R/srs_discrete.R

In SilviaTerra/forestsamplr: Standard Forest Sampling Design Workups

#' @title Summarize simple random sample for discrete attributes
#' @description Summarizes population-level statistics for
#' simple random sample for attribute data. The calculations are
#' derived from Chapter 3 in Avery and Burkhart's (1967)
#' Forest Measurements, Fifth Edition. The variance terms refer
#' to the variance of the mean.
#' @param data data frame containing observations of variable of
#' interest. Attribute must be coded as either TRUE and FALSE or
#' 1 and 0.
#' @param attribute character name of attribute to be summarized.
#' @param popTot numeric population size. Equivalent to total
#' number of individuals.
#' @param desiredConfidence numeric desired confidence level (e.g. 0.9).
#' @return data frame of stand-level statistics. Includes standard error and
#' 95% confidence interval.
#' @author Karin Wolken
#' @import dplyr
#' @examples
#' \dontrun{
#' data <- data.frame(
#'   alive = c(T, T, F, T, F, F),
#'   tree = c(1, 2, 3, 4, 5, 6)
#' )
#' attribute <- "alive"
#' popTot <- 50
#' }
#' @export

summarize_simple_random_discrete <- function(data, attribute = "attr",
                                             popTot = NA, desiredConfidence = 0.95) {
  attrTemp <- unlist(data %>% dplyr::select(one_of(attribute)))
  data$attr <- attrTemp

  if (is.na(popTot)) {
    stop("Total number of units is required as input.")
  }

  calculations <- data.frame(
    sampTot = length(data$attr),
    sampAttr = sum(data$attr)
  ) %>%
    summarize(
      totalSample = sampTot,
      attribute = sampAttr,
      totalPopulation = popTot,
      P = sampAttr / sampTot, # proportion
      SE = sqrt(((P * (1 - P)) / (sampTot - 1)) * (1 - (sampTot / popTot))),
      lowerLimitCI = P - (qt(1 - ((1 - desiredConfidence) / 2), sampAttr - 1)
      * SE + 1 / (2 * sampTot)),
      upperLimitCI = P + (qt(1 - ((1 - desiredConfidence) / 2), sampAttr - 1)
      * SE + 1 / (2 * sampTot))
    )

  return(calculations)
}