Estimate Gamma Parameters

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.

Usage

util_gamma_param_estimate(.x, .auto_gen_empirical = TRUE)

Arguments

.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 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 see if the given vector .x is a numeric vector.

See also

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()

Author

Steven P. Sanderson II, MPH

Examples

library(dplyr)
library(ggplot2)

tg <- tidy_gamma(.shape = 1, .scale = .3) |> pull(y)
output <- util_gamma_param_estimate(tg)

output$parameter_tbl
#> # A tibble: 3 × 10
#>   dist_type samp_size    min   max  mean variance method shape scale shape_ratio
#>   <chr>         <int>  <dbl> <dbl> <dbl>    <dbl> <chr>  <dbl> <dbl>       <dbl>
#> 1 Gamma            50 0.0153 0.968 0.283    0.232 NIST_…  1.48 0.191        7.77
#> 2 Gamma            50 0.0153 0.968 0.283    0.232 EnvSt…  1.45 0.191        7.62
#> 3 Gamma            50 0.0153 0.968 0.283    0.232 EnvSt…  1.41 0.191        7.38

output$combined_data_tbl |>
  tidy_combined_autoplot()