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# Estimate Hypergeometric Parameters

Source: `R/est-param-hypergeometric.R`
`util_hypergeometric_param_estimate.Rd`

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_exponential_param_estimate()`, `util_gamma_param_estimate()`, `util_geometric_param_estimate()`, `util_logistic_param_estimate()`, `util_lognormal_param_estimate()`, `util_negative_binomial_param_estimate()`, `util_normal_param_estimate()`, `util_pareto_param_estimate()`, `util_poisson_param_estimate()`, `util_triangular_param_estimate()`, `util_uniform_param_estimate()`, `util_weibull_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   30.6    NA
    #> 2 Hypergeometric        10 EnvStats_MVUE  30      50

    output$combined_data_tbl |>
      tidy_combined_autoplot()