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:
WARNING: The class labels will be re-coded to
range from |
f |
A two-sided formula, if |
learnind |
An index vector specifying the observations that
belong to the learning set. May be |
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 |
models |
a logical value indicating whether the model object shall be returned |
... |
Further arguments passed to the function
|
An object of class cloutput
.
Excessive variable selection is usually necessary if eigengenes = FALSE
Different runs of this method on the same dataset not necessarily produce the same results due to the fact that optimization for Feed-Forward Neural Networks is rather difficult and depends on the choice of (normally randomly chosen) starting values for the network weights.
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)