Distribution Statistics for Paralogistic Distribution
Source:R/stats-paarlogistic-tbl.R
util_paralogistic_stats_tbl.RdReturns distribution statistics in 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