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- New S3 method
network and cim for results from PLS model
- New code for the
valid function to PLS-DA and SPLS-DA models validation
- The S3 method
plot.valid was modified to display graphical results from valid function for PLS-DA and SPLS-DA models
cim and network functions were modified to obtain the similarity matrix in return value- The S3 method
plotVar was modified to obtain the coordinates for X and Y variables in return value
- The
predict function has been modified to simultaneously run either several or all prediction methods available to predict the classes of the test data from PLS-DA and SPLS-DA models
- New function
pca and spca are now available to perform Principal Component Analysis (PCA) and sparse PCA for variable selection
- The S3 methods
plotVar, plot3dVar, plotIndiv, plot3dIndiv were modified to generate graphical results for pca and spca
- New function
plot.valid to display the results of the valid function
- New code for
imgCor function for a nicer representation of the correlation matrices
- In
predict function the argument 'method' were replaced by method = c("max.dist", "class.dist", "centroids.dist", "mahalanobis.dist")
- The arguments
dendrogram, ColSideColors and RowSideColors were added to the cim function
valid function can also been performed with missing values- Functions
pls, plsda, spls and splsda were modified to identify zero- or near-zero variance predictors
- The functions
plotVar and plot3dVar were modified to represent only the X variables in the case of PLS-DA and SPLS-DA
- The
pca function has been improved so that the S3 methods plotIndiv, plot3dIndiv, plotVarand plot3dVar can be used with these new classe
- 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)
