Multiple Regression on distance Matrices

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Description

Multiple regression on distance matrices (MRM) using permutation tests of significance for regression coefficients and R-squared.

Usage

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MRM(formula = formula(data), data = sys.parent(), nperm = 1000, mrank = FALSE)

Arguments

formula

formula in R/S-Plus format describing the test to be conducted.

data

an optional dataframe containing the variables in the model as columns of dissimilarities. By default the variables are taken from the current environment.

nperm

number of permutations to use. If set to 0, the permutation test will be omitted.

mrank

if this is set to FALSE (the default option), Pearson correlations will be used. If set to TRUE, the Spearman correlation (correlation ranked distances) will be used.

Details

Performs multiple regression on distance matrices following the methods outlined in Legendre et al. 1994.

Value

coef

A matrix with regression coefficients and associated p-values from the permutation test (using the pseudo-t of Legendre et al. 1994).

r.squared

Regression R-squared and associated p-value from the permutation test.

F.test

F-statistic and p-value for overall F-test for lack of fit.

Author(s)

Sarah Goslee, Sarah.Goslee@ars.usda.gov

References

Lichstein, J. 2007. Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecology 188: 117-131.

Legendre, P.; Lapointe, F. and Casgrain, P. 1994. Modeling brain evolution from behavior: A permutational regression approach. Evolution 48: 1487-1499.

See Also

mantel

Examples

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data(graze)
LOAR10.mrm <- MRM(dist(LOAR10) ~ dist(sitelocation) + dist(forestpct), data=graze, nperm=100)