wrapper.fkt: Wrapper function for different classification methods

Description Usage Arguments Value Author(s) See Also Examples

Description

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

Usage

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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.

Author(s)

Markus Ruschhaupt mailto:m.ruschhaupt@dkfz.de

See Also

MCRestimate

Examples

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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)


MCRestimate documentation built on May 20, 2017, 9:37 p.m.
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