## 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

## 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:

---------------------------------------------

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

---------------------------------------------

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

-----------------------------------------------

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

## Wednesday, December 01, 2010

Subscribe to:
Posts (Atom)