Dear R-helpers, I am planning a course on Statistical Computing and Computational Statistics for the Fall semester, aimed at first year Masters students in Statistics. Among the topics that I would like to cover are linear algebra related to least squares calculations, optimization and root-finding, numerical integration, Monte Carlo methods (possibly including MCMC), bootstrap, smoothing and nonparametric density estimation. Needless to say, the software I will be using is R. 1. Does anybody have a suggestion about a book to follow that covers (most of) the topics above at a reasonable revel for my audience? Are there any on-line publicly-available manuals, lecture notes, instructional documents that may be useful? 2. I do most of my work in R using Emacs and ESS. That means that I keep a file in an emacs window and I submit it to R one line at a time or one region at a time, making corrections and iterating as needed. When I am done, I just save the file with the last, working, correct (hopefully!) version of my code. Is there a way of doing something like that, or in the same spirit, without using Emacs/ESS? What approach would you use to polish and save your code in this case? For my course I will be working in a Windows environment. While I am looking for simple and effective solutions that do not require installing emacs in our computer lab, the answer "you should teach your students emacs/ess on top of R" is perfecly acceptable. Thank you for your consideration, and thank you in advance for the useful replies. Have a good day, Giovanni -- Giovanni Petris <GPetris at uark.edu> Department of Mathematical Sciences University of Arkansas - Fayetteville, AR 72701 Ph: (479) 575-6324, 575-8630 (fax) http://definetti.uark.edu/~gpetris/
Hi Giovanni, You may want to consider: "Numerical analysis for statisticians" (Springer) by Ken Lange. We used when I was taking a graduate level (MS and PhD students) course in statistical computing. I really like it and still use it frequently. Ravi. ---------------------------------------------------------------------------- ------- Ravi Varadhan, Ph.D. Assistant Professor, The Center on Aging and Health Division of Geriatric Medicine and Gerontology Johns Hopkins University Ph: (410) 502-2619 Fax: (410) 614-9625 Email: rvaradhan at jhmi.edu Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html ---------------------------------------------------------------------------- -------- -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Giovanni Petris Sent: Friday, April 20, 2007 9:34 AM To: r-help at stat.math.ethz.ch Subject: [R] Suggestions for statistical computing course Dear R-helpers, I am planning a course on Statistical Computing and Computational Statistics for the Fall semester, aimed at first year Masters students in Statistics. Among the topics that I would like to cover are linear algebra related to least squares calculations, optimization and root-finding, numerical integration, Monte Carlo methods (possibly including MCMC), bootstrap, smoothing and nonparametric density estimation. Needless to say, the software I will be using is R. 1. Does anybody have a suggestion about a book to follow that covers (most of) the topics above at a reasonable revel for my audience? Are there any on-line publicly-available manuals, lecture notes, instructional documents that may be useful? 2. I do most of my work in R using Emacs and ESS. That means that I keep a file in an emacs window and I submit it to R one line at a time or one region at a time, making corrections and iterating as needed. When I am done, I just save the file with the last, working, correct (hopefully!) version of my code. Is there a way of doing something like that, or in the same spirit, without using Emacs/ESS? What approach would you use to polish and save your code in this case? For my course I will be working in a Windows environment. While I am looking for simple and effective solutions that do not require installing emacs in our computer lab, the answer "you should teach your students emacs/ess on top of R" is perfecly acceptable. Thank you for your consideration, and thank you in advance for the useful replies. Have a good day, Giovanni -- Giovanni Petris <GPetris at uark.edu> Department of Mathematical Sciences University of Arkansas - Fayetteville, AR 72701 Ph: (479) 575-6324, 575-8630 (fax) http://definetti.uark.edu/~gpetris/ ______________________________________________ R-help at 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.
