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
options = simca('options');.
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, discrimprob, pca, plsda, plsdaroc, plsdthres
