pls (version 1.0-1)

mvrCv: Cross-validation

Description

Performs the cross-validation calculations for mvr.

Usage

mvrCv(X, Y, ncomp,
      method = c("kernelpls", "simpls", "oscorespls", "svdpc"),
      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.

Details

This function is not meant to be called directly, but through the generic functions pcr, plsr or mvr with the argument CV = TRUE. 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, 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, CV = TRUE, segments = 10)
plot(MSEP(NIR.pcr))