npmv (version 2.4.0)

npmv-package: Nonparametric Comparison of Multivariate Samples

Description

Performs analysis of one-way multivariate data using nonparametric techniques developed since 2008. Allows for small samples and ordinal variables, or even mixture of the different variable types ordinal, quantitative, binary. Using F-approximations for ANOVA Type, Wilks' Lambda Type, Lawley Hotelling Type, and Bartlett Nanda Pillai Type test statics, as well as a permutation test for each, the package compares the multivariate distributions of the different samples. Also computes nonparametric relative effects and produces plots.

Warning

The nonparametric methods implemented in the code have been developed for complete data with no missing values.The code automatically produces a warning if there is missing data. Under certain conditions, the matrices H and G are singular (See literature for explanation of H and G), for example when the number of response variables exceeds the sample size. When this happens, only the ANOVA type statistic can be computed. The code automatically produces a warning if H or G are singular.

Details

Package:

npmv

Type:

Package

Version:

2.4

Date:

2017-01-08

License:

GPL-2

This package provides the R functions nonpartest and ssnonpartest to compute nonparametric test statistics. The function nonpartest computes the global nonparametric test statistics, their permutation test analogs, and calculates nonparametric relative effects. The function ssnonpartest performs an all-subset algorithm to determine which variables cause significant effects, and between which factor levels. See the examples below for some basic uses and look in the help pages for each function for a much more detailed look.

References

Arne C. Bathke , Solomon W. Harrar, and Laurence V. Madden. "How to compare small multivariate samples using nonparametric tests," Computational Statistics and Data Analysis 52 (2008) 4951-4965

Woodrow W. Burchett, Amanda R. Ellis, Solomon W. Harrar, Arne C. Bathke (2017). "Nonparametric Inference for Multivariate Data: The R Package npmv.," Journal of Statistical Software, 76(4), 1-18.

Brunner E, Domhof S, Langer F (2002), Nonparametric Analysis of Longitudinal Data in Factorial Experiments. Wiley, New York.

Chunxu Liu, Arne C. Bathke, Solomon W. Harrar. "A nonparametric version of Wilks' lambda-Asymptotic results and small sample approximations" Statistics and Probability Letters 81 (2011) 1502-1506

Horst, L.E., Locke, J., Krause, C.R., McMahaon, R.W., Madden, L.V., Hoitink, H.A.J., 2005. Suppression of Botrytis blight of Begonia by Trichoderma hamatum 382 in peat and compost-amended potting mixes. Plant Disease 89, 1195-1200.

Examples

data(sberry)
nonpartest(weight|bot|fungi|rating~treatment,sberry,permreps=1000)
ssnonpartest(weight|bot|fungi|rating~treatment,sberry,test=c(1,0,0,0),alpha=.05,
            factors.and.variables=TRUE)