similar to: "na.action" parameter in princomp() (PR#3481)

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)

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 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) # =^=

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() ##

Dear R users, When I tried to use princomp() from stats packages to do Principal Components Analysis, I am not very clear what is the "scale". And the scores are different from "PROC PRINCOMP" procedure from SAS. Using the example data from this package: restpc <- princomp(USArrests, cor = TRUE) > restpc$scale Murder Assault UrbanPop Rape 4.311735 82.500075

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

Full_Name: Jerome Asselin Version: 1.6.2 OS: RedHat Linux 7.2 Submission from: (NULL) (142.103.173.179) Should be an easy fix... Consider the examble below: plot(0,0) legend(0,0,c("Hello!","Hi!"),pch=1:2,lty=1:2,trace=T) It gives the following trace: > plot(0,0) > legend(0,0,c("Hello!","Hi!"),pch=1:2,lty=1:2,trace=T) xchar= 0.05178 ;

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

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 run example(princomp) I get the following error message: prncmp> ## The variances of the variables in the prncmp> ## USArrests data vary by orders of magnitude, so scaling is appropriate prncmp> (pc.cr <- princomp(USArrests)) # inappropriate Error in cov.wt(z) : 'x' must contain finite values only Seth Roberts -- blog.sethroberts.net www.shangriladiet.com

Full_Name: Jerome Asselin Version: 1.8.0 OS: RedHat Linux 7.2 Submission from: (NULL) (142.103.177.13) I would not expect a segmentation fault; perhaps an error message. > .Call("log") Segmentation fault This is always reproducable for me. Sincerely, Jerome Asselin

Is there a simpler way then the solution to the one that was posted here? I'm not very proficient with legend, and I don't understand this solution. All I have is two or more lines on one plot that I want to put a legend on and I can't figure out how to do it from the examples. Can you give a very simple example? It does not have to be fancy!! I have never worked with a

Dear all, i wonder if there?s a command to obtain the actual values of a principal component or a factor (not as.factor, but factanal) . test=princomp(USArrests, cor = TRUE) summary(test) just outputs, standard deviation, Prop of Variance and cumulative proportion of variance. test$loadings offers yet another proportion of variance scheme. why is that? Apart from that: Is there a

R version: 1.7.1 OS: Red Hat Linux 7.2 In this example, I would expect an error for the overly long variable name. This is always reproducable for me. > formula(paste("y~",paste(rep("x",50000),collapse=""))) Segmentation fault Sincerely, Jerome Asselin -- Jerome Asselin (Jérôme), Statistical Analyst British Columbia Centre for Excellence in HIV/AIDS St.

Full_Name: Jerome Asselin Version: 1.6.1 OS: RedHat Linux 7.2 Submission from: (NULL) (142.103.173.179) Hello, I get a segmentation fault when I run the following code. I wouldn't expect meaningful results because my response variable contains only missing values. However, I would expect something like a regular error (not a segmentation fault). library(survival) data <-

Hi All, would appreciate an answer on this if you have a moment; Is there a function (before I try and write it !) that allows the input of a covariance or correlation matrix to calculate PCA, rather than the actual data as in princomp() Regards Glenn [[alternative HTML version deleted]]

Full_Name: Jerome Asselin Version: 1.6.2 OS: RedHat Linux 7.2 Submission from: (NULL) (142.103.173.179) In the following example, the line type lty=2 does not show properly across the entire line. x <- c(seq(0,.5,.001),seq(.6,1,.1)) y <- rep(1,length(x)) plot(x,y,type="s",lty=2) Sincerely, Jerome Asselin

Full_Name: Jerome Asselin Version: 1.5.1 OS: linux redhat 7.2 Submission from: (NULL) (142.103.173.179) gsub() return different answers depending on how the input variables were created. Here is an example of code that replicates the problem. The vectors y and yy appear to be the same, but gsub() doesn't return the same answer. It should remove all the blanks when I use the vector y, but it

Dear listserv, I am trying to perform a principal components analysis and create an output table of the eigenvalues for the dependent variables. What I want is to see which variables are driving each principal components axis, so I can make statements like, "PC1 mostly refers to seed size" or something like that. For instance, if I try the example from ?prcomp > prcomp(USArrests,

Hi everyone. I performing a simple PCA using the princomp function. Then, I use the biplot function to show it. However, the function use line number to represent samples. I would like to know if there's a way to use a dot (point) instead of the line number when using the biplot function. With regards, Phil -- View this message in context: