similar to: Errors in Compiling R

Has anyone written a function that returns all possible subsets of r elements out of a vector that contains n elements? I have not been able to find an answer in the FAQ or the archives. Say I have a vector: letters[1:10]. I want to get a matrix of 120 rows that contains all possible subsets of 3 letters out of the 10. And I would like to be able to generalize to any r out of n, like

This question is about compiling R-1.6.2 from source on a Sparcstation-20 machine running Solaris 7, gcc-2.95.3 and readline-4.2 (both gcc and readline from sunfreeware.com). I searched the r-help archive and the FAQ. The errors seem to be related to the binary readline libraries, and I have /usr/local/lib/libreadline.a,libreadline.so@, and libreadline.so.4. The attached errors are from

I remember reading about post hoc statistical power on R-help. But I can't seem to find them with RSiteSearch("post hoc statistical power") and variations of it. I would like to learn more about post hoc statistical power, its meaningfulness, advantages and disadvantages. I thought the issue was settled after Tukey's 1993 paper about post hoc statistical power being

In the hypothetical example below, how do I add two segments() into the two panels, respectively? Say segments(x0=5, y0=10, x1=5, y1=20) on the left and segments(x0=15, y0=-10, x1=15, y1=-2) on the right? Many thanks in advance, Yuelin Li. ps. part of the code came from a solution given by Deepayan Sarkar. ------------------- library(lattice) set.seed(12345) x <- 0:20 y.male.obs <- - 1.2

This is a question about removing the intercept in a binomial glm() model with categorical predictors. V&R (3rd Ed. Ch7) and Chambers & Hastie (1993) were very helpful but I wasn't sure I got all the answers. In a simplistic example suppose I want to explore how disability (3 levels, profound, severe, and mild) affects the dichotomized outcome. The glm1 model (see below) is

Is there a function that calculates the power of a repeated-measure ANOVA design, e.g., 2 groups, 4 within-subject factors, an average 0.40 correlation between the 4 within factors, etc. I don't think I can use power.anova.test() because it does not consider corr=0.40. I am hoping that someone has already implemented the method by Muller & Barton (1989, JASA 84: 549-555) on how to

On a ubuntu linux computer (Feisty, i386), I compile R and additional packages from source. The compiler is gcc 4.1.2. The problem is, I can run "sudo R" and successfully compile all packages (e.g., MASS, lattice) except rggobi. The error seems to be in display.c. My ggobi is in /usr/local/, which R can find. I don't think this is a dependence issue because install.packages(...,

I just successfully compiled R-1.6.0 on a Red Hat 7.2 machine. When I tried to install "grid" from source, I got this error. Any suggestions? I don't think it is a dependency problem. I also attach the output from library(). Yuelin Li. ------------- > install.packages("grid") trying URL `http://cran.r-project.org/src/contrib/PACKAGES' Content type

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

For those new to this list, I point out that Yuelin Li and I have drafted an introduction to R for psychology, trying to emphasize the things the psychologists usually do. It is at http://www.psych.upenn.edu/~baron/rpsych.pdf and http://www.psych.upenn.edu/~baron/rpsych.htm Because it is a draft, with frequent changes, we have not yet asked for it to be placed in the CRAN page. We welcome

I am trying to build R with 64bit support so I can access more than 2GB of memory on an IBM running AIX. (IBM NightHawk II RS/6000 node with 16 64-bit, 375-MHz POWER3 CPUs and 32 GB of memory, if you want specifics) The 32bit install required little tweaking, but the 64 bit is sufficiently complicated that I thought I would report my findings here and ask for help with the latest (and hopefully

Why is as.numeric("3/2") NA plus a warning? Thanks, Mike. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch

I have been trying to analyse an unreplicated repeated measures 2-level factorial design with 11 factors using aov() with Error(), similar to that described in "Notes on the use of R for psychology experiments and questionnaires" by Jonathan Baron and Yuelin Li (see the example of Hay's) I have found that there seems to be a limit, in the number of factors in Error() . For

I forgot to mention that this analysis was carried out using the Windows version . Perhaps this problem is windows specific. Peter Peter Ho wrote: > Prof. Ripley, > > I am sending this to you again, as the first one was undelivered. > > > Peter > > ripley at stats.ox.ac.uk wrote: > > >>> There is no intended limit, but there was a bug fix in 1.5.0. If

When building the X11 modules under HPUX 11.23 I get the following errors ld: Unsatisfied symbol "Rf_isNull" in file dataentry.lo ld: Unsatisfied symbol "Rf_length" in file dataentry.lo ld: Unsatisfied symbol "Rf_warningcall" in file devX11.lo ld: Unsatisfied symbol "UNIMPLEMENTED" in file dataentry.lo ld: Unsatisfied symbol "R_alloc" in file

Hi, I have sort of a newbie question. I've seriously put a lot of effort into how to handle simple replicates in a repeated ANOVA design, but haven't had much luck. I really liked reading "Notes on the use of R for psychology experiments and questionnaires", by Jonathan Baron and Yuelin Li ( http://www.psych.upenn.edu/~baron/rpsych/rpsych.html ) but still didn't run across

We now have a draft of: Notes on the use of R for psychology experiments and questionnaires by Jonathan Baron and Yuelin Li at http://www.psych.upenn.edu/~baron/rpsych.htm and http://www.psych.upenn.edu/~baron/rpsych.pdf and http://www.psych.upenn.edu/~baron/rpsych.tex It is intended for students and others who are doing research in psychology. What makes it "for psychology" is that

Yuelin Li and I have made some revisions in our introductory document, "Notes on the use of R for psychology experiments and questionnaires," which is still available through my R page (below) in html or pdf. We fixed typos. We updated and organized the list of commands. ("Re"-organized isn't quite right.) We made a few other additions throughout. And we revised the

A question about comparing symptom reduction over time and across treatment groups. Each respondent is asked twice if they experience symptoms (coded 1), at baseline and then at 6 months later. They are randomly assigned to either the control or the intervention group. The 2x2x2 table below shows the frequency counts. As can be seen in the margins of the table, 21% of the control group

How can I get the log odds associated with the levels in strata() within a clogit() model? I'm running R-1.9.0 on a Linux platform. I am using clogit() to run a Rasch model in Item Response Theory in psychometrics. Symbolically, the model is: logit(p_{j,k}) = \log({\Pr(p_{j,k}) \over \Pr(1-p_{j,k})}) = \theta_j - \alpha_k, That is, the log odds of answering an test item correctly is