I really like John Monahan's Numerical Methods of Statistics (Cambridge University Press). As to running/editing R scripts, you may want to look into JGR. The built-in editor is not as "smart" as ESS in some respect, but "smarter" than ESS in others. The only thing that keep me from using it regularly is the fact that it won't take arguments to R itself (at least on Windows): I need the --internet2 argument to be able to access the net from R. Andy From: Giovanni Petris> > Dear R-helpers, > > I am planning a course on Statistical Computing and Computational > Statistics for the Fall semester, aimed at first year Masters students > in Statistics. Among the topics that I would like to cover are linear > algebra related to least squares calculations, optimization and > root-finding, numerical integration, Monte Carlo methods (possibly > including MCMC), bootstrap, smoothing and nonparametric density > estimation. Needless to say, the software I will be using is R. > > 1. Does anybody have a suggestion about a book to follow that covers > (most of) the topics above at a reasonable revel for my audience? > Are there any on-line publicly-available manuals, lecture notes, > instructional documents that may be useful? > > 2. I do most of my work in R using Emacs and ESS. That means that I > keep a file in an emacs window and I submit it to R one line at a > time or one region at a time, making corrections and iterating as > needed. When I am done, I just save the file with the last, > working, correct (hopefully!) version of my code. Is there a way of > doing something like that, or in the same spirit, without using > Emacs/ESS? What approach would you use to polish and save your code > in this case? For my course I will be working in a Windows > environment. > > While I am looking for simple and effective solutions that do not > require installing emacs in our computer lab, the answer "you > should teach your students emacs/ess on top of R" is perfecly > acceptable. > > > Thank you for your consideration, and thank you in advance for the > useful replies. > > Have a good day, > Giovanni > > -- > > Giovanni Petris <GPetris at uark.edu> > Department of Mathematical Sciences > University of Arkansas - Fayetteville, AR 72701 > Ph: (479) 575-6324, 575-8630 (fax) > http://definetti.uark.edu/~gpetris/ > > ______________________________________________ > R-help at 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. > > >------------------------------------------------------------------------------ Notice: This e-mail message, together with any attachments,...{{dropped}}
> 2. I do most of my work in R using Emacs and ESS. That means that I > keep a file in an emacs window and I submit it to R one line at a > time or one region at a time, making corrections and iterating as > needed. When I am done, I just save the file with the last, > working, correct (hopefully!) version of my code. Is there a way of > doing something like that, or in the same spirit, without using > Emacs/ESS? What approach would you use to polish and save your code > in this case? For my course I will be working in a Windows > environment. > > While I am looking for simple and effective solutions that do not > require installing emacs in our computer lab, the answer "you > should teach your students emacs/ess on top of R" is perfecly > acceptable. >TINN-R (http://www.sciviews.org/Tinn-R/) could be an alternative for Emacs. But hen you would still have to install it on each computer. And there still is the build-in code editor. Cheers, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Reseach Institute for Nature and Forest Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx op inbo.be www.inbo.be Do not put your faith in what statistics say until you have carefully considered what they do not say. ~William W. Watt A statistical analysis, properly conducted, is a delicate dissection of uncertainties, a surgery of suppositions. ~M.J.Moroney
--- Ravi Varadhan <rvaradhan at jhmi.edu> wrote:> Hi Giovanni, >I have been quite satisfied with Tinn-R (http://www.sciviews.org/Tinn-R/ ) in a Windows environment. It is small fast and I can run both it and R from a USB if I need a portable setup.> 2. I do most of my work in R using Emacs and ESS. > That means that I > keep a file in an emacs window and I submit it to > R one line at a > time or one region at a time, making corrections > and iterating as > needed. When I am done, I just save the file with > the last, > working, correct (hopefully!) version of my code. > Is there a way of > doing something like that, or in the same spirit, > without using > Emacs/ESS? What approach would you use to polish > and save your code > in this case? For my course I will be working in > a Windows > environment. > > While I am looking for simple and effective > solutions that do not > require installing emacs in our computer lab, the > answer "you > should teach your students emacs/ess on top of R" > is perfecly > acceptable. > > > Thank you for your consideration, and thank you in > advance for the > useful replies. > > Have a good day, > Giovanni > > -- > > Giovanni Petris <GPetris at uark.edu> > Department of Mathematical Sciences > University of Arkansas - Fayetteville, AR 72701 > Ph: (479) 575-6324, 575-8630 (fax) > http://definetti.uark.edu/~gpetris/ > > ______________________________________________ > R-help at 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. > > ______________________________________________ > R-help at 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. >
