Estimate Paralogistic 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 paralogistic data.

The method of parameter estimation is:

• MLE

Usage

util_paralogistic_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 paralogistic shape and rate 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_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 Paralogistic: util_paralogistic_aic(), util_paralogistic_stats_tbl()

Examples

library(dplyr)
library(ggplot2)

x <- mtcars$mpg
output <- util_paralogistic_param_estimate(x)

output$parameter_tbl
#> # A tibble: 1 × 10
#>   dist_type    samp_size   min   max  mean   var method shape   rate
#>   <chr>            <int> <dbl> <dbl> <dbl> <dbl> <chr>  <dbl>  <dbl>
#> 1 Paralogistic        32  10.4  33.9  20.1  36.3 MLE     4.14 0.0336
#> # ℹ 1 more variable: shape_rate_ratio <dbl>

output$combined_data_tbl |>
  tidy_combined_autoplot()


t <- tidy_paralogistic(50, 2.5, 1.4)[["y"]]
util_paralogistic_param_estimate(t)$parameter_tbl
#> # A tibble: 1 × 10
#>   dist_type    samp_size    min   max  mean    var method shape  rate
#>   <chr>            <int>  <dbl> <dbl> <dbl>  <dbl> <chr>  <dbl> <dbl>
#> 1 Paralogistic        50 0.0946 0.955 0.452 0.0442 MLE     2.76  1.48
#> # ℹ 1 more variable: shape_rate_ratio <dbl>