Dunnett.GLM.bMDD: Dunnett.GLM bootstrap MDD (bMDD)

In qountstat: Statistical Analysis of Count Data and Quantal Data

Dunnett.GLM bootstrap MDD (bMDD)

Description

The basic idea of the calculation of bootstrap MDD (bMDD) using the Dunnett.GLM approach is to shift the lambda parameter of Poisson distribution until there is a certain proportion of results significantly different from the control.

Usage

Dunnett.GLM.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,
  Dunnett.GLM.zero.treatment.action = "log(x+1)",
  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.
Dunnett.GLM.zero.treatment.action	Dunnett.GLM method to be used for treatments only containing zeros
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 Dunnett.GLM bootstrap MDD
Dunnett.GLM.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
	Dunnett.GLM.zero.treatment.action = "log(x+1)",
	show.progress = TRUE,
	show.results = TRUE)

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