nparml: Nonparametric Test For Multivariate Data With Two-Way Layout...

In nparMD: Nonparametric Analysis of Multivariate Data in Factorial Designs

Nonparametric Test For Multivariate Data With Two-Way Layout Factorial Design - Large Samples

Description

Analysis of multivariate data with two-way completely randomized factorial design - version for large samples. The analysis is based on fully nonparametric, rank-based methods and uses an F-approximation for 'Dempster's ANOVA' and a chisquare-approximation for the criteria called 'Wilks Lambda', 'Lawley-Hotelling' and 'Bartlett-Nanda-Pillai'. These approximations are given by the asymtotic distribution of these statistics under true null-hypothesis. In contrast to the normal-approximated test (as used in the small sample version) it is designed for data with large samples (see details) while the number of factorial levels is allowed to be small. The multivariate response is allowed to be ordinal, quantitative, binary or a mixture of the different variable types. The test statistics are constructed using nonparametric relative effect estimators.

Usage

nparml(formula, data)

Arguments

formula	an object of class "formula" with two explanatory variables (factors), see examples.
data	an object of class "data.frame" containing the variables in the formula

Details

The data is analysed for main effects and interaction effect of the explanatory factors. In each case the null hypothesis "no effect" is testet. In order to obtain reliable results the considered data should include at least 7 observations per factor level combination. This method is only implemented for complete data sets without missing values.

Value

Returns a list of data frames providing the values of the test statistics, p-values, degrees of freedom, factor levels, and groupsize per factor level combination.

References

Kiefel M., Bathke A.C. (2020) Rank-Based Analysis of Multivariate Data in Factorial Designs and Its Implementation in R In: Nonparametric Statistics (285-294) Springer Proceedings in Mathematics & Statistics Springer International Publishing, Cham

Bathke A.C., Harrar S.W. (2016) Rank-Based Inference for Multivariate Data in Factorial Designs. In: Liu R., McKean J. (eds) Robust Rank-Based and Nonparametric Methods. Springer Proceedings in Mathematics & Statistics, vol 168. Springer, Cham

Harrar S.W., Bathke A.C. (2012) A modified two-factor multivariate analysis of variance: asymptotics and small sample approximations (and erratum). In: Annals of the Institute of Statistical Mathematics, 64(1&5):135-165&1087, 2012.

Brunner E., Dette H., Munk A. (1997) Box-Type Approximations in Nonparametric Factorial Designs In: Journal of the American Statistical Association, 92(440):1494-1502

Examples

data(pseudostudy1)
nparml(resp1|resp2|resp3~treatment*age, pseudostudy1)

nparMD documentation built on March 18, 2022, 6:33 p.m.