Cross-validation
Performs the cross-validation calculations for mvr.
mvrCv(X, Y, ncomp, Y.add = NULL, weights = NULL,
method = pls.options()$mvralg, scale = FALSE,
segments = 10, segment.type = c("random", "consecutive", "interleaved"),
length.seg, jackknife = FALSE, trace = FALSE, ...)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.
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
cross-validated, 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 cross-validation 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 cross-validated 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 cross-validation. |
ncomp |
the actual number of components used. |
gamma |
if method |
The PRESS0 is always cross-validated using leave-one-out
cross-validation. This usually makes little difference in practice,
but should be fixed for correctness.
The current implementation of the jackknife stores all jackknife-replicates of the regression coefficients, which can be very costly for large matrices. This might change in a future version.
Ron Wehrens and Bjørn-Helge 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.
data(yarn) yarn.pcr <- pcr(density ~ NIR, 6, data = yarn, validation = "CV", segments = 10) ## Not run: plot(MSEP(yarn.pcr))
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