k-near-neighbors with cross-validation
model = knnda(x,y_class,split,knn,(scale))
a matrix (n x q) or a Div structure
classification of the observations; a disjunctive matrix (n x nclass) or a conjunctive vector (n x 1) or Div structures
for the cross-validation; a number of blocks, or a vector of dimensions (n x 1) identifying by a number the CV group of each observation
the number of neighbors
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 confusion matrix obtained with the calibration models,expressed as numbers of observations
model.conf_cal.d is an hyper-matrix of dimensions (nclass x nclass x lv)
the confusion matrix obtained with the calibration models, expressed as percentages
model.conf_cal.d is an hyper-matrix of dimensions (nclass x nclass x lv)
the confusion matrix obtained with the cross-validations, expressed as percentages
model.conf_cv.d is an hypermatrix of dimensions (nclass x nclass x lv)
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 scalar
the percentage of not-classed observations (all predictions lower than the threshold), for each class
model.notclassed_bycl.d is a vector (nclass x 1)
the discriminant method; here: 'knnda'
the calibration dataset
the applied standardisation