TidyDensity (version 1.5.0)

util_beta_param_estimate: Estimate Beta Parameters

Description

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()

Usage

util_beta_param_estimate(.x, .auto_gen_empirical = TRUE)

Value

A tibble/list

Arguments

.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 tidy_empirical() output for the .x parameter and use the tidy_combine_distributions(). The user can then plot out the data using $combined_data_tbl from the function output.

Author

Steven P. Sanderson II, MPH

Details

This function will attempt to estimate the beta shape1 and shape2 parameters given some vector of values.

See Also

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()

Examples

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_tbl