variable selection using Covsel, then factorial discriminant analysis on the selected variables
model = covsel_fda(x,y_class,split,lv,nbvar,(metric),(scale),(threshold) )
a matrix (n x q) or a Div structure
a conjunctive vector (n x 1) or a disjonctive matrix (n x nclass) or a Div structure
for the cross-validation; the number of blocks or a vector of dimensions (n x 1) which identifies by a number the group of cross-validation of each observation
the maximum number of latent variables or eigenvectors used for the building of the model
lv inf. to nclass
the number max of selected variables
nbvar sup. to lv
the metric used for the calculation of the distances between the observations and the samples
metric=0: Mahalanobis distance (by default)
metric=1: usual Euclidian distance
the columns of x are always centered; scale defines the standardization
scale='c': no standardization (by default)
scale='cs': standardisation; the columns of x are divided by their standard deviation
the lowest value for attributing an observation to a class (by default= 1/nclass )
the confusion matrices for calibration, expressed as numbers of observations
model.conf_cal is a list of lv objects, each of dimensions (nclass x nclass) in the general case, and of dimensions (nclass x nclass x nbvar) for covsel_fda
the confusion matrices for calibration, expressed as percentages
model.conf_cal is a list of lv objects, each of dimensions (nclass x nclass) in the general case, and of dimensions (nclass x nclass x nbvar) for covsel_fda
the confusion matrices for cross-validation, expressed as percentages
model.conf_cv is a list of lv objects, each of dimensions (nclass x nclass) in the general case, and of dimensions (nclass x nclass x nbvar) for covsel_fda
the percentage of classification errors for calibrations and cross-validations
model.err.d is a matrix (lv x 2) in the general case, and (2 x lv x nbvar) for covsel_fda
the percentage of calibration error, for each class
model.errbycl_cal.d is a matrix (lv x nclass) in the general case, and (nclass x lv x nbvar) for covsel_fda
the percentage of cross-validation error, for each class
model.errbycl_cv.d is a matrix (lv x nclass) in the general case, and (nclass x lv x nbvar) for covsel_fda
the percentage of not-classed observations (all predictions lower than the threshold)
model.notclassed.d is a vector (lv x 1) in the general case, (lv x nbvar) for covsel_fda
the percentage of not-classed observations (all predictions lower than the threshold), for each class
model.notclassed_bycl.d is a matrix (lv x nclass) in the general case, and (lv x nclass x nbvar) for covsel_fda
the discriminant method; here: 'covsel_fda'
the original dataset (x); a div structure
model.xcal.d is of dimensions (n x q)
the original classes (y_class)
model.ycal.d is of dimensions (n x nclass) if disjuctive, or (n x 1) if conjunctive
the observations scores T; they verify: T=XR, X being obtained by applying model.scale to x; T is a div structure
model.scores.d is of dimensions (n x lv) in the general case, and (n x lv x nbvar) for covsel_fda
the loadings R, a div structure
model.rloadings.d is a matrix of dimensions (q x lv) in the general case, and (q x lv x nbvar) for covsel_fda
the metric used for the calculation of the distances between observations and groups
the standardization which was applied
the threshold which was applied