crossval

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Cross-validation of PLSR and PCR models

A “stand alone” cross-validation function for mvr objects.

Keywords
multivariate, regression
Usage
crossval(object, segments = 10,
         segment.type = c("random", "consecutive", "interleaved"),
         length.seg, jackknife = FALSE, trace = 15, …)
Arguments
object

an mvr object; the regression to cross-validate.

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

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.

Details

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(…, validation = "CV"), but slower.

Note that to use crossval, the data must be specified with a data argument when fitting object.

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).

When tracing is turned on, the segment number is printed for each segment.

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. See also Examples below.

Value

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.

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 number of components.

gammas

if method cppls is used, gamma values for the powers of each CV segment are returned.

Note

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.

References

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.

See Also

mvr mvrCv cvsegments MSEP var.jack jack.test

Aliases
  • crossval
Examples
# NOT RUN {
data(yarn)
yarn.pcr <- pcr(density ~ msc(NIR), 6, data = yarn)
yarn.cv <- crossval(yarn.pcr, segments = 10)
# }
# NOT RUN {
plot(MSEP(yarn.cv))
# }
# NOT RUN {
# }
# NOT RUN {
## Parallelised cross-validation, using transient cluster:
pls.options(parallel = 4) # use mclapply (not available on Windows)
pls.options(parallel = quote(parallel::makeCluster(4, type = "PSOCK"))) # parLapply
## A new cluster is created and stopped for each cross-validation:
yarn.cv <- crossval(yarn.pcr)
yarn.loocv <- crossval(yarn.pcr, length.seg = 1)

## Parallelised cross-validation, using persistent cluster:
library(parallel)
## This creates the cluster:
pls.options(parallel = makeCluster(4, type = "FORK")) # not available on Windows
pls.options(parallel = makeCluster(4, type = "PSOCK"))
## The cluster can be used several times:
yarn.cv <- crossval(yarn.pcr)
yarn.loocv <- crossval(yarn.pcr, length.seg = 1)
## The cluster should be stopped manually afterwards:
stopCluster(pls.options()$parallel)

## Parallelised cross-validation, using persistent MPI cluster:
## This requires the packages snow and Rmpi to be installed
library(parallel)
## This creates the cluster:
pls.options(parallel = makeCluster(4, type = "MPI"))
## The cluster can be used several times:
yarn.cv <- crossval(yarn.pcr)
yarn.loocv <- crossval(yarn.pcr, length.seg = 1)
## The cluster should be stopped manually afterwards:
stopCluster(pls.options()$parallel)
## It is good practice to call mpi.exit() or mpi.quit() afterwards:
mpi.exit()
# }
Documentation reproduced from package pls, version 2.7-2, License: GPL-2

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