| Title: | Functions for Tidy Analysis and Generation of Random Data |
|---|---|
| Description: | To make it easy to generate random numbers based upon the underlying stats distribution functions. All data is returned in a tidy and structured format making working with the data simple and straight forward. Given that the data is returned in a tidy 'tibble' it lends itself to working with the rest of the 'tidyverse'. |
| Authors: | Steven Sanderson [aut, cre, cph]
|
| Maintainer: | Steven Sanderson <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.5.0.9000 |
| Built: | 2024-09-27 04:45:00 UTC |
| Source: | https://github.com/spsanderson/TidyDensity |
Add density information to the output of tidy_bootstrap(), and
bootstrap_unnest_tbl().
bootstrap_density_augment(.data)bootstrap_density_augment(.data)
.data |
The data that is passed from the |
This function takes as input the output of the tidy_bootstrap() or
bootstrap_unnest_tbl() and returns an augmented tibble that has the following
columns added to it: x, y, dx, and dy.
It looks for an attribute that comes from using tidy_bootstrap() or
bootstrap_unnest_tbl() so it will not work unless the data comes from one of
those functions.
A tibble
Steven P. Sanderson II, MPH
Other Bootstrap:
bootstrap_p_augment(),
bootstrap_p_vec(),
bootstrap_q_augment(),
bootstrap_q_vec(),
bootstrap_stat_plot(),
bootstrap_unnest_tbl(),
tidy_bootstrap()
Other Augment Function:
bootstrap_p_augment(),
bootstrap_q_augment()
x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_density_augment() tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_density_augment()x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_density_augment() tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_density_augment()
Takes a numeric vector and will return the ecdf probability.
bootstrap_p_augment(.data, .value, .names = "auto")bootstrap_p_augment(.data, .value, .names = "auto")
.data |
The data being passed that will be augmented by the function. |
.value |
This is passed |
.names |
The default is "auto" |
Takes a numeric vector and will return the ecdf probability of that vector. This function is intended to be used on its own in order to add columns to a tibble.
A augmented tibble
Steven P. Sanderson II, MPH
Other Augment Function:
bootstrap_density_augment(),
bootstrap_q_augment()
Other Bootstrap:
bootstrap_density_augment(),
bootstrap_p_vec(),
bootstrap_q_augment(),
bootstrap_q_vec(),
bootstrap_stat_plot(),
bootstrap_unnest_tbl(),
tidy_bootstrap()
x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_p_augment(y)x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_p_augment(y)
This function takes in a vector as it's input and will return the ecdf probability of a vector.
bootstrap_p_vec(.x)bootstrap_p_vec(.x)
.x |
A numeric |
A function to return the ecdf probability of a vector.
A vector
Steven P. Sanderson II, MPH
Other Bootstrap:
bootstrap_density_augment(),
bootstrap_p_augment(),
bootstrap_q_augment(),
bootstrap_q_vec(),
bootstrap_stat_plot(),
bootstrap_unnest_tbl(),
tidy_bootstrap()
Other Vector Function:
bootstrap_q_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmean(),
cmedian(),
csd(),
cskewness(),
cvar(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
x <- mtcars$mpg bootstrap_p_vec(x)x <- mtcars$mpg bootstrap_p_vec(x)
Takes a numeric vector and will return the quantile.
bootstrap_q_augment(.data, .value, .names = "auto")bootstrap_q_augment(.data, .value, .names = "auto")
.data |
The data being passed that will be augmented by the function. |
.value |
This is passed |
.names |
The default is "auto" |
Takes a numeric vector and will return the quantile of that vector. This function is intended to be used on its own in order to add columns to a tibble.
A augmented tibble
Steven P. Sanderson II, MPH
Other Augment Function:
bootstrap_density_augment(),
bootstrap_p_augment()
Other Bootstrap:
bootstrap_density_augment(),
bootstrap_p_augment(),
bootstrap_p_vec(),
bootstrap_q_vec(),
bootstrap_stat_plot(),
bootstrap_unnest_tbl(),
tidy_bootstrap()
x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_q_augment(y)x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_q_augment(y)
This function takes in a vector as it's input and will return the quantile of a vector.
bootstrap_q_vec(.x)bootstrap_q_vec(.x)
.x |
A numeric |
A function to return the quantile of a vector.
A vector
Steven P. Sanderson II, MPH
Other Bootstrap:
bootstrap_density_augment(),
bootstrap_p_augment(),
bootstrap_p_vec(),
bootstrap_q_augment(),
bootstrap_stat_plot(),
bootstrap_unnest_tbl(),
tidy_bootstrap()
Other Vector Function:
bootstrap_p_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmean(),
cmedian(),
csd(),
cskewness(),
cvar(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
x <- mtcars$mpg bootstrap_q_vec(x)x <- mtcars$mpg bootstrap_q_vec(x)
This function produces a plot of a cumulative statistic function applied to the
bootstrap variable from tidy_bootstrap() or after bootstrap_unnest_tbl()
has been applied to it.
bootstrap_stat_plot( .data, .value, .stat = "cmean", .show_groups = FALSE, .show_ci_labels = TRUE, .interactive = FALSE )bootstrap_stat_plot( .data, .value, .stat = "cmean", .show_groups = FALSE, .show_ci_labels = TRUE, .interactive = FALSE )
.data |
The data that comes from either |
.value |
The value column that the calculations are being applied to. |
.stat |
The cumulative statistic function being applied to the |
.show_groups |
The default is FALSE, set to TRUE to get output of all simulations of the bootstrap data. |
.show_ci_labels |
The default is TRUE, this will show the last value of the upper and lower quantile. |
.interactive |
The default is FALSE, set to TRUE to get a plotly plot object back. |
This function will take in data from either tidy_bootstrap() directly or
after apply bootstrap_unnest_tbl() to its output. There are several different
cumulative functions that can be applied to the data.The accepted values are:
"cmean" - Cumulative Mean
"chmean" - Cumulative Harmonic Mean
"cgmean" - Cumulative Geometric Mean
"csum" = Cumulative Sum
"cmedian" = Cumulative Median
"cmax" = Cumulative Max
"cmin" = Cumulative Min
"cprod" = Cumulative Product
"csd" = Cumulative Standard Deviation
"cvar" = Cumulative Variance
"cskewness" = Cumulative Skewness
"ckurtosis" = Cumulative Kurtotsis
A plot either ggplot2 or plotly.
Steven P. Sanderson II, MPH
Other Bootstrap:
bootstrap_density_augment(),
bootstrap_p_augment(),
bootstrap_p_vec(),
bootstrap_q_augment(),
bootstrap_q_vec(),
bootstrap_unnest_tbl(),
tidy_bootstrap()
Other Autoplot:
tidy_autoplot(),
tidy_combined_autoplot(),
tidy_four_autoplot(),
tidy_multi_dist_autoplot(),
tidy_random_walk_autoplot()
x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_stat_plot(y, "cmean") tidy_bootstrap(x, .num_sims = 10) |> bootstrap_stat_plot(y, .stat = "chmean", .show_groups = TRUE, .show_ci_label = FALSE )x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_stat_plot(y, "cmean") tidy_bootstrap(x, .num_sims = 10) |> bootstrap_stat_plot(y, .stat = "chmean", .show_groups = TRUE, .show_ci_label = FALSE )
Unnest the data output from tidy_bootstrap().
bootstrap_unnest_tbl(.data)bootstrap_unnest_tbl(.data)
.data |
The data that is passed from the |
This function takes as input the output of the tidy_bootstrap()
function and returns a two column tibble. The columns are sim_number and y
It looks for an attribute that comes from using tidy_bootstrap() so it will
not work unless the data comes from that function.
A tibble
Steven P. Sanderson II, MPH
Other Bootstrap:
bootstrap_density_augment(),
bootstrap_p_augment(),
bootstrap_p_vec(),
bootstrap_q_augment(),
bootstrap_q_vec(),
bootstrap_stat_plot(),
tidy_bootstrap()
tb <- tidy_bootstrap(.x = mtcars$mpg) bootstrap_unnest_tbl(tb) bootstrap_unnest_tbl(tb) |> tidy_distribution_summary_tbl(sim_number)tb <- tidy_bootstrap(.x = mtcars$mpg) bootstrap_unnest_tbl(tb) bootstrap_unnest_tbl(tb) |> tidy_distribution_summary_tbl(sim_number)
A function to return the cumulative geometric mean of a vector.
cgmean(.x)cgmean(.x)
.x |
A numeric vector |
A function to return the cumulative geometric mean of a vector.
exp(cummean(log(.x)))
A numeric vector
Steven P. Sanderson II, MPH
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
chmean(),
ckurtosis(),
cmean(),
cmedian(),
csd(),
cskewness(),
cvar(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
x <- mtcars$mpg cgmean(x)x <- mtcars$mpg cgmean(x)
This function checks for duplicate rows in a data frame.
check_duplicate_rows(.data)check_duplicate_rows(.data)
.data |
A data frame. |
This function checks for duplicate rows by comparing each row in the data frame to every other row. If a row is identical to another row, it is considered a duplicate.
A logical vector indicating whether each row is a duplicate or not.
Steven P. Sanderson II, MPH
Other Utility:
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
data <- data.frame( x = c(1, 2, 3, 1), y = c(2, 3, 4, 2), z = c(3, 2, 5, 3) ) check_duplicate_rows(data)data <- data.frame( x = c(1, 2, 3, 1), y = c(2, 3, 4, 2), z = c(3, 2, 5, 3) ) check_duplicate_rows(data)
A function to return the cumulative harmonic mean of a vector.
chmean(.x)chmean(.x)
.x |
A numeric vector |
A function to return the cumulative harmonic mean of a vector.
1 / (cumsum(1 / .x))
A numeric vector
Steven P. Sanderson II, MPH
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
ckurtosis(),
cmean(),
cmedian(),
csd(),
cskewness(),
cvar(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
x <- mtcars$mpg chmean(x)x <- mtcars$mpg chmean(x)
Gets the upper 97.5% quantile of a numeric vector.
ci_hi(.x, .na_rm = FALSE)ci_hi(.x, .na_rm = FALSE)
.x |
A vector of numeric values |
.na_rm |
A Boolean, defaults to FALSE. Passed to the quantile function. |
Gets the upper 97.5% quantile of a numeric vector.
A numeric value.
Steven P. Sanderson II, MPH
Other Statistic:
ci_lo(),
tidy_kurtosis_vec(),
tidy_range_statistic(),
tidy_skewness_vec(),
tidy_stat_tbl()
x <- mtcars$mpg ci_hi(x)x <- mtcars$mpg ci_hi(x)
Gets the lower 2.5% quantile of a numeric vector.
ci_lo(.x, .na_rm = FALSE)ci_lo(.x, .na_rm = FALSE)
.x |
A vector of numeric values |
.na_rm |
A Boolean, defaults to FALSE. Passed to the quantile function. |
Gets the lower 2.5% quantile of a numeric vector.
A numeric value.
Steven P. Sanderson II, MPH
Other Statistic:
ci_hi(),
tidy_kurtosis_vec(),
tidy_range_statistic(),
tidy_skewness_vec(),
tidy_stat_tbl()
x <- mtcars$mpg ci_lo(x)x <- mtcars$mpg ci_lo(x)
A function to return the cumulative kurtosis of a vector.
ckurtosis(.x)ckurtosis(.x)
.x |
A numeric vector |
A function to return the cumulative kurtosis of a vector.
A numeric vector
Steven P. Sanderson II, MPH
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
chmean(),
cmean(),
cmedian(),
csd(),
cskewness(),
cvar(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
x <- mtcars$mpg ckurtosis(x)x <- mtcars$mpg ckurtosis(x)
A function to return the cumulative mean of a vector.
cmean(.x)cmean(.x)
.x |
A numeric vector |
A function to return the cumulative mean of a vector. It uses dplyr::cummean()
as the basis of the function.
A numeric vector
Steven P. Sanderson II, MPH
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmedian(),
csd(),
cskewness(),
cvar(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
x <- mtcars$mpg cmean(x)x <- mtcars$mpg cmean(x)
A function to return the cumulative median of a vector.
cmedian(.x)cmedian(.x)
.x |
A numeric vector |
A function to return the cumulative median of a vector.
A numeric vector
Steven P. Sanderson II, MPH
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmean(),
csd(),
cskewness(),
cvar(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
x <- mtcars$mpg cmedian(x)x <- mtcars$mpg cmedian(x)
8 Hex RGB color definitions suitable for charts for colorblind people.
color_blind()color_blind()
This function converts data in a data frame or tibble into a time series format. It is designed to
work with data generated from tidy_ distribution functions. The function can return time series data, pivot it
into long format, or both.
convert_to_ts(.data, .return_ts = TRUE, .pivot_longer = FALSE)convert_to_ts(.data, .return_ts = TRUE, .pivot_longer = FALSE)
.data |
A data frame or tibble to be converted into a time series format. |
.return_ts |
A logical value indicating whether to return the time series data. Default is TRUE. |
.pivot_longer |
A logical value indicating whether to pivot the data into long format. Default is FALSE. |
The function takes a data frame or tibble as input and processes it based on the specified options. It performs the following actions:
Checks if the input is a data frame or tibble; otherwise, it raises an error.
Checks if the data comes from a tidy_ distribution function; otherwise, it raises an error.
Converts the data into a time series format, grouping it by "sim_number" and transforming the "y" column into a time series.
Returns the result based on the chosen options:
If ret_ts is set to TRUE, it returns the time series data.
If pivot_longer is set to TRUE, it pivots the data into long format.
If both options are set to FALSE, it returns the data as a tibble.
The function returns the processed data based on the chosen options:
If ret_ts is set to TRUE, it returns time series data.
If pivot_longer is set to TRUE, it returns the data in long format.
If both options are set to FALSE, it returns the data as a tibble.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Convert data to time series format without returning time series data x <- tidy_normal() result <- convert_to_ts(x, FALSE) head(result) # Example 2: Convert data to time series format and pivot it into long format x <- tidy_normal() result <- convert_to_ts(x, FALSE, TRUE) head(result) # Example 3: Convert data to time series format and return the time series data x <- tidy_normal() result <- convert_to_ts(x) head(result)# Example 1: Convert data to time series format without returning time series data x <- tidy_normal() result <- convert_to_ts(x, FALSE) head(result) # Example 2: Convert data to time series format and pivot it into long format x <- tidy_normal() result <- convert_to_ts(x, FALSE, TRUE) head(result) # Example 3: Convert data to time series format and return the time series data x <- tidy_normal() result <- convert_to_ts(x) head(result)
A function to return the cumulative standard deviation of a vector.
csd(.x)csd(.x)
.x |
A numeric vector |
A function to return the cumulative standard deviation of a vector.
A numeric vector. Note: The first entry will always be NaN.
Steven P. Sanderson II, MPH
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmean(),
cmedian(),
cskewness(),
cvar(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
x <- mtcars$mpg csd(x)x <- mtcars$mpg csd(x)
A function to return the cumulative skewness of a vector.
cskewness(.x)cskewness(.x)
.x |
A numeric vector |
A function to return the cumulative skewness of a vector.
A numeric vector
Steven P. Sanderson II, MPH
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmean(),
cmedian(),
csd(),
cvar(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
x <- mtcars$mpg cskewness(x)x <- mtcars$mpg cskewness(x)
A function to return the cumulative variance of a vector.
cvar(.x)cvar(.x)
.x |
A numeric vector |
A function to return the cumulative variance of a vector.
exp(cummean(log(.x)))
A numeric vector. Note: The first entry will always be NaN.
Steven P. Sanderson II, MPH
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmean(),
cmedian(),
csd(),
cskewness(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
x <- mtcars$mpg cvar(x)x <- mtcars$mpg cvar(x)
Get the distribution name in title case from the tidy_ distribution
function.
dist_type_extractor(.x)dist_type_extractor(.x)
.x |
The attribute list passed from a |
This will extract the distribution type from a tidy_ distribution
function output using the attributes of that object. You must pass the attribute
directly to the function. It is meant really to be used internally.
You should be passing if using manually the $tibble_type attribute.
A character string
Steven P. Sanderson II,
tn <- tidy_normal() atb <- attributes(tn) dist_type_extractor(atb$tibble_type)tn <- tidy_normal() atb <- attributes(tn) dist_type_extractor(atb$tibble_type)
This function will perform quantile normalization on two or more distributions of equal length. Quantile normalization is a technique used to make the distribution of values across different samples more similar. It ensures that the distributions of values for each sample have the same quantiles. This function takes a numeric matrix as input and returns a quantile-normalized matrix.
quantile_normalize(.data, .return_tibble = FALSE)quantile_normalize(.data, .return_tibble = FALSE)
.data |
A numeric matrix where each column represents a sample. |
.return_tibble |
A logical value that determines if the output should be a tibble. Default is 'FALSE'. |
This function performs quantile normalization on a numeric matrix by following these steps:
Sort each column of the input matrix.
Calculate the mean of each row across the sorted columns.
Replace each column's sorted values with the row means.
Unsort the columns to their original order.
A list object that has the following:
A numeric matrix that has been quantile normalized.
The row means of the quantile normalized matrix.
The sorted data
The ranked indices
Steven P. Sanderson II, MPH
rowMeans: Calculate row means.
apply: Apply a function over the margins of an array.
order: Order the elements of a vector.
