mantel.correlog {vegan} | R Documentation |
Function mantel.correlog
computes a multivariate
Mantel correlogram. Proposed by Sokal (1986) and Oden and Sokal
(1986), the method is also described in Legendre and Legendre (1998,
pp. 736-738).
mantel.correlog(D.eco, D.geo=NULL, XY=NULL, n.class=0, break.pts=NULL, cutoff=TRUE, r.type="pearson", nperm=999, mult="holm", progressive=TRUE) ## S3 method for class 'mantel.correlog': plot(x, alpha=0.05, ...)
D.eco |
An ecological distance matrix, with class
either dist or matrix . |
D.geo |
A geographic distance matrix, with class either
dist or matrix . Provide either D.geo or
XY . Default: D.geo=NULL . |
XY |
A file of Cartesian geographic coordinates of the
points. Default: XY=NULL . |
n.class |
Number of classes. If n.class=0 , the Sturge
equation will be used unless break points are provided. |
break.pts |
Vector containing the break points of the distance
distribution. Default: break.pts=NULL . |
cutoff |
For the second half of the distance classes,
cutoff = TRUE limits the correlogram to the distance classes
that include all points. If cutoff = FALSE , the correlogram
includes all distance classes. |
r.type |
Type of correlation in calculation of the Mantel
statistic. Default: r.type="pearson" . Other choices are
r.type="spearman" and r.type="kendall" , as in functions
cor and mantel . |
nperm |
Number of permutations for the tests of
significance. Default: nperm=999 . For large data files,
permutation tests are rather slow. |
mult |
Correct P-values for multiple testing. The correction
methods are "holm" (default), "hochberg" ,
"sidak" , and other methods available in the
p.adjust function: "bonferroni" (best known, but
not recommended because it is overly conservative), "hommel" ,
"BH" , "BY" , "fdr" , and "none" . |
progressive |
Default: progressive=TRUE for progressive
correction of multiple-testing, as described in Legendre and Legendre
(1998, p. 721). Test of the first distance class: no correction;
second distance class: correct for 2 simultaneous tests; distance
class k: correct for k simultaneous tests. progressive=FALSE :
correct all tests for n.class simultaneous tests. |
x |
Output of mantel.correlog . |
alpha |
Significance level for the points drawn with black
symbols in the correlogram. Default: alpha=0.05 . |
... |
Other parameters passed from other functions. |
A correlogram is a graph in which spatial correlation values
are plotted, on the ordinate, as a function of the geographic distance
classes among the study sites along the abscissa. In a Mantel
correlogram, a Mantel correlation (Mantel 1967) is computed between a
multivariate (e.g. multi-species) distance matrix of the user's choice
and a design matrix representing each of the geographic distance
classes in turn. The Mantel statistic is tested through a
permutational Mantel test performed by vegan
's
mantel
function.
When a correction for multiple testing is applied, more permutations are necessary than in the no-correction case, to obtain significant p-values in the higher correlogram classes.
The print.mantel.correlog
function prints out the
correlogram. See examples.
mantel.res |
A table with the distance classes as rows and the
class indices, number of distances per class, Mantel statistics
(computed using Pearson's r, Spearman's r, or Kendall's tau), and
p-values as columns. A positive Mantel statistic indicates positive
spatial correlation. An additional column with p-values corrected for
multiple testing is added unless mult="none" . |
n.class |
The n umber of distance classes. |
break.pts |
The break points provided by the user or computed by the program. |
mult |
The name of the correction for multiple testing. No
correction: mult="none" . |
progressive |
A logical (TRUE , FALSE ) value
indicating whether or not a progressive correction for multiple
testing was requested. |
n.tests |
The number of distance classes for which Mantel tests have been computed and tested for significance. |
call |
The function call. |
Pierre Legendre, Universite de Montreal
Legendre, P. and L. Legendre. 1998. Numerical ecology, 2nd English edition. Elsevier Science BV, Amsterdam.
Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Res. 27: 209-220.
Oden, N. L. and R. R. Sokal. 1986. Directional autocorrelation: an extension of spatial correlograms to two dimensions. Syst. Zool. 35: 608-617.
Sokal, R. R. 1986. Spatial data analysis and historical processes. 29-43 in: E. Diday et al. [eds.] Data analysis and informatics, IV. North-Holland, Amsterdam.
# Mite data from "vegan" data(mite) data(mite.xy) mite.hel <- decostand(mite, "hellinger") mite.hel.D <- dist(mite.hel) mite.correlog <- mantel.correlog(mite.hel.D, XY=mite.xy, nperm=99) summary(mite.correlog) mite.correlog plot(mite.correlog) mite.correlog2 <- mantel.correlog(mite.hel.D, XY=mite.xy, cutoff=FALSE, r.type="spearman", nperm=99) summary(mite.correlog2) mite.correlog2 plot(mite.correlog2) ## Mite correlogram after spatially detrending the mite data mite.h.det <- resid(lm(as.matrix(mite.hel.D) ~ ., data=mite.xy)) mite.correlog3 <- mantel.correlog(mite.h.det, XY=mite.xy, nperm=99) mite.correlog3 plot(mite.correlog3)