similar to: Bug in points.formula (PR#6652)

Full_Name: Steven McKinney Version: R 2.8.0 Patched svn rev 46845 OS: powerpc-apple-darwin9.5.0 Submission from: (NULL) (142.103.207.10) <<insert bug report here>> lines.formula() throws an error when subset argument is used but nothing is provided for data argument. Reproduce: x<-1:5 y<-c(1,3,NA,2,5) plot(y~x, type="n") # set up frame lines(y~x,

Here's a simple example: > x <- rnorm(20) > y <- 1 + 10*x + rnorm(20) > plot(y ~ x, subset=2*(1:10),xlab="In",ylab="Out") Error in length(x) == l : comparison (1) is possible only for vector types The problem seems to be in this section of the code for plot.formula: if (!missing(subset)) { s <- eval(m$subset, data, parent.frame()) l

Many thanks to those who answered my question about how to test for the existence of a file: Berwin Turlach, Jari Oksanen, Henrik Bengtsson, Uwe Ligges, and Joerg Maeder. The simplest solution is to use the built-in (!!!) function file.exists() which returns TRUE if the file exists, and FALSE if it doesn't. Like, duhhhhhh!!! It's exactly what I wanted. Special thanks to Berwin Turlach

--fupGvOGOQM Content-Type: text/plain; charset=us-ascii Content-Description: message body and .signature Content-Transfer-Encoding: 7bit There's a simple bug in the handling of the start and offset arguments in glm and glm.fit. The bug exists in the latest development version of R (version information below), but it appears that glm.R has not been touched much lately, so the bug affects at

Hi all I'm getting a NaN returned on using dffits, as explained below. To me, there seems no obvious (or non-obvious reason for that matter) reason why a NaN appears. Before I start digging further, can anyone see why dffits might be failing? Is there a problem with the data? Consider: # Load data dep <-

Hello, I finally run my measurement model in sem - successfully. Now, I am trying to print out the path diagram that is based on the results - but for some reason it's not working. Below is my script - but the problem is probably in my very last line: # ANALYSIS OF ANXIETY, DEPRESSION, AND FEAR - LISREL P.31 library(sem) # Creating the ANXIETY, DEPRESSION, AND FEAR intercorrelation matrix

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =

G'day Erin, On Sat, 16 Dec 2017 08:00:38 -0600 Erin Hodgess <erinm.hodgess at gmail.com> wrote: > I'm in the process of writing a package, and I'm using the lovely "R > Package" book as a guideline. > > However, in the midst of my work, I discovered that I had omitted a > function and am now putting in it the package. Not a problem. But > the

/* Cc: samba list */ Vincent Marty wrote: > Hi Ogi ! > Did you find a solution to the problem you described in a post in > linux.samba in dec 2004 ? > I get the same messages from Fedora Core 3 when accessing to a iMax > under Mac Os 10.3. Hi! Since I started to use Debian kernel 2.6.10 (in unstable), the problem disappeared. Fedora Core 3 uses 2.6.10 too so I don't know

Dear all, While showing some commands of R to my students, I came across the following behaviour of * which surprised me. > set.seed(20) # to make it reproducible # create some objects > z <- matrix(rnorm(6), ncol=2) # 3x2 matrix > x1 <- rnorm(3) # vector of length 3 > y1 <- rnorm(4) #

Dear all, When installing the usual packages that I use, after installing R 3.0.1, I noticed that the installation of some packages that query R about its configuration did not succeed. The problem is exemplified by: berwin at bossiaea:~$ R-3.0.1 CMD config CC /opt/R/R-3.0.1/lib/R/bin/config: 222: .: Can't open /opt/R/R-3.0.1/lib/R/etc/Renviron Prior to R 3.0.1 such commands worked fine:

*Dear experts,* Please excuse me for disturbing... Right now I am struggling with GLM a bit... Would you be so kind to provide me a solution on using nuisance variables. The problem is that I have data on Depression (volumetric measurements of different brain regions) and I want to include age, gender and education as nuisance parameters in the model. In the other words I would like to model the

Hi all, people who don't follow Linux Today regularly may want to check out: http://linuxtoday.com/developer/2003082000626OSSVDV My apologies if this is considered spam. Cheers, Berwin ========================== Full address ============================ Berwin A Turlach Tel.: +61 (8) 9380 3338 (secr) School of Mathematics and Statistics +61

Is there a formal way to prove the need of a mixed model, apart from e.g. comparing the intervals estimated by lmList fit? For example, should I compare (with AIC ML?) a model with seperately (unpooled) estimated fixed slopes (i.e.using an index for each group) with a model that treats this parameter as a random effect (both models treat the remaining parameters as random)? Thank you!

Hey everyone, Thanks for all the input. It's happening again. This time for the packages "DBI", "parallelly", "segmented", "survival", "V8". So, RStudio shows updates for those and updating them via RStudio leads to this output: ``` > install.packages(c("DBI", "parallelly", "segmented", "survival",

A bit of a perennial I'm afraid. My wife has filled up her iPhone 6 with photos, and wants them moved onto my computer. I'm running CentOS 7 patched about 30 minutes ago. Needless to say the computer can't see the data on the iPhone, though it does recognise the phone as an iPhone. Any suggestions (well any that don't involve a steam roller, sledge hammer or GBH to the whole of

I am writing to inquire about a warning message that I receive when trying to model a random slope and intercept at the second level of a 3-level model. Specifically, I am testing a 3-level model in which time (WEEK) is nested in participants (PARTICIP) and participants are nested in dyads (DYADID). The goal is to examine how an interpersonal style (CORUMTO) one week predicts changes in

Dear Berwin, Simply stacking the problems and treating the resulting observations as independent will give you the correct coefficients, but incorrect coefficient variances and artificially zero covariances. The approach that I suggested originally -- testing a linear hypothesis using the coefficient estimates and covariances from the multivariate linear model -- seems simple enough. For

Hi all, I have received the following error from summary.manova: Error in summary.manova(manova.test, test = "Pillai") : residuals have rank 36 < 64 The data is simulated data for 64 variables. The design is a 2*2 factorial with 10 replicates per treatment. Looking at the code for summary.manova, the error involves a problem with qr(). Does anyone have a suggestion as to how to

Thanks, I was getting to try this, but got side tracked by actual work... Your analysis reproduces the SPSS unscaled estimates. It still remains to figure out how Nick got > coefficients(lm(ZDEPRESSION ~ ZMEAN_PA * ZDIVERSITY_PA, data=s1)) (Intercept) ZMEAN_PA ZDIVERSITY_PA ZMEAN_PA:ZDIVERSITY_PA 0.07342198 -0.39650356