Multi.group.test.bMDD: Multi-group test bMDD

In qountstat: Statistical Analysis of Count Data and Quantal Data

Multi-group test bMDD

Description

Idea: shift lambda of Poisson distribution until there is a certain proportion of significant results

Usage

Multi.group.test.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,
  Dunnett.GLM.zero.treatment.action = "log(x+1)",
  show.progress = TRUE,
  show.results = TRUE,
  get.effect.and.power = FALSE,
  use.CMP.distribution = FALSE,
  CMP.dispersion.factor = 1,
  test = "CPCAT"
)

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
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
get.effect.and.power	Return effect size (percent of control) and power for each step (only for last treatment)
use.CMP.distribution	Use Conway-Maxwell-Poisson distribution for sampling
CMP.dispersion.factor	Dispersion parameter phi has to be sqrt(factor) to scale the variance by this factor
test	Either "CPCAT" or "GLM.Dunnett"

Value

Data frame with results from bMDD analysis

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