A while back, I posted some TextWrangler scripts that allow for commenting and uncommenting blocks of code for codeless language modules such as Stata, R and LaTeX. The problem with my scripting approach was that it required two different scripts—one for commenting, one for uncommenting.

Steven Samuels has generously corrected this problem by writing one script that handles both functions — based on whether the line is already commented, running the script will either remove the existing comment or comment out the (currently uncommented) selection.

Download the new and improved script here. Visit this page for information on how to assign a keyboard shortcut to the script.

Note: The old script is still available. The only reason I can think of to use the old version is in case you want to apply multiple comments to the same line (for example, if you use a comment to create a heading in your code, then you comment out the entire section).

Many thanks to Steve for supplying the script!

Alex Yuffa has created an excellent (and beautiful) set of LaTeX tutorials: LaTeX 101, which introduces LaTeX, and LaTeX 102, which introduces advanced material through a series of exercies. The first document is based on my tutorial LaTeX: From Beginner to TeXPert, but Alex’s contains more examples and detail. If you’re new to LaTeX, these tutorials are a great place to start.

The word is more or less official that OOXML, the specification that Microsoft Office 2007 uses to write documents, has been approved as an ISO standard (the same body that approved ODF, the specification used by OpenOffice). The standardization process was very controversial, largely because (i) many felt that Microsoft was bucking the already approved standard (ODF) in order to compete with OpenOffice and other products, and, to a lesser extent, (ii) some felt that the format was unsatisfactory, in some cases because of its complexity. That being said, Microsoft and ECMA (another standards organization) worked closely to bring the OOXML specification up to the standards of the ISO, and Microsoft has made a pledge to work towards interoperability, adding the OOXML specifications to their open-specification promise.

What does standardization mean for you?

• For most people, it means (i) that it will be easier to share documents between applications, and (ii) that you can create OOXML documents without worrying that you will not be able to access them in the future.
• For people that prefer ODF to OOXML, it means that you will not be able to count on OpenOffice overtaking Microsoft Office on standards-compliance grounds alone (though price and performance competition, as always, will remain fair game).
• For people that write mathematical documents, it means that you can add Microsoft Word to the list of applications/formats that make it easy to create such documents and save them in a standardized, interoperable format [i.e., LaTeX (and LyX, Scientific Word, etc.) and ODF (and OpenOffice, KOffice, etc.)].

I hope I don’t sound like a Microsoft shill — as it happens, I just really like the way Office 2007 handles math; it combines LaTeX’s powerful ability to encode math with the convenience of WYSIWYG editing. And now you get that combination with the comfort of knowing that your documents are saved in an industry-standard format (at least, you will once Microsoft rolls out the next update to Office).

USBTeX is a complete, portable LaTeX environment for Windows. It includes portable versions of MikTeX, TeXMaker, Ghostscript, Ghostview, and Sumatra PDF. As the name suggests, you can install it on a USB drive and run it on any Windows machine (provided that you have a decent USB drive). Since LaTeX tends to be “special interest” software, USBTeX could be a very convenient resource.

If this interests you, you may also be interested in using an Online LaTeX Compiler.

Hopefully this post will be preaching to the choir, but lately I’ve been noticing that many students, programmers, professors, etc. write their statistical code as though there were no such thing as missing data. Unless your data have been cleaned and missing values imputed (I think the Public Use Census Data are like this), overlooking missing data is a mistake that could lead to, inter alia, biased estimates that act like measurement error.

The most common error that I see occurs in the creation of new variables. Variables are created according to rules regarding other variables (e.g., if x>5, set y=1). But what happens if x is a missing value? Do you know how your statistical software treats missing values? I’m going to go through this example using Stata, but the same logic applies to any programming environment.

I have created a random variable c that takes 3 values (1, 2 and 3). There are 3 missing observations for the variable c. I want to generate a new variable that takes the form

.

Consider the following two approaches to creating this variable:

Approach 1: Initialize everything to zero

. gen x=0
. replace x=1 if (c==1 | c==2) (9 real changes made)

Approach 2: Initialize everything to missing

. gen y=.
(25 missing values generated)
. replace y=1 if (c==1 | c==2)
(9 real changes made)
. replace y=0 if c==3
(13 real changes made)

Results

The variable x, created using approach 1, has too many observations. There should be 22, but there are 25:

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
           x |        25         .36    .4898979          0          1
           y |        22    .4090909    .5032363          0          1

The variable y, on the other hand, has the correct number. The difference arises because what I want to do is define NewVar to be 0 whenever c is 3. Approach one defines NewVar to be 0 whenever c is not in {1,2}. This is a big difference from an econometric/statistical perspective. If we define NewVar to be 0 when we do not know the value of c, we are essentially imputing values. These imputed values will reduce the accuracy of estimates and future calculations.

A note about missing values in Stata

Stata saves missing values as very large numbers. This means that if you used the command

replace y=0 if c>2

Stata would set y=0 for observations with c=3 as well as observations with c=. (missing). In this case, you could just use the command in Approach 2 above. For continuous variables, however, this is not an option. The optimal workaround, in my opinion, is to use the following:

replace y=0 if c>2 and !missing(c)

This would set y=0 for all observations where c is not missing and the value of c is greater than two.

The American Institutes for Research offers a free application called AM Statistical Software (Windows only). The application is still in early development, but it already offers several modeling and replication procedures, including OLS, logit, probit and marginal maximum likelihood procedures. Most impressive, however, is the application’s optional Data Transfer Component, which can import and export SAS, Stata, SPSS, and many other data formats. I was able to use the application to read an SPSS file that the foreign package for R couldn’t handle. AMSS is worth a try, even if all you need to do is convert a dataset.

The only trouble I had installing and running AM Statistical Software was that the help system didn’t install. [Update: Earlier I wrote that the UI was buggy, but I now think that was due to Vista, not AMSS.]

Roger Koenker, a quantile regression crusader, has an R package that implements the procedure. It is called quantreg, and it is documented here. This package has apparently been around for quite some time, but I was only recently turned on to quantile regression, so it was under my radar.

Rattle is a graphical interface to R. It supports basic data management tasks, as well as a number of different modeling functions. I haven’t had a chance to test it out yet since I don’t have my R installation  completely up to date, but it looks promising. For all of R’s strengths, one weakness is the inconsistency of the syntax from command to command and package to package (not that commercial applications don’t have the same problem).

Also see RCommander.

The STIX mathematical fonts are available as a public beta. From the website:

The mission of the Scientific and Technical Information Exchange (STIX) font creation project is the preparation of a comprehensive set of fonts that serve the scientific and engineering community in the process from manuscript creation through final publication, both in electronic and print formats. Toward this purpose, the STIX fonts will be made available, under royalty-free license, to anyone, including publishers, software developers, scientists, students, and the general public.

I’m not sure if these work on all platforms, but they are TrueType fonts.

These fonts can be used to augment the symbols available in the OpenOffice.org equation editor (see this tutorial for more).

As a follow up to this post on estimating panel data models in R, I thought I’d point out that there is an excellent tutorial (PDF) by Hun Myoung Park on panel data models in Stata, SAS, Limbdep and SPSS. The tutorial covers fixed and random effects (and different variations thereon).