Posts tagged with model

There is a certain hype about mixed (and random) effects among statistician and analysts. You can show some love to Douglas Bates and Martin Maechler for maintaing the lme4 package for our cupid, R ;)

I copy the entity of the information of the projects page.

Doxygen documentation of the underlying C functions is here.

The project summary page you can find here.

References to articles and other research using nlme or lme4 can be found here. The LaTeX bibliography file can be accessed from here. If you would like to add your work to this database, please email vasishth.shravan at gmail dot com

Slides from short courses on lme4 are here.

Chapter drafts of the book lme4: Mixed-effects Modeling with R are available here.

To complete this quick post, I append the following vignettes.

Implementation Details
PLS vs GLS for LMMs
Computational Methods

[source]

Let’s say that you’re fitting a cumbersome model so time is not to waste over a PC staring at the screen half anxious-half bored…

Then, you can always leave and go on with meetings and all your daily routine and have R notify you the results! How?

We will illustrate the situation above using some Bayesian Model Averaging code adapted by Martin Feldkircher & Stefan Zeugner. You should download the code and source everything in R except for the example in the end (after the definition of the functions!).

#The code to get a model
fls.data=read.table(url("http://feldkircher.gzpace.net/links/fls_data_adj.txt"))

data.M=as.matrix(fls.data)
K=ncol(dataM)-1  # nr. of regressors

# this setting corresponds to a uniform prior on the model space (prior.msize=K/2 and theta="fix")
# and the ric specification since K^2> N (with N the nr. of observations) as suggested by fls
model.ric=fls(X.data=data.M,burn=60000,iter=700000,g=(1/K^2),nmodel=100,theta="fix",prior.msize=K/2,logfile=T,mcmc="bd",start.value=rep(0,K),beta.save=T)

This is gonna take s o m e time (really!), so you could let R working and go out for a cup of coffee (typical of Greek people!). Add the following at the end of the above code.

library(twitteR)
sess <- initSession('myUser', 'myPass') # Set your user account info
ns <- updateStatus('A model waits for you @ home ;) ', sess)

Would you really care enough to check whether the fit is done when outside?

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