Estimate t Distribution Parameters

Estimate t Distribution Parameters

Usage

util_t_param_estimate(.x, .auto_gen_empirical = TRUE)

Arguments

.x

The vector of data to be passed to the function, where the data comes from the rt() 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 t distribution parameters given some vector of values produced by rt(). The estimation method uses both method of moments and maximum likelihood estimation.

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_chisquare_param_estimate(), util_exponential_param_estimate(), util_f_param_estimate(), util_gamma_param_estimate(), util_generalized_beta_param_estimate(), util_generalized_pareto_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), util_inverse_burr_param_estimate(), util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), util_negative_binomial_param_estimate(), util_normal_param_estimate(), util_paralogistic_param_estimate(), util_pareto1_param_estimate(), util_pareto_param_estimate(), util_poisson_param_estimate(), util_triangular_param_estimate(), util_uniform_param_estimate(), util_weibull_param_estimate(), util_zero_truncated_binomial_param_estimate(), util_zero_truncated_geometric_param_estimate(), util_zero_truncated_negative_binomial_param_estimate(), util_zero_truncated_poisson_param_estimate()

Author

Steven P. Sanderson II, MPH

Examples

library(dplyr)
library(ggplot2)

set.seed(123)
x <- rt(100, df = 10, ncp = 0.5)
output <- util_t_param_estimate(x)

output$parameter_tbl
#> # A tibble: 2 × 7
#>   dist_type      samp_size  mean variance method df_est ncp_est
#>   <chr>              <int> <dbl>    <dbl> <chr>   <dbl>   <dbl>
#> 1 T Distribution       100 0.612    0.949 MME     0.959   0.612
#> 2 T Distribution       100 0.612    0.949 MLE     8.32    0.571

output$combined_data_tbl |>
  tidy_combined_autoplot()