R: Multiple Regression on distance Matrices

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).

### 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")

[Package ecodist version 2.0.7 Index]