knnClassification: knn classification

In lgatto/pRoloc: A unifying bioinformatics framework for spatial proteomics

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 classes 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.

Author(s)

Laurent Gatto

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")

lgatto/pRoloc documentation built on Oct. 23, 2024, 12:51 a.m.