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Create a sample numeric matrix data <- matrix(rnorm(20), ncol = 4) # Perform quantile normalization normalized_data <- quantile_normalize(data) normalized_data as.data.frame(normalized_data$normalized_data) |> sapply(function(x) quantile(x, probs = seq(0, 1, 1 / 4))) quantile_normalize( data.frame(rnorm(30), rnorm(30)), .return_tibble = TRUE)# Create a sample numeric matrix data <- matrix(rnorm(20), ncol = 4) # Perform quantile normalization normalized_data <- quantile_normalize(data) normalized_data as.data.frame(normalized_data$normalized_data) |> sapply(function(x) quantile(x, probs = seq(0, 1, 1 / 4))) quantile_normalize( data.frame(rnorm(30), rnorm(30)), .return_tibble = TRUE)
Provide Colorblind Compliant Colors
td_scale_color_colorblind(..., theme = "td")td_scale_color_colorblind(..., theme = "td")
... |
Data passed to the function |
theme |
This defaults to |
Provide Colorblind Compliant Colors
td_scale_fill_colorblind(..., theme = "td")td_scale_fill_colorblind(..., theme = "td")
... |
Data passed to the function |
theme |
This defaults to |
This is an auto plotting function that will take in a tidy_
distribution function and a few arguments, one being the plot type, which is
a quoted string of one of the following:
density
quantile
probablity
qq
mcmc
If the number of simulations exceeds 9 then the legend will not print. The plot subtitle is put together by the attributes of the table passed to the function.
tidy_autoplot( .data, .plot_type = "density", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )tidy_autoplot( .data, .plot_type = "density", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )
.data |
The data passed in from a tidy_ |
.plot_type |
This is a quoted string like 'density' |
.line_size |
The size param ggplot |
.geom_point |
A Boolean value of TREU/FALSE, FALSE is the default. TRUE
will return a plot with |
.point_size |
The point size param for ggplot |
.geom_rug |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.geom_smooth |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.geom_jitter |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.interactive |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return an interactive |
This function will spit out one of the following plots:
density
quantile
probability
qq
mcmc
A ggplot or a plotly plot.
Steven P. Sanderson II, MPH
Other Autoplot:
bootstrap_stat_plot(),
tidy_combined_autoplot(),
tidy_four_autoplot(),
tidy_multi_dist_autoplot(),
tidy_random_walk_autoplot()
tidy_normal(.num_sims = 5) |> tidy_autoplot() tidy_normal(.num_sims = 20) |> tidy_autoplot(.plot_type = "qq")tidy_normal(.num_sims = 5) |> tidy_autoplot() tidy_normal(.num_sims = 20) |> tidy_autoplot(.plot_type = "qq")
This function will generate n random points from a Bernoulli
distribution with a user provided, .prob, and number of random simulations
to be produced. The function returns a tibble with the simulation number
column the x column which corresponds to the n randomly generated points,
the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_bernoulli(.n = 50, .prob = 0.1, .num_sims = 1, .return_tibble = TRUE)tidy_bernoulli(.n = 50, .prob = 0.1, .num_sims = 1, .return_tibble = TRUE)
.n |
The number of randomly generated points you want. |
.prob |
The probability of success/failure. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the rbinom(), and its underlying
p, d, and q functions. The Bernoulli distribution is a special case
of the Binomial distribution with size = 1 hence this is why the binom
functions are used and set to size = 1.
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://en.wikipedia.org/wiki/Bernoulli_distribution
Other Discrete Distribution:
tidy_binomial(),
tidy_hypergeometric(),
tidy_negative_binomial(),
tidy_poisson(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
tidy_zero_truncated_poisson()
Other Bernoulli:
util_bernoulli_param_estimate(),
util_bernoulli_stats_tbl()
tidy_bernoulli()tidy_bernoulli()
This function will generate n random points from a beta
distribution with a user provided, .shape1, .shape2, .ncp or non-centrality parameter,
and number of random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_beta( .n = 50, .shape1 = 1, .shape2 = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE )tidy_beta( .n = 50, .shape1 = 1, .shape2 = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape1 |
A non-negative parameter of the Beta distribution. |
.shape2 |
A non-negative parameter of the Beta distribution. |
.ncp |
The |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rbeta(), and its underlying
p, d, and q functions. For more information please see stats::rbeta()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://statisticsglobe.com/beta-distribution-in-r-dbeta-pbeta-qbeta-rbeta
https://en.wikipedia.org/wiki/Beta_distribution
Other Continuous Distribution:
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Beta:
tidy_generalized_beta(),
util_beta_param_estimate(),
util_beta_stats_tbl()
tidy_beta()tidy_beta()
This function will generate n random points from a binomial
distribution with a user provided, .size, .prob, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_binomial( .n = 50, .size = 0, .prob = 1, .num_sims = 1, .return_tibble = TRUE )tidy_binomial( .n = 50, .size = 0, .prob = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.size |
Number of trials, zero or more. |
.prob |
Probability of success on each trial. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rbinom(), and its underlying
p, d, and q functions. For more information please see stats::rbinom()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://www.itl.nist.gov/div898/handbook/eda/section3/eda366i.htm
Other Discrete Distribution:
tidy_bernoulli(),
tidy_hypergeometric(),
tidy_negative_binomial(),
tidy_poisson(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
tidy_zero_truncated_poisson()
Other Binomial:
tidy_negative_binomial(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
util_binomial_param_estimate(),
util_binomial_stats_tbl(),
util_negative_binomial_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_negative_binomial_stats_tbl()
tidy_binomial()tidy_binomial()
Takes an input vector of numeric data and produces a bootstrapped nested tibble by simulation number.
tidy_bootstrap( .x, .num_sims = 2000, .proportion = 0.8, .distribution_type = "continuous" )tidy_bootstrap( .x, .num_sims = 2000, .proportion = 0.8, .distribution_type = "continuous" )
.x |
The vector of data being passed to the function. Must be a numeric vector. |
.num_sims |
The default is 2000, can be set to anything desired. A warning will pass to the console if the value is less than 2000. |
.proportion |
How much of the original data do you want to pass through to the sampling function. The default is 0.80 (80%) |
.distribution_type |
This can either be 'continuous' or 'discrete' |
This function will take in a numeric input vector and produce a tibble
of bootstrapped values in a list. The table that is output will have two columns:
sim_number and bootstrap_samples
The sim_number corresponds to how many times you want the data to be resampled,
and the bootstrap_samples column contains a list of the boostrapped resampled
data.
A nested tibble
Steven P. Sanderson II, MPH
Other Bootstrap:
bootstrap_density_augment(),
bootstrap_p_augment(),
bootstrap_p_vec(),
bootstrap_q_augment(),
bootstrap_q_vec(),
bootstrap_stat_plot(),
bootstrap_unnest_tbl()
x <- mtcars$mpg tidy_bootstrap(x)x <- mtcars$mpg tidy_bootstrap(x)
This function will generate n random points from a Burr
distribution with a user provided, .shape1, .shape2, .scale, .rate, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_burr( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )tidy_burr( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape1 |
Must be strictly positive. |
.shape2 |
Must be strictly positive. |
.rate |
An alternative way to specify the |
.scale |
Must be strictly positive. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rburr(), and its underlying
p, d, and q functions. For more information please see actuar::rburr()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Burr:
tidy_inverse_burr(),
util_burr_param_estimate(),
util_burr_stats_tbl()
tidy_burr()tidy_burr()
This function will generate n random points from a cauchy
distribution with a user provided, .location, .scale, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_cauchy( .n = 50, .location = 0, .scale = 1, .num_sims = 1, .return_tibble = TRUE )tidy_cauchy( .n = 50, .location = 0, .scale = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.location |
The location parameter. |
.scale |
The scale parameter, must be greater than or equal to 0. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rcauchy(), and its underlying
p, d, and q functions. For more information please see stats::rcauchy()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Cauchy:
util_cauchy_param_estimate(),
util_cauchy_stats_tbl()
tidy_cauchy()tidy_cauchy()
This function will generate n random points from a chisquare
distribution with a user provided, .df, .ncp, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_chisquare( .n = 50, .df = 1, .ncp = 1, .num_sims = 1, .return_tibble = TRUE )tidy_chisquare( .n = 50, .df = 1, .ncp = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.df |
Degrees of freedom (non-negative but can be non-integer) |
.ncp |
Non-centrality parameter, must be non-negative. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rchisq(), and its underlying
p, d, and q functions. For more information please see stats::rchisq()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Chisquare:
util_chisquare_param_estimate(),
util_chisquare_stats_tbl()
tidy_chisquare()tidy_chisquare()
This allows a user to specify any n number of tidy_
distributions that can be combined into a single tibble. This is the preferred
method for combining multiple distributions of different types, for example
a Gaussian distribution and a Beta distribution.
This generates a single tibble with an added column of dist_type that will give the distribution family name and its associated parameters.
tidy_combine_distributions(...)tidy_combine_distributions(...)
... |
The |
Allows a user to generate a tibble of different tidy_ distributions
A tibble
Steven P. Sanderson II, MPH
Other Multiple Distribution:
tidy_multi_single_dist()
tn <- tidy_normal() tb <- tidy_beta() tc <- tidy_cauchy() tidy_combine_distributions(tn, tb, tc) ## OR tidy_combine_distributions( tidy_normal(), tidy_beta(), tidy_cauchy(), tidy_logistic() )tn <- tidy_normal() tb <- tidy_beta() tc <- tidy_cauchy() tidy_combine_distributions(tn, tb, tc) ## OR tidy_combine_distributions( tidy_normal(), tidy_beta(), tidy_cauchy(), tidy_logistic() )
This is an auto plotting function that will take in a tidy_
distribution function and a few arguments, one being the plot type, which is
a quoted string of one of the following:
density
quantile
probablity
qq
mcmc
If the number of simulations exceeds 9 then the legend will not print. The plot subtitle is put together by the attributes of the table passed to the function.
tidy_combined_autoplot( .data, .plot_type = "density", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )tidy_combined_autoplot( .data, .plot_type = "density", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )
.data |
The data passed in from a the function |
.plot_type |
This is a quoted string like 'density' |
.line_size |
The size param ggplot |
.geom_point |
A Boolean value of TREU/FALSE, FALSE is the default. TRUE
will return a plot with |
.point_size |
The point size param for ggplot |
.geom_rug |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.geom_smooth |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.geom_jitter |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.interactive |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return an interactive |
This function will spit out one of the following plots:
density
quantile
probability
qq
mcmc
A ggplot or a plotly plot.
Steven P. Sanderson II, MPH
Other Autoplot:
bootstrap_stat_plot(),
tidy_autoplot(),
tidy_four_autoplot(),
tidy_multi_dist_autoplot(),
tidy_random_walk_autoplot()
combined_tbl <- tidy_combine_distributions( tidy_normal(), tidy_gamma(), tidy_beta() ) combined_tbl combined_tbl |> tidy_combined_autoplot() combined_tbl |> tidy_combined_autoplot(.plot_type = "qq")combined_tbl <- tidy_combine_distributions( tidy_normal(), tidy_gamma(), tidy_beta() ) combined_tbl combined_tbl |> tidy_combined_autoplot() combined_tbl |> tidy_combined_autoplot(.plot_type = "qq")
Compare some empirical data set against different distributions to help find the distribution that could be the best fit.
tidy_distribution_comparison( .x, .distribution_type = "continuous", .round_to_place = 3 )tidy_distribution_comparison( .x, .distribution_type = "continuous", .round_to_place = 3 )
.x |
The data set being passed to the function |
.distribution_type |
What kind of data is it, can be one of |
.round_to_place |
How many decimal places should the parameter estimates be rounded off to for distibution construction. The default is 3 |
The purpose of this function is to take some data set provided and
to try to find a distribution that may fit the best. A parameter of
.distribution_type must be set to either continuous or discrete in order
for this the function to try the appropriate types of distributions.
The following distributions are used:
Continuous:
tidy_beta
tidy_cauchy
tidy_chisquare
tidy_exponential
tidy_gamma
tidy_logistic
tidy_lognormal
tidy_normal
tidy_pareto
tidy_uniform
tidy_weibull
Discrete:
tidy_binomial
tidy_geometric
tidy_hypergeometric
tidy_poisson
The function itself returns a list output of tibbles. Here are the tibbles that are returned:
comparison_tbl
deviance_tbl
total_deviance_tbl
aic_tbl
kolmogorov_smirnov_tbl
multi_metric_tbl
The comparison_tbl is a long tibble that lists the values of the density
function against the given data.
The deviance_tbl and the total_deviance_tbl just give the simple difference
from the actual density to the estimated density for the given estimated distribution.
The aic_tbl will provide the AIC for liklehood of the distribution.
The kolmogorov_smirnov_tbl for now provides a two.sided estimate of the
ks.test of the estimated density against the empirical.
The multi_metric_tbl will summarise all of these metrics into a single tibble.
An invisible list object. A tibble is printed.
Steven P. Sanderson II, MPH
xc <- mtcars$mpg output_c <- tidy_distribution_comparison(xc, "continuous") xd <- trunc(xc) output_d <- tidy_distribution_comparison(xd, "discrete") output_c output_dxc <- mtcars$mpg output_c <- tidy_distribution_comparison(xc, "continuous") xd <- trunc(xc) output_d <- tidy_distribution_comparison(xd, "discrete") output_c output_d
This function returns a summary statistics tibble. It will use the
y column from the tidy_ distribution function.
tidy_distribution_summary_tbl(.data, ...)tidy_distribution_summary_tbl(.data, ...)
.data |
The data that is going to be passed from a a |
... |
This is the grouping variable that gets passed to |
This function takes in a tidy_ distribution table and
will return a tibble of the following information:
sim_number
mean_val
median_val
std_val
min_val
max_val
skewness
kurtosis
range
iqr
variance
ci_hi
ci_lo
The kurtosis and skewness come from the package healthyR.ai
A summary stats tibble
Steven P. Sanderson II, MPH
library(dplyr) tn <- tidy_normal(.num_sims = 5) tb <- tidy_beta(.num_sims = 5) tidy_distribution_summary_tbl(tn) tidy_distribution_summary_tbl(tn, sim_number) data_tbl <- tidy_combine_distributions(tn, tb) tidy_distribution_summary_tbl(data_tbl) tidy_distribution_summary_tbl(data_tbl, dist_type)library(dplyr) tn <- tidy_normal(.num_sims = 5) tb <- tidy_beta(.num_sims = 5) tidy_distribution_summary_tbl(tn) tidy_distribution_summary_tbl(tn, sim_number) data_tbl <- tidy_combine_distributions(tn, tb) tidy_distribution_summary_tbl(data_tbl) tidy_distribution_summary_tbl(data_tbl, dist_type)
This function takes in a single argument of .x a vector and will
return a tibble of information similar to the tidy_ distribution functions.
The y column is set equal to dy from the density function.
tidy_empirical(.x, .num_sims = 1, .distribution_type = "continuous")tidy_empirical(.x, .num_sims = 1, .distribution_type = "continuous")
.x |
A vector of numbers |
.num_sims |
How many simulations should be run, defaults to 1. |
.distribution_type |
A string of either "continuous" or "discrete". The function will default to "continuous" |
This function takes in a single argument of .x a vector
A tibble
Steven P. Sanderson II, MPH
x <- mtcars$mpg tidy_empirical(.x = x, .distribution_type = "continuous") tidy_empirical(.x = x, .num_sims = 10, .distribution_type = "continuous")x <- mtcars$mpg tidy_empirical(.x = x, .distribution_type = "continuous") tidy_empirical(.x = x, .num_sims = 10, .distribution_type = "continuous")
This function will generate n random points from a exponential
distribution with a user provided, .rate, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_exponential(.n = 50, .rate = 1, .num_sims = 1, .return_tibble = TRUE)tidy_exponential(.n = 50, .rate = 1, .num_sims = 1, .return_tibble = TRUE)
.n |
The number of randomly generated points you want. |
.rate |
A vector of rates |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rexp(), and its underlying
p, d, and q functions. For more information please see stats::rexp()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3667.htm
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Exponential:
tidy_inverse_exponential(),
util_exponential_param_estimate(),
util_exponential_stats_tbl()
tidy_exponential()tidy_exponential()
This function will generate n random points from a rf
distribution with a user provided, df1,df2, and ncp, and number of random
simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_f( .n = 50, .df1 = 1, .df2 = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE )tidy_f( .n = 50, .df1 = 1, .df2 = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.df1 |
Degrees of freedom, Inf is allowed. |
.df2 |
Degrees of freedom, Inf is allowed. |
.ncp |
Non-centrality parameter. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rf(), and its underlying
p, d, and q functions. For more information please see stats::rf()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3665.htm
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other F Distribution:
util_f_param_estimate(),
util_f_stats_tbl()
tidy_f()tidy_f()
This is an auto plotting function that will take in a tidy_
distribution function and a few arguments, one being the plot type, which is
a quoted string of one of the following:
density
quantile
probablity
qq
mcmc
If the number of simulations exceeds 9 then the legend will not print. The plot subtitle is put together by the attributes of the table passed to the function.
tidy_four_autoplot( .data, .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )tidy_four_autoplot( .data, .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )
.data |
The data passed in from a tidy_ |
.line_size |
The size param ggplot |
.geom_point |
A Boolean value of TREU/FALSE, FALSE is the default. TRUE
will return a plot with |
.point_size |
The point size param for ggplot |
.geom_rug |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.geom_smooth |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.geom_jitter |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.interactive |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return an interactive |
This function will spit out one of the following plots:
density
quantile
probability
qq
mcmc
A ggplot or a plotly plot.
