svmClassification: svm classification

Description Usage Arguments Value Author(s) Examples

View source: R/machinelearning-functions-svm.R

Description

Classification using the support vector machine algorithm.

Usage

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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

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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.