util_beta_param_estimate: Estimate Beta Parameters

In TidyDensity: Functions for Tidy Analysis and Generation of Random Data

Estimate Beta Parameters

Description

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:

• Bayes

• NIST mme

• EnvStats mme, see EnvStats::ebeta()

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.

Details

This function will attempt to estimate the beta shape1 and shape2 parameters given some vector of values.

Value

A tibble/list

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Parameter Estimation: util_bernoulli_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_normal_param_estimate(), util_pareto_param_estimate(), util_poisson_param_estimate(), util_uniform_param_estimate(), util_weibull_param_estimate()

Other Beta: tidy_beta(), tidy_generalized_beta(), util_beta_stats_tbl()

Examples

library(dplyr)
library(ggplot2)

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

output$parameter_tbl

output$combined_data_tbl %>%
  tidy_combined_autoplot()

tb <- rbeta(50, 2.5, 1.4)
util_beta_param_estimate(tb)$parameter_tbl

TidyDensity documentation built on Nov. 2, 2023, 5:38 p.m.