search for: sample_y

...Here is the code I am trying to use: #importing data data.file <-read.csv("/file/location", header=TRUE, sep = ",") #selecting a subset of data based on variable "Sample" data.subset1 <- subset(data.file, subset=(Sample !='sample_x' & Sample !='sample_y')). **This leaves me a data file that has 8 levels of the variable "Sample" and 2 empty levels that correspond to sample_x and sample_y. I need to get rid of these two levels for plotting purposes, so I tried using the code below... data.subset2 <- factor(data.subset1$Sample) **...

Hi! how do i get to the source code of kpca or even better predict.kpca(which it tells me doesn't exist but should) ? (And if anyone has too much time: Now if i got that right, the @pcv attribute consists of the principal components, and for kpca, these are defined as projections of some random point x, which was transformed into the other feature space -> f(x), projected onto the actual