ecodist (version 2.0.9)

xmgram: Cross-Mantel correlogram

Description

Calculates simple Mantel correlograms from cross-distance matrices.

Usage

xmgram(species.xd, space.xd, breaks, nclass, stepsize, nperm = 1000,
    mrank = FALSE, alternative = "two.sided", trace = FALSE)

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.

resids is NA for objects calculated by mgram().

Arguments

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.

Author

Sarah Goslee

Details

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.

References

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

See Also

xdistance xmantel, plot.mgram

Examples

# 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))