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est-param-poisson.R
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est-param-poisson.R
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#' Estimate Poisson Parameters
#'
#' @family Parameter Estimation
#' @family Poisson
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @details This function will attempt to estimate the pareto lambda
#' parameter given some vector of values.
#'
#' @description 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 poisson data.
#'
#' @param .x The vector of data to be passed to the function.
#' @param .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 `$combined_data_tbl` from the function output.
#'
#' @examples
#' library(dplyr)
#' library(ggplot2)
#'
#' x <- as.integer(mtcars$mpg)
#' output <- util_poisson_param_estimate(x)
#'
#' output$parameter_tbl
#'
#' output$combined_data_tbl |>
#' tidy_combined_autoplot()
#'
#' t <- rpois(50, 5)
#' util_poisson_param_estimate(t)$parameter_tbl
#'
#' @return
#' A tibble/list
#'
#' @export
#'
util_poisson_param_estimate <- function(.x, .auto_gen_empirical = TRUE) {
# Tidyeval ----
x_term <- as.numeric(.x)
minx <- min(x_term)
maxx <- max(x_term)
n <- length(x_term)
unique_terms <- length(unique(x_term))
# Checks ----
if (!is.vector(x_term, mode = "numeric") || is.factor(x_term)) {
rlang::abort(
message = "'.x' must be a numeric vector.",
use_cli_format = TRUE
)
}
if (n < 1 || any(x_term < 0) || any(x_term != trunc(x_term))) {
rlang::abort(
message = "'.x' must contain at least one non-missing distinct value.
All values of '.x' must be positive integers.",
use_cli_format = TRUE
)
}
# Get params ----
# EnvStats
m <- mean(x_term, na.rm = TRUE)
# Return Tibble ----
if (.auto_gen_empirical) {
te <- tidy_empirical(.x = x_term)
td <- tidy_poisson(.n = n, .lambda = round(m, 3))
combined_tbl <- tidy_combine_distributions(te, td)
}
ret <- dplyr::tibble(
dist_type = "Posson",
samp_size = n,
min = minx,
max = maxx,
method = "MLE",
lambda = m
)
# Return ----
attr(ret, "tibble_type") <- "parameter_estimation"
attr(ret, "family") <- "poisson"
attr(ret, "x_term") <- .x
attr(ret, "n") <- n
if (.auto_gen_empirical) {
output <- list(
combined_data_tbl = combined_tbl,
parameter_tbl = ret
)
} else {
output <- list(
parameter_tbl = ret
)
}
return(output)
}