crossval {cocorresp} | R Documentation |
Performs a leave-one-out cross-validation of a predictive Co-Correspondence Analysis model.
crossval(y, x, n.axes = min(dim(x), dim(y)) - 1,
centre = TRUE, verbose = TRUE)
## S3 method for class 'crossval'
summary(object, axes = c(1:min(6, object$n.axes)), ...)
y |
the response species matrix. |
x |
the predictor species matrix. |
n.axes |
the number of axes to calculate the leave-one-out cross-validation 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 leave-one-out cross-validation of a predictive Co-Correspondence 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 |
n.axes |
the number of axes tested. |
CVfit |
the cross-validatory 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This function is not a bit out-of-date compared to some of the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
## 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
## Leave-one-out crossvalidation - this takes a while
## Not run:
crossval(beetles, plants)
## End(Not run)
## so 2 axes are sufficient