rankMANOVA: Rank-based Tests for Multivariate Data in Nonparametric...

In smn74/rankMANOVA: Rank-Based Tests for Multivariate Data in Nonparametric Factorial Designs

Rank-based Tests for Multivariate Data in Nonparametric Factorial Designs

Description

The rankMANOVA function calculates an ANOVA-type statistic (ATS) with (wild) bootstrap p-values for nonparametric factorial designs with multivariate data.

Usage

rankMANOVA(
  formula,
  data,
  iter = 10000,
  alpha = 0.05,
  para = FALSE,
  CPU,
  dec = 3,
  seed,
  resampling = "bootstrap",
  nested.levels.unique = FALSE
)

Arguments

formula	A model formula object. The left hand side contains the response variables and the right hand side contains the factor variables of interest. Data must be provided in wide format.
data	A data.frame containing the variables in formula.
iter	The number of iterations used for calculating the resampled statistic. The default option is 10,000.
alpha	A number specifying the significance level; the default is 0.05.
para	Logical: should parallel computing be used? Default is FALSE.
CPU	The number of cores used for parallel computing. If omitted, cores are detected via detectCores.
dec	Number of decimals the results should be rounded to. Default is 3.
seed	A random seed for the resampling procedure. If omitted, no reproducible seed is set.
resampling	The resampling method to be used, one of "bootstrap" (sample-specific bootstrap approach) and "WildBS" (wild bootstrap approach with Rademacher weights). The default is "WildBS".
nested.levels.unique	A logical specifying whether the levels of the nested factor(s) are labeled uniquely or not. Default is FALSE, i.e., the levels of the nested factor are the same for each level of the main factor. For an example and more explanations see the GFD package and the corresponding vignette.

Details

Implemented is an ANOVA-type test statistic for testing hypotheses formulated in Mann-Whitney-type effects in nonparametric factorial designs. Statistical inference is based on a wild or a sample-specific bootstrap approach. The unweighted treatment effects considered do not depend on sample sizes and allow for transitive ordering. The package thus provides an extension of the univariate rankFD package to multivariate data.

Value

A rankMANOVA object containing the following components:

Descriptive	Some descriptive statistics of the data for all factor level combinations. Displayed are the number of individuals per factor level combination and the unweighted treatment effects for each dimension.
Test	The test statistic(s) and p-value(s) based on the chosen bootstrap approach.

NOTE

The number of bootstrap iterations has been set to 100 in the examples due to runtime restrictions on CRAN. Usually it is recommended to use at least 1000 iterations.

References

Dobler, D., Friedrich, S., and Pauly, M. (2017). Nonparametric MANOVA in Mann-Whitney effects.

See Also

rankFD

Examples

 data("marketing")
 mymar <- marketing[, c("Sex", "Income", "Edu")]
 mymar2 <- na.omit(mymar)
 test <- rankMANOVA(cbind(Income, Edu) ~ Sex, data = mymar2, iter=100,
  resampling = "WildBS", CPU = 1)
 summary(test)

smn74/rankMANOVA documentation built on Aug. 20, 2023, 10:26 a.m.