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. Orthogonal partial least squares options (O-PLS and O-PLS-DA) are also available.

Usage

## S4 method for signature 'SparseImagingExperiment,ANY'
PLS(x, y, ncomp = 3, method = c("pls", "opls"),
        center = TRUE, scale = FALSE,
        iter.max = 100, ...)

## S4 method for signature 'SparseImagingExperiment,ANY'
OPLS(x, y, ncomp = 3, ...)

## S4 method for signature 'PLS2'
predict(object, newx, newy, ncomp, ...)

## S4 method for signature 'PLS2'
fitted(object, ...)

## S4 method for signature 'PLS2'
summary(object, ...)

## S4 method for signature 'SImageSet,matrix'
PLS(x, y, ncomp = 3,
    method = "nipals",
    center = TRUE,
    scale = FALSE,
    iter.max = 100, ...)

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

## S4 method for signature 'SImageSet,matrix'
OPLS(x, y, ncomp = 3,
    method = "nipals",
    center = TRUE,
    scale = FALSE,
    keep.Xnew = TRUE,
    iter.max = 100, ...)

## S4 method for signature 'SImageSet,ANY'
OPLS(x, y, ...)

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

## S4 method for signature 'OPLS'
predict(object, newx, newy, keep.Xnew = TRUE, ...)

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.

keep.Xnew

Should the new data matrix be kept after filtering out the orthogonal variation?

Value

An object of class PLS2, which is a ResultImagingExperiment, or an object of class PLS, which is a ResultSet. Each elemnt of resultData slot contains at least the following components:

fitted:

The fitted response.

loadings:

A matrix with the explanatory variable loadings.

weights:

A matrix with the explanatory variable weights.

scores:

A matrix with the component scores for the explanatary variable.

Yscores:

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

Yweights:

A matrix objects with the response variable weights.

coefficients:

The matrix of the regression coefficients.

The following components may also be available for classes OPLS and OPLS2.

Oloadings:

A matrix objects with the orthogonal explanatory variable loadings.

Oweights:

A matrix with the orthgonal explanatory variable weights.

If y is a categorical variable, then a categorical class prediction will also be available in addition to the fitted numeric 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

PCA, spatialShrunkenCentroids,

Examples

register(SerialParam())

set.seed(1)
x <- simulateImage(preset=2, npeaks=10, dim=c(10,10),
    snoise=1, sdpeaks=1, representation="centroid")

y <- makeFactor(circle=pData(x)$circle, square=pData(x)$square)

pls <- PLS(x, y, ncomp=1:3)

summary(pls)

opls <- OPLS(x, y, ncomp=1:3)

summary(pls)

[Package Cardinal version 2.2.3 Index]