Shravan Vasishth's Slog (Statistics blog)

How to append R code in appendix using Sweave

2012-01-26T04:39:00.001+01:00

In the statistics course I'm doing, the solutions to assignments have R code, but the instructor doesn't want to see them in the main text but rather in an appendix. Here's apparently how to do that without copying and pasting the code into an appendix (I got this nifty example from here):

\begin{appendix}
\section{Appendix A}
\subsection{R session information}
<<SessionInforamtaion,echo=F,eval=T,results=tex>>=

toLatex(sessionInfo())
@

\subsection{The simulation's source code}

<>=

Stangle(file.path("Projectpath","RnwFile.Rnw"))

SourceCode <- readLines(file.path("Projectpath","Codefile.R"))

writeLines(SourceCode)

@
\end{appendix}

(In-progress) Review of Graduate Certificate in Statistics offered at Sheffield

2011-12-01T07:04:00.001+01:00

[This review is under construction]

Recently, on Andrew Gelman's blog there was a discussion about how to get yourself a statistics education (presumably without going through the whole process of becoming a professional statistician). Here's the discussion on Gelman's blog, with lots of suggestions on how to achieve this goal.

This was the kind of problem that motivated me to look for a full course of study that would give me the background that's missing.

In September 2011 I started a graduate certificate in statistics, at Sheffield (my wife's alma mater, coincidentally). preparatory course, which gets you ready to start an MSc in statistics. It's a nine-month affair with three courses (Math, Probability, Statistics) which are taught more or less in parallel (they cleverly stagger the assignment submissions so that one is only working on one of the three at any one time). The course guide says it's a time commitment of 15 hours a week, which seems amazingly little (imagine that you devote the last three hours of your day to working on something like this---you could easily exceed 15 hours a week). But it's a realistic number; I think I spent about that much time every week on it. Occasionally I have spent more (when I ran into trouble).

This is an incomplete review; I am working on it as I do the course.

Short version: the course is *excellent*. I wish I had done this years ago. But I would recommend it only to people who are really willing to sweat it out. You have to be able to do your own research when you need more detail on a particular topic; I am not sure all my classmates in this course are in full control of the information-search process, esp. as regards the use of R (the course is R based). I don't blame them though; R can be scarily obscure.

Long version:

Preparation for the course: I wish I had reviewed permutations and combinations, and trigonometry before starting the course. I found it the hardest to recall these things while working through the course. I would recommend the following sites for trig:

http://en.wikipedia.org/wiki/Trig_identities

http://www.mecmath.net/trig/

For permutations and combinations, this is badly formatted but it could be useful: http://www.bmlc.ca/PureMath30/Pure%20Math%2030%20-%20Permutations%20and%20Combinations.html

The assignments:

Each assignment I did (I've done 6 so far, out of a total of 18 to be assigned by the time the course ends; only the last 15 are graded) took a lot of time, and if you are typesetting the submission in LaTeX, you should expect a full day to be devoted to that (at least). Most of the questions directly use knowledge that was recently covered in the course, but some of the questions require some thought. There was one situation where one had to provide a closed form solution to a finite geometric series that was something like \sum n+1 r^n. There's a really nifty trick in the book Concrete Math by Knuth and colleagues, that shows you how to solve that (the key observation is that n+1 r^n is the differential of r^{n+1}), but if I hadn't read that book long ago I would probably not hit on the solution... The solution provided by the instructor for that problem was simpler and seemed magical, I have yet to study it carefully.

I wish the assignments were prepared with greater attention to precision in writing. A couple of times I had a hard time trying to figure out what precisely was meant, and judging from the others' questions, I was not the only one.

The grading is tough, but I kind of like that the graders are so anal retentive about giving every point. You have to respect them for that. When I saw how they grade, I felt a bit ashamed that I am so soft on my students in Potsdam. These guys are hard-asses. I guess I've gone soft after eight years in a soft-skills oriented linguistics department. Computer science and math at Ohio State was about as strict.

My grades so far are below (percentages). This excludes the first assignments; they do not count for the final grade. In general, these assignments count for 20% of the final grade. You have to get minimum 70% overall in the final grade (after the exams in June 2012) to be allowed to continue on to the MSc.

