| nnetCMA {CMA} | R Documentation |
This method provides access to the function
nnet in the package of the same name that trains
Feed-forward Neural Networks with one hidden layer.
For S4 method information, see nnetCMA-methods
nnetCMA(X, y, f, learnind, eigengenes = FALSE, models=FALSE,...)
X |
Gene expression data. Can be one of the following:
|
y |
Class labels. Can be one of the following:
0 to K-1, where K is the
total number of different classes in the learning set.
|
f |
A two-sided formula, if X is a data.frame. The
left part correspond to class labels, the right to variables. |
learnind |
An index vector specifying the observations that
belong to the learning set. May be missing;
in that case, the learning set consists of all
observations and predictions are made on the
learning set. |
eigengenes |
Should the training be performed be in the space of
eigengenes obtained from a singular value decomposition
of the Gene expression data matrix ? Default is FALSE;
in this case, variable selection is necessary to reduce
the number of weights that have to be optimized. |
models |
a logical value indicating whether the model object shall be returned |
... |
Further arguments passed to the function nnet
from the package of the same name.Important parameters are:
|
An object of class cloutput.
eigengenes = FALSE
Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
Christoph Bernau bernau@ibe.med.uni-muenchen.de
Ripley, B.D. (1996)
Pattern Recognition and Neural Networks.
Cambridge University Press
compBoostCMA, dldaCMA, ElasticNetCMA,
fdaCMA, flexdaCMA, gbmCMA,
knnCMA, ldaCMA, LassoCMA,
nnetCMA, pknnCMA, plrCMA,
pls_ldaCMA, pls_lrCMA, pls_rfCMA,
pnnCMA, qdaCMA, rfCMA,
scdaCMA, shrinkldaCMA, svmCMA
### load Golub AML/ALL data
data(golub)
### extract class labels
golubY <- golub[,1]
### extract gene expression from first 10 genes
golubX <- as.matrix(golub[,2:11])
### select learningset
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run nnet (not tuned)
nnetresult <- nnetCMA(X=golubX, y=golubY, learnind=learnind, size = 3, decay = 0.01)
### show results
show(nnetresult)
ftable(nnetresult)
plot(nnetresult)
### in the space of eigengenes (not tuned)
golubXfull <- as.matrix(golubX[,-1])
nnetresult <- nnetCMA(X=golubXfull, y=golubY, learnind = learnind, eigengenes = TRUE,
size = 3, decay = 0.01)
### show results
show(nnetresult)
ftable(nnetresult)
plot(nnetresult)