Performs a leaveoneout crossvalidation of a predictive CoCorrespondence Analysis model.
1 2 3 4 5 6 7 
y 
the response species matrix. 
x 
the predictor species matrix. 
n.axes 
the number of axes to calculate the leaveoneout crossvalidation for. Default is to perform the CV for all extractable axes. 
centre 
centre 
verbose 
if 
object 
an object of class 
axes 
the number of axes to summarise results for. 
... 
further arguments to 
Performs a leaveoneout crossvalidation of a predictive CoCorrespondence Analysis model. It can be slow depending on the number of columns in the matrices, and of course the number of sites.
Returns a large list with the following components:
dimx, dimy 
the dimensions of the input matrices 
press0 
the press_0 statistic. 
n.axes 
the number of axes tested. 
CVfit 
the crossvalidatory fit. 
varianceExp 
list with components 
totalVar 
list with components 
nam.dat 
list with components 
call 
the R call used. 
This function is not a bit outofdate compared to some of the
other functions. It should have a formular interface like
coca
or work on the results from coca
,
although that will have to be altered to store a copy of the data?
Gavin L. Simpson, based on Matlab code by C.J.F. ter Braak and A.P. Schaffers.
The model fitting function coca
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## load the data sets
data(beetles)
data(plants)
## log transform the bettle data
beetles < log(beetles + 1)
## predictive CoCA using SIMPLS and formula interface
bp.pred < coca(beetles ~ ., data = plants)
## should retain only the useful PLS components for a
## parsimonious model
## Leaveoneout crossvalidation  this takes a while
## Not run:
crossval(beetles, plants)
## End(Not run)
## so 2 axes are sufficient

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