MRM {ecodist} | R Documentation |
Multiple regression on distance matrices (MRM) using permutation tests of significance for regression coefficients and R-squared.
MRM(formula = formula(data), data = sys.parent(), nperm = 1000, mrank = FALSE)
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. |
Performs multiple regression on distance matrices following the methods outlined in Legendre et al. 1994.
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. |
Sarah Goslee, Sarah.Goslee@ars.usda.gov
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.
data(graze) LOAR10.mrm <- MRM(dist(LOAR10) ~ dist(sitelocation) + dist(forestpct), data=graze, nperm=100)