pls (version 1.1-0)

mvrCv: Cross-validation

Description

Performs the cross-validation calculations for mvr.

Usage

mvrCv(X, Y, ncomp,
      method = c("kernelpls", "simpls", "oscorespls", "svdpc"), scale = FALSE,
      segments = 10, segment.type = c("random", "consecutive", "interleaved"),
      length.seg, trace = FALSE, ...)

Value

• A list with the following 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.

encoding

latin1

Details

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 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.

X and Y do not need to be centered.

The R2 component returned is calculated as the squared correlation between the cross-validated predictions and the responses. 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.

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 crossval cvsegments MSEP

Examples

data(NIR)
NIR.pcr <- pcr(y ~ X, 6, data = NIR, validation = "CV", segments = 10)
plot(MSEP(NIR.pcr))