TidyDensity (version 1.5.0)

util_pareto1_param_estimate: Estimate Pareto Parameters

Description

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)

Value

A tibble/list

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.

Author

Steven P. Sanderson II, MPH

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()

Examples

library(dplyr)
library(ggplot2)

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

output$parameter_tbl

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