Re: [R] Suggestions for statistical computing course
On Fri, 2007-04-20 at 12:13 -0400, Fred Bacon wrote: Ideally, it would work like this: The free VMware player is installed on each of the lab computers. The lab manager uses a licensed copy of VMware Workstation to create a clean image of a computer. You can use the open source QEMU program to create VMware machines. http://fabrice.bellard.free.fr/qemu/ After installing QEMU, the following command creates a machine with 20 Gb disk space, onto which you can load a (licensed!) copy of Windows (or better, Linux :-) ): qemu-img.exe create -f vmdk VMmachine.vmdk 20G The instructor makes a copy of the clean image and installs the necessary software and instructional materials. The instructor can use either the free player or the paid workstation version to do this. After the virtual machine is completed, the image is sent back to the lab where it is made available to the lab computers. If you use the paid workstation version rather than the free player version on the lab computers, then you can use the Snapshot feature to create a consistent image for every student. Every time the virtual machine is shutdown, the system can revert back to the snapshot for the next student. It all depends on your budget. Again, you can do this for free with QEMU, using the -snapshot option. How you handle the OS licensing issue for the guest operating system is up to you. I personally would recommend using Linux, but some of our customers are terrified of anything that doesn't look like a Microsoft OS. The only caveat is the disk space utilization. Having a complete OS image for every student for every class could eat up terabytes of space. But heck, terabyte RAID arrays are readily available these days. Fred -- Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320, Goddard Building (8) T: +61 7 3365 2506 email: S.Blomberg1_at_uq.edu.au The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. __ 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] Suggestions for statistical computing course
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 __ 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] Suggestions for statistical computing course
One additional package you may want to consider is the R2HTML package. If you load this before each lecture/demonstration and use HTMLStart, HTMLplot, and HTMLStop you will end up with a nice html transcript of the entire session (commands and output) that you could place under a course website for the students to refer back to (rather than them having to write down every command you type/paste in). From: [EMAIL PROTECTED] on behalf of Giovanni Petris Sent: Fri 4/20/2007 5:29 PM To: r-help@stat.math.ethz.ch Subject: Re: [R] Suggestions for statistical computing course Thanks to everybody who responded to my query. I got many useful suggestions about books and editors, plus notes and other material online. Summarizing, the books suggested were - Monahan, Numerical Methods of Statistics - Lange, Numerical analysis for statisticians In terms of Editors, TINN-R was mentioned several times, in addition to R's build-in code editor. I will report to the list on my experience in this course and whatever books/tools I will end up using, since there seems to be some interest. Have a good weekend! Giovanni __ 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. [[alternative HTML version deleted]] __ 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] Suggestions for statistical computing course
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: [EMAIL PROTECTED] Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Giovanni Petris Sent: Friday, April 20, 2007 9:34 AM To: r-help@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 [EMAIL PROTECTED] Department of Mathematical Sciences University of Arkansas - Fayetteville, AR 72701 Ph: (479) 575-6324, 575-8630 (fax) http://definetti.uark.edu/~gpetris/ __ 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. __ 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] Suggestions for statistical computing course
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 [EMAIL PROTECTED]
Department of Mathematical Sciences
University of Arkansas - Fayetteville, AR 72701
Ph: (479) 575-6324, 575-8630 (fax)
http://definetti.uark.edu/~gpetris/
__
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.
--
Notice: This e-mail message, together with any attachments,...{{dropped}}
__
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] Suggestions for statistical computing course
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 [EMAIL PROTECTED] 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 __ 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] Suggestions for statistical computing course
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 __ 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] Suggestions for statistical computing course
Giovanni Petris [EMAIL PROTECTED] 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. __ 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] Suggestions for statistical computing course
--- Ravi Varadhan [EMAIL PROTECTED] 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 [EMAIL PROTECTED] Department of Mathematical Sciences University of Arkansas - Fayetteville, AR 72701 Ph: (479) 575-6324, 575-8630 (fax) http://definetti.uark.edu/~gpetris/ __ 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. __ 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. __ 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.
[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 [EMAIL PROTECTED] Department of Mathematical Sciences University of Arkansas - Fayetteville, AR 72701 Ph: (479) 575-6324, 575-8630 (fax) http://definetti.uark.edu/~gpetris/ __ 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] Suggestions for statistical computing course
On Fri, 2007-04-20 at 16:02 +0200, ONKELINX, Thierry wrote: 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. If you want to avoid a complex setup on multiple computers you might try something we did recently for a customer training class. We used VMware to create a virtual machine. Then we installed all of our software on the virtual machine and set up our training materials for the class on it. Then we rented the necessary computers, installed the free VMware player on them, and copied our virtual machine to each computer. This simplified the class setup significantly and guaranteed that we had a uniform, functioning environment for each of the students. We're a small company, but it should be a great solution for university computer labs. The instructor could set up the environment for his class separately from all other courses, and push it out to the computer lab. A student comes in, opens the virtual machine for his course, and has a clean sandbox to work in. Ideally, it would work like this: The free VMware player is installed on each of the lab computers. The lab manager uses a licensed copy of VMware Workstation to create a clean image of a computer. The instructor makes a copy of the clean image and installs the necessary software and instructional materials. The instructor can use either the free player or the paid workstation version to do this. After the virtual machine is completed, the image is sent back to the lab where it is made available to the lab computers. If you use the paid workstation version rather than the free player version on the lab computers, then you can use the Snapshot feature to create a consistent image for every student. Every time the virtual machine is shutdown, the system can revert back to the snapshot for the next student. It all depends on your budget. How you handle the OS licensing issue for the guest operating system is up to you. I personally would recommend using Linux, but some of our customers are terrified of anything that doesn't look like a Microsoft OS. The only caveat is the disk space utilization. Having a complete OS image for every student for every class could eat up terabytes of space. But heck, terabyte RAID arrays are readily available these days. Fred -- --- Fred Bacon Phone: 978 663-9500 x 273 Aerodyne Research, Inc. FAX: 978 663-4918 --- Where is human nature so weak as in the bookstore? -- Henry Ward Beecher __ 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] Suggestions for statistical computing course
Dear Duncan and Giovanni, -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Duncan Murdoch Sent: Friday, April 20, 2007 10:13 AM To: Giovanni Petris Cc: r-help@stat.math.ethz.ch Subject: Re: [R] Suggestions for statistical computing course On 4/20/2007 9:34 AM, Giovanni Petris wrote: . . . 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. Tinn-R is indeed free and also has some support for LaTeX. Information is available at http://www.sciviews.org/Tinn-R/. Regards, John __ 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] Suggestions for statistical computing course
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 révolution 8 1000 Brussels - BELGIUM +32 499 36 33 15 [EMAIL PROTECTED] __ 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] Suggestions for statistical computing course
Thanks to everybody who responded to my query. I got many useful suggestions about books and editors, plus notes and other material online. Summarizing, the books suggested were - Monahan, Numerical Methods of Statistics - Lange, Numerical analysis for statisticians In terms of Editors, TINN-R was mentioned several times, in addition to R's build-in code editor. I will report to the list on my experience in this course and whatever books/tools I will end up using, since there seems to be some interest. Have a good weekend! Giovanni __ 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.
