Re: [R] New project: littler for GNU R
On 9/26/06, Seth Falcon [EMAIL PROTECTED] wrote: Wow, looks neat. OS X users will be unhappy with your naming choice as the default filesystem there is not case-sensitive :-( IOW, r and R do the same thing. I would expect it to otherwise work on OS X so a change of some sort might be worthwhile. Installing as 'littler' on OS X might be a reasonable solution. Then again, adapting /usr/bin/R to have a python-style -c switch might be the best long-term solution for R 2.5+. Chris, waiting for apt-get install littler to work :-) __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Ubuntu and R
On 2/16/06, Clint Harshaw [EMAIL PROTECTED] wrote: I've recently installed Ubuntu 5.10 on a desktop and need R installed, however, even after uncommenting the repos associated with universe, backports and multiverse, the packages available for Ubuntu are somewhat out of date: [EMAIL PROTECTED]:~$ apt-cache policy r-base r-base-core r-base: Installed: (none) Candidate: 2.1.1-1 Version table: 2.1.1-1 0 500 http://archive.ubuntu.com breezy/universe Packages r-base-core: Installed: (none) Candidate: 2.1.1-1 Version table: 2.1.1-1 0 500 http://archive.ubuntu.com breezy/universe Packages How should I edit my /etc/apt/sources.list so that I can proplery maintain a current version of R, and not break my system? I've searched the forums at Ubuntu, and there are several similar requests there, but no definitive answer that I found. What are other Ubuntu users here doing to keep their version of R fresh? I suspect the 2.2.x packages from Debian testing and/or unstable would run fine on breezy (I don't think there's been any libc6 changes that would affect things); you could always rebuild from the Debianized sources for Ubuntu if they don't. You could use apt pins to make sure that only the R packages from Debian are pulled in, if you want to use apt to keep it up to date from Debian's archive. Something like the following in /etc/apt/preferences should work: Package: r-* Pin: release o=Debian Pin-Priority: 500 Package: * Pin: release o=Debian Pin-Priority: -1 Then add a line for the Debian mirror of your choice to /etc/apt/sources.list, using either testing or unstable as your release. Chris __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Factor analysis with dichotomous variables
On Fri, 17 Dec 2004 13:07:08 -0500, Doran, Harold [EMAIL PROTECTED] wrote: You can use factanal to do the analysis. The polychor() package will give you polychorics. You can then the do the factor analysis on this correlation matrix. -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Tom Denson Sent: Friday, December 17, 2004 12:31 PM To: [EMAIL PROTECTED] Subject: [R] Factor analysis with dichotomous variables Hello, I would like to conduct an exploratory factor analysis with dichotomous data. Do any R routines exist for this purpose? I recall reading something about methods with tetrachoric correlations. Any help would be appreciated. You may also want to consider the routines in MCMCpack (MCMCordfactanal and MCMCmixfactanal), depending on your application. Chris -- Chris Lawrence - http://blog.lordsutch.com/ __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] FIML in lme
On Aug 27, Douglas Bates wrote: F Z wrote: I was asked if lme can use FIML (Full Information Maximum Likelihood) instead of REML or ML but I don't know the answer. Does anybody know if this is implemented in R? To the best of my knowledge, FIML is ML so the answer is yes. For example, the phrase Full Information Maximum Likelihood is used in Singer and Willett (2004) Applied Longitudinal Data Analysis (Oxford University Press) as a synonym for maximum likelihood. I have seen FIML used to refer to a type of ML estimation where a missing data treatment is included in the estimation procedure (parameter estimates are derived from incomplete cases for only the variables present in the case, rather than simply discarding the cases), at least in the latent-variable SEM context, specifically in AMOS. This may be what Francisco is getting at. To my knowledge, no R packages implement this sort of FIML, for any class of models, although there are other available missing data treatments (EM, MCMC estimation). Chris -- Christopher N. Lawrence, Ph.D. Visiting Assistant Professor of Political Science Millsaps College 1701 N. State St Jackson, MS 39210 (601) 974-1438 / [EMAIL PROTECTED] __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Stepwise Regression and PLS
Jinsong Zhao wrote: Do you mean different procedures will provide different results? Maybe I don't understand your email correctly. Now, I just hope I could get a reasonable linear model using stepwise method in R, but I don't know how to deal with collinear problem. What Dr. Harrell means (in part) is that stepwise regression leads to models that often overfit the observed data pattern--i.e. models that are not generalizable. More elaboration can be found here (including comments from Dr. Harrell): http://www.gseis.ucla.edu/courses/ed230bc1/notes4/swprobs.html Key quote: Personally, I would no more let an automatic routine select my model than I would let some best-fit procedure pack my suitcase. The bottom line advice here would be: don't use stepwise regression. Peter Kennedy, in A Guide to Econometrics (pp. 187-89) suggests the following options for dealing with collinearity: 1. Do nothing. The main problem in OLS when variables are collinear is that the estimated variances of the parameters are often inflated. 2. Obtain more data. 3. Formalize relationships among regressors (for example, in a simultaneous equation model). 4. Specify a relationship among the *parameters*. 5. Drop one or more variables. (In essence, a subset of #4 where coefficients are set to zero.) 6. Incorporate estimates from other studies. (A Bayesian might consider using a strong prior.) 7. Form a principal component from the variables, and use that instead. 8. Shrink the OLS estimates using the ridge or Stein estimators. Hope this helps. Chris -- Dr. Chris Lawrence [EMAIL PROTECTED] - http://blog.lordsutch.com/ __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Support for Bayesian statistics in R
On Aug 10, Kevin S. Van Horn wrote: I'm just starting to learn to use R, and although I'm seeing lots of functions aimed at doing orthodox statistical analyses, I don't see the same for Bayesian analyses. What support does R have for Bayesian statistics? There are several packages on CRAN that support various Bayesian techniques. I've had considerable success with Martin and Quinn's MCMCpack which includes formulations of a number of common--and some relatively uncommon--models in the social sciences (also requires the coda package), but I believe there are several others as well. You can also interface with the separate BUGS/WinBUGS system (which uses an R-like syntax for its own programming) if you need to do anything that isn't canned already. See http://scythe.wustl.edu/mcmcpack.html for MCMCpack, or search the packages listing at http://cran.r-project.org/ for the word Bayes. Chris -- Chris Lawrence [EMAIL PROTECTED] - http://blog.lordsutch.com/ Computer Systems Manager, Physics and Astronomy, Univ. of Mississippi 125B Lewis Hall - 662-915-5765 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
