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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.

Two different methods of shape parameters are supplied:

  • MLE/MME

  • MMUE

Usage

util_negative_binomial_param_estimate(.x, .size, .auto_gen_empirical = TRUE)

Arguments

.x

The vector of data to be passed to the function.

.size

The size parameter.

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

Author

Steven P. Sanderson II, MPH

Examples

library(dplyr)
library(ggplot2)

x <- as.integer(mtcars$mpg)
output <- util_negative_binomial_param_estimate(x, .size = 1)

output$parameter_tbl
#> # A tibble: 2 × 9
#>   dist_type         samp_size   min   max  mean method   size   prob shape_ratio
#>   <chr>                 <int> <dbl> <dbl> <dbl> <chr>   <dbl>  <dbl>       <dbl>
#> 1 Negative Binomial        32    10    33  19.7 EnvSta…    32 0.0483        662 
#> 2 Negative Binomial        32    10    33  19.7 EnvSta…    32 0.0469        682.

output$combined_data_tbl %>%
  tidy_combined_autoplot()


t <- rnbinom(50, 1, .1)
util_negative_binomial_param_estimate(t, .size = 1)$parameter_tbl
#> # A tibble: 2 × 9
#>   dist_type         samp_size   min   max  mean method   size   prob shape_ratio
#>   <chr>                 <int> <dbl> <dbl> <dbl> <chr>   <dbl>  <dbl>       <dbl>
#> 1 Negative Binomial        50     0    59  11.4 EnvSta…    50 0.0804        622 
#> 2 Negative Binomial        50     0    59  11.4 EnvSta…    50 0.0789        634.