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pRoloc (version 1.12.4)

svmClassification: svm classification

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 cluster 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.

Examples

Run this code
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")

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