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)
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. |
An object of class cloutput
.
There is actually no strong relation of this method to Feed-Forward
Neural Networks, s. nnetCMA
.
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
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)