Distribution Statistics for Paralogistic Distribution

Returns distribution statistics in a tibble.

Usage

util_paralogistic_stats_tbl(.data)

Arguments

.data

The data being passed from a tidy_ distribution function.

Value

A tibble

Details

This function will take in a tibble and returns the statistics of the given type of tidy_ distribution. It is required that data be passed from a tidy_ distribution function.

See also

Other Paralogistic: util_paralogistic_aic(), util_paralogistic_param_estimate()

Other Distribution Statistics: util_bernoulli_stats_tbl(), util_beta_stats_tbl(), util_binomial_stats_tbl(), util_burr_stats_tbl(), util_cauchy_stats_tbl(), util_chisquare_stats_tbl(), util_exponential_stats_tbl(), util_f_stats_tbl(), util_gamma_stats_tbl(), util_generalized_beta_stats_tbl(), util_generalized_pareto_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), util_inverse_burr_stats_tbl(), util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), util_negative_binomial_stats_tbl(), util_normal_stats_tbl(), util_pareto1_stats_tbl(), util_pareto_stats_tbl(), util_poisson_stats_tbl(), util_t_stats_tbl(), util_triangular_stats_tbl(), util_uniform_stats_tbl(), util_weibull_stats_tbl(), util_zero_truncated_binomial_stats_tbl(), util_zero_truncated_geometric_stats_tbl(), util_zero_truncated_negative_binomial_stats_tbl(), util_zero_truncated_poisson_stats_tbl()

Examples

library(dplyr)

set.seed(123)
tidy_paralogistic(.n = 50, .shape = 5, .rate = 6) |>
  util_paralogistic_stats_tbl() |>
  glimpse()
#> Rows: 1
#> Columns: 17
#> $ tidy_function     <chr> "tidy_paralogistic"
#> $ function_call     <chr> "Paralogistic c(5, 6, 0.167)"
#> $ distribution      <chr> "Paralogistic"
#> $ distribution_type <chr> "Continuous"
#> $ points            <dbl> 50
#> $ simulations       <dbl> 1
#> $ mean              <dbl> 1.5
#> $ mode_lower        <dbl> 1
#> $ range             <chr> "0 to Inf"
#> $ std_dv            <dbl> 1.936492
#> $ coeff_var         <dbl> 1.290994
#> $ skewness          <dbl> 6.97137
#> $ kurtosis          <dbl> 70.8
#> $ computed_std_skew <dbl> -0.1662826
#> $ computed_std_kurt <dbl> 2.556048
#> $ ci_lo             <dbl> 0.06320272
#> $ ci_hi             <dbl> 0.1623798