pnnCMA {CMA} | R Documentation

## Probabilistic Neural Networks

### Description

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.

### Usage

    pnnCMA(X, y, f, learnind, sigma = 1,models=FALSE)

### Arguments

`X` | Gene expression data. Can be one of the following: A `matrix`. Rows correspond to observations, columns to variables. A `data.frame`, when `f` is not missing (s. below). An object of class `ExpressionSet`. Each variable (gene) will be scaled for unit variance and zero mean.
`y` | Class labels. Can be one of the following: A `numeric` vector. A `factor`. A `character` if `X` is an `ExpressionSet` that specifies the phenotype variable. `missing`, if `X` is a `data.frame` and a proper formula `f` is provided. WARNING: The class labels will be re-coded to range from `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

### Value

An object of class `cloutput`.

### Note

There is actually no strong relation of this method to Feed-Forward Neural Networks, s. `nnetCMA`.

### Author(s)

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

### References

Specht, D.F. (1990).

Probabilistic Neural Networks. Neural Networks, 3, 109-118.

### See Also

`compBoostCMA`, `dldaCMA`, `ElasticNetCMA`, `fdaCMA`, `flexdaCMA`, `gbmCMA`, `knnCMA`, `ldaCMA`, `LassoCMA`, `nnetCMA`, `pknnCMA`, `plrCMA`, `pls_ldaCMA`, `pls_lrCMA`, `pls_rfCMA`, `qdaCMA`, `rfCMA`, `scdaCMA`, `shrinkldaCMA`, `svmCMA`

### Examples

    ### 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)

[Package CMA version 1.48.0 Index]