Estimate Pareto Parameters

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

Usage

util_pareto1_param_estimate(.x, .auto_gen_empirical = TRUE)

Arguments

.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 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 Pareto shape and scale parameters given some vector of values.

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_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()

Author

Steven P. Sanderson II, MPH

Examples

library(dplyr)
library(ggplot2)

x <- mtcars[["mpg"]]
output <- util_pareto1_param_estimate(x)

output$parameter_tbl
#> # A tibble: 2 × 7
#>   dist_type samp_size   min   max method est_shape est_min
#>   <chr>         <int> <dbl> <dbl> <chr>      <dbl>   <dbl>
#> 1 Pareto           32  10.4  33.9 LSE         2.86    13.7
#> 2 Pareto           32  10.4  33.9 MLE         1.62    10.4

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
#> # A tibble: 2 × 7
#>   dist_type samp_size   min   max method est_shape est_min
#>   <chr>         <int> <dbl> <dbl> <chr>      <dbl>   <dbl>
#> 1 Pareto          100  1.00  137. LSE         1.36   0.936
#> 2 Pareto          100  1.00  137. MLE         1.52   1.00