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Estimate Zero Truncated Binomial Parameters

Source: R/est-param-zt-binom.R
util_zero_truncated_binomial_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 binomial data.

One method of estimating the parameters is done via:

Usage

util_zero_truncated_binomial_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 zero truncated binomial size and prob 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_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_t_param_estimate(), util_triangular_param_estimate(), util_uniform_param_estimate(), util_weibull_param_estimate(), util_zero_truncated_geometric_param_estimate(), util_zero_truncated_negative_binomial_param_estimate(), util_zero_truncated_poisson_param_estimate()

Other Binomial: tidy_binomial(), tidy_negative_binomial(), tidy_zero_truncated_binomial(), tidy_zero_truncated_negative_binomial(), util_binomial_param_estimate(), util_binomial_stats_tbl(), util_negative_binomial_param_estimate(), util_zero_truncated_binomial_stats_tbl(), util_zero_truncated_negative_binomial_param_estimate(), util_zero_truncated_negative_binomial_stats_tbl()

Other Zero Truncated Distribution: tidy_zero_truncated_binomial(), tidy_zero_truncated_geometric(), tidy_zero_truncated_poisson()

Author

Steven P. Sanderson II, MPH

Examples

library(dplyr)
library(ggplot2)

x <- as.integer(mtcars$mpg)
output <- util_zero_truncated_binomial_param_estimate(x)

output$parameter_tbl
#> # A tibble: 1 × 8
#>   dist_type               samp_size   min   max  mean method     size  prob
#>   <chr>                       <int> <dbl> <dbl> <dbl> <chr>     <dbl> <dbl>
#> 1 Zero-Truncated Binomial        32    10    33  19.7 MLE_Optim    33 0.597

output$combined_data_tbl |>
  tidy_combined_autoplot()


set.seed(123)
t <- tidy_zero_truncated_binomial(100, 10, .1)[["y"]]
util_zero_truncated_binomial_param_estimate(t)$parameter_tbl
#> # A tibble: 1 × 8
#>   dist_type               samp_size   min   max  mean method     size  prob
#>   <chr>                       <int> <dbl> <dbl> <dbl> <chr>     <dbl> <dbl>
#> 1 Zero-Truncated Binomial       100     1     4  1.53 MLE_Optim  4.00 0.500