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, ...)
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. |
... |
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 martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
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)