MRM {ecodist} R Documentation

## Multiple Regression on distance Matrices

### Description

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

### Usage

MRM(formula = formula(data), data, nperm = 1000,
method = "linear", mrank = FALSE)


### Arguments

 formula formula 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. method if "linear", the default, uses multiple regression analysis. If "logistic", performs logistic regression with appropriate permutation testing. Note that this may be substantially slower.

### Details

Performs multiple regression on distance matrices following the methods outlined in Legendre et al. 1994. Specificaly, the permutation test uses a pseudo-t test to assess significance, rather than using the regression coefficients directly.

### 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 (linear only). F.test  F-statistic and p-value for overall F-test for lack of fit (linear only). dev  Residual deviance, degrees of freedom, and associated p-value (logistic only).

Sarah Goslee

### 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.

mantel

### Examples


data(graze)

# Abundance of this grass is related to forest cover but not location
MRM(dist(LOAR10) ~ dist(sitelocation) + dist(forestpct), data=graze, nperm=10)

# Abundance of this legume is related to location but not forest cover
MRM(dist(TRRE3) ~ dist(sitelocation) + dist(forestpct), data=graze, nperm=10)

# Compare to presence/absence of grass LOAR10 using logistic regression
LOAR10.presence <- ifelse(graze\$LOAR10 > 0, 1, 0)
MRM(dist(LOAR10.presence) ~ dist(sitelocation) + dist(forestpct),
data=graze, nperm=10, method="logistic")


[Package ecodist version 2.0.9 Index]