On 4/20/2007 9:34 AM, Giovanni Petris wrote:> Dear R-helpers, > > I am planning a course on Statistical Computing and Computational > Statistics for the Fall semester, aimed at first year Masters students > in Statistics. Among the topics that I would like to cover are linear > algebra related to least squares calculations, optimization and > root-finding, numerical integration, Monte Carlo methods (possibly > including MCMC), bootstrap, smoothing and nonparametric density > estimation. Needless to say, the software I will be using is R. > > 1. Does anybody have a suggestion about a book to follow that covers > (most of) the topics above at a reasonable revel for my audience? > Are there any on-line publicly-available manuals, lecture notes, > instructional documents that may be useful?After you're done the course, please write a review of whatever book you choose. I think a lot of people would be interested.> 2. I do most of my work in R using Emacs and ESS. That means that I > keep a file in an emacs window and I submit it to R one line at a > time or one region at a time, making corrections and iterating as > needed. When I am done, I just save the file with the last, > working, correct (hopefully!) version of my code. Is there a way of > doing something like that, or in the same spirit, without using > Emacs/ESS? What approach would you use to polish and save your code > in this case? For my course I will be working in a Windows > environment. > > While I am looking for simple and effective solutions that do not > require installing emacs in our computer lab, the answer "you > should teach your students emacs/ess on top of R" is perfecly > acceptable.The Windows GUI has a simple editor built in, that allows the work flow you want (but it doesn't have all the bells and whistles of ESS). I'd recommend using it if you want simple installation: it's just there. There are a couple of shareware/freeware editors (WinEDT, Tinn-R) that have hooks to R. WinEDT also has support for TeX/LaTeX; if that's important to you, it might be worth the cost/effort to install. I'm less familiar with Tinn-R, but I believe it's free, whereas WinEDT is not. If you want your students to link compiled C/C++/Fortran code to R, you'll need to install a number of tools that don't normally come with Windows. See the R Admin manual or www.murdoch-sutherland.com/Rtools. Duncan Murdoch
Giovanni Petris <GPetris at uark.edu> wrote:> > 2. I do most of my work in R using Emacs and ESS. That means that I > keep a file in an emacs window and I submit it to R one line at a > time or one region at a time, making corrections and iterating as > needed. When I am done, I just save the file with the last, > working, correct (hopefully!) version of my code. Is there a way of > doing something like that, or in the same spirit, without using > Emacs/ESS? What approach would you use to polish and save your code > in this case? For my course I will be working in a Windows > environment.I second the recommendation of Tinn-R. It is quite a good editor, with many R-specific features (including sending R lines, blocks, or files of code). It will be considerably easier for your students to install and learn than Emacs. -- Mike Prager, NOAA, Beaufort, NC * Opinions expressed are personal and not represented otherwise. * Any use of tradenames does not constitute a NOAA endorsement.
Giovanni Petris wrote:> Dear R-helpers, > > I am planning a course on Statistical Computing and Computational > Statistics for the Fall semester, aimed at first year Masters students > in Statistics. Among the topics that I would like to cover are linear > algebra related to least squares calculations, optimization and > root-finding, numerical integration, Monte Carlo methods (possibly > including MCMC), bootstrap, smoothing and nonparametric density > estimation. Needless to say, the software I will be using is R. > > 1. Does anybody have a suggestion about a book to follow that covers > (most of) the topics above at a reasonable revel for my audience? > Are there any on-line publicly-available manuals, lecture notes, > instructional documents that may be useful? >The course notes for `Advanced Statistical Computing' by Robert Gray covers much of the topics you mentioned and is interspersed with R (1.4.0) code. http://www.stat.wisc.edu/~mchung/teaching/stat471/stat_computing.pdf HTH, Tobias -- Tobias Verbeke - Consultant Business & Decision Benelux Rue de la r?volution 8 1000 Brussels - BELGIUM +32 499 36 33 15 tobias.verbeke at businessdecision.com
> 2. I do most of my work in R using Emacs and ESS. That means that I > keep a file in an emacs window and I submit it to R one line at a > time or one region at a time, making corrections and iterating as > needed. When I am done, I just save the file with the last, > working, correct (hopefully!) version of my code. Is there a way of > doing something like that, or in the same spirit, without using > Emacs/ESS? What approach would you use to polish and save your code > in this case? For my course I will be working in a Windows > environment.I do this with kate on linux. Kate has a konsole window in which I run R, and then pipe the lines from the editor to konsole. You can easily define a shortcut key to pipe the lines/regions to konsole. Vikas