nnetClassification {pRoloc} | R Documentation

## nnet classification

### Description

Classification using the artificial neural network algorithm.

### Usage

    nnetClassification(object, assessRes, scores = c("prediction", "all",
      "none"), decay, size, fcol = "markers", ...)

### Arguments

`object` | An instance of class `"MSnSet"`.
`assessRes` | An instance of class `"GenRegRes"`, as generated by `nnetOptimisation`.
`scores` | One of `"prediction"`, `"all"` or `"none"` to report the score for the predicted class only, for all classes or none.
`decay` | If `assessRes` is missing, a `decay` must be provided.
`size` | If `assessRes` is missing, a `size` must be provided.
`fcol` | The feature meta-data containing marker definitions. Default is `markers`.
`...` | Additional parameters passed to `nnet` from package `nnet`.

### Value

An instance of class `"MSnSet"` with `nnet` and `nnet.scores` feature variables storing the classification results and scores respectively.

### Author(s)

Laurent Gatto

### Examples

    library(pRolocdata)
    data(dunkley2006)
    ## reducing parameter search space and iterations
    params <- nnetOptimisation(dunkley2006, decay = 10^(c(-1, -5)), size = c(5, 10), times = 3)
    params
    plot(params)
    f1Count(params)
    levelPlot(params)
    getParams(params)
    res <- nnetClassification(dunkley2006, params)
    getPredictions(res, fcol = "nnet")
    getPredictions(res, fcol = "nnet", t = 0.75)
    plot2D(res, fcol = "nnet")

[Package pRoloc version 1.26.0 Index]