Mathematics:
Assignment-1: 100%
## Notes: I was pleasantly surprised to get this grade, but I do admit the problems were not too hard. The hardest part of this course is not the math but the probability theory.
Assignment-2: 97%
## Made a stupid mistake. Don't ask.
Assignment-3:
Assignment-4:
Assignment-5:

Probability:
Assignment-1: 76%
## Notes: I did relatively badly in the first assignment because I reversed a couple of signs accidentally, and because I didn't leave enough time for working on the assignment. I lost a lot of points on a stupid and mindless exercise involving reading numbers off of a binomial probability table --- shame on me.
Assignment-2:
Assignment-3:
Assignment-4:
Assignment-5:

Statistics:
Assignment-1: 84%
Assignment-2:
Assignment-3:
Assignment-4:
Assignment-5:

The textbooks:

One minor gripe I have in the course is some of the textbooks assigned. I think the course designers should probably invest some time into building complete course-specific lecture notes. Sometimes they do provide lectures notes, and these are great (my only complaint is that the authors don't believe in page numbers, which can be a real hassle if you print out the material and accidentally drop them on the floor). But that said, I realize that producing customized lecture notes is a major undertaking and I don't blame them for relying on existing books.

The math textbook is by Gilbert and Jordan, Guide$^2$ Mathematical Methods. The title is a bit strange, I mean the 2 for to (although the authors cannot be blamed for this naming decision---apparently Macmillan has a Guide 2... series). The book has one positive aspect: it covers the relevant material in the sense that it goes through the topics. So, for someone like me, who doesn't know exactly what I need to know as background to read more advanced textbooks on statistics, this is a good extended listing of the things I should know (or recall from high school). This is all good. What makes the reader's life hard are the super-terse proofs/solutions to exercises, and the large number of typos (especially in the solutions). The course organizers released a list of typos for the book at the start of the course, but there are even more typos than in the errata. The notation can also be sloppy and the reader has to be careful (e.g., at one point they write F-p, where F are the *names* of a function; what they meant was F(x)-p(x)).

An example of the terseness is the proof that the limit of sin(x)/x when x approaches 0 is 1. I had no idea where that proof was going until I watched Strang's online lecture (MIT open courseware). After watching Strang's lecture, I was able to unpack the proof myself, but I doubt that it could be done easily by just working through it (are proofs meant to be hard work to unpack? Read the Salle et al book on Calculus to see that the answer may be no). Gilbert and Jordan should read Knuth's book on writing mathematics. I would recommend Strang's lectures, and Calculus by Salas, Hille and Etgen (I have the 9th Edition); this last book really nailed it for me. This book has the smoothness and feel of Cormen, Rivest et al on Algorithms.

The probability theory book (Ross, A First Course in Probability) is OK in that it covers all the points. But it has the irritating property that each definition is followed by half a dozen totally unrelated examples. This in itself is not bad, but the examples are SCARY. I'm not sure I need to see the toughest applications of the latest idea learnt right away. After a bit of reading this sort of teaching-by-example at least this reader just gets depressed (there is no way I would have worked out those example answers myself after just reading the definitions provided immediately earlier). I found the online book by Jay Kerns much, much more useful for the present course---the course organizers should consider switching to that or at least assigning it alongside Ross, with some warnings to not get intimidated by Ross' style. Here is an excellent review of the Ross book on amazon that pretty much summarizes the main problems in the book.

There is an accompanying book on probability (by Freund, but authors are Miller and Miller) which is a straightforward and formal introduction to mathematical statistics; I like that more. It's part of the statistics course, not the probability course.

The statistics textbook is by Moore, McCabe and someone else, is absolutely terrible (apparently I'm not the only one complaining; see here). The book seems to be written for first year undergrads in statistics (nothing wrong with that of course). The large number of disconnected and silly examples (for example, for planned experiments vs observational studies) following every new concept lead to a feeling of total disorientation. There is also a painful attempt to make the book relevant to the modern user: examples about cell phones and iPhone Apps abound, presumably to draw in the young reader's eyes away from the cell phone as they read.

There are so many really good books on introductory statistics using R (e.g., in the Use R series); I wish they had used one of them. As it is, you have to be either real good at R, or be able to quickly get on board with R, if you want to do this course. It's completely based on R. This makes the course absolutely wonderful for me, but several of the students went into a state of blind panic (for example, a beginner often cannot easily figure out how to find out how to change a directory within R---in my own courses at Potsdam, I think we spend about 90 minutes just getting them used to the interface). Using an R-based book for introductory statistics would have been much better than Moore et al's. I have to admit though that I cannot name an alternative to Moore et al's right away that covers exactly the same material.

