Plsda

Purpose

Partial least squares discriminate analysis.

Synopsis

model = plsda(x,y,ncomp,options)

model = plsda(x,ncomp,options)

pred = plsda(x,model,options)

valid = plsda(x,y,model,options)

Description

PLSDA is a multivariate inverse least squares discrimination method used to classify samples. The y-block in a PLSDA model indicates which samples are in the class(es) of interest through either:

(A) a column vector of class numbers indicating class assignments:

   y = [1 1 3 2]';

NOTE: if classes are assigned in the input (x), y can be omitted and this option will be assumed using the first class set of the x-block rows.

(B) a matrix of one or more columns containing a logical zero (= not in class) or one (= in class) for each sample (row):

   y = [1 0 0;
        1 0 0;
        0 0 1;
        0 1 0]

NOTE: When a vector of class numbers is used (case A, above), class zero (0) is reserved for "unknown" samples and, thus, samples of class zero are never used when calibrating a PLSDA model. The model will include predictions for these samples.

The prediction from a PLSDA model is a value of nominally zero or one. A value closer to zero indicates the new sample is NOT in the modeled class; a value of one indicates a sample is in the modeled class. In practice a threshold between zero and one is determined above which a sample is in the class and below which a sample is not in the class (See, for example, PLSDTHRES). Similarly, a probability of a sample being inside or outside the class can be calculated using DISCRIMPROB. The predicted probability of each class is included in the output model structure in the field:

model.details.predprobability

Inputs

• x = X-block (predictor block) class "double" or "dataset",
• y = Y-block - OPTIONAL if x is a dataset containing classes for sample mode (mode 1) otherwise, y is one of:

(A) column vector of sample classes for each sample in x -OPTIONAL if x is a dataset containing classes for sample mode (mode 1)

(B) a logical array with 1 indicating class membership for each sample (rows) in one or more classes (columns)

or (C) a cell array of class groupings of classes from the x-block data. For example: {[1 2] [3]} would model classes 1 and 2 as a single group against class 3.

• ncomp = the number of latent variables to be calculated (positive integer scalar).
• options = an optional input options structure (see Options below)

Outputs

• model = standard model structure containing the PLSDA model (See MODELSTRUCT).

• pred = structure array with predictions

• valid = structure array with predictionsz

Note: Calling plsda with no inputs starts the graphical user interface (GUI) for this analysis method.

Options

• display: [ 'off' | {'on'} ] governs level of display to command window.

• plots: [ 'none' | {'final'} ] governs level of plotting.

• preprocessing: {[] []} preprocessing structures for x and y blocks (see PREPROCESS).

• algorithm: [ 'nip' | {'sim'} ] PLS algorithm to use: NIPALS or SIMPLS

• blockdetails: [ 'compact' | {'standard'} | 'all' ] Extent of detail included in model. 'standard' keeps only y-block, 'all' keeps both x- and y- blocks

See Also

class2logical, crossval, pls, plsdthres, simca