svmClassification {pRoloc} | R Documentation |
Classification using the support vector machine algorithm.
svmClassification(object, assessRes, scores = c("prediction", "all", "none"), cost, sigma, fcol = "markers", ...)
object |
An instance of class |
assessRes |
An instance of class
|
scores |
One of |
cost |
If |
sigma |
If |
fcol |
The feature meta-data containing marker definitions.
Default is |
... |
Additional parameters passed to |
An instance of class "MSnSet"
with
svm
and svm.scores
feature variables storing the
classification results and scores respectively.
Laurent Gatto
library(pRolocdata) data(dunkley2006) ## reducing parameter search space and iterations params <- svmOptimisation(dunkley2006, cost = 2^seq(-2,2,2), sigma = 10^seq(-1, 1, 1), times = 3) params plot(params) f1Count(params) levelPlot(params) getParams(params) res <- svmClassification(dunkley2006, params) getPredictions(res, fcol = "svm") getPredictions(res, fcol = "svm", t = 0.75) plot2D(res, fcol = "svm")