Estimate Normal Gaussian Parameters — util_normal_param_estimate • TidyDensity
Description: 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 normal data. Three different methods of shape parameters are supplied: MLE/MME MVUE

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# Estimate Normal Gaussian Parameters

Source: `R/est-param-normal.R`
`util_normal_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 normal data.

Three different methods of shape parameters are supplied:

- MLE/MME

- MVUE

## Usage

    util_normal_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 normal gaussian mean and standard deviation 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_logistic_param_estimate()`, `util_lognormal_param_estimate()`, `util_negative_binomial_param_estimate()`, `util_pareto_param_estimate()`, `util_poisson_param_estimate()`, `util_uniform_param_estimate()`, `util_weibull_param_estimate()`

Other Gaussian: `tidy_inverse_normal()`, `tidy_normal()`, `util_normal_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    library(dplyr)
    library(ggplot2)

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

    output$parameter_tbl
    #> # A tibble: 2 × 8
    #>   dist_type samp_size   min   max method              mu stan_dev shape_ratio
    #>   <chr>         <int> <dbl> <dbl> <chr>            <dbl>    <dbl>       <dbl>
    #> 1 Gaussian         32  10.4  33.9 EnvStats_MME_MLE  20.1     5.93        3.39
    #> 2 Gaussian         32  10.4  33.9 EnvStats_MVUE     20.1     6.03        3.33

    output$combined_data_tbl %>%
      tidy_combined_autoplot()

    t <- rnorm(50, 0, 1)
    util_normal_param_estimate(t)$parameter_tbl
    #> # A tibble: 2 × 8
    #>   dist_type samp_size   min   max method              mu stan_dev shape_ratio
    #>   <chr>         <int> <dbl> <dbl> <chr>            <dbl>    <dbl>       <dbl>
    #> 1 Gaussian         50 -2.49  2.16 EnvStats_MME_MLE 0.260    0.893       0.291
    #> 2 Gaussian         50 -2.49  2.16 EnvStats_MVUE    0.260    0.902       0.288