Displaying 2 results from an estimated 2 matches for "sample_i".
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sample_id
2012 Apr 21
2
using "factor" to eliminate unused levels without dropping other variables
Hello,
I have been banging my head against the wall trying to figure out this
seemingly simple problem with no success. I'm hoping that one or some
of you can help.
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 <-
2012 Apr 26
1
kernlab kpca code
...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 PC (eigenvector of Covariance). This can be computed as the sum of the (eigenvectors of the Kernel matrix * the kernel function(sample_i,x))
Now assume i have some new points and want to project them, how can i do that with only having @pcv?
Wouldn't i rather need the eigenvectors of K?
)
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