similar to: faraway tutorial: cryptic command to newbie

am I right in the assumption, that for calculation of the condition numbers I have to use the correlation matrix of X, and not t(x) %*% x? > e <- eigen(t(x) %*% x) better (x must not have a first column of ones): > e <- eigen(cor(x)) > e$val [1] 6.6653e+07 2.0907e+05 1.0536e+05 1.8040e+04 2.4557e+01 2.0151e+00 > sqrt(e$val[1]/e$val) [1] 1.000 17.855 25.153 60.785 1647.478

Christoph -- There was just a thread on this earlier this week. You can search in the archives for the title: "refitting lm() with same x, different y". (Actually, it doesn't turn up in the R site search yet, at least for me. But if you just go to the archive of recent messages, available through CRAN, you can search on refitting and find it. The original post was from William

Hi I need to overlay two pixmaps (library (pixmap)). One, a pixmapGrey, is the basis, and on this I need to overlay a pixmapIndexed, BUT: the pixmapIndexed has set only some of its "pixels" to an indexed color, many of its pixels should not cover the basis pixmapGrey pixel, means, for this "in pixmapIndexed not defined pixels" it should be transparent. What would you

I hope this question is adequate for this list I use the nnet code from V&R p. 348: The very nice and general function CVnn2() to choose the number of hidden units and the amount of weight decay by an inner cross-validation- with a slight modification to use it for classification (see below). My data has 2 classes with unequal size: 45 observations for classI and 116 obs. for classII With

Full_Name: José Otero Version: Version 1.5.0 (2002-04-29) OS: Redhat 7.3 Submission from: (NULL) (192.187.16.164) Hi: Installation of package faraway as root, from tarbal: R CMD INSTALL ./faraway.tar.gz ERROR: cannot extract package from './faraway.tar.gz' idem, from zipped package: R CMD INSTALL faraway.zip gzip: faraway.zip has more than one entry--rest ignored ERROR: cannot

Sorry, I am sure, this must be documented somewhere (but there are that many docs and tutorials to scan for topics..., actually a great thing... but if you are in a hurry..): I want to save a plot as .ps (or .eps): > postscript("plot1.eps", horizontal=FALSE, onefile=FALSE,height=8,width=8,pointsize=10) > plot(hpfit$fit,rstudent(hpfit),xlab="Fitted

Let's assume that the columns of the model matrix, apart perhaps from an initial column that corresponds to the overall mean, have been centred. Then: 1) Normal equation methods give an accurate fit to the matrix of centred sums of squares and products. 2) QR methods give an accurate fit to the predicted values. QR will give better precision than normal equation methods (e.g., Cholesky) if

again, under another subject: sorry, maybe an all too trivial question. But we have power data from J frequency spectra and to have the same range for the data of all our subjects, we just transformed them into % values, pseudo-code: power[i,j]=power[i,j]/sum(power[i,1:J]) of course, now we have a perfect linear relationship in our x design-matrix, since all power-values for each subject sum up

Dear R-help, I would like to be able to run the following code sequence as a source routine. If I paste it into R via the clipboard it works as expected, but if I source the code instead then the last 3 statements fail. I've also tried writing to the file in place of the sink sequence, but that also hits a snag. R 1.6, w98e2, dfr is a data frame containing the content of Julian

Dear R-users, dear Prof. Ripley as package maintainer I tried to investigate the odd error, when I call nnet together with a 'weights' parameter, using the 'fgl' example in V&R p 348 The error I get is: Error in eval(expr, envir, enclos) : Object "w" not found I think it is a kind of scoping problem, but I really cannot see, what the problem exactly is. and

Hi, dear R pros I try to understand eigen(). I have seen, that eigen() gives the eigenvectors normalized to unit length. What shall I do to get the eigenvectors not normalized to unit length? E.g. take the example: A [,1] [,2] V1 0.7714286 -0.2571429 V2 -0.4224490 0.1408163 Calculating eigen(A) "by hand" gives the eigenvectors (example from Backhaus,

Dear R-users I use nnet for a classification (2 classes) problem. I use the code CVnn1, CVnn2 as described in V&R. The thing I changed to the code is: I define the (class) weight for each observation in each cv 'bag' and give the vector of weights as parameter of nnet(..weights = weight.vector...) Unfortunately I get an error during some (but not all!) inner-fold cv runs:

I haven't found a function that directly calculates the matrix inverse, does it exist? Otherwise what would be the fastest way to do it? Patrik Waldmann -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

Hello. I am trying to invert a matrix, and I am finding that I can get different answers depending on whether I set LAPACK true or false using "qr". I had understood that LAPACK is, in general more robust and faster than LINPACK, so I am confused as to why I am getting what seems to be invalid answers. The matrix is ostensibly the Hessian for a function I am optimizing. I want to get

Dear R- users and Helpers: I downloaded the package from www.stat.lsa.umich.edu/~faraway/book and installed it from local zip file. It looked fine. But when I input library(faraway) it showed " Error in library(faraway) : 'faraway' is not a valid package --- installed < 2.0.0? What I used is R 2.0.0 version now. What should I do? Thank you very much. Xin

Dear R-users How can I get the eigenvalues out of an lda analysis? thanks a lot christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

I have a situation in R where I would like to find any x (if one exists) that solves the linear system of equations Ax = b, where A is square, sparse, and singular, and b is a vector. Here is some code that mimics my issue with a relatively simple A and b, along with three other methods of solving this system that I found online, two of which give me an error and one of which succeeds on the

Hi R community! I am analyzing the data set "motorins" in the package "faraway" by using the generalized additive model. it shows the following error. Can some one suggest me the right way? library(faraway) data(motorins) motori <- motorins[motorins$Zone==1,] library(mgcv) >amgam <- gam(log(Payment) ~ offset(log(Insured))+ s(as.numeric(Kilometres)) + s(Bonus) + Make +

Hi there, >library(faraway) >pima pregnant glucose diastolic triceps insulin bmi diabetes age test 1 6 148 72 35 0 33.6 0.627 50 1 2 1 85 66 29 0 26.6 0.351 31 0 >data(pima) >pima pregnant glucose diastolic triceps insulin bmi diabetes age test 1 6 148 72 35 0 33.6

Hello List, I would like to orthonormalize vectors contained in a matrix X taking into account row weights (matrix diagonal D). ie, I want to obtain Z=XA with t(Z)%*%D%*%Z=diag(1) I can do the Gram-Schmidt orthogonalization with subsequent weighted regressions. I know that in the case of uniform weights, qr can do the trick. I wonder if there is a way to do it in the case of non uniform