pmgram: Partial Mantel correlogram

Description

This function calculates simple and partial multivariate correlograms.

Usage

pmgram(data, space, partial, breaks, nclass, stepsize, resids = FALSE, nperm = 1000)

Arguments

data

lower-triangular dissimilarity matrix.

space

lower-triangular matrix of geographic distances.

partial

optional, lower-triangular dissimilarity matrix of ancillary data.

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.

resids

if resids=TRUE, will return the residuals for each distance class. Otherwise returns 0.

nperm

number of permutations to use. If set to 0, the permutation test will be omitted.

Value

Returns a object of class mgram, which is a list containing two objects: mgram is a matrix with one row for each distance class and 4 columns:
lagmidpoint of the distance class.
ngroupnumber of distances in that class.
mantelrMantel r value.
pvaltwo-sided p-value.
resids is a vector of the residuals (if calcuated) and can be accessed with the residuals() method.

Details

This function does four different analyses: If data has 1 column and partial is missing, calculates a multivariate correlogram for data.

If data has 2 columns and partial is missing, calculates Mantel r between the two columns for each distance class.

If data has 1 column and partial exists, does a multivariate correlogram for the residuals, taking residuals over whole extent.

If data has 2 columns and partial exists, does a partial multivariate correlogram, calculating partial for each distance class separately.

Examples

Run this code

# generate a simple surface
x <- matrix(1:10, nrow=10, ncol=10, byrow=FALSE)
y <- matrix(1:10, nrow=10, ncol=10, byrow=TRUE)
z1 <- x + 3*y
z2 <- 2*x - y

# look at patterns
par(mfrow=c(1,2))
image(z1)
image(z2)

# analyze the pattern of z across space
z1 <- as.vector(z1)
z2 <- as.vector(z2)
z1.d <- distance(z1, "eucl")
z2.d <- distance(z2, "eucl")

space <- cbind(as.vector(x), as.vector(y))
space.d <- distance(space, "eucl")

# take partial correlogram of z2 on the residuals of z1 ~ space.d
z.pmgram <- pmgram(z1.d, space.d, z2.d, nperm=0)
par(mfrow=c(1,1))
plot(z.pmgram, pval=0.1)