MATLAB File Help: prtOutlierRemovalNStd
  prtOutlierRemovalNStd  Removes outliers from a prtDataSet
    NSTDOUT = prtOutlierRemovalNStd creates a pre-processing
    object that flags as outliers data where any of the feature values is
    more then nStd standard deviations from the mean of that feature.
    prtOutlierRemovalNStd has the following properties:
        nStd - The number of standard deviations at which to flag an
               observation as an outlier an observation (default = 3)
    A prtOutlierRemovalNStd object also inherits all properties and
    functions from the prtOutlierRemoval class.  For more information
    on how to control the behaviour of outlier removal objects, see the
    help for prtOutlierRemoval.
    dataSet = prtDataGenUnimodal;               % Load a data Set
    outlier = prtDataSetClass([-10 -10],1);     % Create and insert
    dataSet = catObservations(dataSet,outlier); % an outlier
    % Create the prtOutlierRemoval object
    nStdRemove = prtOutlierRemovalNStd('runMode','removeObservation');
    nStdRemove = nStdRemove.train(dataSet);    % Train and run    
    dataSetNew =;  
    % Plot the results
    subplot(2,1,1); plot(dataSet);
    title('Original Data');
    subplot(2,1,2); plot(dataSetNew);
    title('NstdOutlierRemove Data');
See Also
Class Details
Superclasses prtOutlierRemoval
Sealed false
Construct on load false
Constructor Summary
prtOutlierRemovalNStd Removes outliers from a prtDataSet 
Property Summary
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
isCrossValidateValid False 
isSupervised False 
isTrained Indicates if prtAction object has been trained. 
meanVector The mean vector 
nStd The number of standard deviations beyond which to remove data 
name Standard Deviation Based Outlier Removal 
nameAbbreviation nStd 
runMode Operation taken during RUN 
runOnTrainingMode Operation taken during TRAIN 
stdVector The standard deviation vector 
userData User specified data 
verboseStorage Specifies whether or not to store the training prtDataset. 
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. 
  run Run a prtAction object on a prtDataSet object. 
  set set the object properties 
  train Train a prtAction object using training a prtDataSet object.