Description Usage Arguments Details Value NOTE References See Also Examples
View source: R/rankMANOVAfunction.R
The rankMANOVA function calculates an ANOVAtype statistic (ATS) with (wild) bootstrap pvalues for nonparametric factorial designs with multivariate data.
1 2  rankMANOVA(formula, data, iter = 10000, alpha = 0.05, CPU, dec = 3,
seed, resampling = "WildBS", nested.levels.unique = FALSE)

formula 
A model 
data 
A data.frame containing the variables in

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. 
CPU 
The number of cores used for parallel computing. If omitted, cores are
detected via 
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" (samplespecific 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. 
Implemented is an ANOVAtype test statistic for testing hypotheses formulated in MannWhitneytype
effects in nonparametric factorial designs. Statistical inference is based on a wild or a samplespecific
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.
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 pvalue(s) based on the chosen bootstrap approach. 
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.
Dobler, D., Friedrich, S., and Pauly, M. (2017). Nonparametric MANOVA in MannWhitney effects.
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