rfClassification {pRoloc} | R Documentation |
Classification using the random forest algorithm.
rfClassification(object, assessRes, scores = c("prediction", "all", "none"), mtry, fcol = "markers", ...)
object |
An instance of class |
assessRes |
An instance of class
|
scores |
One of |
mtry |
If |
fcol |
The feature meta-data containing marker definitions.
Default is |
... |
Additional parameters passed to
|
An instance of class "MSnSet"
with
rf
and rf.scores
feature variables storing the
classification results and scores respectively.
Laurent Gatto
library(pRolocdata) data(dunkley2006) ## reducing parameter search space and iterations params <- rfOptimisation(dunkley2006, mtry = c(2, 5, 10), times = 3) params plot(params) f1Count(params) levelPlot(params) getParams(params) res <- rfClassification(dunkley2006, params) getPredictions(res, fcol = "rf") getPredictions(res, fcol = "rf", t = 0.75) plot2D(res, fcol = "rf")