But overall, a thumbs-up. This is a course every non-statistician who needs to work with data should take. Even in these few months I learnt a lot of interesting and even downright cool things (mostly in the math segment, but also in probability theory). The big advantages of doing this kind of structured course are that:
- you have to solve problems on a daily basis in order to the get the assignments done on time, and someone carefully checks your work. If you try to read books on topics that are specifically relevant to you, like Gelman recommends (of course he recommends his own book), you are not going to get that quality of feedback (no, not even with a solutions manual).
- you can ask a statistician questions that come up as you read or work on real problems that affect your own life, and they will take the time to answer them fully. This is virtually impossible if you just try to talk to a random statistician (generally, they either heap scorn on you, or give a rambling answer that doesn't really answer the question, because they just don't want to pay attention long enough to try to understand the problem---not that I blame them for that; why should they care what your problem is?).

Material I consulted while doing this course (incomplete):
1. On writing math
2. Kerns' book on probability
3. Grinstead and Snell on probability
4. Salle et al Calculus
5. Spivak Calculus

Notes I kept on the course:

I've been keeping notes on what I learnt, these are located here (please send corrections to me). Ask me for the source .Rnw file if you want to expand on them.

[to be continued]

How to present one's reviewing service in one's cv

2011-11-06T09:08:00.001+01:00

I've been listing my reviewing service as an enumerated list in my cv till now, but recently I saw a better option: Brian Roark's cv has a semi-graphical representation of his reviewing record. His presentation is ugly IMHO, but the idea is great. So, inspired by his example, I came up with this as a first attempt:

The code goes like this:

The data file has the outlet (journal etc. name) and year, sorted by the number of times I reviewed for that outlet.

> head(data)
outlet year count.sum
9 Brain_Research 2006 1
10 CBG 2010 1
11 CJAL 2012 1
28 DFG 2007 1
29 Dial_and_Discourse 2010 1
30 EACL 2012 1

Define the x and y axes:

y<-data$year

x<-data$outlet

And plot:

library(ggplot2)

(p<-qplot(x,y,geom=c("point"),

alpha=I(2/3),ylab="year",xlab="journals/conferences/funding agencies/other",main=paste("summary of reviewing service (2003-2011); ",n," items \n public version [sorted by frequency] \n current as of ",Sys.Date()),position=position_jitter(w=0, h=.2))+coord_flip())

Suggestions for improvement are most welcome.

This presentation has so many advantages over a listing: it tells you whom I reviewed for most, and it's more compact.

Of course, I have two versions of this plot, one is public (the one above), and the other is private (because I review for universities and don't want to violate confidentiality). I have a switch in latex that generates a public or private version:

In the preamble write:

\ifx\UseOption\undefined

\def\UseOption{optb}

\fi

\usepackage{optional}

Then the public version goes like this:

%% public version

\opt{optb}{

\begin{figure}[htbp]

\begin{center}

\includegraphics[width=9cm]{"reviewingpublic"}

\caption{ACL: Association of Computational Linguistics; AMLaP: Architectures and Mechanisms for Language Processing; BRM: Behavior Research Methods;

CBG: Copenhagen Business School;

CUNY: CUNY Sentence Processing Conference; Cog\_Sci: Cognitive Science; Cog\_Sci\_Conf: Cognitive Science Conference; DFG: Deutsche Forschungsgemeinschaft; Dial\_and\_Discourse: Dialogue and Discourse; EACL: European ACL; ETAP: Experimental and Theoretical Approaches in Prosody; ESF: European Science Foundation; ICCM: International Conference on Cognitive Modeling; JML: Journal of Memory and Linguistics; J\_Jap\_Ling: Journal of Japanese Linguistics; J\_of\_Ling: Journal of Linguistics; LCP: Language and Cognitive Processes; Lang\_and\_Speech: Language and Speech; Ling\_Evidence: Linguistic Evidence; NLLT: Natural Language and Linguistic Theory; NSF: National Science Foundation (USA); NWO: Netherlands Organization for Scientific Research; PBR: Psychological Bulletin and Review; Research\_Lang\_Comp: Research on Language and Competition; TiCS: Topics in Cognitive Science.}

