Estimate Binomial Parameters

This function will check to see if some given vector .x is either a numeric vector or a factor vector with at least two levels then it will cause an error and the function will abort. 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 binomial data.

Usage

util_binomial_param_estimate(.x, .size = NULL, .auto_gen_empirical = TRUE)

Arguments

.x

The vector of data to be passed to the function. Must be numeric, and all values must be 0 <= x <= 1

.size

Number of trials, zero or more.

.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 binomial p_hat and size parameters given some vector of values.

See also

Other Parameter Estimation: util_bernoulli_param_estimate(), util_beta_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_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 Binomial: tidy_binomial(), tidy_negative_binomial(), tidy_zero_truncated_binomial(), tidy_zero_truncated_negative_binomial(), util_binomial_stats_tbl(), util_negative_binomial_param_estimate(), 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)

tb <- rbinom(50, 1, .1)
output <- util_binomial_param_estimate(tb)

output$parameter_tbl
#> # A tibble: 1 × 10
#>   dist_type samp_size   min   max  mean variance method   prob  size shape_ratio
#>   <chr>         <int> <dbl> <dbl> <dbl>    <dbl> <chr>   <dbl> <int>       <dbl>
#> 1 Binomial         50     0     1  0.04   0.0392 EnvSta…  0.04    50      0.0008

output$combined_data_tbl |>
  tidy_combined_autoplot()