MRM: Multiple Regression on distance Matrices

In phiala/ecodist: Dissimilarity-Based Functions for Ecological Analysis

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).

Author(s)

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.

See Also

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")

phiala/ecodist documentation built on Nov. 5, 2023, 10:47 a.m.