similar to: Scaling data

Dear colleague, Just an e-mail to know if they are people working in the field of chemoinformatic that are using R in their work. If yes I was wondering if we couldn't exchange tips and tricks about the use of R in this area ? Best regards Fred Ooms [[alternative HTML version deleted]]

Hello, I am looking for any packages, tutorials, documents,... about the use of R for the analysis of HTS data. Thanks for your help Fred [[alternative HTML version deleted]]

Dear colleagues, I a m a beginner with R and I would like to add labels (i.e. the variable names) on a pca loading plot to determine the most relevant variables. Could you please tell me the way to do this kind of stuff. The command I use to draw the pca loading plot is the following : Plot(molprop.pc$loading[,1] ~ molprop.pc$loading[,2]) Thanks for your help Fred Ooms

Hi, Another newbie question. Once I have generated a PCA model using the princomp command how can i use it to display a new dataset ? Thanks for your help Fred [[alternative HTML version deleted]]

Hi, I misunderstand the definition of Canberra distance in R. On Internet and in function description pages of dist() from stats and Dist() from amap, Canberra distance between vectors x and y, d(x,y), is : d(x,y) = sum(abs(x-y)/(x+y)) But in use, through simple examples, we find that the formula is : d(x,y) = (NZ + 1)/NZ * sum(abs(x-y)/(x+y)) with NZ = nb of pairs of coordinates that are

Dear R helpers, When producing a PCA biplot, vectors of environmental variables (as red arrows with labels) and scores of the observations (black labels (observation names)) are plotted by default. How can I change the graphical output? Let's say I would like that the scores are plottet only as symbols and not text. The only solution I found was this post in the help archive

Hi There, I have a problem with OggEnc v1.0.2. Can you tell me, where to put or to post a bug report? The problems eare with some speciel charakters - used in Europe (Denmark, Norway, Sweden, Germany, Austria, etc..) Some Examples: Hin Vordende Sod & S? ^ 02_Bruderov paa H?gstadtun.ogg ^ 03_Huldradans - Hin Gr?nnkledde.ogg ^

Hi, Say x.train is a matrix of covariates that I want to do PCA on, so I can do regression on its principal components, and x.test is a test set of the same covariates on which I want to evaluate the regression fit. I would like the covariates to be centred and scaled: p <- prcomp(x.train, center=TRUE, scale=TRUE) x.train.pc <- predict(p) Now I want to get the PCs from the test set.

hello, in my windows vm i installed qemu-guest-agent and rebootet the vm. In the settings for the vm i set via virt-manager a new channel "unix socket" "org.qemu.guest_agent.0" "virtio". when i try to do a snapshot via shell i get: virsh snapshot-create-as --domain win7new win7new-snap1 --disk-only --atomic --quiesce error: Guest agent is not responding:

Hi there, i created a Function Pass to retrieve the Control/Data Flow Graph from a simple program, now i would like to statically schedule the Instructions. Is this possible by starting to modify the SelectionDAG Files ? Or can i even build a "standalone" custom scheduler? Thank you very much for any tipps. Ben

I have been trying to figure out whether I can enable write caching on my PATA hard drives (WD3200JB) and have fsync not return until data is safely on the platters. I am also running software raid. This is currently on FC5 (though soon to be FC6) with a 2.6.18 kernel. >From snippets I have found on the net, it looks like write barriers are pushed down through software raid when using raid 1.

Cordial saludo adjunto la base de datos y el script: eu60<- read.csv(file.choose(), header=T, sep=";", dec=".", row.names=1) eu60.pca <- PCA(eu60, quali.sup=19) eu60.data <- cbind.data.frame(eu60[,19], eu60.pca$ind$coord) eu60.ellipse <- coord.ellipse(eu60.data, bary=TRUE) plot.PCA(ellipse=eu60.ellipse, cex=0.8) El 4 de diciembre de 2013 17:35, Camilo Calle

Hi all, I am doing bootstrap on a distance matrix, in which samples have been drawn with replacement. After that I do PCA on a resulted matrix, and these 2 steps are repeated 1000 times. pca(x) is a vector where I wanted to store all 1000 PCAs; and x is from 1 to 1000 SampleD is a new matrix after resampling; I am getting the following error message, which I don't understand: ....

Hi R-community, I am doing a PCA and I need plots for different combinations of axes (e.g., PC1 vs PC3, and PC2 vs PC3) with the arrows indicating the loadings of each variables. What I need is exactly what I get using biplot (pca.object) but for other axes. I have plotted PC2 and 3 using the scores of the cases, but I don't get the arrows proportional to the loadings of each variables on

Hi, Are SQLite users that are worried about losing data that has been committed (fsynced) better off setting data=journal than data=ordered (or even data=writeback)? The context is trying to reduce the number of writes to a flash file-system without sacrificing data integrity in the event of a power failure or OS crash. Thanks, Dan Kennedy.

Dears, I've been learning biplot (Gabriel, 1971) and I found the function 'biplot', inside of the package 'stats', useful but, a bit limited. So, I'm thinking to start a colaborative package to enhance this methods to other multivariate methods. In this way, I would like to start it, making public a new function (biplot.pca, still in development, but running) that make

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.frame(R,G,B)#convert image data to data frame pca<-princomp(x,retx = TRUE) This is working so far. >From this results then i want to create a new matrix from the first (second..) principal component. Here i stuck.

After doing a PCA using princomp, how do you view how much each component contributes to variance in the dataset. I'm still quite new to the theory of PCA - I have a little idea about eigenvectors and eigenvalues (these determine the variance explained?). Are the eigenvalues related to loadings in R? Thanks, Paul -- View this message in context:

Dear R-helpers, Using the package Lattice, I performed a PCA. For example pca.summary <- summary(pc.cr <- princomp(USArrests, cor = TRUE)) The Output of "pca.summary" looks as follows: Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938 Proportion of Variance 0.6200604

Hi, Suppose I have the following data frame. __BEGIN__ > library(MASS) > data(crabs) > crab.pca <- prcomp(crabs[,4:8],retx=TRUE) > crab.pca$rotation PC1 PC2 PC3 PC4 PC5 FL 0.2889810 0.3232500 -0.5071698 0.7342907 0.1248816 RW 0.1972824 0.8647159 0.4141356 -0.1483092 -0.1408623 CL 0.5993986 -0.1982263 -0.1753299 -0.1435941 -0.7416656 CW