application of a discriminant model to a test dataset
res_da = daapply(da_model,xtest,(ytest))
a structure containing a discriminant analysis model, obtained with one of the following functions: copda, fda, forwda, knnda or plsda
the test dataset; a matrix (n x q) or a Div structure
the classes of the observations of xtest; a conjunctive vector (n x 1) or a disjunctive matrix (n x nclass) or a Div structure
the predicted classes for the observations of xtest
res_da.ypred.d is an hypermatrix of dimensions (n x nclass x lv)
the confusion matrix obtained for the test dataset, expressed as numbers of observations for each class
res_da.confm_test_nobs.d is an hyper-matrix of dimensions (nclass x nclass x lv)
the confusion matrix obtained for the test dataset, expressed as percentages for each class
res_da.confm_test.d is an hyper-matrix of dimensions (nclass x nclass x lv)
the percentage of error for the test dataset
res_da.err_test.d is a vector of dimensions (lv x 1)
the percentage of error for the test dataset, for each class
res_da.errbycl_test.d is a matrix of dimensions (lv x nclass)
the percentage of not-classed observations (all predictions lower than the threshold)
res_da.notclassed.d is a vector (lv x 1)
the percentage of not-classed observations (all predictions lower than the threshold), for each class
res_da.notclassed_bycl.d is a matrix (lv x nclass)