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Estimate Negative Binomial Parameters

Source: R/est-param-negative-binomial.R
util_negative_binomial_param_estimate.Rd

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.

Three different methods of shape parameters are supplied:

  • MLE/MME

  • MMUE

  • MLE via optim function.

Usage

util_negative_binomial_param_estimate(
  .x,
  .size = 1,
  .auto_gen_empirical = TRUE
)

Arguments

.x

The vector of data to be passed to the function.

.size

The size parameter, the default is 1.

.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.

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_normal_param_estimate(), util_paralogistic_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 Binomial: tidy_binomial(), tidy_negative_binomial(), tidy_zero_truncated_binomial(), tidy_zero_truncated_negative_binomial(), util_binomial_param_estimate(), util_binomial_stats_tbl(), util_zero_truncated_binomial_param_estimate(), util_zero_truncated_binomial_stats_tbl(), util_zero_truncated_negative_binomial_param_estimate(), util_zero_truncated_negative_binomial_stats_tbl()

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: 3 × 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. 
#> 3 Negative Binomial        32    10    33  19.7 MLE_Op…  26.9 0.577         46.5

output$combined_data_tbl |>
  tidy_combined_autoplot()


t <- rnbinom(50, 1, .1)
util_negative_binomial_param_estimate(t, .size = 1)$parameter_tbl
#> # A tibble: 3 × 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    48  9.36 EnvSt… 50     0.0965       518  
#> 2 Negative Binomial        50     0    48  9.36 EnvSt… 50     0.0948       528. 
#> 3 Negative Binomial        50     0    48  9.36 MLE_O…  0.901 0.0878        10.3