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copda

classification by orthogonal projection (COP) with cross-validation

Calling sequence

model = copda(x,y_class,split,lv,(metric),(scale),(threshold) )

Arguments

x:

a matrix (n x q) or a Div structure

y_class:

a conjunctive vector (n x 1) or a disjonctive matrix (n x nclass) or a Div structure

split:

for the cross-validation; the number of blocks or a vector of dimensions (n x 1) identifant by a number the group of cross-validation of each observation

lv:

maximum number of latent variables or eigenvectors used for the building of the model

lv inf. or equal to nclass

(metric):

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

(scale):

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)

(threshold):

the lowest value for attributing an observation to a class (by default= 1/nclass )

model.conf_cal_nobs:

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)

model.conf_cal:

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)

model.conf_cv:

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)

model.err:

the percentage of classification errors for calibrations (1st column) and cross-validations (2nd column)

model.err.d is a matrix (lv x 2)

model.errbycl_cal:

the percentage of calibration error, for each class

model.errbycl_cal.d is a matrix (lv x nclass)

model.errbycl_cv:

the percentage of cross-validation error, for each class

model.errbycl_cv.d is a matrix (lv x nclass)

model.notclassed:

the percentage of not-classed observations (all predictions lower than the threshold)

model.notclassed.d is a vector (lv x 1)

model.notclassed_bycl:

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)

model.method:

the discriminant method; here: 'copda'

model.loadings:

the eigenvectors

model.eigenvec.d is a matrix of dimensions (q x lv)

model.classif_metric:

the metric used for the calculation of the distances between observations and groups

model.scale:

applied standardization

model.threshold:

applied threshold

Examples

[result1]=copda(x,y,30,20)
[result1]=copda(x,y,30,20,1,'cs',0.1)

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