Kai Arzheimer » software

Running MLwiN from within Stata

kai — Sat, 21 Jan 2012 16:43:43 +0000

In the past, I did a lot of multi-level modelling with MLwiN 2.02, which I quickly learned to loath. Back in the late 1990s, MLwiN was perhaps the first ML software that had a somewhat intuitive interface, i.e. it allowed one to build a model by pointing and clicking. Moreover, it printed updated estimates on the screen while cycling merrily through the parameter space. That was sort of cool, as it could take minutes to reach convergence, and without the updating, one would never have been sure that the program had not crashed yet. Which it did quite often, even for simple models.

Worse than the bugs was the lack of proper scriptability. Pointing and clicking loses its appeal when you need to run the same model on 12 different datasets, or when you are looking at three variants of the same model and 10 recodes of the same variable. Throw in the desire semi-automatically re-compile the findings from these exercises into two nice tables for inclusion in again and again after finding yet another problem with a model, and you will agree that any piece of software that is not scriptable is pretty useless for scientists.

MLwiN’s command language was unreliable and woefully underdocumented, and everything was a pain. So I embraced xtmixed when it came along with Stata 9/10, which solved all of these problems.

runmlwin presentation (pdf)

But xtmixed is slow with large datsets/complex models. It relies on quadrature, which is exact but computationally intensive. MLwiN works with approximations of the likelihood function (quick and dirty) or MCMC (strictly speaking a Bayesian approach, but people don’t ask to many questions because it tends to be faster than quadrature). Moreover, MLwiN can run a lot of fancy models that xtmixed cannot, because it is a highly specialised program that has been around for a very long time.

Enter the good people over at the Centre for Multilevel Modelling at Bristol, who have come up with runmlwin, an ado that essentially makes the functionality of MLwiN available as a Stata command, postestimation analysis and all. Can’t wait to see if this works with Linux, wine and my ancient binaries, too.

MLwiN 2.10: free for British Academics

kai — Sat, 21 Mar 2009 16:37:37 +0000

MLwiN is one of the granddaddies of multi-level modelling software (the other being HLM). Essentially, it is a 1990s-ish looking and sometimes quirky GUI wrapped around an old DOS program (MLn). The one feature that set MLwiN apart in the late 1990s is point-and-click interface that allows you to build the equations for a multi-level in a stepwise fashion. The underlying command language is still slightly confusing and less than well documented, and some of the modern features (such as modelling categorical dependent variables) are implemented as external macros, which does not need to concern you unless something goes horribly wrong, which happens occassionally.

That said, MLwiN is reasonably fast, does now incorporate modern MCMC estimators, has an interface with WINBUGS and can be convinced to do most things you would possibly want to do with it. I bought version 1.10 ca. 1998, received free upgrades to 2.02 and good support well until 2004/2005 or so. These days, Stata, R and MPlus can all estimate multi-level models, but working with MLwiN may still be worthwhile for you (by the way, you can download the free stata2mlwin addon from UCLA academic technology to export your variables from Stata to MLwiN).

Rather amazingly, MLwiN is now freely available for anyone working in UK universities: just enter your details including your ac.uk-email, and few days later, they will send you a download link.

Software for Social Network Analysis: Pajek and Friends

kai — Tue, 08 Jul 2008 21:27:06 +0000

Our project on social (citation and collaboration) networks in British and German political science involves networks with hundreds and thousands of nodes (scientists and articles). At the moment, our data come from the Social Science Citation Index (part of the ISI web of knowledge), and we use a bundle of rather eclectic (erratic?) scripts written in Perl to convert the ISI records into something that programs like Pajek or Stata can read. Some canned solutions (Wos2pajek, network workbench, bibexcel) are available for free, but I was not aware of them when I started this project, did not manage to install them properly, or was not happy with the results. Perl is the Swiss Army Chainsaw (TM) for data pre-processing, incredibly powerful (my scripts are typically less than 50 lines, and I am not an efficient programmer), and every time I want to do something in a slightly different way (i.e. I spot a bug), all I have to do is to change a few lines in the scripts.
After trying a lot of other programs available on the internet, we have chosen Pajek for doing the analyses and producing those intriguing graphs of cliques and inner circles in Political Science. Pajek is closed source but free for non-commercial use and runs on Windows or (via wine) Linux. It is very fast, can (unlike many other programs) easily handle very large networks, produces decent graphs and does many standard analyses. Its user interface may be slightly less than straightforward but I got used to it rather quickly, and it even has basic scripting capacities.

The Missing Manual

The only thing that is missing is a proper manual, but even this is not really a problem since Pajek’s creators have written a very accessible introduction to social network analysis that doubles up as documentation for the program (order from amazon.co.uk, amazon.com, amazon.de. However, Pajek has been under constant development since the 1990s (!) and has acquired a lot of new features since the book was published. Some of them are documented in an appendix, others are simply listed in the very short document that is the official manual for Pajek. You will want to go through the many presentations which are available via the Pajek wiki.

Of course, there is much more software available, often at no cost. If you do program Java or Python (I don’t), there are several libraries available that look very promising. Amongst the stand-alone programs, visone stands out because it can easily produce very attractive-looking graphs of small networks. Even more software has been developed in the context of other sciences that have an interest in networks (chemistry, biology, engineering etc.)
Here is a rather messy collection of links to sna software. Generally, you will want something that is more systematic and informative. Ines Mergel has recently launched a bid for creating a comprehensive software list on wikipedia. The resulting page on social network analysis software is obviously work in progress but provides very valuable guidance.

Technorati-Tags: sna, software, political science, network, analysis, perl, citation, bibliometrics, networks, social, social networks