util_hypergeometric_param_estimate {TidyDensity}

Estimate Hypergeometric Parameters

Description

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.

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

Value

A tibble/list

Author(s)

Steven P. Sanderson II, MPH

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()

Examples

library(dplyr)
library(ggplot2)

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

output$parameter_tbl

output$combined_data_tbl |>
  tidy_combined_autoplot()