svmClassification: svm classification

In pRoloc: A unifying bioinformatics framework for spatial proteomics

Description

Classification using the support vector machine algorithm.

Usage

svmClassification(object, assessRes, scores = c("prediction", "all",
  "none"), cost, sigma, fcol = "markers", ...)

Arguments

object	An instance of class "MSnSet".
assessRes	An instance of class "GenRegRes", as generated by svmOptimisation.
scores	One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.
cost	If assessRes is missing, a cost must be provided.
sigma	If assessRes is missing, a sigma must be provided.
fcol	The feature meta-data containing marker definitions. Default is markers.
...	Additional parameters passed to svm from package e1071.

Value

An instance of class "MSnSet" with svm and svm.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 <- 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")

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