pRoloc (version 1.12.4)

nnetClassification: 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 cluster 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.

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")