Estimate Beta Parameters

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)

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.

Value

A tibble/list

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()

Author

Steven P. Sanderson II, MPH

Examples

library(dplyr)
library(ggplot2)

x <- mtcars$mpg
output <- util_beta_param_estimate(x)
#> For the beta distribution, its mean 'mu' should be 0 < mu < 1. The data will
#> therefore be scaled to enforce this.

output$parameter_tbl
#> # A tibble: 3 × 10
#>   dist_type samp_size   min   max  mean variance method       shape1 shape2
#>   <chr>         <int> <dbl> <dbl> <dbl>    <dbl> <chr>         <dbl>  <dbl>
#> 1 Beta             32  10.4  33.9 0.412   0.0658 Bayes         13.2   18.8 
#> 2 Beta             32  10.4  33.9 0.412   0.0658 NIST_MME       1.11   1.58
#> 3 Beta             32  10.4  33.9 0.412   0.0658 EnvStats_MME   1.16   1.65
#> # ℹ 1 more variable: shape_ratio <dbl>

output$combined_data_tbl |>
  tidy_combined_autoplot()


tb <- rbeta(50, 2.5, 1.4)
util_beta_param_estimate(tb)$parameter_tbl
#> There was no need to scale the data.
#> # A tibble: 3 × 10
#>   dist_type samp_size   min   max  mean variance method       shape1 shape2
#>   <chr>         <int> <dbl> <dbl> <dbl>    <dbl> <chr>         <dbl>  <dbl>
#> 1 Beta             50 0.119 0.999 0.624   0.0584 Bayes         31.2   18.8 
#> 2 Beta             50 0.119 0.999 0.624   0.0584 NIST_MME       1.88   1.14
#> 3 Beta             50 0.119 0.999 0.624   0.0584 EnvStats_MME   1.93   1.17
#> # ℹ 1 more variable: shape_ratio <dbl>