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This function will automatically scale the data from 0 to 1 if it is not already. This means you can pass a vector like mtcars$mpg and not worry about it.

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 beta data.

Three different methods of shape parameters are supplied:

Usage

util_beta_param_estimate(.x, .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

.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 beta shape1 and shape2 parameters given some vector of values.

Author

Steven P. Sanderson II, MPH

Examples

library(dplyr)
library(ggplot2)

x <- mtcars$mpg
output <- util_beta_param_estimate(x)
#> For the beta distribution, its mean 'mu' should be 0 < mu < 1. The data will
#> therefore be scaled to enforce this.

output$parameter_tbl
#> # A tibble: 3 × 10
#>   dist_type samp_size   min   max  mean variance method    shape1 shape2 shape…¹
#>   <chr>         <int> <dbl> <dbl> <dbl>    <dbl> <chr>      <dbl>  <dbl>   <dbl>
#> 1 Beta             32  10.4  33.9 0.412   0.0658 Bayes      13.2   18.8    0.702
#> 2 Beta             32  10.4  33.9 0.412   0.0658 NIST_MME    1.11   1.58   0.702
#> 3 Beta             32  10.4  33.9 0.412   0.0658 EnvStats…   1.16   1.65   0.702
#> # … with abbreviated variable name ¹​shape_ratio

output$combined_data_tbl %>%
  tidy_combined_autoplot()


tb <- rbeta(50, 2.5, 1.4)
util_beta_param_estimate(tb)$parameter_tbl
#> There was no need to scale the data.
#> # A tibble: 3 × 10
#>   dist_type samp_size   min   max  mean variance method    shape1 shape2 shape…¹
#>   <chr>         <int> <dbl> <dbl> <dbl>    <dbl> <chr>      <dbl>  <dbl>   <dbl>
#> 1 Beta             50 0.223 0.985 0.668   0.0512 Bayes      33.4   16.6     2.02
#> 2 Beta             50 0.223 0.985 0.668   0.0512 NIST_MME    2.22   1.10    2.02
#> 3 Beta             50 0.223 0.985 0.668   0.0512 EnvStats…   2.28   1.13    2.02
#> # … with abbreviated variable name ¹​shape_ratio