PLS discriminant analysis (PLS-DA) with cross-validation: a PLS2 (Simpls) computes scores from the disjonctive matrix of the classes, then each observation is attributed to a class using these scores
model = plsda(x,y_class,split,lv,(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
maximum number of latent variables or eigenvectors used for the building of the model
lv should be lower or equal to nclass
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
scale='cs': standardisation; the columns of x are divided by their standard deviation (by default)
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)
the confusion matrices for calibration, expressed as percentages
model.conf_cal is a list of lv objects, each of dimensions (nclass x nclass)
the confusion matrices for cross-validation, expressed as percentages
model.conf_cv is a list of lv objects, each of dimensions (nclass x nclass)
the percentage of classification errors for calibrations (1st column) and cross-validations (2nd column)
model.err.d is a matrix (lv x 2)
the percentage of calibration error, for each class
model.errbycl_cal.d is a matrix (lv x nclass)
the percentage of cross-validation error, for each class
model.errbycl_cv.d is a matrix (lv x nclass)
the percentage of not-classed observations (all predictions lower than the threshold)
model.notclassed.d is a vector (lv x 1)
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)
the discriminant method; here: 'plsda'
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)
the observations scores from Simpls; a div structure
model.scores.d is of dimensions (n x lv)
the R matrix of simpls, verifying: T=XR
model.rloadings.d is a matrix of dimensions (q x lv)
the metric used for the calculation of the distances between observations and groups
the applied standardization
the applied threshold