R/all_cluster.R

In SilviaTerra/forestsamplr: Standard Forest Sampling Design Workups

#' @title Summarize all cluster sample
#' @description Summarizes population-level statistics for
#' cluster sample data. This function has two options: (1)
#' Cluster sample with a normal distribution and (2) Cluster
#' sample with a Bernoulli distribution.
#' @usage summarize_all_cluster(data, attribute = NA, element = TRUE,
#'                              plotTot = NA,
#'                              desiredConfidence = 0.95,
#'                              bernoulli = F)
#' @param data data frame containing observations of variable of
#' interest for either cluster-level or plot-level data.
#' @param attribute character name of attribute to be summarized.
#' @param element logical true if parameter data is plot-level, false if
#' parameter data is cluster-level. Default is True.
#' @param plotTot numeric population size. Equivalent to the
#' total number of possible elements in the population.
#' @param desiredConfidence numeric desired confidence level (e.g. 0.9).
#' @param bernoulli logical TRUE if data fitting the Bernoulli
#' distribution is used.
#' @return data frame of stand-level statistics including
#' standard error and confidence interval limits.
#' @author Karin Wolken
#' @import dplyr
#' @examples
#' \dontrun{
#' 
#' # See Forest Sampling vignette for more details
#' 
#' # Plot level data can be expressed as:
#' 
#' plotLevelDataExample <- data.frame(
#'   clusterID = c(
#'     1, 1, 1, 1, 1, 2,
#'     2, 3, 4, 4, 4, 4,
#'     4, 4, 5, 5, 5, 5,
#'     5
#'   ),
#'   attr = c(
#'     1000, 1250, 950, 900,
#'     1005, 1000, 1250, 950,
#'     900, 1005, 1000, 1250,
#'     950, 900, 1005, 1000,
#'     1250, 950, 900
#'   ),
#'   isUsed = c(
#'     T, T, T, T, T, T, T,
#'     T, T, T, T, T, T, T,
#'     F, F, F, F, F
#'   )
#' )
#' 
#' # Cluster level data can be expressed as:
#' 
#' clusterLevelDataExample <- data.frame(
#'   clusterID = c(1, 2, 3, 4, 5),
#'   clusterElements = c(
#'     4, 2, 9,
#'     4, 10
#'   ),
#'   sumAttr = c(
#'     1000, 1250, 950,
#'     900, 1005
#'   ),
#'   isUsed = c(T, T, F, T, T)
#' )
#' # Set element = FALSE
#' 
#' 
#' # Bernoulli data can be expressed as:
#' 
#' bernoulliData <- data.frame(
#'   plots = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
#'   propAlive = c(
#'     0.75, 0.80, 0.80, 0.85,
#'     0.70, 0.90, 0.70, 0.75,
#'     0.80, 0.65
#'   )
#' )
#' # Set parameter bernoulli = TRUE
#' }
#' @export


summarize_all_cluster <- function(data, attribute = NA, element = TRUE,
                                  plotTot = NA, desiredConfidence = 0.95, bernoulli = F) {
  if (bernoulli == F) {
    out <- summarize_cluster(data, element, attribute, desiredConfidence)
  } else {
    out <- summarize_cluster_discrete(data, attribute, plotTot, desiredConfidence)
  }

  return(out)
}