Steven P. Sanderson II, MPH
Other Autoplot:
bootstrap_stat_plot(),
tidy_autoplot(),
tidy_combined_autoplot(),
tidy_multi_dist_autoplot(),
tidy_random_walk_autoplot()
tidy_normal(.num_sims = 5) |> tidy_four_autoplot()tidy_normal(.num_sims = 5) |> tidy_four_autoplot()
This function will generate n random points from a gamma
distribution with a user provided, .shape, .scale, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_gamma( .n = 50, .shape = 1, .scale = 0.3, .num_sims = 1, .return_tibble = TRUE )tidy_gamma( .n = 50, .shape = 1, .scale = 0.3, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape |
This is strictly 0 to infinity. |
.scale |
The standard deviation of the randomly generated data. This is strictly from 0 to infinity. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rgamma(), and its underlying
p, d, and q functions. For more information please see stats::rgamma()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://www.statology.org/fit-gamma-distribution-to-dataset-in-r/
https://en.wikipedia.org/wiki/Gamma_distribution
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Gamma:
tidy_inverse_gamma(),
util_gamma_param_estimate(),
util_gamma_stats_tbl()
tidy_gamma()tidy_gamma()
This function will generate n random points from a generalized beta
distribution with a user provided, .shape1, .shape2, .shape3, .rate, and/or
.sclae, and number of random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_generalized_beta( .n = 50, .shape1 = 1, .shape2 = 1, .shape3 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )tidy_generalized_beta( .n = 50, .shape1 = 1, .shape2 = 1, .shape3 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape1 |
A non-negative parameter of the Beta distribution. |
.shape2 |
A non-negative parameter of the Beta distribution. |
.shape3 |
A non-negative parameter of the Beta distribution. |
.rate |
An alternative way to specify the |
.scale |
Must be strictly positive. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rbeta(), and its underlying
p, d, and q functions. For more information please see stats::rbeta()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://statisticsglobe.com/beta-distribution-in-r-dbeta-pbeta-qbeta-rbeta
https://en.wikipedia.org/wiki/Beta_distribution
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Beta:
tidy_beta(),
util_beta_param_estimate(),
util_beta_stats_tbl()
tidy_generalized_beta()tidy_generalized_beta()
This function will generate n random points from a generalized
Pareto distribution with a user provided, .shape1, .shape2, .rate or
.scale and number of #' random simulations to be produced.
The function returns a tibble with the simulation number column the x column
which corresponds to the n randomly generated points, the d_, p_ and q_
data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_generalized_pareto( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )tidy_generalized_pareto( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape1 |
Must be positive. |
.shape2 |
Must be positive. |
.rate |
An alternative way to specify the |
.scale |
Must be positive. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rgenpareto(), and its underlying
p, d, and q functions. For more information please see actuar::rgenpareto()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Pareto:
tidy_inverse_pareto(),
tidy_pareto(),
tidy_pareto1(),
util_pareto1_aic(),
util_pareto1_param_estimate(),
util_pareto1_stats_tbl(),
util_pareto_param_estimate(),
util_pareto_stats_tbl()
tidy_generalized_pareto()tidy_generalized_pareto()
This function will generate n random points from a geometric
distribution with a user provided, .prob, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_geometric(.n = 50, .prob = 1, .num_sims = 1, .return_tibble = TRUE)tidy_geometric(.n = 50, .prob = 1, .num_sims = 1, .return_tibble = TRUE)
.n |
The number of randomly generated points you want. |
.prob |
A probability of success in each trial 0 < prob <= 1. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rgeom(), and its underlying
p, d, and q functions. For more information please see stats::rgeom()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://en.wikipedia.org/wiki/Geometric_distribution
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Geometric:
tidy_zero_truncated_geometric(),
util_geometric_param_estimate(),
util_geometric_stats_tbl()
tidy_geometric()tidy_geometric()
This function will generate n random points from a hypergeometric
distribution with a user provided, m,nn, and k, and number of random
simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_hypergeometric( .n = 50, .m = 0, .nn = 0, .k = 0, .num_sims = 1, .return_tibble = TRUE )tidy_hypergeometric( .n = 50, .m = 0, .nn = 0, .k = 0, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.m |
The number of white balls in the urn |
.nn |
The number of black balls in the urn |
.k |
The number of balls drawn fro the urn. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rhyper(), and its underlying
p, d, and q functions. For more information please see stats::rhyper()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://en.wikipedia.org/wiki/Hypergeometric_distribution
Other Discrete Distribution:
tidy_bernoulli(),
tidy_binomial(),
tidy_negative_binomial(),
tidy_poisson(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
tidy_zero_truncated_poisson()
Other Hypergeometric:
util_hypergeometric_param_estimate(),
util_hypergeometric_stats_tbl()
tidy_hypergeometric()tidy_hypergeometric()
This function will generate n random points from an Inverse Burr
distribution with a user provided, .shape1, .shape2, .scale, .rate, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_inverse_burr( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )tidy_inverse_burr( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape1 |
Must be strictly positive. |
.shape2 |
Must be strictly positive. |
.rate |
An alternative way to specify the |
.scale |
Must be strictly positive. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rinvburr(), and its underlying
p, d, and q functions. For more information please see actuar::rinvburr()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Burr:
tidy_burr(),
util_burr_param_estimate(),
util_burr_stats_tbl()
Other Inverse Distribution:
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull()
tidy_inverse_burr()tidy_inverse_burr()
This function will generate n random points from an inverse exponential
distribution with a user provided, .rate or .scale and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_inverse_exponential( .n = 50, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )tidy_inverse_exponential( .n = 50, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.rate |
An alternative way to specify the |
.scale |
Must be strictly positive. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rinvexp(), and its underlying
p, d, and q functions. For more information please see actuar::rinvexp()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Exponential:
tidy_exponential(),
util_exponential_param_estimate(),
util_exponential_stats_tbl()
Other Inverse Distribution:
tidy_inverse_burr(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull()
tidy_inverse_exponential()tidy_inverse_exponential()
This function will generate n random points from an inverse gamma
distribution with a user provided, .shape, .rate, .scale, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_inverse_gamma( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )tidy_inverse_gamma( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape |
Must be strictly positive. |
.rate |
An alternative way to specify the |
.scale |
Must be strictly positive. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rinvgamma(), and its underlying
p, d, and q functions. For more information please see actuar::rinvgamma()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Gamma:
tidy_gamma(),
util_gamma_param_estimate(),
util_gamma_stats_tbl()
Other Inverse Distribution:
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull()
tidy_inverse_gamma()tidy_inverse_gamma()
This function will generate n random points from an Inverse Gaussian
distribution with a user provided, .mean, .shape, .dispersionThe function
returns a tibble with the simulation number column the x column which corresponds
to the n randomly generated points.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_inverse_normal( .n = 50, .mean = 1, .shape = 1, .dispersion = 1/.shape, .num_sims = 1, .return_tibble = TRUE )tidy_inverse_normal( .n = 50, .mean = 1, .shape = 1, .dispersion = 1/.shape, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.mean |
Must be strictly positive. |
.shape |
Must be strictly positive. |
.dispersion |
An alternative way to specify the |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rinvgauss(). For
more information please see rinvgauss()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Gaussian:
tidy_normal(),
util_normal_param_estimate(),
util_normal_stats_tbl()
Other Inverse Distribution:
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_pareto(),
tidy_inverse_weibull()
tidy_inverse_normal()tidy_inverse_normal()
This function will generate n random points from an inverse
pareto distribution with a user provided, .shape, .scale, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_inverse_pareto( .n = 50, .shape = 1, .scale = 1, .num_sims = 1, .return_tibble = TRUE )tidy_inverse_pareto( .n = 50, .shape = 1, .scale = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape |
Must be positive. |
.scale |
Must be positive. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rinvpareto(), and its underlying
p, d, and q functions. For more information please see actuar::rinvpareto()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Pareto:
tidy_generalized_pareto(),
tidy_pareto(),
tidy_pareto1(),
util_pareto1_aic(),
util_pareto1_param_estimate(),
util_pareto1_stats_tbl(),
util_pareto_param_estimate(),
util_pareto_stats_tbl()
Other Inverse Distribution:
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_weibull()
tidy_inverse_pareto()tidy_inverse_pareto()
This function will generate n random points from a weibull
distribution with a user provided, .shape, .scale, .rate, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_inverse_weibull( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )tidy_inverse_weibull( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape |
Must be strictly positive. |
.rate |
An alternative way to specify the |
.scale |
Must be strictly positive. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rinvweibull(), and its underlying
p, d, and q functions. For more information please see actuar::rinvweibull()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Weibull:
tidy_weibull(),
util_weibull_param_estimate(),
util_weibull_stats_tbl()
Other Inverse Distribution:
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto()
tidy_inverse_weibull()tidy_inverse_weibull()
This function takes in a vector as it's input and will return the kurtosis of that vector. The length of this vector must be at least four numbers. The kurtosis explains the sharpness of the peak of a distribution of data.
((1/n) * sum(x - mu})^4) / ((()1/n) * sum(x - mu)^2)^2
tidy_kurtosis_vec(.x)tidy_kurtosis_vec(.x)
.x |
A numeric vector of length four or more. |
A function to return the kurtosis of a vector.
The kurtosis of a vector
Steven P. Sanderson II, MPH
https://en.wikipedia.org/wiki/Kurtosis
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmean(),
cmedian(),
csd(),
cskewness(),
cvar(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
Other Statistic:
ci_hi(),
ci_lo(),
tidy_range_statistic(),
tidy_skewness_vec(),
tidy_stat_tbl()
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmean(),
cmedian(),
csd(),
cskewness(),
cvar(),
tidy_scale_zero_one_vec(),
tidy_skewness_vec()
tidy_kurtosis_vec(rnorm(100, 3, 2))tidy_kurtosis_vec(rnorm(100, 3, 2))
This function will generate n random points from a logistic
distribution with a user provided, .location, .scale, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresonds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_logistic( .n = 50, .location = 0, .scale = 1, .num_sims = 1, .return_tibble = TRUE )tidy_logistic( .n = 50, .location = 0, .scale = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.location |
The location parameter |
.scale |
The scale parameter |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rlogis(), and its underlying
p, d, and q functions. For more information please see stats::rlogis()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://en.wikipedia.org/wiki/Logistic_distribution
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Logistic:
tidy_paralogistic(),
util_logistic_param_estimate(),
util_logistic_stats_tbl()
tidy_logistic()tidy_logistic()
This function will generate n random points from a lognormal
distribution with a user provided, .meanlog, .sdlog, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_lognormal( .n = 50, .meanlog = 0, .sdlog = 1, .num_sims = 1, .return_tibble = TRUE )tidy_lognormal( .n = 50, .meanlog = 0, .sdlog = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.meanlog |
Mean of the distribution on the log scale with default 0 |
.sdlog |
Standard deviation of the distribution on the log scale with default 1 |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rlnorm(), and its underlying
p, d, and q functions. For more information please see stats::rlnorm()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Lognormal:
util_lognormal_param_estimate(),
util_lognormal_stats_tbl()
tidy_lognormal()tidy_lognormal()
This function performs Markov Chain Monte Carlo (MCMC) sampling on the input data and returns tidy data and a plot representing the results.
tidy_mcmc_sampling(.x, .fns = "mean", .cum_fns = "cmean", .num_sims = 2000)tidy_mcmc_sampling(.x, .fns = "mean", .cum_fns = "cmean", .num_sims = 2000)
.x |
The data vector for MCMC sampling. |
.fns |
The function(s) to apply to each MCMC sample. Default is "mean". |
.cum_fns |
The function(s) to apply to the cumulative MCMC samples. Default is "cmean". |
.num_sims |
The number of simulations. Default is 2000. |
Perform MCMC sampling and return tidy data and a plot.
The function takes a data vector as input and performs MCMC sampling with the specified number of simulations. It applies user-defined functions to each MCMC sample and to the cumulative MCMC samples. The resulting data is formatted in a tidy format, suitable for further analysis. Additionally, a plot is generated to visualize the MCMC samples and cumulative statistics.
A list containing tidy data and a plot.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Generate MCMC samples set.seed(123) data <- rnorm(100) result <- tidy_mcmc_sampling(data, "median", "cmedian", 500) result# Generate MCMC samples set.seed(123) data <- rnorm(100) result <- tidy_mcmc_sampling(data, "median", "cmedian", 500) result
Create mixture model data and resulting density and line plots.
tidy_mixture_density(...)tidy_mixture_density(...)
... |
The random data you want to pass. Example rnorm(50,0,1) or something like tidy_normal(.mean = 5, .sd = 1) |
This function allows you to make mixture model data. It allows you to produce density data and plots for data that is not strictly of one family or of one single type of distribution with a given set of parameters.
For example this function will allow you to mix say tidy_normal(.mean = 0, .sd = 1) and tidy_normal(.mean = 5, .sd = 1) or you can mix and match distributions.
The output is a list object with three components.
Data
input_data (The random data passed)
dist_tbl (A tibble of the passed random data)
density_tbl (A tibble of the x and y data from stats::density())
Plots
line_plot - Plots the dist_tbl
dens_plot - Plots the density_tbl
Input Functions
input_fns - A list of the functions and their parameters passed to the function itself
A list object
Steven P. Sanderson II, MPH
output <- tidy_mixture_density(rnorm(100, 0, 1), tidy_normal(.mean = 5, .sd = 1)) output$data output$plots output$input_fnsoutput <- tidy_mixture_density(rnorm(100, 0, 1), tidy_normal(.mean = 5, .sd = 1)) output$data output$plots output$input_fns
This is an auto plotting function that will take in a tidy_
distribution function and a few arguments, one being the plot type, which is
a quoted string of one of the following:
density
quantile
probablity
qq
mcmc
If the number of simulations exceeds 9 then the legend will not print. The plot subtitle is put together by the attributes of the table passed to the function.
tidy_multi_dist_autoplot( .data, .plot_type = "density", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )tidy_multi_dist_autoplot( .data, .plot_type = "density", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )
.data |
The data passed in from a the function |
.plot_type |
This is a quoted string like 'density' |
.line_size |
The size param ggplot |
.geom_point |
A Boolean value of TREU/FALSE, FALSE is the default. TRUE
will return a plot with |
.point_size |
The point size param for ggplot |
.geom_rug |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.geom_smooth |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.geom_jitter |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.interactive |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return an interactive |
This function will spit out one of the following plots:
density
quantile
probability
qq
mcmc
A ggplot or a plotly plot.
Steven P. Sanderson II, MPH
Other Autoplot:
bootstrap_stat_plot(),
tidy_autoplot(),
tidy_combined_autoplot(),
tidy_four_autoplot(),
tidy_random_walk_autoplot()
tn <- tidy_multi_single_dist( .tidy_dist = "tidy_normal", .param_list = list( .n = 100, .mean = c(-2, 0, 2), .sd = 1, .num_sims = 5, .return_tibble = TRUE ) ) tn |> tidy_multi_dist_autoplot() tn |> tidy_multi_dist_autoplot(.plot_type = "qq")tn <- tidy_multi_single_dist( .tidy_dist = "tidy_normal", .param_list = list( .n = 100, .mean = c(-2, 0, 2), .sd = 1, .num_sims = 5, .return_tibble = TRUE ) ) tn |> tidy_multi_dist_autoplot() tn |> tidy_multi_dist_autoplot(.plot_type = "qq")
Generate multiple distributions of data from the same tidy_
distribution function.
tidy_multi_single_dist(.tidy_dist = NULL, .param_list = list())tidy_multi_single_dist(.tidy_dist = NULL, .param_list = list())
.tidy_dist |
The type of |
.param_list |
This must be a |
Generate multiple distributions of data from the same tidy_
distribution function. This allows you to simulate multiple distributions of
the same family in order to view how shapes change with parameter changes. You
can then visualize the differences however you choose.
