The new package R2MLwiN package bridges R and MLwinN software for multilevel analysis. From the examples provided, it looks very promising. It will be great if a command line version of MLwiN can be made available under Linux so the package can be usef...
Blog Archives
R_inferno
Knowing the weakness of your tool is a shortcut toward getting really good at it.
Some Rcpp benchmarks
I ran the Fibonacci number example from the Rcpp package on a number of computers and operating systems. Here are the results:A. On my main computer (Core 2 Extreme 3.06GHz, 8 GB memory) running Ubuntu 10.04 (g++ 4.4.3): test...
Visual debugger and the debug mode of the autorun R console
The StatET team kept their promise and delivered the autorun R console with debug mode on. This, combined with the visual debugger, makes the StatET a very appealing cross-platform environment for working with R.
SabreR
SabreR just released an update. It is another software package that can estimate multivariate multilevel model (other options are aML, MCMCglmm, etc.). They seem to also have a book dedicated to the software, which be worth checking out.It will be grea...
Performance difference between Stata and R
With respect to multinomial logit model, the performance difference between the two packages are quite large, based on this post.
GEE using Stata vs. R
I am running GEE logistic regression model for my fetal loss paper. As usual, I compare results between Stata and R and make sure they are consistent. To my surprise, the models assuming independent correlation structure give similar results but the mo...
Updated examples that combine Rcpp and CppBugs
This example is very informative in illustrating how to use CppBugs module with Rcpp to conduct fast MCMC simulation in R.
Ra vs. compiler package
R seems to have two byte code compilers: the Ra add-on module (and the accompanying "jit" package) and the "compiler" package came with the default installation. I wonder how they differentiate from each other and what the strengths and weaknesses...
Legends in ggplot2
A simple plot takes a few lines of coding:g1 <- ggplot(d, aes(birth.year))g2 <- g1 + geom_line(aes(y=alive0, linetype="Famale")) + geom_line(aes(y=alive1, linetype="Male")) + scale_linetype_discrete(name = "")g3 <- g2 + geom_point(aes(y=...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
