Finally, I am getting really excited about penalized regression. Specifically ridge, lasso, and elastic-net regression. I will hopefully be back to blog about this soon with some examples of how to do this in R. The benefits of these approaches seem limitless and would be a great addition to the arsenal of methodologies that an applied researcher in education or psychology might use. Specifically I think these types of regressions could work well as a substitute for factor analysis (as these techniques act to reduce the dimensionality of your data).

Do the following
Edit -> Preferences...


Under the Typesetting tab, in the Processing tools section look for the ‘+’ and click it. This will bring up a dialog box to add your own ‘tool’

I added the following.

In the Name field:
Sweave

In the Program field:
R

And I gave it 3 arguments, each is a separate argument and should not be on the same line!
CMD
Sweave
$fullname


Also, I deselected view PDF as it doesn’t make sense as you now need to run LaTeX.

Click OK, then OK, and now you’ll have Sweave available in the drop down box.

It should look like the screenshot below.

1. Install wine-1.0 not wine-1.2 (wine-1.2 will seg fault!)
sudo apt-get install wine-1.0


2. Download WinBUGS from here.

3. Navigate to the directory where you downloaded WinBUGS, I’ll assume it was /home/user/Downloads, and run the following:
wine WinBUGS14.exe


4. Launch WinBUGS
wine ~/.wine/drive_c/Program Files/WinBUGS14/WinBUGS14.exe


5. Patch WinBUGS with the following patch – available here. Apply that patch as described on the WinBUGS website. Then close WinBUGS and launch it again as previously described.

6. Add the immortality key – available here. Apply it the same way you did the patch!

7. Launch R and install R2WinBUGS
R
install.packages("R2WinBUGS")


8. Load R2WinBUGS and run the Gelman example to check that it worked
library(R2WinBUGS)
model.file <- system.file(package="R2WinBUGS","model",
"schools.txt")
data(schools)
J <- nrow(schools)
y <- schools$estimate
sigma.y <- schools$sd
data <- list ("J", "y", "sigma.y")
inits <- function(){
list(theta=rnorm(J, 0, 100), mu.theta=rnorm(1, 0, 100),
sigma.theta=runif(1, 0, 100))
}
parameters <- c("theta", "mu.theta", "sigma.theta")
schools.sim <- bugs(data, inits, parameters, model.file, n.chains=3, n.iter=5000)
print(schools.sim)
plot(schools.sim)


You will get a couple of messages from Wine, but I believe you can ignore these. I have not thoroughly tested this but I can corroborate that it gives similar estimates to R2WinBUGS from Windows (see below gnome-terminal vs. Windows XP in VirtualBox). I still recommend JAGS but if you need to use WinBUGS here is how.

Also this should work on any Linux distribution or Mac OS X provided that you can get Wine 1.0 as the latest Wine versions do not work with WinBUGS. Let me know if you have troubles.

9. Add a desktop launcher (OPTIONAL)
Right Click gnome-panel
Click 'Add to Panel ...'
Click 'Custom Application Launcher'


Add the following to the Name field
WinBUGS14

Add the following to the Command field (do include the quotes!)
wine '/home/USER/.wine/drive_c/Program Files/WinBUGS14/WinBUGS14.exe'

Finally, if you want to add the WinBUGS icon, click here. Right click the image and select:
Save image as ...

Save it to /home/user/Downloads/ and then add it by clicking the Wine icon, navigating to the directory where you downloaded it and finally selecting the icon. Then close the Custom Application Launcher dialog box by clicking Close.

Also, I’ve recently updated to JAGS 2.1.0 and have rolled my own Ubuntu 10.04 64 bit packages. I will continue to keep JAGS updated by rebuilding Dirk’s packages from Debian Sid while 10.04 is supported (unless I move back to Debian once Squeeze is released). I will not roll packages for Ubuntu releases that are not LTS. You can get JAGS by clicking here. If anyone is willing to roll 32 bit package I am happy to host it here. Please let me know.

First, I am continuing to work with the Fedora project to get JAGS formally into Fedora. Afterwards, I do hope to get R-rjags and R-coda into Fedora as well but these are much more easily installable via the fantastic R package manager and admittedly are very low on my priorities.

The second big thing that I’ve been working on is writing a KDE/Qt R editor. I know cantor and rkward exist and that you can use kate, kwrite, etc., however, none of these editors really meet my R needs. Rkward is the closest, but is way too feature rich for my needs. I am hoping that my R editor will be similar to ESS and TINN-R. I mostly just want syntax highlighting (which I already have partially implemented), the ability to easily send a line(s) of coding to a terminal using a keystroke (will default to Ctrl+R like RGui in Windows but will be configurable in a preferences dialog), the ability to launch R from the editor in a separate window (e.g. konsole, gnome-terminal, etc.), and the ability to run Sweave on the document. Maybe by the end of the summer I will be ready to release a preview or a beta version, but I am not sure.

Finally, and lowest down on the priority list, I plan on writing a MCMC program in C++. After creating this program and bolstering my C++ and MCMC skills, I will stop writing the program once I’ve learned enough (as it’s a didactic experience) and instead will actively contribute to JAGS or MCMCglmm or some other Bayesian program that can interact with R. Alternatively, I might consider trying to create a KDE/Qt frontend to JAGS that could also call R and rjags just to run the JAGS engine and nothing else. I am uninterested in a Qt fronted to R (btw, that’s called rkward).

But that’s a long way down the road.

Thoughts? Ideas? Comments?

For the time being I am hosting the RPMs for jags i386 and jags 64 bit and jags-devel i386 and jags-devel 64 bit on Dropbox. Once they are accepted into Fedora they will be removed from Dropbox.

EDIT: This afternoon I packaged up rjags and coda for Fedora (also submitted for review). I hope to start maintaining these in Fedora. If you want to use them and try them out click here for rjags 64 bit and here for coda (both i386 and 64 bit) . Coda is platform independent but rjags is for 64 bit only. If you want to install rjags and you’re on a i386, just install the two JAGS rpms (jags and jags-devel) from above and fire up R and run:
install.packages("rjags")

And that should be it.

yum install R R-devel libblas-devel


Next thing you’ll want to do is to download the tarball and unpack it .

tar xvzf JAGS*


Next you need to tell it to install the libraries in /usr/local/lib64 otherwise you’ll need to tell rjags where to look for JAGS.

./configure --libdir=/usr/local/lib64
make
sudo make install


Next you can test that JAGS installed properly by typing:

jags


Once this is done, fire up R and install rjags like any other package and that’s it.

R
install.packages("rjags")


Feel free to leave comments on the talk.

This summer I am in the process of going through and reading Gelman’s Bayesian textbook. Last summer I worked through Bradley Carlin’s book and a few other books. I plan to adapt his code, especially the hierarchical modeling code, from BUGS to JAGS and I may post it here.

Also, it should be noted that all of my materials presented on this blog, including attachments, are released under a Creative Commons license or GNU GPL 3. Feel free to modify and distribute my materials as specified under the terms of these licenses.

This script is available here and the manual is available here .

The script contains two functions:

criterion.pattern()


profile.cv()


And you should source the script at the start of a R session:
source("/path/to/profile_analysis-0.1.R")