similar to: [Fwd: Libraries loading, but not really?] - it really IS a problem :-(

I'm a newbie at R, and can't get libraries to really work. I did this: > library(help = mva) cancor Canonical Correlations cmdscale Classical (Metric) Multidimensional Scaling dist Distance Matrix Computation hclust Hierarchical Clustering kmeans K-Means Clustering prcomp

Peter, As I understand your Q. You probably have data that is similar to each other like stock Prices for all RHS variable. In that case the difference between corr and cov is not significant; however, if your RHS contains totally dissimilar variables it matters a great deal. If x1 income, x2 job type, x3 Education level, etc..., then taking cov of these variables would not be desireable

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an

Hello, I'm a beginning user of R, now I have a question about canonical correlation analysis (cca). In R,there is a function "cancor.R" used for cca; For example X(n*p1) and Y(n*p2)are the two matrix to be analyzed. In the example given by R, when n> max(p1,n2), cancor(X,Y) works; but when n<p1 or n<p2, cancor(X,Y) doesn't work well because cancor$cor == 1; how to cope

Dear R's! I am strugling with cancor procedure in R. I cannot figure out the meaning of xcoef and of yxcoef. Are these: 1. standardized coefficients 2. structural coefficients 3. something else? I have tried to simulate canonical correlation analysis by checking the eigenstructure of the expression: Sigma_xx %*% Sigma_xy %*% Sigma_yy %*% t(Sigma_xy). The resulting eigenvalues were the same

Hi, from what I understand about the canonical correlation function 'cancor', it looks for correlations in two sets of variables, each represented in matrix form. Right? Sounds exactly like what I need. I have tried the following but I am not sure how to interpret the results. AudioPCs <- c(ArTHarF0PCA$x[,2], ArTHarF1PCA$x[,2], ArTHarF2PCA$x[,2], ArTHarF3PCA$x[,2],

Hi All, We're having some problems using Samba and supposedly "standalone" rogue NT servers on one of our network domains. When an authenticated samba-served NT domain user on a client machine tries to access a share on a non BDC/PDC NT server that it part of the same samba domain, the standalone NT server sends it a message saying "Your logon time at NTDOMAIN has

Hi all, I get two different results when using sum() and the switch na.rm. The result is correct when na.rm=FALSE. Linux Redhat 7.3, R version 1.6.1. I've had no luck searching the mail archives, so I was hoping somebody could explain/check this one for me. I will need to apply the function to missing data, simple as it is. Code: x<-matrix(runif(20,0,5)%/%1,4,5) # random matrix

Hi everyone, Can I suggest an additional output component in cancor, from package mva? It would be useful to have the number of canonical correlation vectors, equivalently the rank of the covariance between x and y (label "rank"). This would usually be min(dx, dy), where dx and dy have already been computed for the svd function, but there might be situations where it was less than

Dear all, I didn't find any graphical solution in the package "mva" to plot the canonical scores from a CCA (canonical correlation analysis). Does anybody knows how to plot or has anybody already programmed : - the map of the canonical scores, - the graph of the canonical weights, - the correlation circle i.e. the canonical loadings ? Thank you for help ...

Dear all, I'm trying to get a two dimensional embedding of some data using different meythods, among which princomp(), cmds(), sammon() and isoMDS(). I have a problem with sammon() because the coordinates I get are all equal to NA. What does it mean? Why the method fails in finding the coordinates? Can I do anything to get some meaningful results? Thank you very much Domenico

Dear all I am tryiing to fit the following lme with an ARMA correlation structure: test <- lme(fixed=fev1f~year, random=~1|id2, data=pheno2, correlation=corARMA(value=0.2, form=~year|id2), na.action=na.omit) But I get the following error message: Error in getGroupsFormula.default(correlation, asList = TRUE) : "Form" argument must be a formula I have used this same form

Hello, Is there any possibility in R to see the body of the non-visible function, for example princomp? If I do : > methods(princomp) so, I get that princomp.default and princomp.formula are non-visible functions and body(princomp.default) doesnt show it. In particular, I guess I have a very nave question Id like to see how scores calculation is implemented in the function princomp.

When I use the function cancor of mva package, I found that it doesn't work when one of the matrix has only one column, or both have only one column. The function in Splus 5 with the same name works under those situations. The version of R I am using is 0.63.2 (released on Jan., 1999) on Solaris. Kenny Ye Assistant Professor Department of Applied Math and Statistics SUNY at Stony Brook

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

... 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 R buddies, I’m trying to run Principal Component Analysis, package princomp: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/princomp.html. My question is: why do I get different results with pca = princomp (x, cor = TRUE) and pca = princomp (x, cor = FALSE) even when I standardize variables in my matrix? Best regards, Blaž Simčič [[alternative HTML version deleted]]

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() ##