similar to: lda() called with data=subset() command

With the approach of R 2.12.0: with mild apologies for re-opening this perennial issue: is there any hope, if appropriate patches are submitted, of adding a drop.levels argument (with default equal to FALSE to preserve backward compatibility/efficiency) to the subset function ... ? If not, would a patch to the documentation and/or the R FAQ be accepted? This does seem to be a continuing

Hi I used princomp on a dataset x[!sub,]. How can I get the scores for another dataset, say x[sub,]? I didn't succeed using predict() thanks for a hint cheers christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

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

Dear R folk: Perhaps I'm just dense today, but I am having trouble reproducing the principal components plotted and summarized by clusplot. Here is a brief example using the pluton dataset. clusplot reports that the first two principal components explain 99.7% of the variability. But this is not what princomp is reporting. I would greatly appreciate any advice. With best regards, -- Tom

Dear R-users How can I get the eigenvalues out of an lda analysis? thanks a lot christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

Hi!! I'm having trouble selecting 10 out of 41 attributes of the KDD data set. In order to identify the components with the higher variance I'm using princomp. the result i get for summary(pca1) is: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9

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

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, I just wonder if this is a rounding error by the princomp command in R. Although this does not make much sense, using a hypothetical dataset, a, a<-matrix(runif(1000),100,10) I did PCA with the princomp, and compared it with the results estimated with the eigen and the prcomp commands. And I found some differences in the results: opposite signs in the loadings; slight differences in

Hi, I am using R to do a principal components analysis for a class which is generally using SPSS - so some of my question relates to SPSS output (and this might not be the right place). I have scoured the mailing list and the web but can't get a feel for this. It is annoying because they will be marking to the SPSS output. Basically I'm getting different values for the component

Hi, I've performed an lda and obtained a classification table for some of my data: > efa.dfa<-lda(groups~.,efa.scores.8,CV=T) > str(efa.dfa) List of 5 $ class : Factor w/ 2 levels "1","2": 1 2 1 2 1 1 2 2 1 2 ... $ posterior: num [1:160, 1:2] 0.99083 0.00852 0.93983 0.23186 0.85931 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:160]

Full_Name: Sahotra Sarkar Version: 2.2.0 OS: Windows XP Professional Submission from: (NULL) (146.6.130.180) The following two commands should give the same results for the eigenvectors but do not (there is a sign reversal for the first one): > summary(princomp(bumpus),loading = TRUE) Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5

[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

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

Hi All, We are using the following dataset which contains UTF-8 characters and is stored in a MySQL database. When we use RODBC and read the data, the characters are read correctly.But when we read using RJDBC ,the characters are read like ???????????????????????????1 Can you please let me know how we can read the UTF-8 characters as it is done by RODBC. ID code Outlook Temperature Humidity

Hi I'm sure this question has been asked before but I can't find it in the archives. I have a data frame which includes interval and ordered nominal results. It looks something like "Measured" "Eyeball" 46.5 Normal 43.5 Mild 56.2 Normal 41.1 Mild 37.8 Moderate 12.6 Severe 17.3 Moderate 39.1 Normal 26.7 Mild NULL Normal 27.9 NULL 68.1

I have a question about princomp(mva) that I hope isn't too newbie. I used the sample data from Table 1.1 in "Manly (1986/1994) Multivariate Statistical Methods: a primer. Chapman and Hall" on sparrow body measurements. I rescaled the data to mean 0 and SD 1, and the covariance matrix is: V1 V2 V3 V4 V5 V1 1.0000000 0.7349642 0.6618119

Hi, I have a data.frame like this: y <- rnorm(60) lev <- gl(3,20, labels=paste("lev", 1:3, sep="")) date1 <- as.Date(seq(ISOdate(2007,9,1), ISOdate(2007,11,5), by=60*60*24)) date1 <- date1[-c(3,4,15,34,38,40)] df <- data.frame(lev=lev, date1=date1, y=y) I would like to produce a new data.frame with missing days in df$date1 in each df$lev, like this: lev

Hi All, I have been trying to get to plot my PCA legend outside of the PCA plot, but success still alludes me. Can you guys please advise how I can achieve this. I used locater() to obtain coordinates for below the Comp.1 axis. Using these coordinates the legend disappears. Below is the code for the PCA and legend. Thanks in advance for the help. Regards Tinus r.cols <-