Re: [R] PCA on image data

Hi, You have the values of the principal component in "scores" (for each "location" where you have a RGB reading) and the eigenvectors in "loadings", see ?princomp So your first pca component would be: comp1 <- pca$scores[,1] Now you can transform this in the matrix you need to display your

Re: [R] PCA on image data

Dear Jorge, i think it works. I have to investigate it further but have a visible result from your suggestion. Thank you very much for your help! With kind regards Marcel Austenfeld -- View this message in context: http://www.nabble.com/PCA-on-image-data-tp18255217p18259659.html Sent from the

Re: [R] PCA on image data

Dear Marcel, I'm sorry for the misunderstanding. I think what you really need are the scores values provided by PCA. See ?princomp for details. Here's an example: # PCA prin.comp=princomp(USArrests, cor = TRUE) summary(prin.comp) # What do you have in prin.comp? names(prin.comp) # Extracting th

Re: [R] PCA on image data

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

Re: [R] PCA on image data

Dear Marcel, See ?princomp. HTH, Jorge On Thu, Jul 3, 2008 at 5:15 AM, Bio7 <[EMAIL PROTECTED]> wrote: > > Dear R users, > > i would like to apply a PCA on image data for data reduction. > The image data is available as three matrices for the > RGB values. At the moment i use > > x <- data.fra