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# Estimate Logistic Parameters

Source: `R/est-param-logistic.R`
`util_logistic_param_estimate.Rd`

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 logistic data.

Three different methods of shape parameters are supplied:

- MLE

- MME

- MMUE

## Usage

    util_logistic_param_estimate(.x, .auto_gen_empirical = TRUE)

## Arguments

.x

  The vector of data to be passed to the function.

.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 logistic location and scale parameters given some vector of values.

## 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_hypergeometric_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 Logistic: `tidy_logistic()`, `tidy_paralogistic()`, `util_logistic_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    library(dplyr)
    library(ggplot2)

    x <- mtcars$mpg
    output <- util_logistic_param_estimate(x)

    output$parameter_tbl
    #> # A tibble: 3 × 10
    #>   dist_type samp_size   min   max  mean basic_scale method        location scale
    #>   <chr>         <int> <dbl> <dbl> <dbl>       <dbl> <chr>            <dbl> <dbl>
    #> 1 Logistic         32  10.4  33.9  20.1        3.27 EnvStats_MME      20.1  3.27
    #> 2 Logistic         32  10.4  33.9  20.1        3.27 EnvStats_MMUE     20.1  3.32
    #> 3 Logistic         32  10.4  33.9  20.1        3.27 EnvStats_MLE      20.1 12.6
    #> # ℹ 1 more variable: shape_ratio <dbl>

    output$combined_data_tbl |>
      tidy_combined_autoplot()

    t <- rlogis(50, 2.5, 1.4)
    util_logistic_param_estimate(t)$parameter_tbl
    #> # A tibble: 3 × 10
    #>   dist_type samp_size   min   max  mean basic_scale method        location scale
    #>   <chr>         <int> <dbl> <dbl> <dbl>       <dbl> <chr>            <dbl> <dbl>
    #> 1 Logistic         50 -1.86  8.80  2.58        1.32 EnvStats_MME      2.58  1.32
    #> 2 Logistic         50 -1.86  8.80  2.58        1.32 EnvStats_MMUE     2.58  1.33
    #> 3 Logistic         50 -1.86  8.80  2.58        1.32 EnvStats_MLE      2.58  1.58
    #> # ℹ 1 more variable: shape_ratio <dbl>