nnetClassification: nnet classification

In pRoloc: A unifying bioinformatics framework for spatial proteomics

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")

pRoloc documentation built on Nov. 8, 2020, 6:26 p.m.