mgram: Mantel correlogram

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

Mantel correlogram

Description

Calculates simple Mantel correlograms.

Usage

mgram(species.d, space.d, breaks, nclass, stepsize, equiprobable = FALSE, 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.
equiprobable	if TRUE, create nclass classes of equal number of distances; if FALSE, create nclass classes of equal width
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, and tests the hypothesis that the mean compositional dissimilarity within a distance class differs from the mean of all the other distance classes combined. 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, and significance is tested in reference to all distance classes.

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))

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