lsplsCv: Cross-Validate LS-PLS Models
In lspls: LS-PLS Models

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/lsplsCv.R

Calculate cross-validated predictions for LS-PLS models.

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lsplsCv(formula, ncomp, data, subset, na.action, segments = 10,
        segment.type = c("random", "consecutive", "interleaved"),
        length.seg, model = TRUE, ...)

`formula`	model formula. See Details.
`ncomp`	list or vector of positive integers, giving the number of components to use for each PLS matrix. See Details.
`data`	an optional data frame with the data to fit the model from.
`subset`	an optional vector specifying a subset of observations to be used in the fitting process.
`na.action`	a function which indicates what should happen when the data contain missing values.
`segments`	the number of segments to use, or a list with segments (see Details).
`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.
`model`	logical. If `TRUE`, the model frame is returned.
`...`	additional arguments, passed to the underlying cross-validation function (currently `orthlsplsCv`).

The function performs a cross-validation, using the model and segments specified in the call. It returns an object of class "lsplsCv", which has a plot method (see plot.lsplsCv). See lspls-package for typical usage and more about LS-PLS models.

See lspls for details about specifying the model with formula and ncomp. Note that lsplsCv cross-validates models with from 0 components to the numbers of components specified with ncomp.

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.

An object of class "lsplsCv", with components

`pred`	the cross-validated predictions. An array with one dimension for the observations, one for the responses, and one for each of the PLS matrices.
`segments`	the list of segments used in the cross-validation.
`na.action`	if observations with missing values were removed, `na.action` contains a vector with their indices.
`ncomp`	the list of number of components used in the model.
`call`	the function call.
`terms`	the model terms.
`model`	if `model = TRUE`, the model frame.

Currently, lsplsCv handles the formula and the data, and calls orthlsplsCv for the actual cross-validation. The formula interface is experimental, and might change in future versions.

Bjørn-Helge Mevik

Jørgensen, K., Segtnan, V. H., Thyholt, K., Næs, T. (2004) A Comparison of Methods for Analysing Regression Models with Both Spectral and Designed Variables. Journal of Chemometrics, 18(10), 451–464.

Jørgensen, K., Mevik, B.-H., Næs, T. Combining Designed Experiments with Several Blocks of Spectroscopic Data. (Submitted)

Mevik, B.-H., Jørgensen, K., Måge, I., Næs, T. LS-PLS: Combining Categorical Design Variables with Blocks of Spectroscopic Measurements. (Submitted)

lspls, plot.lsplsCv, cvsegments, orthlsplsCv, lspls-package