| pnnCMA {CMA} | R Documentation |
Probabilistic Neural Networks is the term Specht (1990) used for a Gaussian kernel estimator for the conditional class densities.
For S4 method information, see pnnCMA-methods.
pnnCMA(X, y, f, learnind, sigma = 1,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. For this method, this
must not be missing. |
sigma |
Standard deviation of the Gaussian Kernel used.
This hyperparameter should be tuned, s. tune.
The default is 1, but this generally does not
lead to good results. Actually, this method reacts
very sensitively to the value of sigma. Take care
if warnings appear related to the particular choice. |
models |
a logical value indicating whether the model object shall be returned |
An object of class cloutput.
There is actually no strong relation of this method to Feed-Forward
Neural Networks, s. nnetCMA.
Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
Specht, D.F. (1990).
Probabilistic Neural Networks. Neural Networks, 3, 109-118.
compBoostCMA, dldaCMA, ElasticNetCMA,
fdaCMA, flexdaCMA, gbmCMA,
knnCMA, ldaCMA, LassoCMA,
nnetCMA, pknnCMA, plrCMA,
pls_ldaCMA, pls_lrCMA, pls_rfCMA,
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 PNN pnnresult <- pnnCMA(X=golubX, y=golubY, learnind=learnind, sigma = 3) ### show results show(pnnresult) ftable(pnnresult) plot(pnnresult)