knntlClassification: knn transfer learning classification

In pRoloc: A unifying bioinformatics framework for spatial proteomics

Description

Classification using a variation of the KNN implementation of Wu and Dietterich's transfer learning schema

Usage

knntlClassification(primary, auxiliary, fcol = "markers", bestTheta, k,
  scores = c("prediction", "all", "none"), seed)

Arguments

primary	An instance of class "MSnSet".
auxiliary	An instance of class "MSnSet".
fcol	The feature meta-data containing marker definitions. Default is markers.
bestTheta	Best theta vector as output from knntlOptimisation, see knntlOptimisation for details
k	Numeric vector of length 2, containing the best k parameters to use for the primary and auxiliary datasets. If k k is not specified it will be calculated internally.
scores	One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.
seed	The optional random number generator seed.

Value

A character vector of the classifications for the unknowns

Author(s)

Lisa Breckels

See Also

knntlOptimisation

Examples

library(pRolocdata)
data(andy2011)
data(andy2011goCC)
## reducing calculation time of k by pre-running knnOptimisation
x <- c(andy2011, andy2011goCC)
k <- lapply(x, function(z)
            knnOptimisation(z, times=5,
                            fcol = "markers.orig",
                            verbose = FALSE))
k <- sapply(k, function(z) getParams(z))
k
## reducing parameter search with theta = 1,
## weights of only 1 or 0 will be considered
opt <- knntlOptimisation(andy2011, andy2011goCC,
                         fcol = "markers.orig",
                         times = 2,
                         by = 1, k = k)
opt
th <- getParams(opt)
plot(opt)
res <- knntlClassification(andy2011, andy2011goCC,
                           fcol = "markers.orig", th, k)
res

pRoloc documentation built on Nov. 8, 2020, 6:26 p.m.