Blossom (version 1.3)

mrpp: Multiresponse permutation procedures

Description

Multiresponse permutation procedures (MRPP) are used for univariate and multivariate analyses of grouped data in a completely randomized one-way design. MRPP are used for comparing equality of treatment groups analogous to one-way analysis of variance (or t-test) for univariate data, or multivariate analysis of variance (Hotelling's T^2) for multivariate data.

Usage

mrpp(variables, group, data, expon = 1, c.form = 1, hotelling = FALSE,
  commens = TRUE, interv = 0, number.perms, exact = FALSE,
  has.excess = FALSE, excess.value, max.dist, save.test)

Value

mrpp returns an object of either class MRPPObj or EMRPPObj. The functions summary as well as print can be used to obtain a summary of the test. Generic accessor functions pvalue and ResampVals (for MRPPObj) can be used to obtain the p-value and Monte Carlo resampled test statistic values respectively.

Details

The default Euclidean distance function in MRPP provides an omnibus test of distributional equivalence among groups or a test for common medians if the assumption of equal dispersions is applicable. Options allow MRPP to perform permutation (randomization) versions of t-tests, one-way analysis of variance, Kruskal-Wallis tests (for ranked data), Mann-Whitney Wilcoxon tests (for ranked data), and one-way multivariate analysis of variance. Options in MRPP also allow you to truncate distances to evaluate multiple clumping of data, establish an excess group, and select arc distances to compare circular distributions of grouped data. Multivariate data are commensurated (standardized) to a common scale but an option allows you to turn off commensuration. Commensuration can be done by using average Euclidean distance (default) or the variance/covariance matrix for the dependent variables. Multivariate medians and distance quantiles (MEDQ) are provided as estimates to be used in describing distributional changes detected by MRPP analyses.

References

Mielke, P.W., Jr., and K.J. Berry. 2001. Permutation methods: A distance function approach. Springer-Verlag.

See Also

pvalue, and ResampVals

Examples

out <- mrpp(variables = c(distance,elev),group = sex,data = bgrouse,
 exact = TRUE)
summary(out)