\label{reviewing}

\end{center}

\end{figure}

}

And the private version goes like this:

\opt{opta}{your figure here}

When compiling one does (> is the command line):

for the public version:

> pdflatex vasishthcv.tex

for the restricted version:

> pdflatex -jobname vasishthcv "\def\UseOption{opta}\input{vasishthcv}"

Graduate certificate in statistics

2011-10-31T15:46:00.001+01:00

I finally have a sabbatical; such a relief not to have to teach for a bit. What can one usefully do during a sabbatical? Here's a preliminary list:

(a) bird watching

(b) lounge around

(d) surf the web for six months straight

(e) do a statistics degree

I decided to go for (e). I never really learned statistics systematically (some would say, not at all, and they would not be far off the mark ;), which is absurd if you consider that it's practically my bread and butter (using statistics for data analysis). Why isn't everyone in psycholinguistics a professional statistician? God knows we all need that expertise.

So I surfed the web a bit and found something that kills two birds with one stone. A part time distance MSc in statistics, at Sheffield. Time commitment is 20 hours a week (i.e., 3 hours a day if you work on it seven days a week); very minimal I would say. If I cut out on reading fiction and other non-work books in the evenings after dinner, that's three hours right there every day.

At Ohio State, one of my professors, Brian Joseph, related the story of a physicist at OSU who decided to learn Sanskrit by taking one of Brian's classes; he did his studies in the evenings after dinner. Before long he was teaching the course with Brian. So my only question is: why the hell didn't I think of formally learning statistics earlier?

I'm doing the prep course first, to review the math, stats, and probability theory that is assumed for the MSc, and plan to start the MSc next year (I do have to pass the prep course, would be pretty annoying if I failed ;). And the course is great! I never realized back in school that math is FUN, probability theory is FUN, I only understood that once I got to Ohio State. And I'm again enjoying struggling with unfamiliar problems. The only annoying thing is that the homework assignments have to, of course, be submitted online, and so I feel compelled to typeset them nicely using Sweave+LaTeX, which is very time consuming. At Ohio State I never did that, I just wrote them up by hand (e.g., in formal language theory or discrete math or logic classes). Somehow the idea of an online submission demands typesetting, I can't bring myself to write it with a pn, scan the solution, and send it...

The course itself is good so far (too early to tell; e.g., in the stats segment we are drawing boxplots "by hand", which I did for the first time in my life today). But the textbooks could have been better, IMHO.

Lower p-values apparently give you more confidence in the alternative hypothesis

2011-09-01T10:53:00.000+02:00

"But an isolated finding, especially when embodied in a 2 X 2 design, at the .05 level or even the .01 level was frequently judged not sufficiently impressive to warrant archival publication." (p. 554)

From: Melton, A. W. (1962). Editorial. Journal of Experimental Psychology, 64, 553–557.

According to Gigerenzer et al (Published in: D. Kaplan (Ed.). (2004). The Sage handbook of quantitative methodology for the social sciences (pp. 391–408)), this quote is where the common convention comes from to use p-values as a measure of one's belief in a result.

Gigerenzer et al write:

"Editors of major journals such as A. W. Melton (1962) made null hypothesis testing a necessary

condition for the acceptance of papers and made small p-values the hallmark of excellent

experimentation."

Potsdam Mind Research Repository

2011-07-17T18:11:00.000+02:00

Here's a revolutionary website: all data and code accompanying papers.

http://read.psych.uni-potsdam.de/pmr2/

Imagine if it was mandatory to release data with your publication! It would make life so much easier.

The book is out

2010-12-09T20:55:00.002+01:00

You can read it online.

2010-10-15T14:47:00.001+02:00

I foolishly tried to convert a matrix M to a vector using vector:

vector(M)

But this is done column wise, so that adjacent row items end up non-adjacent.

The right way to do this seems to be:

library(data)
unmatrix(M, byrow=TRUE)

Mike's and my book is coming out

2010-09-29T17:46:00.010+02:00

Our book is finally coming out:

You can buy it on
Amazon.com, Amazon.de, or Springer.com.

tilde's in URLs (LaTeX)

2010-09-29T13:41:00.001+02:00

Obscure LaTeX command:

\textasciitilde{} % for tilde's in URLs as text.

