[SerializableAttribute] public class PLS1NipalsAlgorithm : IPLS1Calc
<SerializableAttribute> Public Class PLS1NipalsAlgorithm Inherits IPLS1Calc
[SerializableAttribute] public ref class PLS1NipalsAlgorithm : public IPLS1Calc
[<SerializableAttribute>] type PLS1NipalsAlgorithm = class inherit IPLS1Calc end
Thetype exposes the following members.
Whether the most recent calculation was successful.(Overrides IPLS1CalcIsGood.)
Gets the loadings matrix for PredictorMatrix. The loadings matrix is described in the class summary.(Overrides IPLS1CalcLoadings.)
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.)
Gets the vector of means for the predictor variables.
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
Gets the vector of means for the response variables.
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.
Gets the scores matrix for PredictorMatrix. The scores matrix is described in the class summary.(Overrides IPLS1CalcScores.)
Returns the matrix of weights computed by the algorithm.
Calculates a partial least squares from the given data and number of components.(Overrides IPLS1CalcCalculate(DoubleMatrix, DoubleVector, Int32).)
Creates a deep copy of this PLS1NipalsAlgorithm.(Overrides IPLS1CalcClone.)
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.)
Sets most of the attributes only if isGood_
Conditionally sets most of the values for serialization only if isGood_
Use the calculated model to predict the response values, ResponseVector, from the given set of predictor variables.(Overrides IPLS1CalcPredict(DoubleMatrix).)
Use the calculated model to predict the response value, y, from the given value for the predictor variable.(Overrides IPLS1CalcPredict(DoubleVector).)
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