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\name{mgram}
\alias{mgram}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ Mantel correlogram }
\description{
Calculates simple and partial 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{
\item{species.d}{ lower-triangular dissimilarity matrix. }
\item{space.d}{ lower-triangular matrix of geographic distances. }
\item{breaks}{ locations of class breaks. If specified, overrides nclass and stepsize. }
\item{nclass}{ number of distance classes. If not specified, Sturge's rule will be used
to determine an appropriate number of classes. }
\item{stepsize}{ width of each distance class. If not specified, nclass and the range of space.d will be used to calculate an appropriate default. }
\item{nperm}{ number of permutations to use. If set to 0, the permutation test will be omitted. }
\item{mrank}{ if this is set to F (the default option), Pearson correlations will be used. If
set to T, the Spearman correlation (correlation ranked distances) will be used. }
\item{nboot}{ number of iterations to use for the bootstrapped confidence limits. If set to 0,
the bootstrapping will be omitted. }
\item{pboot}{ the level at which to resample the data for the bootstrapping procedure. }
\item{cboot}{ the level of the confidence limits to estimate. }
\item{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. }
\item{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:
\item{lag }{midpoint of the distance class.}
\item{ngroup }{number of distances in that class.}
\item{mantelr }{Mantel r value.}
\item{pval }{p-value for the test chosen.}
\item{llim }{lower bound of confidence limit for mantelr.}
\item{ulim }{upper bound of confidence limit for mantelr.}
resids is NA for objects calculated by mgram().
}
\references{ Legendre, P. and M. Fortin. 1989. Spatial pattern and ecological analysis.
Vegetatio 80:107-138. }
\author{ Sarah Goslee, Sarah.Goslee@ars.usda.gov }
\seealso{ \code{\link{mantel}}, \code{\link{plot.mgram}}, \code{\link{pmgram}} }
\examples{
\dontrun{
# 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)
}
}
\keyword{ multivariate }% at least one, from doc/KEYWORDS
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