A tibble
Steven P. Sanderson II, MPH
Other Multiple Distribution:
tidy_combine_distributions()
tidy_multi_single_dist( .tidy_dist = "tidy_normal", .param_list = list( .n = 50, .mean = c(-1, 0, 1), .sd = 1, .num_sims = 3, .return_tibble = TRUE ) ) tidy_multi_single_dist( .tidy_dist = "tidy_normal", .param_list = list( .n = 50, .mean = c(-1, 0, 1), .sd = 1, .num_sims = 3, .return_tibble = FALSE ) )tidy_multi_single_dist( .tidy_dist = "tidy_normal", .param_list = list( .n = 50, .mean = c(-1, 0, 1), .sd = 1, .num_sims = 3, .return_tibble = TRUE ) ) tidy_multi_single_dist( .tidy_dist = "tidy_normal", .param_list = list( .n = 50, .mean = c(-1, 0, 1), .sd = 1, .num_sims = 3, .return_tibble = FALSE ) )
This function will generate n random points from a negative binomial
distribution with a user provided, .size, .prob, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_negative_binomial( .n = 50, .size = 1, .prob = 0.1, .num_sims = 1, .return_tibble = TRUE )tidy_negative_binomial( .n = 50, .size = 1, .prob = 0.1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.size |
target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution). Must be strictly positive, need not be integer. |
.prob |
Probability of success on each trial where 0 < .prob <= 1. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rnbinom(), and its underlying
p, d, and q functions. For more information please see stats::rnbinom()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Discrete Distribution:
tidy_bernoulli(),
tidy_binomial(),
tidy_hypergeometric(),
tidy_poisson(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
tidy_zero_truncated_poisson()
Other Binomial:
tidy_binomial(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
util_binomial_param_estimate(),
util_binomial_stats_tbl(),
util_negative_binomial_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_negative_binomial_stats_tbl()
tidy_negative_binomial()tidy_negative_binomial()
This function will generate n random points from a Gaussian
distribution with a user provided, .mean, .sd - standard deviation and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the dnorm, pnorm and qnorm data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_normal(.n = 50, .mean = 0, .sd = 1, .num_sims = 1, .return_tibble = TRUE)tidy_normal(.n = 50, .mean = 0, .sd = 1, .num_sims = 1, .return_tibble = TRUE)
.n |
The number of randomly generated points you want. |
.mean |
The mean of the randomly generated data. |
.sd |
The standard deviation of the randomly generated data. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rnorm(), stats::pnorm(),
and stats::qnorm() functions to generate data from the given parameters. For
more information please see stats::rnorm()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Gaussian:
tidy_inverse_normal(),
util_normal_param_estimate(),
util_normal_stats_tbl()
tidy_normal()tidy_normal()
This function will generate n random points from a paralogistic
distribution with a user provided, .shape, .rate, .scale and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_paralogistic( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )tidy_paralogistic( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape |
Must be strictly positive. |
.rate |
An alternative way to specify the |
.scale |
Must be strictly positive. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rparalogis(), and its underlying
p, d, and q functions. For more information please see actuar::rparalogis()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://en.wikipedia.org/wiki/Logistic_distribution
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Logistic:
tidy_logistic(),
util_logistic_param_estimate(),
util_logistic_stats_tbl()
tidy_paralogistic()tidy_paralogistic()
This function will generate n random points from a
pareto distribution with a user provided, .shape, .scale, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_pareto( .n = 50, .shape = 10, .scale = 0.1, .num_sims = 1, .return_tibble = TRUE )tidy_pareto( .n = 50, .shape = 10, .scale = 0.1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape |
Must be positive. |
.scale |
Must be positive. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rpareto(), and its underlying
p, d, and q functions. For more information please see actuar::rpareto()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Pareto:
tidy_generalized_pareto(),
tidy_inverse_pareto(),
tidy_pareto1(),
util_pareto1_aic(),
util_pareto1_param_estimate(),
util_pareto1_stats_tbl(),
util_pareto_param_estimate(),
util_pareto_stats_tbl()
tidy_pareto()tidy_pareto()
This function will generate n random points from a single parameter
pareto distribution with a user provided, .shape, .min, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_pareto1( .n = 50, .shape = 1, .min = 1, .num_sims = 1, .return_tibble = TRUE )tidy_pareto1( .n = 50, .shape = 1, .min = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape |
Must be positive. |
.min |
The lower bound of the support of the distribution. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rpareto1(), and its underlying
p, d, and q functions. For more information please see actuar::rpareto1()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Pareto:
tidy_generalized_pareto(),
tidy_inverse_pareto(),
tidy_pareto(),
util_pareto1_aic(),
util_pareto1_param_estimate(),
util_pareto1_stats_tbl(),
util_pareto_param_estimate(),
util_pareto_stats_tbl()
tidy_pareto1()tidy_pareto1()
This function will generate n random points from a Poisson
distribution with a user provided, .lambda, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_poisson(.n = 50, .lambda = 1, .num_sims = 1, .return_tibble = TRUE)tidy_poisson(.n = 50, .lambda = 1, .num_sims = 1, .return_tibble = TRUE)
.n |
The number of randomly generated points you want. |
.lambda |
A vector of non-negative means. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rpois(), and its underlying
p, d, and q functions. For more information please see stats::rpois()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://r-coder.com/poisson-distribution-r/
https://en.wikipedia.org/wiki/Poisson_distribution
Other Poisson:
tidy_zero_truncated_poisson(),
util_poisson_param_estimate(),
util_poisson_stats_tbl(),
util_zero_truncated_poisson_param_estimate(),
util_zero_truncated_poisson_stats_tbl()
Other Discrete Distribution:
tidy_bernoulli(),
tidy_binomial(),
tidy_hypergeometric(),
tidy_negative_binomial(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
tidy_zero_truncated_poisson()
tidy_poisson()tidy_poisson()
Takes in the data from a tidy_ distribution function and applies
a random walk calculation of either cum_prod or cum_sum to y.
tidy_random_walk( .data, .initial_value = 0, .sample = FALSE, .replace = FALSE, .value_type = "cum_prod" )tidy_random_walk( .data, .initial_value = 0, .sample = FALSE, .replace = FALSE, .value_type = "cum_prod" )
.data |
The data that is being passed from a |
.initial_value |
The default is 0, this can be set to whatever you want. |
.sample |
This is a boolean value TRUE/FALSE. The default is FALSE. If
set to TRUE then the |
.replace |
This is a boolean value TRUE/FALSE. The default is FALSE. If
set to TRUE AND |
.value_type |
This can take one of three different values for now. These are the following:
|
Monte Carlo simulations were first formally designed in the 1940’s while developing nuclear weapons, and since have been heavily used in various fields to use randomness solve problems that are potentially deterministic in nature. In finance, Monte Carlo simulations can be a useful tool to give a sense of how assets with certain characteristics might behave in the future. While there are more complex and sophisticated financial forecasting methods such as ARIMA (Auto-Regressive Integrated Moving Average) and GARCH (Generalised Auto-Regressive Conditional Heteroskedasticity) which attempt to model not only the randomness but underlying macro factors such as seasonality and volatility clustering, Monte Carlo random walks work surprisingly well in illustrating market volatility as long as the results are not taken too seriously.
An ungrouped tibble.
Steven P. Sanderson II, MPH
tidy_normal(.sd = .1, .num_sims = 25) %>% tidy_random_walk()tidy_normal(.sd = .1, .num_sims = 25) %>% tidy_random_walk()
This is an auto-plotting function that will take in a tidy_
distribution function and a few arguments with regard to the output of the
visualization.
If the number of simulations exceeds 9 then the legend will not print. The plot subtitle is put together by the attributes of the table passed to the function.
tidy_random_walk_autoplot( .data, .line_size = 0.5, .geom_rug = FALSE, .geom_smooth = FALSE, .interactive = FALSE )tidy_random_walk_autoplot( .data, .line_size = 0.5, .geom_rug = FALSE, .geom_smooth = FALSE, .interactive = FALSE )
.data |
The data passed in from a tidy_ |
.line_size |
The size param ggplot |
.geom_rug |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.geom_smooth |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return the use of |
.interactive |
A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
will return an interactive |
This function will produce a simple random walk plot from a tidy_
distribution function.
A ggplot or a plotly plot.
Steven P. Sanderson II, MPH
Other Autoplot:
bootstrap_stat_plot(),
tidy_autoplot(),
tidy_combined_autoplot(),
tidy_four_autoplot(),
tidy_multi_dist_autoplot()
tidy_normal(.sd = .1, .num_sims = 5) |> tidy_random_walk(.value_type = "cum_sum") |> tidy_random_walk_autoplot() tidy_normal(.sd = .1, .num_sims = 20) |> tidy_random_walk(.value_type = "cum_sum", .sample = TRUE, .replace = TRUE) |> tidy_random_walk_autoplot()tidy_normal(.sd = .1, .num_sims = 5) |> tidy_random_walk(.value_type = "cum_sum") |> tidy_random_walk_autoplot() tidy_normal(.sd = .1, .num_sims = 20) |> tidy_random_walk(.value_type = "cum_sum", .sample = TRUE, .replace = TRUE) |> tidy_random_walk_autoplot()
Takes in a numeric vector and returns back the range of that vector
tidy_range_statistic(.x)tidy_range_statistic(.x)
.x |
A numeric vector |
Takes in a numeric vector and returns the range of that vector using
the diff and range functions.
A single number, the range statistic
Steven P. Sandeson II, MPH
Other Statistic:
ci_hi(),
ci_lo(),
tidy_kurtosis_vec(),
tidy_skewness_vec(),
tidy_stat_tbl()
tidy_range_statistic(seq(1:10))tidy_range_statistic(seq(1:10))
Takes a numeric vector and will return a vector that has been scaled from [0,1]
tidy_scale_zero_one_vec(.x)tidy_scale_zero_one_vec(.x)
.x |
A numeric vector to be scaled from |
Takes a numeric vector and will return a vector that has been scaled from [0,1]
The input vector must be numeric. The computation is fairly straightforward.
This may be helpful when trying to compare the distributions of data where a
distribution like beta which requires data to be between 0 and 1
A numeric vector
Steven P. Sanderson II, MPH
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmean(),
cmedian(),
csd(),
cskewness(),
cvar(),
tidy_kurtosis_vec(),
tidy_skewness_vec()
vec_1 <- rnorm(100, 2, 1) vec_2 <- tidy_scale_zero_one_vec(vec_1) dens_1 <- density(vec_1) dens_2 <- density(vec_2) max_x <- max(dens_1$x, dens_2$x) max_y <- max(dens_1$y, dens_2$y) plot(dens_1, asp = max_y / max_x, main = "Density vec_1 (Red) and vec_2 (Blue)", col = "red", xlab = "", ylab = "Density of Vec 1 and Vec 2" ) lines(dens_2, col = "blue")vec_1 <- rnorm(100, 2, 1) vec_2 <- tidy_scale_zero_one_vec(vec_1) dens_1 <- density(vec_1) dens_2 <- density(vec_2) max_x <- max(dens_1$x, dens_2$x) max_y <- max(dens_1$y, dens_2$y) plot(dens_1, asp = max_y / max_x, main = "Density vec_1 (Red) and vec_2 (Blue)", col = "red", xlab = "", ylab = "Density of Vec 1 and Vec 2" ) lines(dens_2, col = "blue")
This function takes in a vector as it's input and will return the skewness of that vector. The length of this vector must be at least four numbers. The skewness explains the 'tailedness' of the distribution of data.
((1/n) * sum(x - mu})^3) / ((()1/n) * sum(x - mu)^2)^(3/2)
tidy_skewness_vec(.x)tidy_skewness_vec(.x)
.x |
A numeric vector of length four or more. |
A function to return the skewness of a vector.
The skewness of a vector
Steven P. Sanderson II, MPH
https://en.wikipedia.org/wiki/Skewness
Other Statistic:
ci_hi(),
ci_lo(),
tidy_kurtosis_vec(),
tidy_range_statistic(),
tidy_stat_tbl()
Other Vector Function:
bootstrap_p_vec(),
bootstrap_q_vec(),
cgmean(),
chmean(),
ckurtosis(),
cmean(),
cmedian(),
csd(),
cskewness(),
cvar(),
tidy_kurtosis_vec(),
tidy_scale_zero_one_vec()
tidy_skewness_vec(rnorm(100, 3, 2))tidy_skewness_vec(rnorm(100, 3, 2))
A function to return the stat function values of a given tidy_ distribution
output.
tidy_stat_tbl( .data, .x = y, .fns, .return_type = "vector", .use_data_table = FALSE, ... )tidy_stat_tbl( .data, .x = y, .fns, .return_type = "vector", .use_data_table = FALSE, ... )
.data |
The input data coming from a |
.x |
The default is |
.fns |
The default is |
.return_type |
The default is "vector" which returns an |
.use_data_table |
The default is FALSE, TRUE will use data.table under the
hood and still return a tibble. If this argument is set to TRUE then the
|
... |
Addition function arguments to be supplied to the parameters of
|
A function to return the value(s) of a given tidy_ distribution function
output and chosen column from it. This function will only work with tidy_
distribution functions.
There are currently three different output types for this function. These are:
"vector" - which gives an sapply() output
"list" - which gives an lapply() output, and
"tibble" - which returns a tibble in long format.
Currently you can pass any stat function that performs an operation on a vector
input. This means you can pass things like IQR, quantile and their associated
arguments in the ... portion of the function.
This function also by default will rename the value column of the tibble to
the name of the function. This function will also give the column name of sim_number
for the tibble output with the corresponding simulation numbers as the values.
For the sapply and lapply outputs the column names will also give the
simulation number information by making column names like sim_number_1 etc.
There is an option of .use_data_table which can greatly enhance the speed of
the calculations performed if used while still returning a tibble. The calculations
are performed after turning the input data into a data.table object, performing
the necessary calculation and then converting back to a tibble object.
A return of object of either sapply lapply or tibble based upon user input.
Steven P. Sanderson II, MPH
Other Statistic:
ci_hi(),
ci_lo(),
tidy_kurtosis_vec(),
tidy_range_statistic(),
tidy_skewness_vec()
tn <- tidy_normal(.num_sims = 3) p <- c(0.025, 0.25, 0.5, 0.75, 0.95) tidy_stat_tbl(tn, y, quantile, "vector", probs = p, na.rm = TRUE) tidy_stat_tbl(tn, y, quantile, "list", probs = p) tidy_stat_tbl(tn, y, quantile, "tibble", probs = p) tidy_stat_tbl(tn, y, quantile, .use_data_table = TRUE, probs = p, na.rm = TRUE)tn <- tidy_normal(.num_sims = 3) p <- c(0.025, 0.25, 0.5, 0.75, 0.95) tidy_stat_tbl(tn, y, quantile, "vector", probs = p, na.rm = TRUE) tidy_stat_tbl(tn, y, quantile, "list", probs = p) tidy_stat_tbl(tn, y, quantile, "tibble", probs = p) tidy_stat_tbl(tn, y, quantile, .use_data_table = TRUE, probs = p, na.rm = TRUE)
This function will generate n random points from a rt
distribution with a user provided, df, ncp, and number of random
simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_t(.n = 50, .df = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE)tidy_t(.n = 50, .df = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE)
.n |
The number of randomly generated points you want. |
.df |
Degrees of freedom, Inf is allowed. |
.ncp |
Non-centrality parameter. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rt(), and its underlying
p, d, and q functions. For more information please see stats::rt()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3664.htm
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other T Distribution:
util_t_stats_tbl()
tidy_t()tidy_t()
This function generates tidy data from the triangular distribution.
tidy_triangular( .n = 50, .min = 0, .max = 1, .mode = 1/2, .num_sims = 1, .return_tibble = TRUE )tidy_triangular( .n = 50, .min = 0, .max = 1, .mode = 1/2, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of x values for each simulation. |
.min |
The minimum value of the triangular distribution. |
.max |
The maximum value of the triangular distribution. |
.mode |
The mode (peak) value of the triangular distribution. |
.num_sims |
The number of simulations to perform. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
The function takes parameters for the triangular distribution
(minimum, maximum, mode), the number of x values (n), the number of
simulations (num_sims), and an option to return the result as a tibble
(return_tibble). It performs various checks on the input parameters to ensure
validity. The result is a data frame or tibble with tidy data for
further analysis.
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Triangular:
util_triangular_param_estimate(),
util_triangular_stats_tbl()
tidy_triangular(.return_tibble = TRUE)tidy_triangular(.return_tibble = TRUE)
This function will generate n random points from a uniform
distribution with a user provided, .min and .max values, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_uniform(.n = 50, .min = 0, .max = 1, .num_sims = 1, .return_tibble = TRUE)tidy_uniform(.n = 50, .min = 0, .max = 1, .num_sims = 1, .return_tibble = TRUE)
.n |
The number of randomly generated points you want. |
.min |
A lower limit of the distribution. |
.max |
An upper limit of the distribution |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::runif(), and its underlying
p, d, and q functions. For more information please see stats::runif()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3662.htm
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Uniform:
util_uniform_param_estimate(),
util_uniform_stats_tbl()
tidy_uniform()tidy_uniform()
This function will generate n random points from a weibull
distribution with a user provided, .shape, .scale, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_weibull( .n = 50, .shape = 1, .scale = 1, .num_sims = 1, .return_tibble = TRUE )tidy_weibull( .n = 50, .shape = 1, .scale = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.shape |
Shape parameter defaults to 0. |
.scale |
Scale parameter defaults to 1. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying stats::rweibull(), and its underlying
p, d, and q functions. For more information please see stats::rweibull()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_zero_truncated_geometric()
Other Weibull:
tidy_inverse_weibull(),
util_weibull_param_estimate(),
util_weibull_stats_tbl()
tidy_weibull()tidy_weibull()
This function will generate n random points from a zero truncated binomial
distribution with a user provided, .size, .prob, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_zero_truncated_binomial( .n = 50, .size = 1, .prob = 1, .num_sims = 1, .return_tibble = TRUE )tidy_zero_truncated_binomial( .n = 50, .size = 1, .prob = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.size |
Number of trials, zero or more. |
.prob |
Probability of success on each trial 0 <= prob <= 1. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rztbinom(), and its underlying
p, d, and q functions. For more information please see actuar::rztbinom()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Discrete Distribution:
tidy_bernoulli(),
tidy_binomial(),
tidy_hypergeometric(),
tidy_negative_binomial(),
tidy_poisson(),
tidy_zero_truncated_negative_binomial(),
tidy_zero_truncated_poisson()
Other Binomial:
tidy_binomial(),
tidy_negative_binomial(),
tidy_zero_truncated_negative_binomial(),
util_binomial_param_estimate(),
util_binomial_stats_tbl(),
util_negative_binomial_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_negative_binomial_stats_tbl()
Other Zero Truncated Distribution:
tidy_zero_truncated_geometric(),
tidy_zero_truncated_poisson(),
util_zero_truncated_binomial_param_estimate()
tidy_zero_truncated_binomial()tidy_zero_truncated_binomial()
This function will generate n random points from a zero truncated
Geometric distribution with a user provided, .prob, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_zero_truncated_geometric( .n = 50, .prob = 1, .num_sims = 1, .return_tibble = TRUE )tidy_zero_truncated_geometric( .n = 50, .prob = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.prob |
A probability of success in each trial 0 < prob <= 1. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rztgeom(), and its underlying
p, d, and q functions. For more information please see actuar::rztgeom()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Geometric:
tidy_geometric(),
util_geometric_param_estimate(),
util_geometric_stats_tbl()
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull()
Other Zero Truncated Distribution:
tidy_zero_truncated_binomial(),
tidy_zero_truncated_poisson(),
util_zero_truncated_binomial_param_estimate()
tidy_zero_truncated_geometric()tidy_zero_truncated_geometric()
This function will generate n random points from a zero truncated binomial
distribution with a user provided, .size, .prob, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_zero_truncated_negative_binomial( .n = 50, .size = 0, .prob = 1, .num_sims = 1, .return_tibble = TRUE )tidy_zero_truncated_negative_binomial( .n = 50, .size = 0, .prob = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.size |
Number of trials, zero or more. |
.prob |
Probability of success on each trial 0 <= prob <= 1. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rztnbinom(), and its underlying
p, d, and q functions. For more information please see actuar::rztnbinom()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Discrete Distribution:
tidy_bernoulli(),
tidy_binomial(),
tidy_hypergeometric(),
tidy_negative_binomial(),
tidy_poisson(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_poisson()
Other Binomial:
tidy_binomial(),
tidy_negative_binomial(),
tidy_zero_truncated_binomial(),
util_binomial_param_estimate(),
util_binomial_stats_tbl(),
util_negative_binomial_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_negative_binomial_stats_tbl()
Other Zero Truncated Negative Distribution:
util_zero_truncated_negative_binomial_param_estimate()
tidy_zero_truncated_negative_binomial()tidy_zero_truncated_negative_binomial()
This function will generate n random points from a Zero Truncated
Poisson distribution with a user provided, .lambda, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the d_, p_ and q_ data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number The current simulation number.
x The current value of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::density() function.
p The values from the resulting p_ function of the distribution family.
q The values from the resulting q_ function of the distribution family.
tidy_zero_truncated_poisson( .n = 50, .lambda = 1, .num_sims = 1, .return_tibble = TRUE )tidy_zero_truncated_poisson( .n = 50, .lambda = 1, .num_sims = 1, .return_tibble = TRUE )
.n |
The number of randomly generated points you want. |
.lambda |
A vector of non-negative means. |
.num_sims |
The number of randomly generated simulations you want. |
.return_tibble |
A logical value indicating whether to return the result as a tibble. Default is TRUE. |
This function uses the underlying actuar::rztpois(), and its underlying
p, d, and q functions. For more information please see actuar::rztpois()
A tibble of randomly generated data.
