MCRestimate (version 2.26.0)

RF.wrap: Wrapper function for different classification methods

Description

Wrapper function for different classification methods used by MCRestimator. These functions are mainly used within the function MCRestimate

Usage

RF.wrap(x,y,...)
PAM.wrap(x,y,threshold,...)
PLR.wrap(x,y,kappa=0,eps=1e-4,...)
SVM.wrap(x,y,gamma = NULL, kernel = "radial", ...)
GPLS.wrap(x,y,...)

Arguments

x,y

x is a matrix where each row refers to a sample a each column refers to a gene; y is a factor which includes the class for each sample

threshold

the threshold for PAM

kappa

the penalty parameter for the penalised logistic regression

eps

precision of convergence

gamma

parameter for support vector machines

kernel

parameter for support vector machines

...

Further parameters

Value

Every function return a predict function which can be used to predict the classes for a new data set.

See Also

MCRestimate

Examples

library(golubEsets)
data(Golub_Train)

class.column <- "ALL.AML"
Preprocessingfunctions <- c("varSel.highest.var")
list.of.poss.parameter <- list(threshold = 6)

Preprocessingfunctions <- c("identity")
class.function <- "PAM.wrap"
plot.label <- "Samples"

cross.outer <- 10
cross.repeat <- 7
cross.inner <- 5

PAM.estimate <- MCRestimate(Golub_Train,
		class.column,
		classification.fun = class.function,
		thePreprocessingMethods = Preprocessingfunctions,
		poss.parameters = list.of.poss.parameter,
		cross.outer = cross.outer, cross.inner = cross.inner,
		cross.repeat = cross.repeat, plot.label = plot.label)