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- Currently improving the
pca and nipals for further graphical outputs
plsda and splsda have been further improved so that all the S3 functions predict, print, plotIndiv, plot3dIndiv can be used with these new classes- Several prediction methods are now available to predict the classes of test data with
plsda andsplsda, see argument 'method' (max.dist, class.dist, centroids.dist, mahalanobis.dist) in thepredict function
plsda and splsda functions are implemented to perform PLS Discriminant Analysis (PLS-DA) and sparse PLS-DA respectivelybreast.tumors data set is introduced to illustrate the (s)PLS-DA- PCA can also been performed with missing values using the NIPALS algorithm and 3D plots are also available for PCA
- Network (updated) to display relevant associations between variables for (r)CCA and (s)PLS, with a new similarity function
- A new similarity measure has been included in
cim function and the arguments hclusfuncand distfunc to display Clustered image maps (heatmaps)
- 3D representation to display samples and variables for (r)CCA
- 3D representation to display samples and variables for (s)PLS
- The argument
scaleY has been added to the pls and spls functions
- (s)PLS can also be applied when there is only 1 predictor variable