Steven P. Sanderson II, MPH
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Poisson:
tidy_poisson(),
util_poisson_param_estimate(),
util_poisson_stats_tbl(),
util_zero_truncated_poisson_param_estimate(),
util_zero_truncated_poisson_stats_tbl()
Other Zero Truncated Distribution:
tidy_zero_truncated_binomial(),
tidy_zero_truncated_geometric(),
util_zero_truncated_binomial_param_estimate()
Other Discrete Distribution:
tidy_bernoulli(),
tidy_binomial(),
tidy_hypergeometric(),
tidy_negative_binomial(),
tidy_poisson(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial()
tidy_zero_truncated_poisson()tidy_zero_truncated_poisson()
This function generates a probability density function (PDF) plot for the triangular distribution.
triangle_plot(.data, .interactive = FALSE)triangle_plot(.data, .interactive = FALSE)
.data |
Tidy data from the |
.interactive |
A logical value indicating whether to return an interactive plot using plotly. Default is FALSE. |
The function checks if the input data is a data frame or tibble, and if it comes from the tidy_triangular
function. It then extracts necessary attributes for the plot and creates a PDF plot using ggplot2. The plot
includes data points and segments to represent the triangular distribution.
The function returns a ggplot2 object representing the probability density function plot for the triangular distribution.
Steven P. Sanderson II, MPH
# Example: Generating a PDF plot for the triangular distribution data <- tidy_triangular(.n = 50, .min = 0, .max = 1, .mode = 1/2, .num_sims = 1, .return_tibble = TRUE) triangle_plot(data)# Example: Generating a PDF plot for the triangular distribution data <- tidy_triangular(.n = 50, .min = 0, .max = 1, .mode = 1/2, .num_sims = 1, .return_tibble = TRUE) triangle_plot(data)
This function will attempt to estimate the Bernoulli prob parameter
given some vector of values .x. The function will return a list output by default,
and if the parameter .auto_gen_empirical is set to TRUE then the empirical
data given to the parameter .x will be run through the tidy_empirical()
function and combined with the estimated Bernoulli data.
util_bernoulli_param_estimate(.x, .auto_gen_empirical = TRUE)util_bernoulli_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be non-negative integers. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will see if the given vector .x is a numeric vector.
It will attempt to estimate the prob parameter of a Bernoulli distribution.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Bernoulli:
tidy_bernoulli(),
util_bernoulli_stats_tbl()
library(dplyr) library(ggplot2) tb <- tidy_bernoulli(.prob = .1) |> pull(y) output <- util_bernoulli_param_estimate(tb) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) tb <- tidy_bernoulli(.prob = .1) |> pull(y) output <- util_bernoulli_param_estimate(tb) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_bernoulli_stats_tbl(.data)util_bernoulli_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Bernoulli:
tidy_bernoulli(),
util_bernoulli_param_estimate()
Other Distribution Statistics:
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_bernoulli() |> util_bernoulli_stats_tbl() |> glimpse()library(dplyr) tidy_bernoulli() |> util_bernoulli_stats_tbl() |> glimpse()
This function estimates the parameters of a beta distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_beta_aic(.x)util_beta_aic(.x)
.x |
A numeric vector containing the data to be fitted to a beta distribution. |
This function calculates the Akaike Information Criterion (AIC) for a beta distribution fitted to the provided data.
Initial parameter estimates: The choice of initial values can impact the convergence of the optimization.
Optimization method: You might explore different optimization methods within
optim for potentially better performance.
Data transformation: Depending on your data, you may need to apply
transformations (e.g., scaling to [0,1] interval) before fitting the beta
distribution.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted beta distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rbeta(30, 1, 1) util_beta_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rbeta(30, 1, 1) util_beta_aic(x)
This function will automatically scale the data from 0 to 1 if
it is not already. This means you can pass a vector like mtcars$mpg and not
worry about it.
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated beta data.
Three different methods of shape parameters are supplied:
Bayes
NIST mme
EnvStats mme, see EnvStats::ebeta()
util_beta_param_estimate(.x, .auto_gen_empirical = TRUE)util_beta_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be numeric, and all values must be 0 <= x <= 1 |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the beta shape1 and shape2 parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Beta:
tidy_beta(),
tidy_generalized_beta(),
util_beta_stats_tbl()
library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_beta_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() tb <- rbeta(50, 2.5, 1.4) util_beta_param_estimate(tb)$parameter_tbllibrary(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_beta_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() tb <- rbeta(50, 2.5, 1.4) util_beta_param_estimate(tb)$parameter_tbl
Returns distribution statistics in a tibble.
util_beta_stats_tbl(.data)util_beta_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Beta:
tidy_beta(),
tidy_generalized_beta(),
util_beta_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_beta() |> util_beta_stats_tbl() |> glimpse()library(dplyr) tidy_beta() |> util_beta_stats_tbl() |> glimpse()
This function estimates the size and probability parameters of a binomial distribution from the provided data and then calculates the AIC value based on the fitted distribution.
util_binomial_aic(.x)util_binomial_aic(.x)
.x |
A numeric vector containing the data to be fitted to a binomial distribution. |
This function calculates the Akaike Information Criterion (AIC) for a binomial distribution fitted to the provided data.
This function fits a binomial distribution to the provided data. It estimates the size and probability parameters of the binomial distribution from the data. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the size and probability parameters of the binomial distribution.
Optimization method: Since the parameters are directly calculated from the data, no optimization is needed.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted binomial distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rbinom(30, size = 10, prob = 0.2) util_binomial_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rbinom(30, size = 10, prob = 0.2) util_binomial_aic(x)
This function will check to see if some given vector .x is
either a numeric vector or a factor vector with at least two levels then it
will cause an error and the function will abort. The function will return a
list output by default, and if the parameter .auto_gen_empirical is set to
TRUE then the empirical data given to the parameter .x will be run through
the tidy_empirical() function and combined with the estimated binomial data.
util_binomial_param_estimate(.x, .size = NULL, .auto_gen_empirical = TRUE)util_binomial_param_estimate(.x, .size = NULL, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be numeric, and all values must be 0 <= x <= 1 |
.size |
Number of trials, zero or more. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the binomial p_hat and size parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Binomial:
tidy_binomial(),
tidy_negative_binomial(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
util_binomial_stats_tbl(),
util_negative_binomial_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_negative_binomial_stats_tbl()
library(dplyr) library(ggplot2) tb <- rbinom(50, 1, .1) output <- util_binomial_param_estimate(tb) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) tb <- rbinom(50, 1, .1) output <- util_binomial_param_estimate(tb) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_binomial_stats_tbl(.data)util_binomial_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Binomial:
tidy_binomial(),
tidy_negative_binomial(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
util_binomial_param_estimate(),
util_negative_binomial_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_negative_binomial_stats_tbl()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_binomial() |> util_binomial_stats_tbl() |> glimpse()library(dplyr) tidy_binomial() |> util_binomial_stats_tbl() |> glimpse()
This function will attempt to estimate the Burr prob parameter
given some vector of values .x. The function will return a list output by default,
and if the parameter .auto_gen_empirical is set to TRUE then the empirical
data given to the parameter .x will be run through the tidy_empirical()
function and combined with the estimated Burr data.
util_burr_param_estimate(.x, .auto_gen_empirical = TRUE)util_burr_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be non-negative integers. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will see if the given vector .x is a numeric vector.
It will attempt to estimate the prob parameter of a Burr distribution.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Burr:
tidy_burr(),
tidy_inverse_burr(),
util_burr_stats_tbl()
library(dplyr) library(ggplot2) tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) |> pull(y) output <- util_burr_param_estimate(tb) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) |> pull(y) output <- util_burr_param_estimate(tb) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_burr_stats_tbl(.data)util_burr_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Burr:
tidy_burr(),
tidy_inverse_burr(),
util_burr_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_burr() |> util_burr_stats_tbl() |> glimpse()library(dplyr) tidy_burr() |> util_burr_stats_tbl() |> glimpse()
This function estimates the parameters of a Cauchy distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_cauchy_aic(.x)util_cauchy_aic(.x)
.x |
A numeric vector containing the data to be fitted to a Cauchy distribution. |
This function calculates the Akaike Information Criterion (AIC) for a Cauchy distribution fitted to the provided data.
This function fits a Cauchy distribution to the provided data using maximum likelihood estimation. It first estimates the initial parameters of the Cauchy distribution using the method of moments. Then, it optimizes the negative log-likelihood function using the provided data and the initial parameter estimates. Finally, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates for the initial location and scale parameters of the Cauchy distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted Cauchy distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rcauchy(30) util_cauchy_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rcauchy(30) util_cauchy_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated cauchy data.
util_cauchy_param_estimate(.x, .auto_gen_empirical = TRUE)util_cauchy_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the cauchy location and scale parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Cauchy:
tidy_cauchy(),
util_cauchy_stats_tbl()
library(dplyr) library(ggplot2) x <- tidy_cauchy(.location = 0, .scale = 1)$y output <- util_cauchy_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) x <- tidy_cauchy(.location = 0, .scale = 1)$y output <- util_cauchy_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_cauchy_stats_tbl(.data)util_cauchy_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Cauchy:
tidy_cauchy(),
util_cauchy_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_cauchy() |> util_cauchy_stats_tbl() |> glimpse()library(dplyr) tidy_cauchy() |> util_cauchy_stats_tbl() |> glimpse()
This function estimates the parameters of a chi-square distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_chisq_aic(.x)util_chisq_aic(.x)
.x |
A numeric vector containing the data to be fitted to a chi-square distribution. |
This function calculates the Akaike Information Criterion (AIC) for a chi-square distribution fitted to the provided data.
The AIC value calculated based on the fitted chi-square distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rchisq(30, df = 3) util_chisq_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rchisq(30, df = 3) util_chisq_aic(x)
This function will attempt to estimate the Chisquare prob parameter
given some vector of values .x. The function will return a list output by default,
and if the parameter .auto_gen_empirical is set to TRUE then the empirical
data given to the parameter .x will be run through the tidy_empirical()
function and combined with the estimated Chisquare data.
util_chisquare_param_estimate(.x, .auto_gen_empirical = TRUE)util_chisquare_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be non-negative integers. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will see if the given vector .x is a numeric vector.
It will attempt to estimate the prob parameter of a Chisquare distribution.
The function first performs tidyeval on the input data to ensure it's a
numeric vector. It then checks if there are at least two data points, as this
is a requirement for parameter estimation.
The estimation of the chi-square distribution parameters is performed using
maximum likelihood estimation (MLE) implemented with the bbmle package.
The negative log-likelihood function is minimized to obtain the estimates for
the degrees of freedom (doff) and the non-centrality parameter (ncp).
Initial values for the optimization are set based on the sample variance and
mean, but these can be adjusted if necessary.
If the estimation fails or encounters an error, the function returns NA
for both doff and ncp.
Finally, the function returns a tibble containing the following information:
The type of distribution, which is "Chisquare" in this case.
The sample size, i.e., the number of data points in the input vector.
The minimum value of the data points.
The maximum value of the data points.
The mean of the data points.
The estimated degrees of freedom (doff) for the chi-square distribution.
The estimated non-centrality parameter (ncp) for the chi-square distribution.
Additionally, if the argument .auto_gen_empirical is set to TRUE
(which is the default behavior), the function also returns a combined tibble
containing both empirical and chi-square distribution data, obtained by
calling tidy_empirical and tidy_chisquare, respectively.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Chisquare:
tidy_chisquare(),
util_chisquare_stats_tbl()
library(dplyr) library(ggplot2) tc <- tidy_chisquare(.n = 500, .df = 6, .ncp = 1) |> pull(y) output <- util_chisquare_param_estimate(tc) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) tc <- tidy_chisquare(.n = 500, .df = 6, .ncp = 1) |> pull(y) output <- util_chisquare_param_estimate(tc) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_chisquare_stats_tbl(.data)util_chisquare_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Chisquare:
tidy_chisquare(),
util_chisquare_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_chisquare() |> util_chisquare_stats_tbl() |> glimpse()library(dplyr) tidy_chisquare() |> util_chisquare_stats_tbl() |> glimpse()
This function estimates the rate parameter of an exponential distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_exponential_aic(.x)util_exponential_aic(.x)
.x |
A numeric vector containing the data to be fitted to an exponential distribution. |
This function calculates the Akaike Information Criterion (AIC) for an exponential distribution fitted to the provided data.
This function fits an exponential distribution to the provided data using maximum likelihood estimation. It estimates the rate parameter of the exponential distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the reciprocal of the mean of the data as the initial estimate for the rate parameter.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted exponential distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rexp(30) util_exponential_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rexp(30) util_exponential_aic(x)
This function will attempt to estimate the exponential rate parameter
given some vector of values. The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated exponential data.
util_exponential_param_estimate(.x, .auto_gen_empirical = TRUE)util_exponential_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be numeric. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will see if the given vector .x is a numeric vector.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Exponential:
tidy_exponential(),
tidy_inverse_exponential(),
util_exponential_stats_tbl()
library(dplyr) library(ggplot2) te <- tidy_exponential(.rate = .1) |> pull(y) output <- util_exponential_param_estimate(te) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) te <- tidy_exponential(.rate = .1) |> pull(y) output <- util_exponential_param_estimate(te) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_exponential_stats_tbl(.data)util_exponential_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Exponential:
tidy_exponential(),
tidy_inverse_exponential(),
util_exponential_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_exponential() |> util_exponential_stats_tbl() |> glimpse()library(dplyr) tidy_exponential() |> util_exponential_stats_tbl() |> glimpse()
This function estimates the parameters of a F distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_f_aic(.x)util_f_aic(.x)
.x |
A numeric vector containing the data to be fitted to an F distribution. |
This function calculates the Akaike Information Criterion (AIC) for an F distribution fitted to the provided data.
This function fits an F distribution to the input data using maximum likelihood estimation and then computes the Akaike Information Criterion (AIC) based on the fitted distribution.
The AIC value calculated based on the fitted F distribution to the provided data.
Steven P. Sanderson II, MPH
rf for generating F-distributed data,
optim for optimization.
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Generate F-distributed data set.seed(123) x <- rf(100, df1 = 5, df2 = 10, ncp = 1) # Calculate AIC for the generated data util_f_aic(x)# Generate F-distributed data set.seed(123) x <- rf(100, df1 = 5, df2 = 10, ncp = 1) # Calculate AIC for the generated data util_f_aic(x)
Estimate F Distribution Parameters
util_f_param_estimate(.x, .auto_gen_empirical = TRUE)util_f_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function, where the data
comes from the |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the F distribution parameters
given some vector of values produced by rf(). The estimation method
is from the NIST Engineering Statistics Handbook.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other F Distribution:
tidy_f(),
util_f_stats_tbl()
library(dplyr) library(ggplot2) set.seed(123) x <- rf(100, df1 = 5, df2 = 10, ncp = 1) output <- util_f_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) set.seed(123) x <- rf(100, df1 = 5, df2 = 10, ncp = 1) output <- util_f_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_f_stats_tbl(.data)util_f_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other F Distribution:
tidy_f(),
util_f_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_f() |> util_f_stats_tbl() |> glimpse()library(dplyr) tidy_f() |> util_f_stats_tbl() |> glimpse()
This function estimates the shape and scale parameters of a gamma distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_gamma_aic(.x)util_gamma_aic(.x)
.x |
A numeric vector containing the data to be fitted to a gamma distribution. |
This function calculates the Akaike Information Criterion (AIC) for a gamma distribution fitted to the provided data.
