| RF.wrap {MCRestimate} | R Documentation |
Wrapper function for different classification methods used
by MCRestimator. These functions are mainly used within the
function MCRestimate
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,...)
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 |
Every function return a predict function which can be used to predict the classes for a new data set.
Markus Ruschhaupt mailto:m.ruschhaupt@dkfz.de
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)