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Estimate Burr Parameters

Source: R/est-param-burr.R
util_burr_param_estimate.Rd

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.

Usage

util_burr_param_estimate(.x, .auto_gen_empirical = TRUE)

Arguments

.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 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. It will attempt to estimate the prob parameter of a Burr distribution.

See also

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()

Author

Steven P. Sanderson II, MPH

Examples

library(dplyr)
library(ggplot2)

tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) |> pull(y)
output <- util_burr_param_estimate(tb)

output$parameter_tbl
#> # A tibble: 1 × 9
#>   dist_type samp_size   min   max  mean shape1 shape2  rate scale
#>   <chr>         <int> <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>
#> 1 Burr             50 0.478  32.4  4.39  0.948   2.75 0.306  3.27

output$combined_data_tbl |>
  tidy_combined_autoplot()