Dear Jorge, As i've already written the princomp method works for me. But i'm interested to produce from the results a matrix which i can visualize (e.g the first pc) in an image application and which is then the source for a clustering algorithm. The background is that i've an application which can transfer images to R and create images from R very fast (using ImageJ and RServe). I've already clustered successfully image data with the cluster package. If i now have more than three channels (or bands - > R,G,B) i want to reduce the dataset with the PCA. So which data (as a matrix) represents the first pc or how can i calculate from the results this matrix? Since i've not worked with the PCA method before any explanation beyond the R help would really help me.
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