Estimate Binomial Parameters — util_binomial_param_estimate • TidyDensity
Description: 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.

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# Estimate Binomial Parameters

Source: `R/est-param-binomial.R`
`util_binomial_param_estimate.Rd`

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_exponential_param_estimate()`, `util_gamma_param_estimate()`, `util_geometric_param_estimate()`, `util_hypergeometric_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_uniform_param_estimate()`, `util_weibull_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()`

## 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()