Publications by Wingfeet
Trying a prefmap
Preference mapping is a key technique in sensory and consumer research. It links the sensory perception on products to the liking of products and hence provides clues to the development of new, well tasting, products. Even though it is a key technique, it is also a long standing problem how to perform such an analysis. In R the SensoM...
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Trying dplyr on triathon data
There was a triathlon in Almere last week, like every year since 1983. I pulled the data of all years to get some idea how things have changed in that sport. To get a visual I decided to plot the best 10% athletes. Then later I decided this was an ideal moment to look at plyr and dplyr again, so rewrote everything using those tools. I...
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Bayesian models in R
There are many ways to run general Bayesian calculations in or from R. The best known are JAGS, OpenBUGS and STAN. Then some time ago Rasmus Bååth had a post Three ways to run Bayesian models in R in which he mentioned LaplacesDemon (not on CRAN) on top of those. A check of the Bayes task view gives ‘MCMCpack (…) contains a gene...
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Bayes models from SAS PROC MIXED in R, post 2
This is my second post in converting SAS’s PROC MCMC examples in R. The task in his week is determining the transformation parameter in a Box-Cox transformation. SAS only determines Lambda, but I am not so sure about that. What I used to do was get an interval for Lambda, select an interpretable value (e.g. 1/2 is square root, -1 is...
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Tuning LaplacesDemon
I was continuing with my Bayesian algorithms in R exercise. For these exercises I port SAS PROC MCMC examples to the various R solutions. However, the next example was logit model and that’s just too simple, especially after last week’s Jacobian for the Box-Cox transformation. Examples for logit and probit models abound on the web...
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Tuning Laplaces Demon II
I am continuing with my trying all algorithms of Laplaces Demon. It is actually quite a bit more work than I expected but I do find that some of the things get clearer. Now that I am close to the end of calculating this second batch I learned that there is loads of adaptive algorithms. The point of those adaptations is not so much get...
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Tuning Laplaces Demon III
This is the third post with LaplacesDemon tuning. same problem, different algorithms. For introduction and other code see this post. The current post takes algorithms Independence Metropolis to Reflective Slice Sampler.Independence MetropolisIndependence Metropolis expects first a run of e.g. LaplaceApproximation and to be fed the res...
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Tuning Laplaces Demon IV
This is the last post of testing Laplaces Demon algorithms. In the last algorithms there are some which are completely skipped because they are not suitable for the problem. Reversible Jump is for variable selection. Sequential Metropolis-within-Gibbs, Updating Sequential Metropolis-within-Gibbs and their adaptive versions are for state space mod...
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The completeness of online gun shooting victim counts
There are a number of on line efforts to register victims of shootings online. Shootingtracker tries to register all mass shootings, those with four or more victims. Slate had the gun death tally (GDT), gun deaths starting at Newtown, running through to December 31, 2013. This project is continued in the Gun Violence Archive.In this ...
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SAS PROC MCMC example in R; Poisson Regression
In this post I will try to copy the calculations of SAS’s PROC MCMC example 61.5 (Poisson Regression) into the various R solutions. In this post Jags, RStan, MCMCpack, LaplacesDemon solutions are shown. Compared to the first post in this series, rcppbugs and mcmc are not used. Rcppbugs has no poisson distribution and while I know ho...
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