Description Usage Arguments Details Value Note Author(s) References See Also Examples
Performs the crossvalidation calculations for mvr
.
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X 
a matrix of observations. 
Y 
a vector or matrix of responses. 
ncomp 
the number of components to be used in the modelling. 
Y.add 
a vector or matrix of additional responses containing
relevant information about the observations. Only used for 
weights 
a vector of individual weights for the observations.
Only used for 
method 
the multivariate regression method to be used. 
scale 
logical. If 
segments 
the number of segments to use, or a list with segments (see below). 
segment.type 
the type of segments to use. Ignored if

length.seg 
Positive integer. The length of the segments to
use. If specified, it overrides 
jackknife 
logical. Whether jackknifing of regression coefficients should be performed. 
trace 
logical; if 
... 
additional arguments, sent to the underlying fit function. 
This function is not meant to be called directly, but through
the generic functions pcr
, plsr
, cppls
or mvr
with the argument validation
set to "CV"
or
"LOO"
. All arguments to mvrCv
can be specified in the
generic function call.
If segments
is a list, the arguments segment.type
and
length.seg
are ignored. The elements of the list should be
integer vectors specifying the indices of the segments. See
cvsegments
for details.
Otherwise, segments of type segment.type
are generated. How
many segments to generate is selected by specifying the number of
segments in segments
, or giving the segment length in
length.seg
. If both are specified, segments
is
ignored.
If jackknife
is TRUE
, jackknifed regression coefficients
are returned, which can be used for for variance estimation
(var.jack
) or hypothesis testing (jack.test
).
X
and Y
do not need to be centered.
Note that this function cannot be used in situations where X
needs to be recalculated for each segment (except for scaling by the
standard deviation), for instance with
msc
or other preprocessing. For such models, use the more
general (but slower) function crossval
.
Also note that if needed, the function will silently(!) reduce
ncomp
to the maximal number of components that can be
crossvalidated, which is n  l  1, where n is the
number of observations and l is the length of the longest
segment. The (possibly reduced) number of components is returned as
the component ncomp
.
By default, the crossvalidation will be performed serially. However,
it can be done in parallel using functionality in the
parallel
package by setting the option parallel
in
pls.options
. See pls.options
for the
different ways to specify the parallelism.
A list with the following components:
method 
equals 
pred 
an array with the crossvalidated predictions. 
coefficients 
(only if 
PRESS0 
a vector of PRESS values (one for each response variable) for a model with zero components, i.e., only the intercept. 
PRESS 
a matrix of PRESS values for models with 1, ...,

adj 
a matrix of adjustment values for calculating bias
corrected MSEP. 
segments 
the list of segments used in the crossvalidation. 
ncomp 
the actual number of components used. 
gamma 
if method 
The PRESS0
is always crossvalidated using leaveoneout
crossvalidation. This usually makes little difference in practice,
but should be fixed for correctness.
The current implementation of the jackknife stores all jackknifereplicates of the regression coefficients, which can be very costly for large matrices. This might change in a future version.
Ron Wehrens and BjørnHelge Mevik
Mevik, B.H., Cederkvist, H. R. (2004) Mean Squared Error of Prediction (MSEP) Estimates for Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR). Journal of Chemometrics, 18(9), 422–429.
mvr
crossval
cvsegments
MSEP
var.jack
jack.test
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