CPCAT.bMDD: CPCAT bootstrap MDD (bMDD)

In qountstat: Statistical Analysis of Count Data and Quantal Data

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.

Usage

CPCAT.bMDD(
  groups,
  counts,
  control.name = NULL,
  alpha = 0.05,
  shift.step = -0.25,
  bootstrap.runs = 200,
  power = 0.8,
  max.iterations = 1000,
  use.fixed.random.seed = NULL,
  CPCAT.bootstrap.runs = 200,
  show.progress = TRUE,
  show.results = TRUE
)

Arguments

groups	Group vector
counts	Vector with count data
control.name	Character string with control group name (optional)
alpha	Significance level
shift.step	Step of shift (negative as a reduction is assumed)
bootstrap.runs	Number of bootstrap runs
power	Proportion of bootstrap.runs that return significant differences
max.iterations	Max. number of iterations to not get stuck in the while loop
use.fixed.random.seed	Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set.
CPCAT.bootstrap.runs	Bootstrap runs within CPCAT method
show.progress	Show progress for each shift of lambda
show.results	Show results

Value

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)

qountstat documentation built on April 4, 2025, 12:18 a.m.