similar to: Finding code for R functions

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"

Hello, I am trying to use the package "exams" to construct problem sets. I have constructed an exercise which generates a list of integers and asks the student to compute the median. rx is the vector of n numbers >str(rx) num [1:16] 21 9 8 18 4 12 17 2 9 7 ... I want to print out the entire vector as part of the problem. When I use \Sexpr(rx) only the first value (in this case

Hi, I am trying to understand how the biplot.prcomp is constructed so I can manipulate it to emphasise particular observations and reduce the number of variables shown. The prcomp model I have ran has cor=TRUE and scale=TRUE I have worked out from looking at str(prcomp.model) that... prcomp.model$x = the observations ploted in the biplot prcomp.model$rotation = the variables that form the

Dear all, I have a very large dataset (1712351 , 20) and would like to plot only the arrows that represent the contribution of each variables. On the sample below I woild like to plot only the explanatory variables (Murder, Assault..) and not the sites. prcomp(USArrests) # inappropriate prcomp(USArrests, scale = TRUE) prcomp(~ Murder + Assault + Rape, data = USArrests, scale = TRUE)

Hello, I did a pca with over 200000 snps for 340 observations (ids). If I plot the eigenvectors (called rotation in prcomp) 2,3 and 4 (e.g. plot (rotation[,2]) I see a strange "column" in my data (see attachment). I suggest it is an artefact (but of what?). Suggestion: I used prcomp this way: prcomp (mat), where mat is a matrix with the column means already substracted followed by a

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)

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

The following seems to be an bug in prcomp(): > test <- ts( matrix( c(NA, 2:5, NA, 7:10), 5, 2)) > test Time Series: Start = 1 End = 5 Frequency = 1 Series 1 Series 2 1 NA NA 2 2 7 3 3 8 4 4 9 5 5 10 > prcomp(test, scale.=TRUE, na.action=na.omit) Erro en svd(x, nu = 0) : infinite or missing values in 'x'

Full_Name: Ryszard Czerminski Version: 1.8.1 OS: GNU/Linux Submission from: (NULL) (205.181.102.120) prcomp(..., scale = TRUE) does not work correctly: $ uname -a Linux 2.4.20-28.9bigmem #1 SMP Thu Dec 18 13:27:33 EST 2003 i686 i686 i386 GNU/Linux $ gcc --version gcc (GCC) 3.2.2 20030222 (Red Hat Linux 3.2.2-5) > a <- matrix(rnorm(6), nrow = 3) > sum((scale(a %*% svd(cov(a))$u, scale

Hi All: I am running prcomp on a very large array, roughly [500000, 3650]. The array itself is 16GB. I am running on a Unix machine and am running ?top? at the same time and am quite surprised to see that the application memory usage is 76GB. I have the ?tol? set very high (.8) so that it should only pull out a few components. I am surprised at this memory usage because prcomp uses the SVD

Hi All: I am running prcomp on a very large array, roughly [500000, 3650]. The array itself is 16GB. I am running on a Unix machine and am running ?top? at the same time and am quite surprised to see that the application memory usage is 76GB. I have the ?tol? set very high (.8) so that it should only pull out a few components. I am surprised at this memory usage because prcomp uses the SVD

To my knowledge, this has not been reported previously, and doesn't seem to have been changed in R-devel or R-patched. If M is a matrix with coloumn names, and mod <- prcomp(M) # or princomp then predicting a single observation (row) with predict() gives the error Error in scale.default(newdata, object$center, object$scale) : length of 'center' must equal the number of

Dear R-Helpers, I suspect I'm about to ask a FAQ, but I haven't been able to find an answer in the FAQ, AItR or an R Site Search. When I look at the methods of summary (below) it says, "Non-visible functions are asterisked". I looked at the help file for summary.princomp, which did not comment on it being non-visible. I ran its help file example, which printed visible output. I

Hi, I'm doing PCA on wide matrices and I don't understand why calling predict.prcomp on it throws an error: > x1 <- matrix(rnorm(100), 5, 20) > x2 <- matrix(rnorm(100), 5, 20) > p <- prcomp(x1) > predict(p, x2) Error in predict.prcomp(p, x2) : 'newdata' does not have the correct number of columns > dim(x2) [1] 5 20 > dim(p$rotation) [1] 20 5

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:

I'm looking for someone to explain the difference between these procedures. The function prcomp() does principal components anaylsis, and the function cmdscale() does classical multi-dimensional scaling (also called principal coordinates analysis). My confusion stems from the fact that they give very similar results: my.d <- matrix(rnorm(50), ncol=5) rownames(my.d) <-

Hi everybody, Excuse me for this silly question, but after searching the help archives I'm still unable to find my way to read the source code of the "biplot" function in R. I have installed the mvbutils package, and tried: > fixr(biplot) which only gives me: function (x, ...) UseMethod("biplot") with no further details.... I'd like to read the code to see if

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]

Full_Name: Juha Heljoranta Version: R 2.1.1 (2005-06-20) OS: Gentoo Linux Submission from: (NULL) (88.112.29.250) prcomp has a bug which causes following error Error in svd(x, nu = 0) : infinite or missing values in 'x' on a valid data set (no Infs, no missing values). The error is most likely caused by the zeros in data. My code and temporary workaround: m = matrix(... ...

Dear all, I've used the 'prcomp' command to calculate the eigenvalues and eigenvectors of a matrix(gg). Using the command 'principal' from the 'psych' package  I've performed the same exercise. I got the same eigenvalues but different eigenvectors. Is there any reason for that difference? Below are the steps I've followed: 1. PRCOMP #defining the matrix