mso {vegan}  R Documentation 
The function mso
adds an attribute vario
to
an object of class "cca"
that describes the spatial
partitioning of the cca
object and performs an optional
permutation test for the spatial independence of residuals. The
function plot.mso
creates a diagnostic plot of the spatial
partitioning of the "cca"
object.
mso(object.cca, object.xy, grain = 1, round.up = FALSE, permutations = FALSE) msoplot(x, alpha = 0.05, explained = FALSE, ...)
object.cca 
An object of class cca, created by the cca or
rda function. 
object.xy 
A vector, matrix or data frame with the spatial
coordinates of the data represented by object.cca. Must have the
same number of rows as object.cca\$CA\$Xbar (see
cca.object ). 
grain 
Interval size for distance classes. 
round.up 
Determines the choice of breaks. If false, distances are rounded to the nearest multiple of grain. If true, distances are rounded to the upper multiple of grain. 
permutations 
If false, suppresses the permutation test. If an integer, determines the number of permutations for the Mantel test of spatial independence of residual inertia. 
x 
A result object of mso . 
alpha 
Significance level for the twosided permutation test of the Mantel statistic for spatial independence of residual inertia and for the pointwise envelope of the variogram of the total variance. A Bonferronitype correction can be achieved by dividing the overall significance value (e.g. 0.05) by the number of distance classes. 
explained 
If false, suppresses the plotting of the variogram of explained variance. 
... 
Other arguments passed to functions. 
The Mantel test is an adaptation of the function mantel
of the
vegan package to the parallel testing of several distance classes. It
compares the mean inertia in each distance class to the pooled mean
inertia of all other distance classes.
If there are explanatory variables (RDA, CCA, pRDA, pCCA) and a
significance test for residual autocorrelation was performed when
running the function mso
, the function plot.mso
will
print an estimate of how much the autocorrelation (based on
significant distance classes) causes the global error variance of the
regression analysis to be underestimated
The function mso
returns an amended cca
or rda
object with the additional attributes grain
, H
,
H.test
and vario
.
grain 
The grain attribute defines the interval size of the distance classes . 
H 
H is an object of class 'dist' and contains the geographic distances between observations. 
H.test 
H.test contains a set of dummy variables that describe
which pairs of observations (rows = elements of object\$H ) fall in
which distance class (columns). 
vario 
The vario attribute is a data frame that contains some
or all of the following components for the rda case (cca case in
brackets):

The function is based on the code published in the Ecological Archives E085006 (http://www.esapubs.org/archive/ecol/E085/006/default.htm).
The responsible author was Helene Wagner.
Wagner, H.H. 2004. Direct multiscale ordination with canonical correspondence analysis. Ecology 85: 342–351.
Function cca
and rda
,
cca.object
.
## Reconstruct worked example of Wagner (submitted): X < matrix(c(1, 2, 3, 2, 1, 0), 3, 2) Y < c(3, 1, 2) tmat < c(1:3) ## Canonical correspondence analysis (cca): Example.cca < cca(X, Y) Example.cca < mso(Example.cca, tmat) msoplot(Example.cca) Example.cca$vario ## Correspondence analysis (ca): Example.ca < mso(cca(X), tmat) msoplot(Example.ca) ## Unconstrained ordination with test for autocorrelation ## using oribatid mite data set as in Wagner (2004) data(mite) data(mite.env) data(mite.xy) mite.cca < cca(log(mite + 1)) mite.cca < mso(mite.cca, mite.xy, grain = 1, permutations = 100) msoplot(mite.cca) mite.cca ## Constrained ordination with test for residual autocorrelation ## and scaleinvariance of speciesenvironment relationships mite.cca < cca(log(mite + 1) ~ SubsDens + WatrCont + Substrate + Shrub + Topo, mite.env) mite.cca < mso(mite.cca, mite.xy, permutations = 100) msoplot(mite.cca) mite.cca