I'd been using $\sim$.

Public and private cv's (LaTeX)

2010-09-25T07:43:00.004+02:00

Not directly related to statistics but:

Often one wants to have a public cv that one can put on the web, and a more restricted one that has private information that one only needs for a job application or something. Instead of maintaining two cvs, there's an easy way to automate it if you are a latex user.

1. For a public cv, type:

## public
pdflatex vasishthcv.tex

2. For a restricted cv, type:
## restricted
pdflatex -jobname vasishthcv "\def\UseOption{opta}\input{vasishthcv}"

where in the tex file, you have in the preamble:

\ifx\UseOption\undefined
\def\UseOption{optb}
\fi
\usepackage{optional}

and

in the text itself for restricted sections use:

\opt{opta}{Home address:...}

Statistics in linguistics

2009-12-11T06:52:00.002+01:00

People in linguistics tend to treat statistical theory as something that can be outsourced--we don't really need to know anything about the details, we just need to know which button to click.

People easily outsource statistical knowledge in an empirical paper, but the same people would be appalled if they hired an assistant to work out the technical details of syntactic theory for a syntax paper.

The statistics *is* the science, it's not some extra appendage that can be outsourced.

How to get ESS style indentation in textmate

2009-04-23T12:18:00.002+02:00

This should be standard in Textmate, I don't know why one has to go through so many steps to get it working:

http://gragusa.wordpress.com/2007/11/11/textmate-emacs-like-indentation-for-r-files/

How to update R bundle in textmate

2009-04-23T12:00:00.002+02:00

Got this from the web somewhere:

Just create a script with the following content:

#!/bin/sh

LC_CTYPE=en_US.UTF-8
SVN=`which svn`

echo Changing to Bundles directory...
mkdir -p /Library/Application\ Support/TextMate/Bundles
cd /Library/Application\ Support/TextMate/Bundles

if [ -d /Library/Application\ Support/TextMate/Bundles/R.tmbundle ]; then
echo R bundle already exists - updating...
$SVN up "R.tmbundle"
else
echo Checking out R bundle...
$SVN --username anon --password anon co http://macromates.com/svn/Bundles/trunk/Bundles/R.tmbundle/
fi

echo Reloading bundles in TextMate...
osascript -e 'tell app "TextMate" to reload bundles'

Selection bias in journal articles

2007-07-04T21:03:00.000+02:00

Journals dealing in psycholinguistic research do not publish null results generally, because they are "inconclusive". So it's completely possible that out of 100 experiments, 95 are inconclusive, and 5 are "significant", but that all five are Type I errors. But it's those 5 experiments that will get published.

The naive rebuttal to this would be that such a situation would only rarely arise. But the non-obvious thing is that rare events do happen. If we published only those five articles, then how would we draw the conclusion that we are not in Type I la la land?

Rlang mailing list

2007-04-28T20:46:00.001+02:00

Roger Levy has created a possibly useful wiki for exchanging questions about the use of R for language research:

https://ling.ucsd.edu/mailman/listinfo.cgi/r-lang

How to choose between a multiplicity of sexy models

2007-04-21T10:53:00.000+02:00

It's websites like this that give model selection such a bad reputation in science:

http://www.modelselection.org/

Nice introduction to R

2007-04-21T09:36:00.001+02:00

http://heather.cs.ucdavis.edu/~matloff/r.html

A blog most amazing

2007-04-21T08:34:00.000+02:00

I just found a most astounding blog via Gelman's blog:

http://emotion.inrialpes.fr/~dangauthier/blog/

How to extract SEs from lmer fixed effects estimates

2007-04-17T22:42:00.000+02:00

Extracting fixed effects coefficients from lmer is easy:

fixef(lmer.fit)

But extracting SEs of those coefficients is, well, trivial, but you have to know what to do. It's not obvious:

Vcov <- vcov(lmer.fit, useScale = FALSE)
se <- sqrt(diag(Vcov))

Hmisc: how to increase magnification

2007-02-17T12:48:00.000+01:00

One non-obvious thing (at least to me) about Hmisc's xYplot function is that to increase magnification or other parameters of a graph component, you have to do the following.

xlab=list("Condition",cex=2)

I.e., you have to make a list out of the parameter, and add whatever information you need. This works generally for any of the xYplot parameters.

