R: Mantel correlogram

mgram {ecodist} | R Documentation

## Mantel correlogram

### Description

Calculates simple Mantel correlograms.

### Usage

    mgram(species.d, space.d, breaks, nclass, stepsize, nperm = 1000,
        mrank = FALSE, nboot = 500, pboot = 0.9, cboot = 0.95,
        alternative = "two.sided", trace = FALSE)

### Arguments

`species.d` | lower-triangular dissimilarity matrix.
`space.d` | lower-triangular matrix of geographic distances.
`breaks` | locations of class breaks. If specified, overrides nclass and stepsize.
`nclass` | number of distance classes. If not specified, Sturge's rule will be used to determine an appropriate number of classes.
`stepsize` | width of each distance class. If not specified, nclass and the range of space.d will be used to calculate an appropriate default.
`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.
`nboot` | number of iterations to use for the bootstrapped confidence limits. If set to 0, the bootstrapping will be omitted.
`pboot` | the level at which to resample the data for the bootstrapping procedure.
`cboot` | the level of the confidence limits to estimate.
`alternative` | default is "two.sided", and returns p-values for H0: rM = 0. The alternative is "one.sided", which returns p-values for H0: rM <= 0.
`trace` | if TRUE, returns progress indicators.

### Details

This function calculates Mantel correlograms. The Mantel correlogram is essentially a multivariate autocorrelation function. The Mantel r represents the dissimilarity in variable composition (often species composition) at a particular lag distance.

### Value

Returns an object of class mgram, which is a list with two elements. mgram is a matrix with one row for each distance class and 6 columns:

`lag` | midpoint of the distance class.
`ngroup` | number of distances in that class.
`mantelr` | Mantel r value.
`pval` | p-value for the test chosen.
`llim` | lower bound of confidence limit for mantelr.
`ulim` | upper bound of confidence limit for mantelr.

resids is NA for objects calculated by mgram().

### Author(s)

Sarah Goslee

### References

Legendre, P. and M. Fortin. 1989. Spatial pattern and ecological analysis. Vegetatio 80:107-138.

### See Also

`mantel`, `plot.mgram`, `pmgram`

### Examples

    # generate a simple surface
    x <- matrix(1:10, nrow=10, ncol=10, byrow=FALSE)
    y <- matrix(1:10, nrow=10, ncol=10, byrow=TRUE)
    z <- x + 3*y
    image(z)

    # analyze the pattern of z across space
    space <- cbind(as.vector(x), as.vector(y))
    z <- as.vector(z)
    space.d <- distance(space, "eucl")
    z.d <- distance(z, "eucl")
    z.mgram <- mgram(z.d, space.d, nperm=0)
    plot(z.mgram)

    #

    data(graze)
    space.d <- dist(graze$sitelocation)
    forest.d <- dist(graze$forestpct)

    grasses <- graze[, colnames(graze) %in% c("DAGL", "LOAR10", "LOPE", "POPR")]
    legumes <- graze[, colnames(graze) %in% c("LOCO6", "TRPR2", "TRRE3")]

    grasses.bc <- bcdist(grasses)
    legumes.bc <- bcdist(legumes)

    # Does the relationship of composition with distance vary for
    # grasses and legumes?
    par(mfrow=c(2, 1))
    plot(mgram(grasses.bc, space.d, nclass=8))
    plot(mgram(legumes.bc, space.d, nclass=8))

[Package ecodist version 2.0.7 Index]