Estimate Hypergeometric Parameters

This function will attempt to estimate the geometric prob parameter given some vector of values .x. Estimate m, the number of white balls in the urn, or m+n, the total number of balls in the urn, for a hypergeometric distribution.

Usage

util_hypergeometric_param_estimate(
  .x,
  .m = NULL,
  .total = NULL,
  .k,
  .auto_gen_empirical = TRUE
)

Arguments

.x

A non-negative integer indicating the number of white balls out of a sample of size .k drawn without replacement from the urn. You cannot have missing, undefined or infinite values.

.m

Non-negative integer indicating the number of white balls in the urn. You must supply .m or .total, but not both. You cannot have missing values.

.total

A positive integer indicating the total number of balls in the urn (i.e., m+n). You must supply .m or .total, but not both. You cannot have missing values.

.k

A positive integer indicating the number of balls drawn without replacement from the urn. You cannot have missing values.

.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 see if the given vector .x is a numeric integer. It will attempt to estimate the prob parameter of a geometric distribution. Missing (NA), undefined (NaN), and infinite (Inf, -Inf) values are not allowed. Let .x be an observation from a hypergeometric distribution with parameters .m = M, .n = N, and .k = K. In R nomenclature, .x represents the number of white balls drawn out of a sample of .k balls drawn without replacement from an urn containing .m white balls and .n black balls. The total number of balls in the urn is thus .m + .n. Denote the total number of balls by T = .m + .n

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_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_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 Hypergeometric: tidy_hypergeometric(), util_hypergeometric_stats_tbl()

Author

Steven P. Sanderson II, MPH

Examples

library(dplyr)
library(ggplot2)

th <- rhyper(10, 20, 30, 5)
output <- util_hypergeometric_param_estimate(th, .total = 50, .k = 5)

output$parameter_tbl
#> # A tibble: 2 × 5
#>   dist_type      samp_size method            m total
#>   <chr>              <int> <chr>         <dbl> <dbl>
#> 1 Hypergeometric        10 EnvStats_MLE   20.4    NA
#> 2 Hypergeometric        10 EnvStats_MVUE  20      50

output$combined_data_tbl |>
  tidy_combined_autoplot()