Simca

Purpose

Create soft independent method of class analogy models for classification.

Synopsis

model = simca(x,ncomp,options) %creates simca model on dataset x

model = simca(x,classid,labels) %models double x with class id

pred = simca(x,model,options); %predictions on x with model

Description

The function SIMCA develops a SIMCA model, which is really a collection of PCA models, one for each class of data in the data set and is used for supervised pattern recognition.

SIMCA cross-validates the PCA model of each class using leave-one-out cross-validation if the number of samples in the class is <= 20. If there are more than 20 samples, the data is split into 10 contiguous blocks.

Inputs

• x = M x N matrix of class "dataset" where class information is extracted from x.class{1,1} and labels from x.label{1,1}, or

• x = M x N data matrix of class "double" and

• classid = M x 1 vector of class identifiers where each element is an integer identifying the class number of the corresponding sample.

• model = when making predictions, input model is a SIMCA model structure.

OPIONAL INPUTS:

• ncomp = integer, number of PCs to use in each model. This is rarely known a priori. When ncomp=[] {default} the user is querried for number of PCs for each class.

• labels = a character array with M rows that is used to label samples on Q vs. T² plots, otherwise the class identifiers are used.

options = a structure array discussed below.

OUPUT:

• model = model structure array with the following fields:

• modeltype: 'SIMCA',

• datasource: structure array with information about input data,

• date: date of creation,

• time: time of creation,

• info: additional model information,

• description: cell array with text description of model,

• submodel: structure array with each record containing the PCA model of each class (see PCA), and

• detail: sub-structure with additional model details and results.

• pred = is a structure, similar to model, that contains the SIMCA predictions. Additional, or other, fields in pred are:

• rtsq: the reduced T² (T² divided by it's 95Found confidence limit line) where each column corresponds to each class in the SIMCA model,

• rq: the reduced Q (Q divided by it's 95Found confidence limit line) where each column corresponds to each class in the SIMCA model,

• nclass: the predicted class number (class to which the sample was closest when considering T² and Q combined), and

• submodelpred: structure array with each record containing the PCA model predictions for each class (see PCA).

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

Options

options = a structure array with the following fields:

• display: [ {'on'} | 'off' ], governs level of display,

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

• staticplots: ['no' | {'yes'} ], produce ole-style "static" plots,

• rule: [{'combined'} | 'final' | 'T2' | 'Q'], decision rule,

• preprocessing: { [ ] }, a preprocessing structure (see PREPROCESS) that is used to preprocess data in each class.

The default options can be retreived using: options = simca('options');.

Note: with display='off', plots='none', nocomp=(>0 integer) and preprocessing specified that SIMCA can be run without command line interaction.

See Also

cluster, crossval, pca, plsdthres, discrimprob, plsdaroc, plsdthres