CPFISH.bMDD: CPFISH bootstrap MDD (bMDD)

In qountstat: Statistical Analysis of Count Data and Quantal Data

CPFISH bootstrap MDD (bMDD)

Description

The basic idea of the calculation of bootstrap MDD (bMDD) using the CPCAT approach is to shift the probability of binomial distribution until there is a certain proportion of results significantly different from the control.

Usage

CPFISH.bMDD(
  contingency.table,
  control.name = NULL,
  alpha = 0.05,
  shift.step = -0.01,
  bootstrap.runs = 200,
  power = 0.8,
  max.iterations = 1000,
  simulate.p.value = TRUE,
  use.fixed.random.seed = NULL,
  show.progress = TRUE,
  show.results = TRUE
)

Arguments

contingency.table	Matrix with observed data (e.g. survival counts, survival must be in first row)
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 (draw Poisson data n times)
power	Proportion of bootstrap.runs that return significant differences
max.iterations	Max. number of iterations to not get stuck in the while loop
simulate.p.value	Use simulated p-values in Fisher test or not
use.fixed.random.seed	Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set.
show.progress	Show progress for each shift of the probability
show.results	Show results

Value

Data frame with results from bMDD analysis

Examples

CPFISH.contingency.table	# example data provided alongside the package

# Test CPFISH bootstrap MDD
CPFISH.bMDD(contingency.table = CPFISH.contingency.table,
		   control.name = NULL,
		   alpha = 0.05,
		   shift.step = -0.1,		# Caution: big step size for testing
		   bootstrap.runs = 10,		# Caution: low number of bootstrap runs for testing
		   power = 0.8,
		   max.iterations = 1000,
		   simulate.p.value = TRUE,
		   use.fixed.random.seed = 123,  #fixed seed for reproducible results
		   show.progress = TRUE,
		   show.results = TRUE)

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