MSEP.lsplsCv: MSEP, RMSEP and R^2 for LS-PLS

In lspls: LS-PLS Models

Description

(Root) Mean Squared Error of Prediction ((R)MSEP) and R^2 methods for LS-PLS cross-validations ("lsplsCv" objects).

Usage

## S3 method for class 'lsplsCv'
MSEP(object, scale = FALSE, ...)
## S3 method for class 'lsplsCv'
RMSEP(object, scale = FALSE, ...)
## S3 method for class 'lsplsCv'
R2(object, ...)

Arguments

object	an "lsplsCv" object, typically the output from lsplsCv.
scale	logical. Whether the responses and predicted values should be divided by the standard deviation of the response prior to calculating the measure. This is most useful when comparing several responses. Default is not to scale. Note that this argument is ignored by the R2 method, since R^2 is independent of scale.
...	Further arguments. Currently unused.

Value

An array. The first dimension corresponds to the responses (for single-response models, the length of this dimension is 1). The rest of the dimensions correspond to the number of components from the PLS matrices.

Author(s)

Bjørn-Helge Mevik

See Also

lsplsCv, plot.lsplsCv

lspls documentation built on May 2, 2019, 12:19 p.m.