spsanderson
commented
2 years ago Function:
#' Distribution Statistics
#'
#' @family Binomaial
#' @family Negative Binomial
#' @fmaily Distribution Statistics
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @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.
#'
#' @description Returns distribution statistics in a tibble.
#'
#' @param .data The data being passed from a `tidy_` distribution function.
#'
#' @examples
#' tidy_negative_binomial() %>%
#' util_negative_binomial_stats_tbl()
#'
#' @return
#' A tibble
#'
#' @export
#'
util_negative_binomial_stats_tbl <- function(.data){
# Immediate check for tidy_ distribution function
if (!"tibble_type" %in% names(attributes(.data))){
rlang::abort(
message = "You must pass data from the 'tidy_dist' function.",
use_cli_format = TRUE
)
}
if (attributes(.data)$tibble_type != "tidy_negative_binomial"){
rlang::abort(
message = "You must use 'tidy_negative_binomial()'",
use_cli_format = TRUE
)
}
# Data
data_tbl <- tibble::as_tibble(.data)
atb <- attributes(data_tbl)
r <- atb$.size
p <- atb$.prob
stat_mean <- (p*r)/(1 - p)
stat_mode <- ifelse(r > 1, floor((p*(r-1))/(1 - p)), 0)
stat_coef_var <- (p*r)/((1 - p)^2)
stat_sd <- sqrt(stat_coef_var)
stat_skewness <- (1 + p)/sqrt(p*r)
stat_kurtosis <- 6/r + ((1-p)^2)/(p*r)
# Data Tibble
ret <- tibble::tibble(
tidy_function = atb$tibble_type,
function_call = atb$dist_with_params,
distribution = atb$tibble_type %>%
stringr::str_remove("tidy_") %>%
stringr::str_to_title(),
distribution_type = atb$distribution_family_type,
points = atb$.n,
simulations = atb$.num_sims,
mean = stat_mean,
mode_lower = stat_mode,
range = paste0("0 to Inf"),
std_dv = stat_sd,
coeff_var = stat_coef_var,
skewness = stat_skewness,
kurtosis = stat_kurtosis,
computed_std_skew = tidy_skewness_vec(data_tbl$y),
computed_std_kurt = tidy_kurtosis_vec(data_tbl$y)
)
# Return
return(ret)
}Examples:
tidy_negative_binomial() %>%
util_negative_binomial_stats_tbl() %>%
glimpse()
Rows: 1
Columns: 15
$ tidy_function <chr> "tidy_negative_binomial"
$ function_call <chr> "Negative Binomial c(1, 0.1)"
$ distribution <chr> "Negative_binomial"
$ distribution_type <chr> "discrete"
$ points <dbl> 50
$ simulations <dbl> 1
$ mean <dbl> 0.1111111
$ mode_lower <dbl> 0
$ range <chr> "0 to Inf"
$ std_dv <dbl> 0.3513642
$ coeff_var <dbl> 0.1234568
$ skewness <dbl> 3.478505
$ kurtosis <dbl> 14.1
$ computed_std_skew <dbl> 1.718317
$ computed_std_kurt <dbl> 6.09096
https://openacttexts.github.io/Loss-Data-Analytics/C-SummaryDistributions.html