This function fits a gamma distribution to the provided data using maximum likelihood estimation. It estimates the shape and scale parameters of the gamma distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape and scale parameters of the gamma distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted gamma distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rgamma(30, shape = 1) util_gamma_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rgamma(30, shape = 1) util_gamma_aic(x)
This function will attempt to estimate the gamma shape and scale
parameters given some vector of values. The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated gamma data.
util_gamma_param_estimate(.x, .auto_gen_empirical = TRUE)util_gamma_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be numeric. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will see if the given vector .x is a numeric vector.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Gamma:
tidy_gamma(),
tidy_inverse_gamma(),
util_gamma_stats_tbl()
library(dplyr) library(ggplot2) tg <- tidy_gamma(.shape = 1, .scale = .3) |> pull(y) output <- util_gamma_param_estimate(tg) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) tg <- tidy_gamma(.shape = 1, .scale = .3) |> pull(y) output <- util_gamma_param_estimate(tg) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_gamma_stats_tbl(.data)util_gamma_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Gamma:
tidy_gamma(),
tidy_inverse_gamma(),
util_gamma_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_gamma() |> util_gamma_stats_tbl() |> glimpse()library(dplyr) tidy_gamma() |> util_gamma_stats_tbl() |> glimpse()
This function estimates the shape1, shape2, shape3, and rate parameters of a generalized Beta distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_generalized_beta_aic(.x)util_generalized_beta_aic(.x)
.x |
A numeric vector containing the data to be fitted to a generalized Beta distribution. |
This function calculates the Akaike Information Criterion (AIC) for a generalized Beta distribution fitted to the provided data.
This function fits a generalized Beta distribution to the provided data using maximum likelihood estimation. It estimates the shape1, shape2, shape3, and rate parameters of the generalized Beta distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses reasonable initial estimates for the shape1, shape2, shape3, and rate parameters of the generalized Beta distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted generalized Beta distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_generalized_beta(100, .shape1 = 2, .shape2 = 3, .shape3 = 4, .rate = 5)[["y"]] util_generalized_beta_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_generalized_beta(100, .shape1 = 2, .shape2 = 3, .shape3 = 4, .rate = 5)[["y"]] util_generalized_beta_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated generalized Beta data.
util_generalized_beta_param_estimate(.x, .auto_gen_empirical = TRUE)util_generalized_beta_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the generalized Beta shape1, shape2, shape3, and rate parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Generalized Beta:
util_generalized_beta_stats_tbl()
library(dplyr) library(ggplot2) set.seed(123) x <- tidy_generalized_beta(100, .shape1 = 2, .shape2 = 3, .shape3 = 4, .rate = 5)[["y"]] output <- util_generalized_beta_param_estimate(x) output$parameter_tbl output$combined_data_tbl %>% tidy_combined_autoplot()library(dplyr) library(ggplot2) set.seed(123) x <- tidy_generalized_beta(100, .shape1 = 2, .shape2 = 3, .shape3 = 4, .rate = 5)[["y"]] output <- util_generalized_beta_param_estimate(x) output$parameter_tbl output$combined_data_tbl %>% tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_generalized_beta_stats_tbl(.data)util_generalized_beta_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and return the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Generalized Beta:
util_generalized_beta_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) set.seed(123) tidy_generalized_beta() |> util_generalized_beta_stats_tbl() |> glimpse()library(dplyr) set.seed(123) tidy_generalized_beta() |> util_generalized_beta_stats_tbl() |> glimpse()
This function estimates the shape1, shape2, and rate parameters of a generalized Pareto distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_generalized_pareto_aic(.x)util_generalized_pareto_aic(.x)
.x |
A numeric vector containing the data to be fitted to a generalized Pareto distribution. |
This function calculates the Akaike Information Criterion (AIC) for a generalized Pareto distribution fitted to the provided data.
This function fits a generalized Pareto distribution to the provided data using maximum likelihood estimation. It estimates the shape1, shape2, and rate parameters of the generalized Pareto distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape1, shape2, and rate parameters of the generalized Pareto distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted generalized Pareto distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- actuar::rgenpareto(100, shape1 = 1, shape2 = 2, scale = 3) util_generalized_pareto_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- actuar::rgenpareto(100, shape1 = 1, shape2 = 2, scale = 3) util_generalized_pareto_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated generalized Pareto data.
util_generalized_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)util_generalized_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the generalized Pareto shape1, shape2, and rate parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Generalized Pareto:
util_generalized_pareto_stats_tbl()
library(dplyr) library(ggplot2) set.seed(123) x <- tidy_generalized_pareto(100, .shape1 = 1, .shape2 = 2, .scale = 3)[["y"]] output <- util_generalized_pareto_param_estimate(x) output$parameter_tbl output$combined_data_tbl %>% tidy_combined_autoplot()library(dplyr) library(ggplot2) set.seed(123) x <- tidy_generalized_pareto(100, .shape1 = 1, .shape2 = 2, .scale = 3)[["y"]] output <- util_generalized_pareto_param_estimate(x) output$parameter_tbl output$combined_data_tbl %>% tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_generalized_pareto_stats_tbl(.data)util_generalized_pareto_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and return the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Generalized Pareto:
util_generalized_pareto_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_generalized_pareto() |> util_generalized_pareto_stats_tbl() |> glimpse()library(dplyr) tidy_generalized_pareto() |> util_generalized_pareto_stats_tbl() |> glimpse()
This function estimates the probability parameter of a geometric distribution from the provided data and then calculates the AIC value based on the fitted distribution.
util_geometric_aic(.x)util_geometric_aic(.x)
.x |
A numeric vector containing the data to be fitted to a geometric distribution. |
This function calculates the Akaike Information Criterion (AIC) for a geometric distribution fitted to the provided data.
This function fits a geometric distribution to the provided data. It estimates the probability parameter of the geometric distribution from the data. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimate as a starting point for the probability parameter of the geometric distribution.
Optimization method: Since the parameter is directly calculated from the data, no optimization is needed.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted geometric distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rgeom(100, prob = 0.2) util_geometric_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rgeom(100, prob = 0.2) util_geometric_aic(x)
This function will attempt to estimate the geometric prob parameter
given some vector of values .x. The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated geometric data.
util_geometric_param_estimate(.x, .auto_gen_empirical = TRUE)util_geometric_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be non-negative integers. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will see if the given vector .x is a numeric vector.
It will attempt to estimate the prob parameter of a geometric distribution.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Geometric:
tidy_geometric(),
tidy_zero_truncated_geometric(),
util_geometric_stats_tbl()
library(dplyr) library(ggplot2) tg <- tidy_geometric(.prob = .1) |> pull(y) output <- util_geometric_param_estimate(tg) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) tg <- tidy_geometric(.prob = .1) |> pull(y) output <- util_geometric_param_estimate(tg) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_geometric_stats_tbl(.data)util_geometric_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Geometric:
tidy_geometric(),
tidy_zero_truncated_geometric(),
util_geometric_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_geometric() |> util_geometric_stats_tbl() |> glimpse()library(dplyr) tidy_geometric() |> util_geometric_stats_tbl() |> glimpse()
This function estimates the parameters m, n, and k of a hypergeometric distribution from the provided data and then calculates the AIC value based on the fitted distribution.
util_hypergeometric_aic(.x)util_hypergeometric_aic(.x)
.x |
A numeric vector containing the data to be fitted to a hypergeometric distribution. |
This function calculates the Akaike Information Criterion (AIC) for a hypergeometric distribution fitted to the provided data.
This function fits a hypergeometric distribution to the provided data. It estimates the parameters m, n, and k of the hypergeometric distribution from the data. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function does not estimate parameters; they are directly calculated from the data.
Optimization method: Since the parameters are directly calculated from the data, no optimization is needed.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted hypergeometric distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rhyper(100, m = 10, n = 10, k = 5) util_hypergeometric_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rhyper(100, m = 10, n = 10, k = 5) util_hypergeometric_aic(x)
This function will attempt to estimate the geometric prob parameter
given some vector of values .x. Estimate m, the number of white balls in
the urn, or m+n, the total number of balls in the urn, for a hypergeometric
distribution.
util_hypergeometric_param_estimate( .x, .m = NULL, .total = NULL, .k, .auto_gen_empirical = TRUE )util_hypergeometric_param_estimate( .x, .m = NULL, .total = NULL, .k, .auto_gen_empirical = TRUE )
.x |
A non-negative integer indicating the number of white balls out of a
sample of size |
.m |
Non-negative integer indicating the number of white balls in the urn.
You must supply |
.total |
A positive integer indicating the total number of balls in the
urn (i.e., m+n). You must supply |
.k |
A positive integer indicating the number of balls drawn without replacement from the urn. You cannot have missing values. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will see if the given vector .x is a numeric integer.
It will attempt to estimate the prob parameter of a geometric distribution.
Missing (NA), undefined (NaN), and infinite (Inf, -Inf) values are not allowed.
Let .x be an observation from a hypergeometric distribution with parameters
.m = M, .n = N, and .k = K. In R nomenclature, .x represents
the number of white balls drawn out of a sample of .k balls drawn without
replacement from an urn containing .m white balls and .n black balls.
The total number of balls in the urn is thus .m + .n. Denote the total
number of balls by T = .m + .n
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Hypergeometric:
tidy_hypergeometric(),
util_hypergeometric_stats_tbl()
library(dplyr) library(ggplot2) th <- rhyper(10, 20, 30, 5) output <- util_hypergeometric_param_estimate(th, .total = 50, .k = 5) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) th <- rhyper(10, 20, 30, 5) output <- util_hypergeometric_param_estimate(th, .total = 50, .k = 5) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_hypergeometric_stats_tbl(.data)util_hypergeometric_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Hypergeometric:
tidy_hypergeometric(),
util_hypergeometric_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_hypergeometric() |> util_hypergeometric_stats_tbl() |> glimpse()library(dplyr) tidy_hypergeometric() |> util_hypergeometric_stats_tbl() |> glimpse()
This function estimates the shape1, shape2, and rate parameters of an inverse Burr distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_inverse_burr_aic(.x)util_inverse_burr_aic(.x)
.x |
A numeric vector containing the data to be fitted to an inverse Burr distribution. |
This function calculates the Akaike Information Criterion (AIC) for an inverse Burr distribution fitted to the provided data.
This function fits an inverse Burr distribution to the provided data using maximum likelihood estimation. It estimates the shape1, shape2, and rate parameters of the inverse Burr distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape1, shape2, and rate parameters of the inverse Burr distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted inverse Burr distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_inverse_burr(100, .shape1 = 2, .shape2 = 3, .scale = 1)[["y"]] util_inverse_burr_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_inverse_burr(100, .shape1 = 2, .shape2 = 3, .scale = 1)[["y"]] util_inverse_burr_aic(x)
This function will attempt to estimate the inverse Burr shape1, shape2, and rate parameters
given some vector of values .x. The function will return a list output by default,
and if the parameter .auto_gen_empirical is set to TRUE then the empirical
data given to the parameter .x will be run through the tidy_empirical()
function and combined with the estimated inverse Burr data.
util_inverse_burr_param_estimate(.x, .auto_gen_empirical = TRUE)util_inverse_burr_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be non-negative integers. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will see if the given vector .x is a numeric vector.
It will attempt to estimate the shape1, shape2, and rate parameters of an inverse
Burr distribution.
A tibble/list
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Inverse Burr:
util_inverse_burr_stats_tbl()
library(dplyr) library(ggplot2) set.seed(123) tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) |> pull(y) output <- util_inverse_burr_param_estimate(tb) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) set.seed(123) tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) |> pull(y) output <- util_inverse_burr_param_estimate(tb) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_inverse_burr_stats_tbl(.data)util_inverse_burr_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Inverse Burr:
util_inverse_burr_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) set.seed(123) tidy_inverse_burr() |> util_inverse_burr_stats_tbl() |> glimpse()library(dplyr) set.seed(123) tidy_inverse_burr() |> util_inverse_burr_stats_tbl() |> glimpse()
This function estimates the shape and scale parameters of an inverse Pareto distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_inverse_pareto_aic(.x)util_inverse_pareto_aic(.x)
.x |
A numeric vector containing the data to be fitted to an inverse Pareto distribution. |
This function calculates the Akaike Information Criterion (AIC) for an inverse Pareto distribution fitted to the provided data.
This function fits an inverse Pareto distribution to the provided data using maximum likelihood estimation. It estimates the shape and scale parameters of the inverse Pareto distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape and scale parameters of the inverse Pareto distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted inverse Pareto distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]] util_inverse_pareto_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]] util_inverse_pareto_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated inverse Pareto data.
util_inverse_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)util_inverse_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the inverse Pareto shape and scale parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Inverse Pareto:
util_inverse_pareto_stats_tbl()
library(dplyr) library(ggplot2) set.seed(123) x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]] output <- util_inverse_pareto_param_estimate(x) output$parameter_tbl output$combined_data_tbl %>% tidy_combined_autoplot()library(dplyr) library(ggplot2) set.seed(123) x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]] output <- util_inverse_pareto_param_estimate(x) output$parameter_tbl output$combined_data_tbl %>% tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_inverse_pareto_stats_tbl(.data)util_inverse_pareto_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Inverse Pareto:
util_inverse_pareto_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_inverse_pareto() |> util_inverse_pareto_stats_tbl() |> glimpse()library(dplyr) tidy_inverse_pareto() |> util_inverse_pareto_stats_tbl() |> glimpse()
This function estimates the shape and scale parameters of an inverse Weibull distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_inverse_weibull_aic(.x)util_inverse_weibull_aic(.x)
.x |
A numeric vector containing the data to be fitted to an inverse Weibull distribution. |
This function calculates the Akaike Information Criterion (AIC) for an inverse Weibull distribution fitted to the provided data.
This function fits an inverse Weibull distribution to the provided data using maximum likelihood estimation. It estimates the shape and scale parameters of the inverse Weibull distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape and scale parameters of the inverse Weibull distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted inverse Weibull distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_inverse_weibull(.n = 100, .shape = 2, .scale = 1)[["y"]] util_inverse_weibull_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_inverse_weibull(.n = 100, .shape = 2, .scale = 1)[["y"]] util_inverse_weibull_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated inverse Weibull data.
util_inverse_weibull_param_estimate(.x, .auto_gen_empirical = TRUE)util_inverse_weibull_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the inverse Weibull shape and rate parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Inverse Weibull:
util_inverse_weibull_stats_tbl()
library(dplyr) library(ggplot2) set.seed(123) x <- tidy_inverse_weibull(100, .shape = 2, .scale = 1)[["y"]] output <- util_inverse_weibull_param_estimate(x) output$parameter_tbl output$combined_data_tbl %>% tidy_combined_autoplot()library(dplyr) library(ggplot2) set.seed(123) x <- tidy_inverse_weibull(100, .shape = 2, .scale = 1)[["y"]] output <- util_inverse_weibull_param_estimate(x) output$parameter_tbl output$combined_data_tbl %>% tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_inverse_weibull_stats_tbl(.data)util_inverse_weibull_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Inverse Weibull:
util_inverse_weibull_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) set.seed(123) tidy_inverse_weibull() |> util_inverse_weibull_stats_tbl() |> glimpse()library(dplyr) set.seed(123) tidy_inverse_weibull() |> util_inverse_weibull_stats_tbl() |> glimpse()
This function estimates the location and scale parameters of a logistic distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_logistic_aic(.x)util_logistic_aic(.x)
.x |
A numeric vector containing the data to be fitted to a logistic distribution. |
This function calculates the Akaike Information Criterion (AIC) for a logistic distribution fitted to the provided data.
This function fits a logistic distribution to the provided data using maximum likelihood estimation. It estimates the location and scale parameters of the logistic distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the location and scale parameters of the logistic distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted logistic distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rlogis(30) util_logistic_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rlogis(30) util_logistic_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated logistic data.
Three different methods of shape parameters are supplied:
MLE
MME
MMUE
util_logistic_param_estimate(.x, .auto_gen_empirical = TRUE)util_logistic_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the logistic location and scale parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Logistic:
tidy_logistic(),
tidy_paralogistic(),
util_logistic_stats_tbl()
library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_logistic_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- rlogis(50, 2.5, 1.4) util_logistic_param_estimate(t)$parameter_tbllibrary(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_logistic_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- rlogis(50, 2.5, 1.4) util_logistic_param_estimate(t)$parameter_tbl
Returns distribution statistics in a tibble.
util_logistic_stats_tbl(.data)util_logistic_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Logistic:
tidy_logistic(),
tidy_paralogistic(),
util_logistic_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_logistic() |> util_logistic_stats_tbl() |> glimpse()library(dplyr) tidy_logistic() |> util_logistic_stats_tbl() |> glimpse()
This function estimates the meanlog and sdlog parameters of a log-normal distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_lognormal_aic(.x)util_lognormal_aic(.x)
.x |
A numeric vector containing the data to be fitted to a log-normal distribution. |
This function calculates the Akaike Information Criterion (AIC) for a log-normal distribution fitted to the provided data.
This function fits a log-normal distribution to the provided data using maximum likelihood estimation. It estimates the meanlog and sdlog parameters of the log-normal distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the meanlog and sdlog parameters of the log-normal distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted log-normal distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rlnorm(100, meanlog = 0, sdlog = 1) util_lognormal_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rlnorm(100, meanlog = 0, sdlog = 1) util_lognormal_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated lognormal data.
Three different methods of shape parameters are supplied:
mme, see EnvStats::elnorm()
mle, see EnvStats::elnorm()
util_lognormal_param_estimate(.x, .auto_gen_empirical = TRUE)util_lognormal_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the lognormal meanlog and log sd parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Lognormal:
tidy_lognormal(),
util_lognormal_stats_tbl()
library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_lognormal_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() tb <- tidy_lognormal(.meanlog = 2, .sdlog = 1) |> pull(y) util_lognormal_param_estimate(tb)$parameter_tbllibrary(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_lognormal_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() tb <- tidy_lognormal(.meanlog = 2, .sdlog = 1) |> pull(y) util_lognormal_param_estimate(tb)$parameter_tbl
Returns distribution statistics in a tibble.
util_lognormal_stats_tbl(.data)util_lognormal_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Lognormal:
tidy_lognormal(),
util_lognormal_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_lognormal() |> util_lognormal_stats_tbl() |> glimpse()library(dplyr) tidy_lognormal() |> util_lognormal_stats_tbl() |> glimpse()
This function estimates the parameters size (r) and probability (prob) of a negative binomial distribution from the provided data and then calculates the AIC value based on the fitted distribution.
util_negative_binomial_aic(.x)util_negative_binomial_aic(.x)
.x |
A numeric vector containing the data to be fitted to a negative binomial distribution. |
This function calculates the Akaike Information Criterion (AIC) for a negative binomial distribution fitted to the provided data.
