MATLAB File Help: prtClassPlsda
  prtClassPlsda  Partial least squares discriminant classifier
     CLASSIFIER = prtClassPlsda returns a Partial least squares
     discriminant classifier
     CLASSIFIER = prtClassPlsda(PROPERTY1, VALUE1, ...) constructs a
     prtClassMAP object CLASSIFIER with properties as specified by
     PROPERTY/VALUE pairs.
     A prtClassPlsda object inherits all properties from the abstract
     class prtClass. In addition is has the following properties:
     nComponents  -  The number of components
     Bpls         -  The regression weights, estimated during training
     xMeans       -  The xMeans, estimated during training
     yMeans       -  The yMeana, estimated during training   
     For information on the partial least squares discriminant
     algorithm, please refer to the following URL:
     A prtClassPlsda object inherits the TRAIN, RUN, CROSSVALIDATE and
     KFOLDS methods from prtAction. It also inherits the PLOT method
     from prtClass.
    TestDataSet = prtDataGenUnimodal;      % Create some test and 
    TrainingDataSet = prtDataGenUnimodal;  % training data
    classifier = prtClassPlsda;           % Create a classifier
    classifier = classifier.train(TrainingDataSet);    % Train
    classified = run(classifier, TestDataSet);         % Test
    [pf,pd] = prtScoreRoc(classified,TestDataSet);
    h = plot(pf,pd,'linewidth',3);
    title('ROC'); xlabel('Pf'); ylabel('Pd');
See also
Class Details
Superclasses prtClass
Sealed false
Construct on load false
Constructor Summary
prtClassPlsda Partial least squares discriminant classifier 
Property Summary
Bpls The prediction weights 
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
internalDecider Optional prtDecider object for making decisions 
isCrossValidateValid True 
isNativeMary True 
isSupervised True 
isTrained Indicates if prtAction object has been trained. 
nComponents w is a DataSet.nDimensions x 1 vector of projection weights 
name Partial Least Squares Discriminant 
nameAbbreviation PLSDA 
twoClassParadigm Whether the classifier retures one output (binary) or two outputs (m-ary) when there are only two unique class labels 
userData User specified data 
verboseStorage Specifies whether or not to store the training prtDataset. 
yMeansFactor Factor to be added into regression output (accounts for X means and yMeans); 
Method Summary
  crossValidate Cross validate prtAction using prtDataSet and cross validation keys. 
  get get the object properties 
  kfolds Perform K-folds cross-validation of prtAction 
  optimize Optimize action parameter by exhaustive function maximization. 
  plot Plot the output confidence of a prtClass object 
  run Run a prtAction object on a prtDataSet object. 
  set set the object properties 
  train Train a prtAction object using training a prtDataSet object.