nnetClassification {pRoloc} | R Documentation |
Classification using the artificial neural network algorithm.
nnetClassification(object, assessRes, scores = c("prediction", "all", "none"), decay, size, fcol = "markers", ...)
object |
An instance of class |
assessRes |
An instance of class
|
scores |
One of |
decay |
If |
size |
If |
fcol |
The feature meta-data containing marker definitions.
Default is |
... |
Additional parameters passed to |
An instance of class "MSnSet"
with
nnet
and nnet.scores
feature variables storing
the classification results and scores respectively.
Laurent Gatto
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")