similar to: prcomp & princomp - revised

I've been working on a revised version of prcomp and princomp. Below is my current draft of prcomp, which is marginally different from V&R. I've added center and scale as optional arguments. However, scale causes the following: > zi _ prcomp(iris[,,2]) Warning: ignored non function "scale" because scale is both a variable and a function. Is there any way to avoid this

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 call to sweep in this function which was working in 0.62.2 is giving me trouble in 62.3: prcomponents <- function(x, center=TRUE, scale=TRUE, N=nrow(x)-1) {if (center) center <- apply(x,2,mean) else center <- rep(0, ncol(x)) if (scale) scale <- sqrt(apply(x,2,var)) else scale <- rep(1, ncol(x)) s <- svd(sweep(sweep(x,2, center),2, scale,

Hi, I run the example for princomp for R211 I got the following error for biplot > ## The variances of the variables in the > ## USArrests data vary by orders of magnitude, so scaling is appropriate > (pc.cr <http://pc.cr> <- princomp(USArrests)) # inappropriate Erreur dans cov.wt(z) : 'x' must contain finite values only > princomp(USArrests, cor = TRUE) # =^=

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

Per the discussion about the problems with prcomp() when n << p, which boils down to a problem with svd() when n << p, here is a patch to prcomp() which substitutes La.svd() instead of svd(). -Greg (This is really a feature enhancement, but submitted to R-bugs to make sure it doesn't get lost. ) *** R-1.6.0/src/library/mva/R/prcomp.R Mon Aug 13 17:41:50 2001 ---

(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 <-

... I checked and Brian and I are both right (see bottom for prior mail exchange). Let me explain: ============================================================= 1. Indeed, in principle, princomp allows data matrices with are wider than high. Example: > x1 [,1] [,2] [,3] [,4] [1,] 1 1 2 2 [2,] 1 1 2 2 > princomp(x1) Call: princomp(x = x1) Standard deviations:

... I checked and Brian and I are both right (see bottom for prior mail exchange). Let me explain: ============================================================= 1. Indeed, in principle, princomp allows data matrices with are wider than high. Example: > x1 [,1] [,2] [,3] [,4] [1,] 1 1 2 2 [2,] 1 1 2 2 > princomp(x1) Call: princomp(x = x1) Standard deviations:

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

Full_Name: Gavin Simpson Version: 2.5.0 OS: Linux (FC5) Submission from: (NULL) (128.40.33.76) Screeplots are a common plot-type used to interpret the results of various ordination methods and other techniques. A number of packages include ordination techniques not included in a standard R installation. screeplot() works for princomp and prcomp objects, but not for these other techniques as it

How does one rotate the loadings from a principal component analysis? Help on function prcomp() from package mva mentions rotation: Arguments retx a logical value indicating whether the rotated variables should be returned. Values rotation the matrix of variable loadings (i.e., a matrix whose olumns contain the eigenvectors). The function princomp returns this in the element

Dear R-list, Among the (R-)tools, I've seen on the net, for (bivariate) Principal Component scatter plots (+histograms), "plotpc" [1] is the one I like most. By default it performs PCA on a bivariate dataset based on R's "princomp()" (which is the eigenvector-based algebraic solution to PCA). I would like to modify "plotpc()" in order be able, as an

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, I think that the documentation for the biplot function `biplot.princomp' is inconsistent with what it actually does. Here is what the documentation states: pc.biplot If true, use what Gabriel (1971) refers to as a "principal component biplot", with lambda = 1 and observations scaled up by sqrt(n) and variables scaled down by sqrt(n). Then inner products between

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

I am working with data sets in which the number and order of columns may vary, but each column is uniquely identified by its name. E.g., one data set might have columns MW logP Num_Rings Num_H_Donors while another has columns Num_Rings Num_Atoms Num_H_Donors logP MW I would like to be able to perform a principal component analysis (PCA) on one data set and save the PCA object to

[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'm trying to make a 3d plot showing a point cloud, the corresponding data ellipse and the principal axes of the ellipse as vectors. library(rgl) data(trees) cov <- cov(trees) mu <- mean(trees) plot3d(trees, type="s", size=0.5, col="blue", cex=2) In this step, an extra box is added. I've tried using box=FALSE, but it has no effect. # how to avoid the

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