knntlClassification {pRoloc} | R Documentation |
Classification using a variation of the KNN implementation of Wu and Dietterich's transfer learning schema
knntlClassification(primary, auxiliary, fcol = "markers", bestTheta, k, scores = c("prediction", "all", "none"), seed)
primary |
An instance of class |
auxiliary |
An instance of class
|
fcol |
The feature meta-data containing marker definitions.
Default is |
bestTheta |
Best theta vector as output from
|
k |
Numeric vector of length 2, containing the best |
scores |
One of |
seed |
The optional random number generator seed. |
A character vector of the classifications for the unknowns
Lisa Breckels
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