nbClassification: nb classification

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

nb classification

Description

Classification using the naive Bayes algorithm.

Usage

nbClassification(
  object,
  assessRes,
  scores = c("prediction", "all", "none"),
  laplace,
  fcol = "markers",
  ...
)

Arguments

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

Value

An instance of class "MSnSet" with nb and nb.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 <- nbOptimisation(dunkley2006, laplace = c(0, 5),  times = 3)
params
plot(params)
f1Count(params)
levelPlot(params)
getParams(params)
res <- nbClassification(dunkley2006, params)
getPredictions(res, fcol = "naiveBayes")
getPredictions(res, fcol = "naiveBayes", t = 1)
plot2D(res, fcol = "naiveBayes")

lgatto/pRoloc documentation built on March 14, 2024, 7:10 a.m.