PLS1NipalsAlgorithm Class 
Namespace: CenterSpace.NMath.Core
The PLS1NipalsAlgorithm type exposes the following members.
Name  Description  

PLS1NipalsAlgorithm 
Constructs an instance of the PLS1NipalsAlgorithm class.

Name  Description  

IsGood 
Whether the most recent calculation was successful.
(Overrides IPLS1CalcIsGood.)  
Loadings 
Gets the loadings matrix for PredictorMatrix. The loadings matrix
is described in the class summary.
(Overrides IPLS1CalcLoadings.)  
Message 
Gets any message that may have been generated by the algorithm. For example,
if the calculation is unsuccessful, the message indicates the reason.
(Overrides IPLS1CalcMessage.)  
PredictorMean 
Gets the vector of means for the predictor variables.
 
RegressionVector 
Gets the vector of regression, r, which can be used for making
predictions as follows:
Let ybar and xbar be the means of the response and predictor variables, respectively, used to create the model. Then the predicted response, yhat, for a predictor vector, z is given by the formula yhat = ybar + (z  xbar)'r  
ResponseMean 
Gets the vector of means for the response variables.
 
ResponseWeights 
Gets the vector of response weights. The ith element of this vector
corresponds to the regression coefficient calculated by ordinary linear
regression of the response vector on the ith score vector.
 
Scores 
Gets the scores matrix for PredictorMatrix. The scores matrix
is described in the class summary.
(Overrides IPLS1CalcScores.)  
Weights 
Returns the matrix of weights computed by the algorithm.

Name  Description  

Calculate 
Calculates a partial least squares from the given data and number of
components.
(Overrides IPLS1CalcCalculate(DoubleMatrix, DoubleVector, Int32).)  
Clone 
Creates a deep copy of this PLS1NipalsAlgorithm.
(Overrides IPLS1CalcClone.)  
HotellingsT2 
Calculaties Hotelling's T2 statistic for each sample. T2 can be viewed as the
squared distance from a samples projection into the subspace to the centroid
of the subspace, or, more simply, the variation of the sample point within
the model.
(Inherited from IPLS1Calc.)  
OnDeserialized 
Sets most of the attributes only if isGood_
 
OnSerializing 
Conditionally sets most of the values for serialization only if isGood_
 
Predict(DoubleMatrix) 
Use the calculated model to predict the response values, ResponseVector,
from the given set of predictor variables.
(Overrides IPLS1CalcPredict(DoubleMatrix).)  
Predict(DoubleVector) 
Use the calculated model to predict the response value, y, from
the given value for the predictor variable.
(Overrides IPLS1CalcPredict(DoubleVector).)  
QResiduals 
Calculates the Q residuals for in sample in the model. The Q residual
for a given sample is the distance between the sample and its projection
in the subspace of the model.
(Overrides IPLS1CalcQResiduals.) 
PredictorMatrix = TP' + Xg
g