crossval {pls} | R Documentation |
A “stand alone” cross-validation function for mvr
objects.
crossval(object, segments = 10, segment.type = c("random", "consecutive", "interleaved"), length.seg, trace = 15, ...)
object |
a mvr object; the regression to cross-validate. |
segments |
the number of segments to use, or a list with segments
(see below). Ignored if loo = TRUE . |
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 . |
trace |
if TRUE , tracing is turned on. If numeric, it
denotes a time limit (in seconds). If the estimated total time of
the cross-validation exceeds this limit, tracing is turned on. |
... |
additional arguments, sent to the underlying fit function. |
This function performs cross-validation on a model fit by mvr
.
It can handle models such as plsr(y ~ msc(X), ...)
or other
models where the predictor variables need to be recalculated for each
segment. When recalculation is not needed, the result of
crossval(mvr(...))
is identical to mvr(..., CV = TRUE)
,
but slower.
If length.seg
is specified, segments of the requested length
are used. Otherwise:
If segments
is a number, it specifies the number of segments to
use, and segment.type
is used to select the type of segments.
If segments
is a list, the elements of the list should be
integer vectors specifying the indices of the segments. See
cvsegments
for details.
The R2 component returned is calculated as the squared correlation between the cross-validated predictions and the responses.
When tracing is turned on, the segment number is printed for each segment.
The supplied object
is returned, with an additional component
validation
, which is a list with components
method |
euqals "CV" for cross-validation. |
pred |
an array with the cross-validated predictions. |
MSEP0 |
a vector of MSEP values (one for each response variable) for a model with zero components, i.e., only the intercept. |
MSEP |
a matrix of MSEP 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. |
R2 |
a matrix of R2 values for models with 1, ...,
ncomp components. Each row corresponds to one response variable. |
segments |
the list of segments used in the cross-validation. |
The MSEP0
is always cross-validated using leave-one-out
cross-validation. This usually makes little difference in practice,
but should be fixed for correctness.
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(NIR) NIR.pcr <- pcr(y ~ msc(X), 6, data = NIR) NIR.cv <- crossval(NIR.pcr, CV = TRUE, segments = 10) plot(MSEP(NIR.cv))