similar to: non-ideal behavior in princomp

... 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:

This message is for people who know the mechanics of model.frame within lm. I am fitting lm models for different responses on a data frame which has (at least some) factors. As it happens, some of the responses are NA for some of the factor levels. Logically, lm creates an xlevels list for which the length of levels is different from the original number of levels in the data frame (a

Here is the first one. It concerns the handling of multiple offsets. The following lines creates a list with 3 explanatory variables and one response. > x<-seq(0,1,length=10);y<-sin(x);z<-cos(x); w<-x+y+z+rnorm(x) > data<-list(x=x,y=y,z=z,w=w) A lm is fitted with one explanatory variable and two offsets. So far, so good. >

R version: 1.7.1 OS: Red Hat Linux 7.2 When "covmat" is supplied in princomp(), the output value "center" is all NA's, even though the input matrix was indeed centered. I haven't read anything about this in the help file for princomp(). See code below for an example: pc2$center is all NA's. Jerome Asselin x <- rnorm(6) y <- rnorm(6) X <- cbind(x,y)

I'm attempting to modify how biplot draws its red vectors (among other things). This is how I've started: Biplot <- function(xx, comps = c(1, 2), cex = c(.6, .4)) { ## Purpose: Makes a biplot with princomp() object to not show arrows ## ---------------------------------------------------------------------- ## Arguments: xx is an object made using princomp() ##

Full_Name: Jerome Asselin Version: 1.7.1 OS: Red Hat Linux 7.2 Submission from: (NULL) (24.77.125.119) Setting the parameter na.action=na.omit should remove incomplete records in princomp. However this does not seem to work as expected. See example below. Sincerely, Jerome Asselin data(USArrests) princomp(USArrests, cor = TRUE) #THIS WORKS USArrests[1,3] <- NA princomp(USArrests, cor =

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)

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

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) # =^=

Hi all, I have been using princomp() recently, its very useful indeed, but I have a question about how to specify the rows of data you want it to choose. I have a set of variables relating to bird characteristics and I have been using princomp to produce PC scores from these. However since I have multiple duplicate entries per individual (each bird had a varying number of chicks), I only want

Hi all! I am working with R package cluster and I have a little problem: let's say I have two datasets...first one ("A") is divided into 4 clusters by means of Pam algorythm. Let's say I want to project the second database ("B") onto the Comp.1 X Comp.2 graph, and see where its elements are placed. The two datasets are made of different dim (54x19 and 28x19). I tried

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

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

(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 <-

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, 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

Hello! I am running principle components analysis using princomp function in pacakge psych. mypc <- princomp(mydataforpc, cor=TRUE) Question: I'd like to use pairwise deletion of missing cases when correlations are calculated. I.e., I'd like to have a correlation between any 2 variables to be based on all cases that have valid values on both variables. What should my na.action be in

[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

$ R --version R 1.6.1 (2002-11-01). So I would like to perform principal components analysis on a 16X16 correlation matrix, [princomp(cov.mat=x) where x is correlation matrix], the problem is princomp complains that it is not non-negative definite. I called eigen() on the correlation matrix and found that one of the eigenvectors is close to zero & negative (-0.001832311). Is there any way