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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_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_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_t_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()

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 -1.33  8.29  2.87        1.23 EnvStats_MME      2.87  1.23
#> 2 Logistic         50 -1.33  8.29  2.87        1.23 EnvStats_MMUE     2.87  1.24
#> 3 Logistic         50 -1.33  8.29  2.87        1.23 EnvStats_MLE      2.87  1.63
#> # ℹ 1 more variable: shape_ratio <dbl>