pRoloc (version 1.12.4)

knnClassification: knn classification

Description

Classification using for the k-nearest neighbours algorithm.

Usage

knnClassification(object, assessRes, scores = c("prediction", "all", "none"), k, fcol = "markers", ...)

Arguments

object

An instance of class "MSnSet".

assessRes

An instance of class "GenRegRes", as generated by knnOptimisation.

scores

One of "prediction", "all" or "none" to report the score for the predicted class only, for all cluster or none.

k

If assessRes is missing, a k must be provided.

fcol

The feature meta-data containing marker definitions. Default is markers.

...

Additional parameters passed to knn from package class.

Value

An instance of class "MSnSet" with knn and knn.scores feature variables storing the classification results and scores respectively.

Examples

library(pRolocdata)
data(dunkley2006)
## reducing parameter search space and iterations 
params <- knnOptimisation(dunkley2006, k = c(3, 10), times = 3)
params
plot(params)
f1Count(params)
levelPlot(params)
getParams(params)
res <- knnClassification(dunkley2006, params)
getPredictions(res, fcol = "knn")
getPredictions(res, fcol = "knn", t = 0.75)
plot2D(res, fcol = "knn")