asmbPLS (version 1.0.0)

asmbPLSDA.vote.predict: Using an asmbPLS-DA vote model for classification of new samples

Description

Function to make the classification using the weights and fitted model obtained from asmbPLSDA.vote.fit. The final classification results are the weighted classification using the decision rules included.

Usage

asmbPLSDA.vote.predict(fit.results, X.matrix.new)

Value

Y_pred

Predicted class for the new sampels.

Arguments

fit.results

The output of asmbPLSDA.vote.fit.

X.matrix.new

A predictors matrix, whose predictors are the same as the predictors in model fitting.

Examples

## Use the example dataset
data(asmbPLSDA.example)
X.matrix = asmbPLSDA.example$X.matrix
X.matrix.new = asmbPLSDA.example$X.matrix.new
Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary
X.dim = asmbPLSDA.example$X.dim
PLS.comp = asmbPLSDA.example$PLS.comp
quantile.comb.table.cv = asmbPLSDA.example$quantile.comb.table.cv

## Cross validaiton based on fixed cutoff
cv.results.cutoff <- asmbPLSDA.cv(X.matrix = X.matrix, 
                                  Y.matrix = Y.matrix.binary,
                                  PLS.comp = PLS.comp, 
                                  X.dim = X.dim, 
                                  quantile.comb.table = quantile.comb.table.cv, 
                                  outcome.type = "binary", 
                                  method = "fixed_cutoff",
                                  k = 3,
                                  ncv = 1)
quantile.comb.cutoff <- cv.results.cutoff$quantile_table_CV

## Cross validation using Euclidean distance of X super score
cv.results.EDX <- asmbPLSDA.cv(X.matrix = X.matrix, 
                               Y.matrix = Y.matrix.binary,
                               PLS.comp = PLS.comp, 
                               X.dim = X.dim, 
                               quantile.comb.table = quantile.comb.table.cv, 
                               outcome.type = "binary", 
                               method = "Euclidean_distance_X",
                               k = 3,
                               ncv = 1)
quantile.comb.EDX <- cv.results.EDX$quantile_table_CV

## Cross validation using Mahalanobis distance of X super score
cv.results.MDX <- asmbPLSDA.cv(X.matrix = X.matrix, 
                                  Y.matrix = Y.matrix.binary,
                                  PLS.comp = PLS.comp, 
                                  X.dim = X.dim, 
                                  quantile.comb.table = quantile.comb.table.cv, 
                                  outcome.type = "binary", 
                                  method = "Mahalanobis_distance_X",
                                  k = 3,
                                  ncv = 1)
quantile.comb.MDX <- cv.results.MDX$quantile_table_CV

#### vote list ####
cv.results.list = list(fixed_cutoff = quantile.comb.cutoff,
                       Euclidean_distance_X = quantile.comb.EDX,
                       Mahalanobis_distance_X = quantile.comb.MDX)

## vote models fit
vote.fit <- asmbPLSDA.vote.fit(X.matrix = X.matrix, 
                               Y.matrix = Y.matrix.binary, 
                               X.dim = X.dim, 
                               nPLS = c(cv.results.cutoff$optimal_nPLS, 
                               cv.results.EDX$optimal_nPLS, 
                               cv.results.MDX$optimal_nPLS),
                               cv.results.list = cv.results.list, 
                               outcome.type = "binary",
                               method = "weighted")

## classification
vote.predict <- asmbPLSDA.vote.predict(vote.fit, X.matrix.new)