similar to: PCA model

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

Dear all, not really a R question but: If I want to check for the classification accuracy of a LDA with previous PCA for dimensionality reduction by means of the LOOCV method: Is it ok to do the PCA on the WHOLE dataset ONCE and then run the LDA with the CV option set to TRUE (runs LOOCV) -- OR-- do I need - to compute for each 'test-bag' (the n-1 observations) a PCA

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 There, I'm attempting to plot 10 values on a three-dimensional PCA with text labels next to each point. While i have no trouble doing this on 2D plots using the 'text' or 'textxy' function, I cannot find a function to do this on a 3D plot. I am using princomp for my PCA: >PCA<-princomp(eucdata, cor=TRUE) >PCA$scores [,1:3] # the three principal components i

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.

hi, I have two questions: #first (SPSS vs. R): I just compared the output of different PCA routines in R (pca, prcomp, princomp) with results from SPSS. the loadings of the variables differ vastly! in SPSS the variables load constantly higher than in R. I made sure that both progr. use the correlation matrix as basis. I found the same problem with rotated values (varimax rotation and rtex=T

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:

Hi all, I am using the vegan package to run a prcincipal components analysis on forest structural variables (tree density, basal area, average height, regeneration density) in R. However, I could not find out how to extract factor loadings (correlations of each variable with each pca axis), as is straightforwar in princomp. Do anyone know how to do that? Moreover, do anyone knows

Hello, I'm attempting to run a PCA on an example data set. I ran it just fine, but I don't know how to few the output? I listed what the variable got stored in it, but I don't know how I can get anything else out of it. Are there other ways to view the results? Also, I'm confused about the last line "6 variables and 8 observations" Aren't the rows the

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: ....

I am trying to run PCA on a matrix (the first column and row are headers). There are several cells with NA's. When I run PCA with the following code: ______________________________________ setwd("I:/PCA") AsianProp<-read.csv("Matrix.csv", sep=",", header=T, row.names=1) attach(AsianProp) AsianProp AsianProp.pca<-princomp(AsianProp, na.omit)

Hi all, I have a difficulty to calculate the PCA scores. The PCA scores I calculated doesn't match with the scores generated by R, mypca<-princomp(mymatrix, cor=T) myscore<-as.matrix(mymatrix)%*%as.matrix(mypca$loadings) Does anybody know how the mypca$scores were calculated? Is my formula not correct? Thanks a lot! Phoebe [[alternative HTML version deleted]]

Hi All, would appreciate an answer on this if you have a moment; Is there a function (before I try and write it !) that allows the input of a covariance or correlation matrix to calculate PCA, rather than the actual data as in princomp() Regards Glenn [[alternative HTML version deleted]]

Dear all, I'm using R 2.8.1 under Vista. I programmed a Simulation with the code enclosed at the end of the eMail. After the simulation I want to analyse the columns of the single simulation-runs, i.e. e.g. Simulation[[1]][,1] sth. like that but I cannot address these columns... Can anybody please help? Best, Thomas ############################ CODE ############################

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]]

Dear all I'm unable to find an example of extracting the rotated scores of a principal components analysis. I can do this easily for the un-rotated version. data(mtcars) .PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars) unclass(loadings(.PC)) # component loadings summary(.PC) # proportions of variance mtcars$PC1 <- .PC$scores[,1] # extract un-rotated scores of

Dear useRs: I'm not sure if it's the correct place to ask but I'll try it out. I've been reading about how to perform Principal Component Analysis (PCA) in microarrays (see [1]) and there's something that I don't get it. Basically it's related with performing PCA over data sets which number of variables is greater than the number of samples. For example in the paper

Dear all, I have a question about the 'sdev' value returned by the princomp function (which does principal components analysis). On the help page for princomp it says 'sdev' is 'the standard deviations of the principal components'. However, when I calculate the principal components for the USArrests data set, I don't find this to be the case: Here is how I

Hi all: I am trying to do PCA on the following matrix. N1 N2 A1 A2 B1 B2 gene_a 90 110 190 210 290 310 gene_b 190 210 390 410 590 610 gene_c 90 110 110 90 120 80 gene_d 200 100 400 90 600 200 >dataf<-read.table("matrix") >

Hello, I am trying to complete a PCA on a set of standardized ring widths from 8 different sites (T10, T9, T8, T7, T6, T5, T3, and T2). The following is a small portion of my data: T10 T9 T8 T7 T6 T5 T3 T2 1.33738 0.92669 0.91146 0.98922 0.9308 0.88201 0.92287 0.91775 0.82181 1.05319 0.92908 0.97971 0.95165 0.98029 1.14048 0.77803 0.88294 0.96413 0.90893 0.87957 0.9961 0.74926 0.71394 0.70877