Cross-Mantel correlogram
Calculates simple Mantel correlograms from cross-distance matrices.
xmgram(species.xd, space.xd, breaks, nclass, stepsize, nperm = 1000,
mrank = FALSE, alternative = "two.sided", trace = FALSE)species.xd |
non-symmetric square cross-distance matrix. |
space.xd |
non-symmetric square 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. |
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. |
This function calculates cross-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. Note that the cross-dissimilarity functions are for research purposes, and are not well-tested.
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. |
resids is NA for objects calculated by mgram().
Sarah Goslee
Legendre, P. and M. Fortin. 1989. Spatial pattern and ecological analysis. Vegetatio 80:107-138.
# Need to develop a cross-dissimilarity example
data(graze)
### EXAMPLE 1: Square matrices
# take two subsets of sites with different dominant grass abundances
# use cut-offs that produce equal numbers of sites
dom1 <- subset(graze, POPR > 50 & DAGL < 20) # 8 sites
dom2 <- subset(graze, POPR < 50 & DAGL > 20) # 8 sites
# first two columns are site info
dom.xd <- xdistance(dom1[, -c(1,2)], dom2[, -c(1,2)], "bray")
# environmental and spatial distances; preserve rownames
forest.xd <- xdistance(dom1[, "forestpct", drop=FALSE],
dom2[, "forestpct", drop=FALSE])
sitelocation.xd <- xdistance(dom1[, "sitelocation", drop=FALSE],
dom2[, "sitelocation", drop=FALSE])
# permutes rows and columns of full nonsymmetric matrix
xmantel(dom.xd ~ forest.xd)
xmantel(dom.xd ~ forest.xd + sitelocation.xd)
plot(xmgram(dom.xd, sitelocation.xd))
### EXAMPLE 2: Non-square matrices
# take two subsets of sites with different dominant grass abundances
# this produces a non-square matrix
dom1 <- subset(graze, POPR > 45 & DAGL < 20) # 13 sites
dom2 <- subset(graze, POPR < 45 & DAGL > 20) # 8 sites
# first two columns are site info
dom.xd <- xdistance(dom1[, -c(1,2)], dom2[, -c(1,2)], "bray")
# environmental and spatial distances; preserve rownames
forest.xd <- xdistance(dom1[, "forestpct", drop=FALSE],
dom2[, "forestpct", drop=FALSE])
sitelocation.xd <- xdistance(dom1[, "sitelocation", drop=FALSE],
dom2[, "sitelocation", drop=FALSE])
# permutes rows and columns of full nonsymmetric matrix
xmantel(dom.xd ~ forest.xd, dims=c(13, 8))
xmantel(dom.xd ~ forest.xd + sitelocation.xd, dims=c(13, 8))
plot(xmgram(dom.xd, sitelocation.xd))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.