Estimate Geometric Parameters — util_geometric_param_estimate • TidyDensity
Description: This function will attempt to estimate the geometric prob parameter given some vector of values .x. 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 geometric data.

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# Estimate Geometric Parameters

Source: `R/est-param-geometric.R`
`util_geometric_param_estimate.Rd`

This function will attempt to estimate the geometric prob parameter given some vector of values `.x`. 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 geometric data.

## Usage

    util_geometric_param_estimate(.x, .auto_gen_empirical = TRUE)

## Arguments

.x

  The vector of data to be passed to the function. Must be non-negative integers.

.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 vector. It will attempt to estimate the prob parameter of a geometric distribution.

## 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_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 Geometric: `tidy_geometric()`, `tidy_zero_truncated_geometric()`, `util_geometric_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    library(dplyr)
    library(ggplot2)

    tg <- tidy_geometric(.prob = .1) %>% pull(y)
    output <- util_geometric_param_estimate(tg)

    output$parameter_tbl
    #> # A tibble: 2 × 9
    #>   dist_type samp_size   min   max  mean variance sum_x method         shape
    #>   <chr>         <int> <dbl> <dbl> <dbl>    <dbl> <dbl> <chr>          <dbl>
    #> 1 Geometric        50     0    46  9.04     113.   452 EnvStats_MME  0.0996
    #> 2 Geometric        50     0    46  9.04     113.   452 EnvStats_MVUE 0.0978

    output$combined_data_tbl %>%
      tidy_combined_autoplot()