similar to: Code behind the Function

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 ############################

When I look through archives at https://stat.ethz.ch/pipermail/r-help/2003-October/040525.html I see this: Liaw, Andy wrote: >In the `Detail' section of ?princomp: > >princomp only handles so-called Q-mode PCA, that is feature extraction of >variables. If a data matrix is supplied (possibly via a formula) it is >required that there are at least as many units as variables. For

Dear R-user, I am trying to use the function 'parLapply' from the 'snow' package which is supposed to work the same wys as 'lapply' but for a parallelized cluster of computers. The function I am trying to call in parallel is 'dudi.pca' (from the 'ade4' package) which performs principal component analyses. When I call this function on a list of

Dear all, perhaps I am using princomp.formula and prcomp.formula in a way that is not documented to work, but then the documentation just says: formula: a formula with no response variable. Thus, to avoid a lot of typing, it would be nice if one could use '.' and '-' in the formula, e.g. > library(DAAG) > res <- prcomp(~ . - case - site - Pop - sex, possum)

Some webpage has described prcomp and princomp, but I am still not quite sure what the major difference between them is. Can they be used interchangeably? In help, it says 'princomp' only handles so-called R-mode PCA, that is feature extraction of variables. If a data matrix is supplied (possibly via a formula) it is required that there are at least as many units as

Dear R buddies, I’m trying to run Principal Component Analysis, package princomp: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/princomp.html. My question is: why do I get different results with pca = princomp (x, cor = TRUE) and pca = princomp (x, cor = FALSE) even when I standardize variables in my matrix? Best regards, Blaž Simčič [[alternative HTML version deleted]]

I start a clean session of R 1.9.1 on Windows and I run the following code: > library(MASS) > data(painters) > pca.painters <- princomp(painters[ ,1:4]) > loadings(pca.painters) Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Composition 0.484 -0.376 0.784 -0.101 Drawing 0.424 0.187 -0.280 -0.841 Colour -0.381 -0.845 -0.211 -0.310 Expression 0.664 -0.330 -0.513

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.

I do not understand, from a PCA point of view, the option center=F of prcomp() According to the help page, the calculation in prcomp() "is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix" (as it's done by princomp()) . "This is generally the preferred method for numerical accuracy"

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, 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

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 have a problem with the princomp method, it seems stupid but I don't know how to handle it. I have a dataset with some regular data and some outliers. I want to calculate a PCA on the regular data and get the scores for all data, including the outliers. Is this possible on R? Thank you for helping!!! -- View this message in context:

In comparing the results of princomp and prcomp I find: 1. The reported standard deviations are similar but about 1% from each other, which seems well above round-off error. 2. princomp returns what I understand are variances and cumulative variances accounted for by each principal component which are all equal. "SS loadings" is always 1. 3. Same happens

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

[Re-directing to R-devel, as I think this needs changes to the code.] Can I suggest a modification to stats:predict.princomp so that it will check for column (variable) names? In src/library/stats/R/princomp-add.R, insert the following after line 4: if (!is.null(cn <- names(object$center))) newdata <- newdata[, cn] Now Dana's example looks like: > predict(pca1, frz) Error in

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: ....