R/CPCATbMDD.R

In qountstat: Statistical Analysis of Count Data and Quantal Data

#' @title CPCAT bootstrap MDD (bMDD)
#' @description The basic idea of the calculation of bootstrap MDD (bMDD) using the CPCAT approach
#' is to shift the lambda parameter of Poisson distribution until there is a certain proportion of
#' results significantly different from the control.
#' @param groups Group vector
#' @param counts Vector with count data
#' @param control.name Character string with control group name (optional)
#' @param alpha Significance level
#' @param shift.step Step of shift (negative as a reduction is assumed)
#' @param bootstrap.runs Number of bootstrap runs
#' @param power Proportion of bootstrap.runs that return significant differences
#' @param max.iterations Max. number of iterations to not get stuck in the while loop
#' @param use.fixed.random.seed Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set.
#' @param CPCAT.bootstrap.runs Bootstrap runs within CPCAT method
#' @param show.progress Show progress for each shift of lambda
#' @param show.results Show results
#' @return Data frame with results from bMDD analysis
#' @examples
#' Daphnia.counts	# example data provided alongside the package
#'
#' # Test CPCAT bootstrap MDD
#' CPCAT.bMDD(groups = Daphnia.counts$Concentration,
#'			  counts = Daphnia.counts$Number_Young,
#'			  control.name = NULL,
#'			  alpha = 0.05,
#'			  shift.step = -1,		# Caution: big step size for testing
#'			  bootstrap.runs = 5,	# Caution: low number of bootstrap runs for testing
#'			  power = 0.8,
#'			  max.iterations = 1000,
#'			  use.fixed.random.seed = 123,  #fixed seed for reproducible results
#'			  CPCAT.bootstrap.runs = 10,
#'			  show.progress = TRUE,
#'			  show.results = TRUE)
#'
#' @export
CPCAT.bMDD = function(groups,						# group vector
					  counts,						# vector with count data
					  control.name = NULL,			# character string with control group name
					  alpha = 0.05,					# significance level
					  shift.step = -0.25,			# step of shift (negative as a reduction is assumed)
					  bootstrap.runs = 200,			# number of bootstrap runs (draw Poisson data n times)
					  power = 0.8,					# proportion of bootstrap.runs that return significant differences
					  max.iterations = 1000,		# max number of iterations to not get stuck in the while loop
					  use.fixed.random.seed = NULL, # fix seed, e.g. 123, for random numbers if desired (enables to reproduce results)
					  CPCAT.bootstrap.runs = 200,	# bootstrap runs within CPCAT method
					  show.progress = TRUE,			# show progress for each shift of lambda
					  show.results = TRUE) {		# show results

	Multi.group.test.bMDD(groups = groups,
						  counts = counts,
						  control.name = control.name,
						  alpha = alpha,
						  shift.step = shift.step,
						  bootstrap.runs = bootstrap.runs,
						  power = power,
						  max.iterations = max.iterations,
						  use.fixed.random.seed = use.fixed.random.seed,
						  CPCAT.bootstrap.runs = CPCAT.bootstrap.runs,
						  show.progress = show.progress,
						  show.results = show.results,
						  test = "CPCAT")
}