This function fits a negative binomial distribution to the provided data. It estimates the parameters size (r) and probability (prob) of the negative binomial distribution from the data. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimate as a starting point for the size (r) parameter of the negative binomial distribution, and the probability (prob) is estimated based on the mean and variance of the data.
Optimization method: Since the parameters are directly calculated from the data, no optimization is needed.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted negative binomial distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) data <- rnbinom(n = 100, size = 5, mu = 10) util_negative_binomial_aic(data)# Example 1: Calculate AIC for a sample dataset set.seed(123) data <- rnbinom(n = 100, size = 5, mu = 10) util_negative_binomial_aic(data)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated negative binomial data.
Three different methods of shape parameters are supplied:
MLE/MME
MMUE
MLE via optim function.
util_negative_binomial_param_estimate( .x, .size = 1, .auto_gen_empirical = TRUE )util_negative_binomial_param_estimate( .x, .size = 1, .auto_gen_empirical = TRUE )
.x |
The vector of data to be passed to the function. |
.size |
The size parameter, the default is 1. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the negative binomial size and prob parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Binomial:
tidy_binomial(),
tidy_negative_binomial(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
util_binomial_param_estimate(),
util_binomial_stats_tbl(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_negative_binomial_stats_tbl()
library(dplyr) library(ggplot2) x <- as.integer(mtcars$mpg) output <- util_negative_binomial_param_estimate(x, .size = 1) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- rnbinom(50, 1, .1) util_negative_binomial_param_estimate(t, .size = 1)$parameter_tbllibrary(dplyr) library(ggplot2) x <- as.integer(mtcars$mpg) output <- util_negative_binomial_param_estimate(x, .size = 1) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- rnbinom(50, 1, .1) util_negative_binomial_param_estimate(t, .size = 1)$parameter_tbl
Returns distribution statistics in a tibble.
util_negative_binomial_stats_tbl(.data)util_negative_binomial_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Negative Binomial:
util_zero_truncated_negative_binomial_stats_tbl()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_negative_binomial() |> util_negative_binomial_stats_tbl() |> glimpse()library(dplyr) tidy_negative_binomial() |> util_negative_binomial_stats_tbl() |> glimpse()
This function estimates the parameters of a normal distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_normal_aic(.x)util_normal_aic(.x)
.x |
A numeric vector containing the data to be fitted to a normal distribution. |
This function calculates the Akaike Information Criterion (AIC) for a normal distribution fitted to the provided data.
The AIC value calculated based on the fitted normal distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) data <- rnorm(30) util_normal_aic(data)# Example 1: Calculate AIC for a sample dataset set.seed(123) data <- rnorm(30) util_normal_aic(data)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated normal data.
Three different methods of shape parameters are supplied:
MLE/MME
MVUE
util_normal_param_estimate(.x, .auto_gen_empirical = TRUE)util_normal_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the normal gaussian mean and standard deviation parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Gaussian:
tidy_inverse_normal(),
tidy_normal(),
util_normal_stats_tbl()
library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_normal_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- rnorm(50, 0, 1) util_normal_param_estimate(t)$parameter_tbllibrary(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_normal_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- rnorm(50, 0, 1) util_normal_param_estimate(t)$parameter_tbl
Returns distribution statistics in a tibble.
util_normal_stats_tbl(.data)util_normal_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Gaussian:
tidy_inverse_normal(),
tidy_normal(),
util_normal_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_normal() |> util_normal_stats_tbl() |> glimpse()library(dplyr) tidy_normal() |> util_normal_stats_tbl() |> glimpse()
This function estimates the shape and rate parameters of a paralogistic distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_paralogistic_aic(.x)util_paralogistic_aic(.x)
.x |
A numeric vector containing the data to be fitted to a paralogistic distribution. |
This function calculates the Akaike Information Criterion (AIC) for a paralogistic distribution fitted to the provided data.
This function fits a paralogistic distribution to the provided data using maximum likelihood estimation. It estimates the shape and rate parameters of the paralogistic distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape and rate parameters of the paralogistic distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted paralogistic distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
Other Paralogistic:
util_paralogistic_param_estimate(),
util_paralogistic_stats_tbl()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_paralogistic(30, .shape = 2, .rate = 1)[["y"]] util_paralogistic_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_paralogistic(30, .shape = 2, .rate = 1)[["y"]] util_paralogistic_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated paralogistic data.
The method of parameter estimation is:
MLE
util_paralogistic_param_estimate(.x, .auto_gen_empirical = TRUE)util_paralogistic_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the paralogistic shape and rate parameters given some vector of values.
A tibble/list
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Paralogistic:
util_paralogistic_aic(),
util_paralogistic_stats_tbl()
library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_paralogistic_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- tidy_paralogistic(50, 2.5, 1.4)[["y"]] util_paralogistic_param_estimate(t)$parameter_tbllibrary(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_paralogistic_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- tidy_paralogistic(50, 2.5, 1.4)[["y"]] util_paralogistic_param_estimate(t)$parameter_tbl
Returns distribution statistics in a tibble.
util_paralogistic_stats_tbl(.data)util_paralogistic_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Other Paralogistic:
util_paralogistic_aic(),
util_paralogistic_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) set.seed(123) tidy_paralogistic(.n = 50, .shape = 5, .rate = 6) |> util_paralogistic_stats_tbl() |> glimpse()library(dplyr) set.seed(123) tidy_paralogistic(.n = 50, .shape = 5, .rate = 6) |> util_paralogistic_stats_tbl() |> glimpse()
This function estimates the shape and scale parameters of a Pareto distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_pareto_aic(.x)util_pareto_aic(.x)
.x |
A numeric vector containing the data to be fitted to a Pareto distribution. |
This function calculates the Akaike Information Criterion (AIC) for a Pareto distribution fitted to the provided data.
This function fits a Pareto distribution to the provided data using maximum likelihood estimation. It estimates the shape and scale parameters of the Pareto distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape and scale parameters of the Pareto distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted Pareto distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- TidyDensity::tidy_pareto()$y util_pareto_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- TidyDensity::tidy_pareto()$y util_pareto_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated pareto data.
Two different methods of shape parameters are supplied:
LSE
MLE
util_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)util_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the pareto shape and scale parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Pareto:
tidy_generalized_pareto(),
tidy_inverse_pareto(),
tidy_pareto(),
tidy_pareto1(),
util_pareto1_aic(),
util_pareto1_param_estimate(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl()
library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_pareto_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- tidy_pareto(50, 1, 1) |> pull(y) util_pareto_param_estimate(t)$parameter_tbllibrary(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_pareto_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- tidy_pareto(50, 1, 1) |> pull(y) util_pareto_param_estimate(t)$parameter_tbl
Returns distribution statistics in a tibble.
util_pareto_stats_tbl(.data)util_pareto_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Pareto:
tidy_generalized_pareto(),
tidy_inverse_pareto(),
tidy_pareto(),
tidy_pareto1(),
util_pareto1_aic(),
util_pareto1_param_estimate(),
util_pareto1_stats_tbl(),
util_pareto_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_pareto() |> util_pareto_stats_tbl() |> glimpse()library(dplyr) tidy_pareto() |> util_pareto_stats_tbl() |> glimpse()
This function estimates the shape and scale parameters of a Pareto distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_pareto1_aic(.x)util_pareto1_aic(.x)
.x |
A numeric vector containing the data to be fitted to a Pareto distribution. |
This function calculates the Akaike Information Criterion (AIC) for a Pareto distribution fitted to the provided data.
This function fits a Pareto distribution to the provided data using maximum likelihood estimation. It estimates the shape and scale parameters of the Pareto distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape and scale parameters of the Pareto distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted Pareto distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
Other Pareto:
tidy_generalized_pareto(),
tidy_inverse_pareto(),
tidy_pareto(),
tidy_pareto1(),
util_pareto1_param_estimate(),
util_pareto1_stats_tbl(),
util_pareto_param_estimate(),
util_pareto_stats_tbl()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_pareto1()$y util_pareto1_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_pareto1()$y util_pareto1_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated Pareto data.
Two different methods of shape parameters are supplied:
LSE
MLE
util_pareto1_param_estimate(.x, .auto_gen_empirical = TRUE)util_pareto1_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the Pareto shape and scale parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Pareto:
tidy_generalized_pareto(),
tidy_inverse_pareto(),
tidy_pareto(),
tidy_pareto1(),
util_pareto1_aic(),
util_pareto1_stats_tbl(),
util_pareto_param_estimate(),
util_pareto_stats_tbl()
library(dplyr) library(ggplot2) x <- mtcars[["mpg"]] output <- util_pareto1_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- tidy_pareto1(.n = 100, .shape = 1.5, .min = 1)[["y"]] util_pareto1_param_estimate(t)$parameter_tbllibrary(dplyr) library(ggplot2) x <- mtcars[["mpg"]] output <- util_pareto1_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- tidy_pareto1(.n = 100, .shape = 1.5, .min = 1)[["y"]] util_pareto1_param_estimate(t)$parameter_tbl
Returns distribution statistics in a tibble.
util_pareto1_stats_tbl(.data)util_pareto1_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Other Pareto:
tidy_generalized_pareto(),
tidy_inverse_pareto(),
tidy_pareto(),
tidy_pareto1(),
util_pareto1_aic(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_pareto_stats_tbl()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_pareto1() |> util_pareto1_stats_tbl() |> glimpse()library(dplyr) tidy_pareto1() |> util_pareto1_stats_tbl() |> glimpse()
This function estimates the lambda parameter of a Poisson distribution from the provided data and then calculates the AIC value based on the fitted distribution.
util_poisson_aic(.x)util_poisson_aic(.x)
.x |
A numeric vector containing the data to be fitted to a Poisson distribution. |
This function calculates the Akaike Information Criterion (AIC) for a Poisson distribution fitted to the provided data.
This function fits a Poisson distribution to the provided data. It estimates the lambda parameter of the Poisson distribution from the data. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimate as a starting point for the lambda parameter of the Poisson distribution.
Optimization method: Since the parameter is directly calculated from the data, no optimization is needed.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted Poisson distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rpois(100, lambda = 2) util_poisson_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rpois(100, lambda = 2) util_poisson_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated poisson data.
util_poisson_param_estimate(.x, .auto_gen_empirical = TRUE)util_poisson_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the pareto lambda parameter given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Poisson:
tidy_poisson(),
tidy_zero_truncated_poisson(),
util_poisson_stats_tbl(),
util_zero_truncated_poisson_param_estimate(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) library(ggplot2) x <- as.integer(mtcars$mpg) output <- util_poisson_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- rpois(50, 5) util_poisson_param_estimate(t)$parameter_tbllibrary(dplyr) library(ggplot2) x <- as.integer(mtcars$mpg) output <- util_poisson_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() t <- rpois(50, 5) util_poisson_param_estimate(t)$parameter_tbl
Returns distribution statistics in a tibble.
util_poisson_stats_tbl(.data)util_poisson_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Poisson:
tidy_poisson(),
tidy_zero_truncated_poisson(),
util_poisson_param_estimate(),
util_zero_truncated_poisson_param_estimate(),
util_zero_truncated_poisson_stats_tbl()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_poisson() |> util_poisson_stats_tbl() |> glimpse()library(dplyr) tidy_poisson() |> util_poisson_stats_tbl() |> glimpse()
This function estimates the parameters of a t distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_t_aic(.x)util_t_aic(.x)
.x |
A numeric vector containing the data to be fitted to a t distribution. |
This function calculates the Akaike Information Criterion (AIC) for a t distribution fitted to the provided data.
This function fits a t distribution to the input data using maximum likelihood estimation and then computes the Akaike Information Criterion (AIC) based on the fitted distribution.
The AIC value calculated based on the fitted t distribution to the provided data.
Steven P. Sanderson II, MPH
rt for generating t-distributed data,
optim for optimization.
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Generate t-distributed data set.seed(123) x <- rt(100, df = 5, ncp = 0.5) # Calculate AIC for the generated data util_t_aic(x)# Generate t-distributed data set.seed(123) x <- rt(100, df = 5, ncp = 0.5) # Calculate AIC for the generated data util_t_aic(x)
Estimate t Distribution Parameters
util_t_param_estimate(.x, .auto_gen_empirical = TRUE)util_t_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function, where the data
comes from the |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the t distribution parameters
given some vector of values produced by rt(). The estimation method
uses both method of moments and maximum likelihood estimation.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
library(dplyr) library(ggplot2) set.seed(123) x <- rt(100, df = 10, ncp = 0.5) output <- util_t_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) set.seed(123) x <- rt(100, df = 10, ncp = 0.5) output <- util_t_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_t_stats_tbl(.data)util_t_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other T Distribution:
tidy_t()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_t() |> util_t_stats_tbl() |> glimpse()library(dplyr) tidy_t() |> util_t_stats_tbl() |> glimpse()
This function estimates the parameters of a triangular distribution (min, max, and mode) from the provided data and calculates the AIC value based on the fitted distribution.
util_triangular_aic(.x)util_triangular_aic(.x)
.x |
A numeric vector containing the data to be fitted to a triangular distribution. |
This function calculates the Akaike Information Criterion (AIC) for a triangular distribution fitted to the provided data.
The function operates in several steps:
Parameter Estimation: The function extracts the minimum, maximum, and
mode values from the data via the TidyDensity::util_triangular_param_estimate
function. It returns these initial parameters as the starting point for
optimization.
Negative Log-Likelihood Calculation: A custom function calculates the
negative log-likelihood using the EnvStats::dtri function to obtain density
values for each data point. The densities are logged manually to simulate the
behavior of a log parameter.
Parameter Validation: During optimization, the function checks that the
constraints min <= mode <= max are met, and returns an infinite loss if not.
Optimization: The optimization process utilizes the "SANN" (Simulated Annealing) method to minimize the negative log-likelihood and find optimal parameter values.
AIC Calculation: The Akaike Information Criterion (AIC) is calculated using the optimized negative log-likelihood and the total number of parameters (3).
The AIC value calculated based on the fitted triangular distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example: Calculate AIC for a sample dataset set.seed(123) data <- tidy_triangular(.min = 0, .max = 1, .mode = 1/2)$y util_triangular_aic(data)# Example: Calculate AIC for a sample dataset set.seed(123) data <- tidy_triangular(.min = 0, .max = 1, .mode = 1/2)$y util_triangular_aic(data)
This function will attempt to estimate the triangular min, mode, and max parameters given some vector of values.
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated beta data.
util_triangular_param_estimate(.x, .auto_gen_empirical = TRUE)util_triangular_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be numeric, and all values must be 0 <= x <= 1 |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the triangular min, mode, and max parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Triangular:
tidy_triangular(),
util_triangular_stats_tbl()
library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_triangular_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() params <- tidy_triangular()$y |> util_triangular_param_estimate() params$parameter_tbllibrary(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_triangular_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() params <- tidy_triangular()$y |> util_triangular_param_estimate() params$parameter_tbl
Returns distribution statistics in a tibble.
util_triangular_stats_tbl(.data)util_triangular_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Triangular:
tidy_triangular(),
util_triangular_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_triangular() |> util_triangular_stats_tbl() |> glimpse()library(dplyr) tidy_triangular() |> util_triangular_stats_tbl() |> glimpse()
This function estimates the min and max parameters of a uniform distribution from the provided data and then calculates the AIC value based on the fitted distribution.
util_uniform_aic(.x)util_uniform_aic(.x)
.x |
A numeric vector containing the data to be fitted to a uniform distribution. |
This function calculates the Akaike Information Criterion (AIC) for a uniform distribution fitted to the provided data.
This function fits a uniform distribution to the provided data. It estimates the min and max parameters of the uniform distribution from the range of the data. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the minimum and maximum values of the data as starting points for the min and max parameters of the uniform distribution.
Optimization method: Since the parameters are directly calculated from the data, no optimization is needed.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted uniform distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- runif(30) util_uniform_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- runif(30) util_uniform_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated uniform data.
util_uniform_param_estimate(.x, .auto_gen_empirical = TRUE)util_uniform_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the uniform min and max parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Uniform:
tidy_uniform(),
util_uniform_stats_tbl()
library(dplyr) library(ggplot2) x <- tidy_uniform(.min = 1, .max = 3)$y output <- util_uniform_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) x <- tidy_uniform(.min = 1, .max = 3)$y output <- util_uniform_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_uniform_stats_tbl(.data)util_uniform_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Uniform:
tidy_uniform(),
util_uniform_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_uniform() |> util_uniform_stats_tbl() |> glimpse()library(dplyr) tidy_uniform() |> util_uniform_stats_tbl() |> glimpse()
This function estimates the shape and scale parameters of a Weibull distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_weibull_aic(.x)util_weibull_aic(.x)
.x |
A numeric vector containing the data to be fitted to a Weibull distribution. |
This function calculates the Akaike Information Criterion (AIC) for a Weibull distribution fitted to the provided data.
