caret (version 3.21)

plsda: Partial Least Squares Discriminant Analysis

Description

plsda is used to fit PLS models for classification.

Usage

plsda(x, ...)
## S3 method for class 'default':
plsda(x,  y, ncomp = 2, ...)
## S3 method for class 'plsda':
predict(object, newdata = NULL, ncomp = NULL, type = "class", ...)

Arguments

x

a matrix or data frame of predictors

y

a factor or indicator matrix for the discrete outcome. If a matrix, the entries must be either 0 or 1 and rows must add to one

ncomp

the number of components to include in the model

...

arguments to pass to plsr (code{plsda} only)

object

an object produced by plsda

newdata

a matrix or data frame of predictors

type

either "class", "prob" or "raw" to produce the predicted class, class probabilities or the raw model scores, respectively.

Value

• For plsda, an object of class "plsda" and "mvr". The predict method produces either a vector, matrix or three-dimensional array, depending on the values of type of ncomp. For example, specifying more than one value of ncomp with type = "class" with produce a three dimensional array but the default specification would produce a factor vector.

Details

If a factor is supplied, the appropriate indicator matrix is created by plsda.

A multivariate PLS model is fit to the indicator matrix using the plsr function.

To predict, the softmax function is used to normalize the model output into probability-like scores. The class with the largest score is the assigned output class.

See Also

plsr

Examples

data(mdrr)

tmpX <- scale(mdrrDescr)

plsFit <- plsda(tmpX, mdrrClass, ncomp = 3)

table(predict(plsFit), mdrrClass)