asmbPLSDA.vote.predict {asmbPLS} | R Documentation |
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)
Arguments
fit.results |
The output of |
X.matrix.new |
A predictors matrix, whose predictors are the same as the predictors in model fitting. |
Value
Y_pred |
Predicted class for the new sampels. |
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)
[Package asmbPLS version 1.0.0 Index]