mbinest: Binary Classification by Multiple Classifier

In mt: Metabolomics Data Analysis Toolbox

Description

Binary classification by multiple classifier.

Usage

mbinest(dat, cl, choices = NULL, method, pars=valipars(),...) 

Arguments

dat	A matrix or data frame containing the explanatory variables.
cl	A factor specifying the class for each observation.
choices	The vector or list of class labels to be chosen for binary classification. For details, see dat.sel.
method	Multiple classification methods to be used. For details, see maccest.
pars	A list of parameters of the resampling method. See valipars for details.
...	Additional parameters to method.

Value

A list with components:

all	All results of classification.
com	A matrix of the combinations of the binary class labels.
acc	A table of classification accuracy for the binary combination.
mar	Prediction margin.
auc	The area under receiver operating curve (AUC).
method	Classification methods used.
niter	Number of iterations.
sampling	Sampling scheme used.
nreps	Number of replications in each iteration if sampling is not loocv.

Author(s)

Wanchang Lin

See Also

maccest, maccest,valipars, dat.sel

Examples

## iris data set
data(iris)
dat    <- subset(iris, select = -Species)
cl     <- iris$Species
method <- c("svm","pcalda")

pars  <- valipars(sampling="cv",niter = 10, nreps = 5)
res   <- mbinest(dat,cl,choices=c("setosa"), method=method,
                  pars = pars, kernel="linear")

## combine prediction accuracy, AUC and margin 
z      <- round(cbind(res$acc,res$auc,res$mar),digits=3)
colnames(z) <- c(paste(method,".acc", sep=""),paste(method,".auc", sep=""),
                 paste(method,".mar", sep=""))

mt documentation built on Feb. 2, 2022, 1:07 a.m.