similar to: prcomp vs. princomp vs fast.prcomp

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 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)

According to R help: princomp() uses eigenvalues of covariance data. prcomp() uses the SVD method. yet when I run the (eg., USArrests) data example and compare with my own "hand-written" versions of PCA I get what looks like the opposite. Example: comparing the variances I see: Using prcomp(USArrests) ------------------------------------- Standard deviations: [1] 83.732400 14.212402

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

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

Hi , To my knowledge, there're two functions that can do principal component analysis, princomp and prcomp. I don't really know the difference; the only thing I know is that when the sample size < number of variable, only prcomp will work. Could someone tell me the difference or where I can find easy-to-read reference? To access the first PC using princomp:

Hello fellow Rers, I have a no-doubt simple question which is turning into a headache so would be grateful for any help. I want to do a principal components analysis directly on a correlation matrix object rather than inputting the raw data (and specifying cor = TRUE or the like). The reason behind this is I need to use polychoric correlation coefficients calculated with John Fox's

Hi, I am a new user of R, and apologize beforehand for the simplistic nature of this question: I ran prcomp on a data set with 4 variables, and am able to see the summary information (variance contribution, rotation matrix, plots, etc.). However, I'd also like to extract the actual values of the principal components (PC) corresponding to each sample. I've looked in the help, on-line

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"

As of R 1.7.0, princomp no longer accept matrices with more coloumns than rows. I'm curious: Why was this decision made? I work a lot with data where more coloumns than rows is more of a rule than an exception (for instance spectroscopic data). To me, princomp have two advantages above prcomp: 1) It has a predict method, and 2) it has a biplot method. A biplot method shouldn't be too

Hello, Can you get eigenvalues in addition to eigevectors using prcomp? If so how? I am unable to use princomp due to small sample sizes. Thank you in advance for your help! Rebecca Young -- Rebecca Young Graduate Student Ecology & Evolutionary Biology, Badyaev Lab University of Arizona 1041 E Lowell Tucson, AZ 85721-0088 Office: 425BSW rlyoung at email.arizona.edu (520) 621-4005

Hello folks, it may be regarded as a user error to scale() your data prior to prcomp() instead of using its 'scale.' argument. However, it is a user thing that may happen and sounds a legitimate thing to do, but in that case predict() with 'newdata' can give wrong results: x <- scale(USArrests) sol <- prcomp(x) all.equal(predict(sol), predict(sol, newdata=x)) ## [1]

Hello, I have a large data set that has more columns than rows (sample data below). I am trying to perform a partitioning cluster analysis and then plot that using pca. I have tried using CLUSPLOT(), but that only allows for 'princomp' where I need 'prcomp' as I do not want to reduce my columns. Is there a way to edit the CLUSPLOT() code to use 'prcomp', please? #

Hello, I have a short question about the prcomp function. First I cite the associated help page (help(prcomp)): "Value: ... SDEV the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix). ROTATION the matrix of variable loadings

[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

Hi ! I've used the example given in the documentation for the prcomp function both in R and SPAD to compare the results obtained. Surprisingly, I do not obtain the same results for the coordinates of the principal composantes with these two softwares. using USArrests data I obtain with R : > summary(prcomp(USArrests)) Importance of components: PC1 PC2

(all in subject). If I want to do a PC analysis in a situation with missing data, I may want to have same flexibility as with "cor(.)", e.g., I may want princomp(x, ..., use.obs = "pairwise.complete") Actually, I may want even more flexibility. Currently, princomp(.) has if (cor) cv <- get("cor", envir = .GlobalEnv)(z) else cv <-

Dear friends, I am beginning to use R software in my academic research and I'm having some problems regarding the use of PCA. I have a table with 24445 rows and 9 columns, and I used the function prcomp() to do the analysis. Working with an example?: x<-read.table("test.txt", header=T) row.names(x)<-x[,1] x<-x[,-1] require(stats) pca<-prcomp(x, scale=T) names(pca) ##

I've noticed that the arguments and result list of prcomp in the mva package (with 61.1) are not quite the same as in the Blue Book and in Splus. Is this intentional or can I change it? If I change it who should I send the code to? Paul Gilbert -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read

hey, im trying hard to display names (given in a seperate variable) instead of "case numbers" in a pca plot (by biplot of a prcomp output). somebody could help me with this? lukas