Saturday, December 25, 2010

Maximum Likelihood Estimation and Inference: With examples in R, SAS and ADMB

As the first textbook on ADMB, this book is highly expected.

Friday, December 17, 2010

Where to find good data sets

This post provides some good information on where to find good data sets for statistical analysis.

Thursday, December 09, 2010

Programming languages on the rise

This post list seven programming languages that are rising, from Python to Ruby to R to... Cobol.

Monday, December 06, 2010

Very interesting blog

That is a very interesting blog on R + LaTeX + Sweave!

Saturday, December 04, 2010

Comparison of results

I am doing a simple comparison of different estimation procedures in dealing with a simple binomial model. Here is where I got started:
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library(INLA)
library(npmlreg)
library(MCMCglmm)
library(DPpackage)


data(Seeds)


# Using INLA
formula = r ~ x1*x2 + f(plate, model="iid")
mod.inla = inla(formula, data=Seeds, family="binomial", Ntrials=n)
summary(mod.seeds)


# Using npmlreg
mod.ml <- alldist(cbind(r, n-r) ~ x1*x2 , random=~1, data=Seeds, family=binomial, random.distribution="gq")
summary(mod.ml)


# Using MCMCglmm
prior <- list(R=list(V=1, nu=0.002))
mod.mcmc <- MCMCglmm(cbind(r, n-r) ~ x1*x2, family="multinomial2", data=Seeds, prior=prior)
summary(mod.mcmc$Sol)


# Using DPpackage
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I will keep updating by adding new things (estimation procedures, predictive simulations, etc.)

Wednesday, December 01, 2010

Tuesday, November 30, 2010

Wrapping text around figures in LaTeX

I found this page very helpful in showing how to create figures with text wrapping around. And this one for making tables.

I am using the LaTeX package by Bruce Donald for my next NIH proposal.

Sunday, November 28, 2010

Top 5 Most Anticipated Tablets of 2010-2011

This is the list. What's interesting is that the Adam can be used as a tablet and a e-book reader and the reader mode is much more power efficient.

Friday, November 26, 2010

Computational tools for Bayesian analysis

The increasing number of R-oriented Bayesian computational tools such as MCMCpack, MCMCglmm, DPpackage, R-INLA, spBayes, have made BUGS less and less crucial for day to day Bayesian computation. Honestly, I cannot figure out a single analysis that BUGS can do but at least one of the above mentioned packages cannot.

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