Description Usage Arguments Details Value Author(s) References Examples
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 (2012,
pp. 819–821).
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D.eco |
An ecological distance matrix, with class
either |
D.geo |
A geographic distance matrix, with class either
|
XY |
A file of Cartesian geographic coordinates of the
points. Default: |
n.class |
Number of classes. If |
break.pts |
Vector containing the break points of the distance
distribution. Provide (n.class+1) breakpoints, that is, a list with
a beginning and an ending point. Default: |
cutoff |
For the second half of the distance classes,
|
r.type |
Type of correlation in calculation of the Mantel
statistic. Default: |
nperm |
Number of permutations for the tests of
significance. Default: |
mult |
Correct P-values for multiple testing. The correction
methods are |
progressive |
Default: |
x |
Output of |
alpha |
Significance level for the points drawn with black
symbols in the correlogram. Default: |
... |
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 |
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: |
progressive |
A logical ( |
n.tests |
The number of distance classes for which Mantel tests have been computed and tested for significance. |
call |
The function call. |
Pierre Legendre, Université de Montréal
Legendre, P. and L. Legendre. 2012. Numerical ecology, 3rd 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.
Sturges, H. A. 1926. The choice of a class interval. Journal of the American Statistical Association 21: 65–66.
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# Mite data available in "vegan"
data(mite)
data(mite.xy)
mite.hel <- decostand(mite, "hellinger")
# Detrend the species data by regression on the site coordinates
mite.hel.resid <- resid(lm(as.matrix(mite.hel) ~ ., data=mite.xy))
# Compute the detrended species distance matrix
mite.hel.D <- dist(mite.hel.resid)
# Compute Mantel correlogram with cutoff, Pearson statistic
mite.correlog <- mantel.correlog(mite.hel.D, XY=mite.xy, nperm=49)
summary(mite.correlog)
mite.correlog
# or: print(mite.correlog)
# or: print.mantel.correlog(mite.correlog)
plot(mite.correlog)
# Compute Mantel correlogram without cutoff, Spearman statistic
mite.correlog2 <- mantel.correlog(mite.hel.D, XY=mite.xy, cutoff=FALSE,
r.type="spearman", nperm=49)
summary(mite.correlog2)
mite.correlog2
plot(mite.correlog2)
# NOTE: 'nperm' argument usually needs to be larger than 49.
# It was set to this low value for demonstration purposes.
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