The SDL Component Suite is an industry leading collection of components supporting scientific and engineering computing. Please visit the SDL Web site for more information.... 
Home MathPack Statis Class TMLRModel How to use TMLRModel  
How to use TMLRModel 

The class TMLRModel implements multiple linear regression (MLR) technique and supports multilinear regression (MLR), linear discriminant analysis (LDA) and ridge regression (RR). The distinction between these methods is controled by the property ModelType. In order to create and use the regression model you have first to load the training data into the array properties XData and YData. By default, XData and YData are 1x1matrices which have to be resized before loading the data. The matrix XData contains the independent variables ("descriptors"), with the rows being the objects and the columns being the variables. The response variables have to be loaded into the vector YData. There are a few additional parameters which you have to set as well: ForceZeroIcpt controls the inclusion of an intercept, the property LDAThreshold has to specify the classifier threshold for LDA models, and the property Lambda controls the regularization of ridge regression models. After setting up the model parameters and loading the data into XData and YData, the model can be calculated by calling the method CalculateModel. This will fill in the regression coefficients (property RegCoeffs) as well a bunch of other matrices which contain additional information on the particular model:
If you establish an LDA model (ModelType = rmLDA) a few additional parameters are calculated which specify the quality of the LDA classifier:
If the model has been computed successfully, you can use it to predict response values of unknown samples by calling the method ApplyModel. A calculated model can be stored on disk by the method StoreModel and loaded from disk by LoadModel. Following is a sample code torso showing the principal usage of the MLR model (it is assumed that the number of objects and the number of independent variables are contained in the variables no_of_objects and no_of_input_vars, respectively): var MLRMod : TMLRModel; ... ... MLRMod := TMLRModel.Create(nil); // create the MLR model instance MLRMod.ModelType := rmMLR; // plain multilinear regression MLRMod.ForceZeroIcpt := false; // including the intercept // resize the data matrices MLRMod.XData.Resize(no_of_input_vars, no_of_objects); MLRMod.YData.NrOfElem := no_of_objects; for iy:=1 to no_of_objects do begin for ix:=1 to no_of_input_vars do MLRMod.XData[ix,iy] := ... // copy the independent data MLRMod.YData[ix] := ... // copy the response data end; MLRMod.CalculateModel; // now calculate the model // at this point apply the model or otherwise process the results MLRMod.Free; // finally free up the memory ... ...Alternatively, the TMLRModel component could be taken from the component palette and put on a form. In this case the Create and Free method must not be called (they are called automatically when the form is created or destroyed).
