PLS-methods {Cardinal}R Documentation

Partial least squares

Description

Performs partial least squares (also called projection to latent structures or PLS) on an imaging dataset. This will also perform discriminant analysis (PLS-DA) if the response is a factor.

Usage

    ## S4 method for signature 'SImageSet,matrix'
PLS(x, y, ncomp = 20,
        method = "nipals",
        center = TRUE,
        scale = FALSE,
        iter.max = 100, ...)
    
    ## S4 method for signature 'SImageSet,numeric'
PLS(x, y, ...)

    ## S4 method for signature 'SImageSet,factor'
PLS(x, y, ...)

    ## S4 method for signature 'SImageSet,character'
PLS(x, y, ...)

    ## S4 method for signature 'PLS'
predict(object, newx, newy, ...)

Arguments

x

The imaging dataset on which to perform partial least squares.

y

The response variable, which can be a matrix or a vector for ordinary PLS, or a factor or a character for PLS-DA.

ncomp

The number of PLS components to calculate.

method

The function used to calculate the projection.

center

Should the data be centered first? This is passed to scale.

scale

Shoud the data be scaled first? This is passed to scale.

iter.max

The number of iterations to perform for the NIPALS algorithm.

...

Passed to the next PLS method.

object

The result of a previous call to PLS.

newx

An imaging dataset for which to calculate their PLS projection and predict a response from an already-calculated PLS object.

newy

Optionally, a new response from which residuals should be calcualted.

Value

An object of class PLS, which is a ResultSet, where each component of the resultData slot contains at least the following components:

scores:

A matrix with the component scores for the explanatary variable.

loadings:

A matrix with the explanatory variable loadings.

weights:

A matrix with the explanatory variable weights.

Yscores:

A matrix objects with the component scores for the response variable.

Yweights:

A matrix objects with the response variable weights.

projection:

The projection matrix.

coefficients:

The matrix of the regression coefficients.

ncomp:

The number of PLS components.

method:

The method used to calculate the projection.

center:

The center of the dataset. Used for calculating PLS scores on new data.

scale:

The scaling factors for the dataset. Used for PLS scores on new data.

Ycenter:

The centers of the response variables. Used for predicting new observations.

Yscale:

The scaling factors for the response variables. Used for predicting new observation.

fitted:

The fitted response.

Author(s)

Kylie A. Bemis

References

Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16(3), 119-128. doi:10.1002/cem.695

See Also

OPLS, PCA, spatialShrunkenCentroids,

Examples

sset <- generateImage(diag(4), range=c(200, 300), step=1)

y <- factor(diag(4))

pls <- PLS(sset, y, ncomp=1:2)
[Package Cardinal version 2.0.4 Index]