similar to: seeking advice for manipulating matrices to find the difference

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Here's a small R program: --------------------------------------------------------------------------- a <- rep(1,10000000) system.time(a <- a + 1) system.time(for (i in 1:10000000) {a[i] <- a[i] + 1}) --------------------------------------------------------------------------- and here's its matlab version:

Are these 225 compile time regressions real? It sure looks bad! Ciao, Duncan. On 01/12/11 09:39, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/380/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 380

On Fri, Sep 14, 2007 at 11:42:18PM -0700, Tanya Lattner wrote: > The 2.1 pre-release (version 1) is available for testing: > http://llvm.org/prereleases/2.1/version1/ > > [...] > > 2) Download llvm-2.1, llvm-test-2.1, and the llvm-gcc4.0 source. > Compile everything. Run "make check" and the full llvm-test suite > (make TEST=nightly report). > > Send

Hi, there: When plot ann predicted results I need to indicate numbers of inputs for each column. V1 V2 V3 V4 1 86.2700 49.9380 30.7630 0.1327 2 89.5127 55.9707 33.7683 0.1186 3 91.1833 58.4670 34.5610 0.1134 matplot(t, pch = 1:4, type = "o", col = rainbow(ncol(t)),xlab = "No. of inputs ", ylab = "Mean of 6 Datasets",

Hi, R-users, data is a matrix like this AMR BS GE HR MO UK SP500 1974 -0.3505 -0.1154 -0.4246 -0.2107 -0.0758 0.2331 -0.2647 1975 0.7083 0.2472 0.3719 0.2227 0.0213 0.3569 0.3720 1976 0.7329 0.3665 0.2550 0.5815 0.1276 0.0781 0.2384 1977 -0.2034 -0.4271 -0.0490 -0.0938 0.0712 -0.2721 -0.0718 1978 0.1663 -0.0452 -0.0573 0.2751 0.1372 -0.1346

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### The multcomp functions work on "lm" objects as anticipated. They do not work on

This is causing me great consternation, and I've spent too much time floundering around on it. My data is in the form of columns in Excel, with the first column being in m/dd/yyyy hh:mm format. The spreadsheet is complicated (headers, merged cells, lines w/o data); so I've tried various ways of exporting the data into a text file for the R processing - CSV, spaced, etc. For example:

Hi, I want to test for a contrast from a regression where I am regressing the columns of a matrix. In short, the following. X <- matrix(rnorm(50),10,5) Y <- matrix(rnorm(50),10,5) lm(Y~X) Call: lm(formula = Y ~ X) Coefficients: [,1] [,2] [,3] [,4] [,5] (Intercept) 0.3350 -0.1989 -0.1932 0.7528 0.0727 X1 0.2007 -0.8505 0.0520

Hello: In the example below (or for a censored data) using survfit.coxph, can anyone point me to a link or a pdf as to how the probabilities appearing in bold under "summary(pred$surv)" are calculated? Do these represent acumulative probability distribution in time (not including censored time)? Thanks very much, parmee *fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)*

Dear all, The standard call to ARIMA in the base package such as arima(y,c(5,0,0),include.mean=FALSE) gives a full 5th order lag polynomial model with for example coeffs Coefficients: ar1 ar2 ar3 ar4 ar5 0.4715 0.067 -0.1772 0.0256 -0.2550 s.e. 0.1421 0.158 0.1569 0.1602 0.1469 Is it possible (I doubt it but am

Hi, i've been trying to apply the lme apprach to growth curves of children, but lme keeps running for ever and ever as soon as I use a reasonable basis. First Example: Data are 39 boys from the Berkeley growth study, each one measured 31 times at the ages of 1.00 1.25 1.50 1.75 2.00 3.00 4.00 5.00 6.00 7.00 8.00 8.50 9.00 9.50 10.00 10.50 11.00 11.50 12.00 12.50 13.00 13.50

My question is about the "Signif. codes" , the output when I run matcoef =cbind(fit$par, se.coef,tval,2*(1-pnorm(abs(tval)))) dimnames(matcoef)=list(names(tval),c("Estimate","Std.Error","t value","pr(>|t|)")) cat("\nCoefficient(s):\n") printCoefmat(matcoef, digits=4, signif.stars = TRUE) Coefficient(s): Estimate

Hi All, Here is my sample data set.. y x 7/4/2009 -0.2368 -1.2727 7/11/2009 -0.5039 -5.2805 7/18/2009 -0.6655 -6.9641 7/25/2009 -0.3936 -3.6937 8/1/2009 -0.3463 -5.6457 8/8/2009 -0.3000 -1.7368 8/15/2009 0.2378 6.4600 8/22/2009 -0.2962 -3.1113 8/29/2009 -0.4346 -4.2039 9/5/2009 -0.6971 -7.8216 9/12/2009 -0.1217 5.1446 9/19/2009 -0.3107 2.0862 9/26/2009 -0.1797 -3.6055 10/3/2009 0.2299 -0.7373

Hello - I am trying to figure out R's transformation for interaction terms in a linear regression. My simple background understanding is that interaction terms are generally calculated by multiplying the centred (0-mean) variables with each other and then doing the regression. However, in this regard I would have expected to see the same p-value when I calculate summary(lm(Y~A:B)) for A:B

Hi all I have another GLIM question. I have been using R as well as Genstat (version 6) in order to fit GLIM models to the data (displayed below). The same models are fitted but the answers supplied by the two packages are not the same. Why? Can anyone help? A discription of the data and the type of model/s fitted can be found below. Regards Allan The

Target: FreeBSD 7.0-RC1 on amd64. autoconf says: configure:2122: checking build system type configure:2140: result: x86_64-unknown-freebsd7.0 [...] configure:2721: gcc -v >&5 Using built-in specs. Target: amd64-undermydesk-freebsd Configured with: FreeBSD/amd64 system compiler Thread model: posix gcc version 4.2.1 20070719 [FreeBSD] [...] objdir != srcdir, for both llvm and gcc. Release

LLVMers, The 2.2 prerelease is now available for testing: http://llvm.org/prereleases/2.2/ If anyone can help test this release, I ask that you do the following: 1) Build llvm and llvm-gcc (or use a binary). You may build release (default) or debug. You may pick llvm-gcc-4.0, llvm-gcc-4.2, or both. 2) Run 'make check'. 3) In llvm-test, run 'make TEST=nightly report'. 4) When