CPCAT: CPCAT

In qountstat: Statistical Analysis of Count Data and Quantal Data

CPCAT

Description

When conducting statistical tests with multiple treatments, such as a control group and increasing concentrations of a test substance, ANOVA and parametric post-hoc tests (e.g. Dunnett's test) are commonly used. However, these tests require the assumptions of homogeneous variances and normally distributed data. For count data (e.g. counts of animals), these assumptions are typically violated, as the data are usually Poisson-distributed. Additionally, multiple testing using post-hoc tests can lead to alpha-inflation. To address these issues, CPCAT was proposed by Lehmann et al. (2016). CPCAT has two components. The first is the Closure Principle (CP) developed by Bretz et al. (2010), which aims to eliminate alpha-inflation. CP applies a stepwise approach to identify at which concentration effects begin to occur. The second part of CPCAT is the actual significance test, CAT (Computational Approach Test; introduced by Chang et al., 2010), which uses a test based on the Poisson distribution rather than a parametric test based on normal distribution assumptions. For details on the structure of the input data, please refer to the dataset 'Daphnia.counts' provided alongside this package.

Usage

CPCAT(
  groups,
  counts,
  control.name = NULL,
  bootstrap.runs = 10000,
  hampel.threshold = 5,
  use.fixed.random.seed = NULL,
  get.contrasts.and.p.values = FALSE,
  show.output = TRUE
)

Arguments

groups	Group vector
counts	Vector with count data
control.name	Character string with control group name (optional)
bootstrap.runs	Number of bootstrap runs
hampel.threshold	Threshold for Hampel identifier (measure for over-/underdispersion)
use.fixed.random.seed	Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set.
get.contrasts.and.p.values	Get each row of the contrast matrices evaluated
show.output	Show/hide output

Value

R object with results and information from CPCAT calculations

References

Bretz, F.; Hothorn, T.; Westfall, P. (2010): Multiple comparisons using R. 1st Edition, Chapman and Hall/CRC, New York

Chang, C.-H.; Pal, N.; Lin, J.-J. (2010): A Note on Comparing Several Poisson Means. In: Commun. Stat. Simul. Comput., 2010, 39(8), p. 1605-1627, https://doi.org/10.1080/03610918.2010.508860

Lehmann, R.; Bachmann, J.; Maletzki, D.; Polleichtner, C.; Ratte, H.; Ratte, M. (2016): A new approach to overcome shortcomings with multiple testing of reproduction data in ecotoxicology. In: Stochastic Environmental Research and Risk Assessment, 2016, 30(3), p. 871-882, https://doi.org/10.1007/s00477-015-1079-4

Examples

Daphnia.counts	# example data provided alongside the package

# Test CPCAT
CPCAT(groups = Daphnia.counts$Concentration,
	 counts = Daphnia.counts$Number_Young,
	 control.name = NULL,
	 bootstrap.runs = 10000,
	 use.fixed.random.seed = 123,  #fixed seed for reproducible results
	 get.contrasts.and.p.values = FALSE,
	 show.output = TRUE)

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