using winbugs with gelman and hill book on intel macs

2007-01-25T09:12:00.000+01:00

I finally installed Windows on my Mac (a traumatic experience) and finally got the code working. However, the startup instructions on the website of the book did not work for me. I offer a working example for other souls as clueless as myself. The first problem is that the libraries have to be installed manually, they do not install automatically as adverstised. Second, the library R2WinBUGS has to be called explicitly to run the critical bugs command.
Also, if anyone out there is thinking of installing a dual boot environment in Mac in order to install WinBUGS, there is a bug (no pun intended) in the licence installation of WinBUGS. The decode command for the license does not work as advertised, but the license installs anyway.
The working version is here: http://www.ling.uni-potsdam.de/~vasishth/temp/schools2.R

Some expensive lessons I recently learnt about R/Sweave

2007-01-22T07:35:00.000+01:00

1. If you are going to generate lots of latex tables automatically from an Rnw file, LABEL THEM.

2. weaver does not work with xYplot. If you are using the Hmisc library, just don't use weaver. I will present a solution here sometime soon.

The solution: set caching to off (cache=off) in the chunk that loads the Hmisc library and runs the xYplot command(s). You can turn caching on before and after the chunk, but xYplots need to be computed without caching.

3. xtable is unable to identify the fact that an R output line containing, e.g., log(sigma^2), has to be in math-environment in the tex. In Sweave this has the disastrous consequence that the .tex file does not compile. My kludgy solution is to search and replace the .tex file after Sweaving it.

It's frustrating that such good tools can sometimes be such a pain in the ass. I guess one should be grateful they are there at all.

Incomplete Review of Gelman and Hill's Data Analysis using Regression and Multilevel/Hierarchical Models

2007-01-13T16:13:00.000+01:00

I'm writing this somewhat cranky review as I read the book. Compared to the Pinheiro and Bates book, the examples in this book are initially irritatingly difficult to get working. A major problem with the book is that code involving BUGS runs only on Windows. This excludes readers like me from the action. So I have to wait until I get a Windows machine--but do I really want to start using Windows now? It would have been more helpful if their webpage prominently mentioned this detail (that the book is Windows specific). Had they done that I would probably not have bought it. But now that I have paid for it I am going to read it.

The website for the book has the data in a pretty disorganized way--why not just make a library? The authors do have a package for arm on the CRAN archive, but it does not install on any OS except Windows (the first R package I have seen with this property in my seven years as an R user). I tried to wget -r the ~gelman/arm/examples directory but ended up with all kinds of other crap in my directory as well, which was annoying. A zip archive could not hurt.

Chapters 1-3

I did not get a huge amount out of these chapters that was deeply interesting, but it is a good intro for newcomers to regression.

The code for the example in chapter 3 doesn't work on non-windows machines. Here is a working version.

Chapter 4

The book becomes more and more exciting from about this point onwards. Only one grouse:

Chapter 4 has some principles doing carrying out regression for prediction (section 4.6) but it is far from clear where they come from and the principles have a cookbookey feel (do this, don't do that, without explaining why). It would have been better if the authors had taught the reader to reason about the problem (surely those are the real principles, and the presented principles the consequences of the thought process generated by those principles).

[to be continued]

Great statistics courses that use R

2007-01-04T07:27:00.000+01:00

1. http://www.unc.edu/courses/2006spring/ecol/145/001/docs/lectures.htm

2. http://www.stat.washington.edu/vanduijn/560/

Statistical learning theory:

3. http://www.ece.rice.edu/~fk1/classes/ELEC697.htm

4. http://www.ulb.ac.be/di/map/gbonte/Stat104.html

Null hypotheses, significance testing and all that jazz

2007-01-03T13:26:00.000+01:00

Some amazing articles I've recently read in my ample spare time:

1. The Insignificance of Null Hypothesis Significance Testing
Jeff Gill
Political Research Quarterly, Vol. 52, No. 3 (Sep., 1999), pp. 647-674
doi:10.2307/449153

2. Andrew Gelman's article

3. And this one: http://www.npwrc.usgs.gov/resource/methods/statsig/index.htm

4. Bowers and Gelman on Exploratory Data Analysis with Hierarchical Linear Models (AKA Multilevel models)

Suitably stunned into silence, the reader may then have the following practical question: how to present one's HPD intervals in a journal, and what else to present?

