mvrCv {pls} | R Documentation |
Performs the cross-validation calculations for mvr
.
mvrCv(X, Y, ncomp, 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. NA s and Inf s are not
allowed. |
Y |
a vector or matrix of responses. NA s and Inf s
are not allowed. |
ncomp |
the number of components to be used in the modelling. |
method |
the multivariate regression method to be used. |
scale |
logical. If TRUE , the learning X data for each
segment is scaled by dividing each variable by its sample standard
deviation. The prediction data is scaled by the same amount. |
segments |
the number of segments to use, or a list with segments (see below). |
segment.type |
the type of segments to use. Ignored if
segments is a list. |
length.seg |
Positive integer. The length of the segments to
use. If specified, it overrides segments unless
segments is a list. |
jackknife |
logical. Whether jackknifing of regression coefficients should be performed. |
trace |
logical; if TRUE , the segment number is printed
for each segment. |
... |
additional arguments, sent to the underlying fit function. |
This function is not meant to be called directly, but through
the generic functions pcr
, plsr
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
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
.
A list with the following components:
method |
equals "CV" for cross-validation. |
pred |
an array with the cross-validated predictions. |
coefficients |
(only if jackknife is TRUE ) an array
with the jackknifed regression coefficients. The dimensions
correspond to the predictors, responses, number of components, and
segments, respectively. |
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, ...,
ncomp components. Each row corresponds to one response variable. |
adj |
a matrix of adjustment values for calculating bias
corrected MSEP. MSEP uses this. |
segments |
the list of segments used in the cross-validation. |
ncomp |
the actual number of components used. |
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.
mvr
crossval
cvsegments
MSEP
var.jack
jack.test
data(yarn) yarn.pcr <- pcr(density ~ NIR, 6, data = yarn, validation = "CV", segments = 10) ## Not run: plot(MSEP(yarn.pcr))