This function fits a Weibull distribution to the provided data using maximum likelihood estimation. It estimates the shape and scale parameters of the Weibull distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape and scale parameters of the Weibull distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted Weibull distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rweibull(100, shape = 2, scale = 1) util_weibull_aic(x)# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rweibull(100, shape = 2, scale = 1) util_weibull_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated weibull data.
util_weibull_param_estimate(.x, .auto_gen_empirical = TRUE)util_weibull_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the weibull shape and scale parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Weibull:
tidy_inverse_weibull(),
tidy_weibull(),
util_weibull_stats_tbl()
library(dplyr) library(ggplot2) x <- tidy_weibull(.shape = 1, .scale = 2)$y output <- util_weibull_param_estimate(x) output$parameter_tbl output$combined_data_tbl %>% tidy_combined_autoplot()library(dplyr) library(ggplot2) x <- tidy_weibull(.shape = 1, .scale = 2)$y output <- util_weibull_param_estimate(x) output$parameter_tbl output$combined_data_tbl %>% tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_weibull_stats_tbl(.data)util_weibull_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Weibull:
tidy_inverse_weibull(),
tidy_weibull(),
util_weibull_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_weibull() |> util_weibull_stats_tbl() |> glimpse()library(dplyr) tidy_weibull() |> util_weibull_stats_tbl() |> glimpse()
This function estimates the parameters (size and prob) of a ZTB
distribution from the provided data using maximum likelihood estimation
(via the optim() function), and then calculates the AIC value based on the
fitted distribution.
util_zero_truncated_binomial_aic(.x)util_zero_truncated_binomial_aic(.x)
.x |
A numeric vector containing the data (non-zero counts) to be fitted to a ZTB distribution. |
This function calculates the Akaike Information Criterion (AIC) for a zero-truncated binomial (ZTB) distribution fitted to the provided data.
Initial parameter estimates: The choice of initial values for size
and prob can impact the convergence of the optimization. Consider using
prior knowledge or method of moments estimates to obtain reasonable starting
values.
Optimization method: The default optimization method used is
"L-BFGS-B," which allows for box constraints to keep the parameters within
valid bounds. You might explore other optimization methods available in
optim() for potentially better performance or different constraint
requirements.
Data requirements: The input data .x should consist of non-zero counts,
as the ZTB distribution does not include zero values. Additionally, the
values in .x should be less than or equal to the estimated size parameter.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen ZTB model using visualization (e.g., probability plots, histograms) and other statistical tests (e.g., chi-square goodness-of-fit test) to ensure it adequately describes the data.
The AIC value calculated based on the fitted ZTB distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
# Example data set.seed(123) x <- tidy_zero_truncated_binomial(30, .size = 10, .prob = 0.4)[["y"]] # Calculate AIC util_zero_truncated_binomial_aic(x)# Example data set.seed(123) x <- tidy_zero_truncated_binomial(30, .size = 10, .prob = 0.4)[["y"]] # Calculate AIC util_zero_truncated_binomial_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated binomial data.
One method of estimating the parameters is done via:
MLE via optim function.
util_zero_truncated_binomial_param_estimate(.x, .auto_gen_empirical = TRUE)util_zero_truncated_binomial_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the zero truncated binomial size and prob parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Binomial:
tidy_binomial(),
tidy_negative_binomial(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
util_binomial_param_estimate(),
util_binomial_stats_tbl(),
util_negative_binomial_param_estimate(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_negative_binomial_stats_tbl()
Other Zero Truncated Distribution:
tidy_zero_truncated_binomial(),
tidy_zero_truncated_geometric(),
tidy_zero_truncated_poisson()
library(dplyr) library(ggplot2) x <- as.integer(mtcars$mpg) output <- util_zero_truncated_binomial_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- tidy_zero_truncated_binomial(100, 10, .1)[["y"]] util_zero_truncated_binomial_param_estimate(t)$parameter_tbllibrary(dplyr) library(ggplot2) x <- as.integer(mtcars$mpg) output <- util_zero_truncated_binomial_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- tidy_zero_truncated_binomial(100, 10, .1)[["y"]] util_zero_truncated_binomial_param_estimate(t)$parameter_tbl
Returns distribution statistics in a tibble for Zero Truncated Binomial distribution.
util_zero_truncated_binomial_stats_tbl(.data)util_zero_truncated_binomial_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Other Binomial:
tidy_binomial(),
tidy_negative_binomial(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
util_binomial_param_estimate(),
util_binomial_stats_tbl(),
util_negative_binomial_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_negative_binomial_stats_tbl()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) set.seed(123) tidy_zero_truncated_binomial(.size = 10, .prob = 0.1) |> util_zero_truncated_binomial_stats_tbl() |> glimpse()library(dplyr) set.seed(123) tidy_zero_truncated_binomial(.size = 10, .prob = 0.1) |> util_zero_truncated_binomial_stats_tbl() |> glimpse()
This function estimates the probability parameter of a Zero-Truncated Geometric distribution from the provided data and calculates the AIC value based on the fitted distribution.
util_zero_truncated_geometric_aic(.x)util_zero_truncated_geometric_aic(.x)
.x |
A numeric vector containing the data to be fitted to a Zero-Truncated Geometric distribution. |
This function calculates the Akaike Information Criterion (AIC) for a Zero-Truncated Geometric distribution fitted to the provided data.
This function fits a Zero-Truncated Geometric distribution to the provided data. It estimates the probability parameter using the method of moments and calculates the AIC value.
Optimization method: Since the parameter is directly calculated from the data, no optimization is needed.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
The AIC value calculated based on the fitted Zero-Truncated Geometric distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_negative_binomial_aic(),
util_zero_truncated_poisson_aic()
library(actuar) # Example: Calculate AIC for a sample dataset set.seed(123) x <- rztgeom(100, prob = 0.2) util_zero_truncated_geometric_aic(x)library(actuar) # Example: Calculate AIC for a sample dataset set.seed(123) x <- rztgeom(100, prob = 0.2) util_zero_truncated_geometric_aic(x)
This function will estimate the prob parameter for a
Zero-Truncated Geometric distribution from a given vector .x. The function
returns a list with a parameter table, and if .auto_gen_empirical is set
to TRUE, the empirical data is combined with the estimated distribution
data.
util_zero_truncated_geometric_param_estimate(.x, .auto_gen_empirical = TRUE)util_zero_truncated_geometric_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must contain non-negative integers and should have no zeros. |
.auto_gen_empirical |
Boolean value (default |
This function will attempt to estimate the prob parameter of the
Zero-Truncated Geometric distribution using given vector .x as input data.
If the parameter .auto_gen_empirical is set to TRUE, the empirical data
in .x will be run through the tidy_empirical() function and combined with
the estimated zero-truncated geometric data.
A tibble/list
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Zero-Truncated Geometric:
util_zero_truncated_geometric_stats_tbl()
library(actuar) library(dplyr) library(ggplot2) library(actuar) set.seed(123) ztg <- rztgeom(100, prob = 0.2) output <- util_zero_truncated_geometric_param_estimate(ztg) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(actuar) library(dplyr) library(ggplot2) library(actuar) set.seed(123) ztg <- rztgeom(100, prob = 0.2) output <- util_zero_truncated_geometric_param_estimate(ztg) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics for Zero-Truncated Geometric distribution in a tibble.
util_zero_truncated_geometric_stats_tbl(.data)util_zero_truncated_geometric_stats_tbl(.data)
.data |
The data being passed from a |
This function takes in a tibble generated by a tidy_ztgeom
distribution function and returns the relevant statistics for a Zero-Truncated
Geometric distribution. It requires data to be passed from a tidy_ztgeom
distribution function.
A tibble
Other Zero-Truncated Geometric:
util_zero_truncated_geometric_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) set.seed(123) tidy_zero_truncated_geometric(.prob = 0.1) |> util_zero_truncated_geometric_stats_tbl() |> glimpse()library(dplyr) set.seed(123) tidy_zero_truncated_geometric(.prob = 0.1) |> util_zero_truncated_geometric_stats_tbl() |> glimpse()
This function estimates the parameters (size and prob) of a ZTNB
distribution from the provided data using maximum likelihood estimation
(via the optim() function), and then calculates the AIC value based on the
fitted distribution.
util_zero_truncated_negative_binomial_aic(.x)util_zero_truncated_negative_binomial_aic(.x)
.x |
A numeric vector containing the data (non-zero counts) to be fitted to a ZTNB distribution. |
This function calculates the Akaike Information Criterion (AIC) for a zero-truncated negative binomial (ZTNB) distribution fitted to the provided data.
Initial parameter estimates: The choice of initial values for size
and prob can impact the convergence of the optimization. Consider using
prior knowledge or method of moments estimates to obtain reasonable starting
values.
Optimization method: The default optimization method used is
"Nelder-Mead". You might explore other optimization methods available in
optim() for potentially better performance or different constraint
requirements.
Data requirements: The input data .x should consist of non-zero counts,
as the ZTNB distribution does not include zero values.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen ZTNB model using visualization (e.g., probability plots, histograms) and other statistical tests (e.g., chi-square goodness-of-fit test) to ensure it adequately describes the data.
The AIC value calculated based on the fitted ZTNB distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_poisson_aic()
library(actuar) # Example data set.seed(123) x <- rztnbinom(30, size = 2, prob = 0.4) # Calculate AIC util_zero_truncated_negative_binomial_aic(x)library(actuar) # Example data set.seed(123) x <- rztnbinom(30, size = 2, prob = 0.4) # Calculate AIC util_zero_truncated_negative_binomial_aic(x)
The function will return a list output by default, and if the parameter
.auto_gen_empirical is set to TRUE then the empirical data given to the
parameter .x will be run through the tidy_empirical() function and combined
with the estimated negative binomial data.
One method of estimating the parameters is done via:
MLE via optim function.
util_zero_truncated_negative_binomial_param_estimate( .x, .auto_gen_empirical = TRUE )util_zero_truncated_negative_binomial_param_estimate( .x, .auto_gen_empirical = TRUE )
.x |
The vector of data to be passed to the function. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the zero truncated negative binomial size and prob parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Binomial:
tidy_binomial(),
tidy_negative_binomial(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
util_binomial_param_estimate(),
util_binomial_stats_tbl(),
util_negative_binomial_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl()
Other Zero Truncated Negative Distribution:
tidy_zero_truncated_negative_binomial()
library(dplyr) library(ggplot2) library(actuar) x <- as.integer(mtcars$mpg) output <- util_zero_truncated_negative_binomial_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- rztnbinom(100, 10, .1) util_zero_truncated_negative_binomial_param_estimate(t)$parameter_tbllibrary(dplyr) library(ggplot2) library(actuar) x <- as.integer(mtcars$mpg) output <- util_zero_truncated_negative_binomial_param_estimate(x) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- rztnbinom(100, 10, .1) util_zero_truncated_negative_binomial_param_estimate(t)$parameter_tbl
Computes distribution statistics for a zero-truncated negative binomial distribution.
util_zero_truncated_negative_binomial_stats_tbl(.data)util_zero_truncated_negative_binomial_stats_tbl(.data)
.data |
The data from a zero-truncated negative binomial distribution. |
This function computes statistics for a zero-truncated negative binomial distribution.
A tibble with distribution statistics.
Steven P. Sanderson II, MPH
Other Binomial:
tidy_binomial(),
tidy_negative_binomial(),
tidy_zero_truncated_binomial(),
tidy_zero_truncated_negative_binomial(),
util_binomial_param_estimate(),
util_binomial_stats_tbl(),
util_negative_binomial_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_negative_binomial_param_estimate()
Other Negative Binomial:
util_negative_binomial_stats_tbl()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) tidy_zero_truncated_negative_binomial(.size = 1, .prob = 0.1) |> util_zero_truncated_negative_binomial_stats_tbl() |> glimpse()library(dplyr) tidy_zero_truncated_negative_binomial(.size = 1, .prob = 0.1) |> util_zero_truncated_negative_binomial_stats_tbl() |> glimpse()
This function estimates the parameters of a zero-truncated poisson distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_zero_truncated_poisson_aic(.x)util_zero_truncated_poisson_aic(.x)
.x |
A numeric vector containing the data to be fitted to a zero-truncated poisson distribution. |
This function calculates the Akaike Information Criterion (AIC) for a zero-truncated poisson distribution fitted to the provided data.
The AIC value calculated based on the fitted zero-truncated poisson distribution to the provided data.
Steven P. Sanderson II, MPH
Other Utility:
check_duplicate_rows(),
convert_to_ts(),
quantile_normalize(),
tidy_mcmc_sampling(),
util_beta_aic(),
util_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_aic(),
util_generalized_pareto_aic(),
util_geometric_aic(),
util_hypergeometric_aic(),
util_inverse_burr_aic(),
util_inverse_pareto_aic(),
util_inverse_weibull_aic(),
util_logistic_aic(),
util_lognormal_aic(),
util_negative_binomial_aic(),
util_normal_aic(),
util_paralogistic_aic(),
util_pareto1_aic(),
util_pareto_aic(),
util_poisson_aic(),
util_t_aic(),
util_triangular_aic(),
util_uniform_aic(),
util_weibull_aic(),
util_zero_truncated_binomial_aic(),
util_zero_truncated_geometric_aic(),
util_zero_truncated_negative_binomial_aic()
library(actuar) # Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rztpois(30, lambda = 3) util_zero_truncated_poisson_aic(x)library(actuar) # Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rztpois(30, lambda = 3) util_zero_truncated_poisson_aic(x)
This function will attempt to estimate the Zero Truncated Poisson
lambda parameter given some vector of values .x. The function will return a
tibble output, and if the parameter .auto_gen_empirical is set to TRUE
then the empirical data given to the parameter .x will be run through the
tidy_empirical() function and combined with the estimated Zero Truncated
Poisson data.
util_zero_truncated_poisson_param_estimate(.x, .auto_gen_empirical = TRUE)util_zero_truncated_poisson_param_estimate(.x, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. Must be non-negative integers. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function estimates the parameter lambda of a Zero-Truncated Poisson distribution
based on a vector of non-negative integer values .x. The Zero-Truncated Poisson
distribution is a discrete probability distribution that models the number of events
occurring in a fixed interval of time, given that at least one event has occurred.
The estimation is performed by minimizing the negative log-likelihood of the observed
data .x under the Zero-Truncated Poisson model. The negative log-likelihood function
used for optimization is defined as:
where \( X_i \) are the observed values in .x and lambda is the parameter
of the Zero-Truncated Poisson distribution.
The optimization process uses the optim function to find the value of lambda
that minimizes this negative log-likelihood. The chosen optimization method is Brent's
method (method = "Brent") within a specified interval [0, max(.x)].
If .auto_gen_empirical is set to TRUE, the function will generate empirical data
statistics using tidy_empirical() for the input data .x and then combine this
empirical data with the estimated Zero-Truncated Poisson distribution using
tidy_combine_distributions(). This combined data can be accessed via the
$combined_data_tbl element of the function output.
The function returns a tibble containing the estimated parameter lambda along
with other summary statistics of the input data (sample size, minimum, maximum).
A tibble/list
Steven P. Sanderson II, MPH
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate()
Other Poisson:
tidy_poisson(),
tidy_zero_truncated_poisson(),
util_poisson_param_estimate(),
util_poisson_stats_tbl(),
util_zero_truncated_poisson_stats_tbl()
library(dplyr) library(ggplot2) tc <- tidy_zero_truncated_poisson() |> pull(y) output <- util_zero_truncated_poisson_param_estimate(tc) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()library(dplyr) library(ggplot2) tc <- tidy_zero_truncated_poisson() |> pull(y) output <- util_zero_truncated_poisson_param_estimate(tc) output$parameter_tbl output$combined_data_tbl |> tidy_combined_autoplot()
Returns distribution statistics in a tibble.
util_zero_truncated_poisson_stats_tbl(.data)util_zero_truncated_poisson_stats_tbl(.data)
.data |
The data being passed from a |
This function will take in a tibble and returns the statistics
of the given type of tidy_ distribution. It is required that data be
passed from a tidy_ distribution function.
A tibble
Steven P. Sanderson II, MPH
Other Poisson:
tidy_poisson(),
tidy_zero_truncated_poisson(),
util_poisson_param_estimate(),
util_poisson_stats_tbl(),
util_zero_truncated_poisson_param_estimate()
Other Distribution Statistics:
util_bernoulli_stats_tbl(),
util_beta_stats_tbl(),
util_binomial_stats_tbl(),
util_burr_stats_tbl(),
util_cauchy_stats_tbl(),
util_chisquare_stats_tbl(),
util_exponential_stats_tbl(),
util_f_stats_tbl(),
util_gamma_stats_tbl(),
util_generalized_beta_stats_tbl(),
util_generalized_pareto_stats_tbl(),
util_geometric_stats_tbl(),
util_hypergeometric_stats_tbl(),
util_inverse_burr_stats_tbl(),
util_inverse_pareto_stats_tbl(),
util_inverse_weibull_stats_tbl(),
util_logistic_stats_tbl(),
util_lognormal_stats_tbl(),
util_negative_binomial_stats_tbl(),
util_normal_stats_tbl(),
util_paralogistic_stats_tbl(),
util_pareto1_stats_tbl(),
util_pareto_stats_tbl(),
util_poisson_stats_tbl(),
util_t_stats_tbl(),
util_triangular_stats_tbl(),
util_uniform_stats_tbl(),
util_weibull_stats_tbl(),
util_zero_truncated_binomial_stats_tbl(),
util_zero_truncated_geometric_stats_tbl(),
util_zero_truncated_negative_binomial_stats_tbl()
library(dplyr) tidy_zero_truncated_poisson() |> util_zero_truncated_poisson_stats_tbl() |> glimpse()library(dplyr) tidy_zero_truncated_poisson() |> util_zero_truncated_poisson_stats_tbl() |> glimpse()