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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 negative binomial data.

One method of estimating the parameters is done via:

Usage

util_ztn_binomial_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 zero truncated negative binomial size and prob parameters given some vector of values.

Author

Steven P. Sanderson II, MPH

Examples

library(dplyr)
library(ggplot2)
library(actuar)

x <- as.integer(mtcars$mpg)
output <- util_ztn_binomial_param_estimate(x)

output$parameter_tbl
#> # A tibble: 1 × 8
#>   dist_type                       samp_size   min   max  mean method  size  prob
#>   <chr>                               <int> <dbl> <dbl> <dbl> <chr>  <dbl> <dbl>
#> 1 Zero-Truncated Negative Binomi…        32    10    33  19.7 MLE_O…  26.9 0.577

output$combined_data_tbl |>
  tidy_combined_autoplot()


set.seed(123)
t <- rztnbinom(100, 10, .1)
util_ztn_binomial_param_estimate(t)$parameter_tbl
#> # A tibble: 1 × 8
#>   dist_type                       samp_size   min   max  mean method  size  prob
#>   <chr>                               <int> <dbl> <dbl> <dbl> <chr>  <dbl> <dbl>
#> 1 Zero-Truncated Negative Binomi…       100    22   183  89.6 MLE_O…  10.7 0.107