Here's an answer from Doug Bates.

Great article: EDA for HLMs

2007-01-02T21:35:00.000+01:00

There's an interesting paper I just read that comes with Sweave/R code that the article uses:

http://www-personal.umich.edu/~jwbowers/papers.html

It's called EDA for HLMs, and advocates an exploratory data analysis when trying to understand data (as opposed to blindly searching for a yes/no answer, did significance fall below 0.05). In psycholinguistics, we are still a long way from conventional plodding along well beaten paths.

Installing weaver

2007-01-02T09:48:00.000+01:00

It is not immediately obvious how one can install weaver (thanks to balajis for telling me about it--see his comment to the Sweave speed issues).

Do the following as superuser for a system-wide install:

1. install digest (from CRAN)
2. install codetools from http://bioconductor.org/packages/1.9/omegahat/html/codetools.html
3. install weaver from http://www.bioconductor.org/packages/1.9/bioc/html/weaver.html

A clunky way to install on Mac OS X and Linux is: as superuser do

R CMD INSTALL packagetarball

I'll add information on how to use it later, but one can always consult the weaver manual/vignette.

How I maintain my data

2007-01-02T09:38:00.000+01:00

As soon as you have a lot of experiments floating around, you tend to get a proliferation of code and data files. Usually, chaos ensues. If someone asks you for the data of some experiment you published, you can (a) ignore the request (this is the most economical but also the least ethical response) (b) send them they can realistically use. This post is about how to carry out (b).

Example.

Suppose I have a collection of data and R code that I will cryptically call intml.

1. package.skeleton("intml")
This will create some directories (see previous post)
2. now add the data to the data directory
3. add the .Rnw file you used to analyze the data as a vignette

Build a package as indicated in the last post, and send it to the person who asked for it. Or make it available on CRAN.

It's simple and it enforces a certain self-discipline. Nothing like the knowledge that anyone can read your code to force you to write it properly :-)

Putting together a library

2007-01-01T17:12:00.000+01:00

The Introduction to R and other documentation CRAN tells you how to build a library. But it is remarkably hard to find a step-by-step how-to for packaging together an R library. Here is a good one.

Sweave for complex projects (speed issues)

2006-12-13T07:05:00.000+01:00

One problem my colleagues and I face is that our statistical analysis projects quickly become very complex, and recompiling Sweave becomes a slow process each time I update the code or just run it again.

I am slowly compiling a list of available solutions to this problem (the real issues are lack of speed, lack of modularity):

Here is what I have found so far:

1. Use \SweaveInput for including modular code
2. Use makefiles a la Deepayan Sarkar:
here
3. Another solution that relates to the present problem:
here
4. Finally, I think one should make vignettes/packages out of one's research projects so that the whole Rnw file does not need to be compiled--the needed objects can be made visible by doing something like:

library(mydata)

There is a bit of work involved in making the package, but the payoff is tremendous. The R documentation provides details on how to build packages, but maybe I will put a simple example here.

How to compute min-F

2006-09-15T13:28:00.000+02:00

Here's how to compute minF in R. You have to give the function the F1 and F2 values and the denominator dfs for each: minf(f1,f2,n1,n2).

minf <- function(f1,f2,n1,n2){
fprime <- (f1*f2)/(f1+f2)
n <- round(((f1+f2)*(f1+f2))/(((f1*f1)/n2)+((f2*f2)/n1)))
return(paste("minF(",n,")=",round(fprime,digits=2),", crit=",round(qf(.95,1,n)),sep=""))
}

If there are any mistakes here, corrections are welcome.

References: Raaijmakers' 1999 and 2003 articles.

Sweave introduction

2006-02-13T07:06:00.000+01:00

Sweave is a package that comes with R, and can be used to interleave LaTeX and R code. Here's how I use it:

(I'm assuming you've got R installed on your machine.)

1. Download the bash script Sweave.sh from here. Install it in your bin directory, and be sure to change the first line to reflect the location of your bash.

2. Download the file Sweave-test-1.Rnw into a temporary directory.

3. cd to that directory and now run the script on this file:
Sweave.sh Sweave-test-1.Rnw. If all goes well, you have a .tex file Sweave-test-1.tex.

4. Compile the .tex file in whatever way is usual for you. The Sweave FAQ is a must-read.