There are several reasons why everyone isn’t using Bayesian methods for regression modeling. One reason is that Bayesian modeling requires more thought: you need pesky things like priors, and you can’t assume that if a procedure runs without throwing an error that the answers are valid. A second reason is that MCMC sampling — the bedrock of practical Bayesian modeling — can be slow compared to closed-form or MLE procedures. A third reason is that existing Bayesian solutions have either been highly-specialized (and thus inflexible), or have required knowing how to use a generalized tool like BUGS, JAGS, or Stan. This third reason has recently been shattered in the R world by not one but two packages:
rstanarm. Interestingly, both of these packages are elegant front ends to Stan, via
This article describes
rstanarm, how they help you, and how they differ.
When I first heard of SSA (Singular Spectrum Analysis) and the EMD (Empirical Mode Decomposition) I though surely I’ve found a couple of magical methods for decomposing a time series into component parts (trend, various seasonalities, various cycles, noise). And joy of joys, it turns out that each of these methods is implemented in R packages:
In this posting, I’m going to document some of my explorations of the two methods, to hopefully paint a more realistic picture of what the packages and the methods can actually do. (At least in the hands of a non-expert such as myself.)
I’m always intrigued by techniques that have cool names: Support Vector Machines, State Space Models, Spectral Clustering, and an old favorite Hidden Markov Models (HMM’s). While going through some of my notes, I stumbled onto a fun experiment with HMM’s where you feed a bunch of English text into a two-state HMM and it will (tend to) discover what letters are vowels.
This is the fourth article in the series, where the techiness builds to a crescendo. If this is too statistical/programming geeky for you, the next posting will return to a more investigative and analytical flavor. Last time, we looked at a fixed-effects model:
m.fe <- lm (dollars ~ 1 + regime + ratetemp * I(dca - 55))
which looks like a plausible model and whose parameters are all statistically significant. A question that might arise is: why not use a hierarchical (AKA multilevel, mixed-effects) model instead? While we’re at it, why not go full-on Bayesian as well? It just so happens that there is a great new tool called
Stan which fits the bill and which also has an
rstan package for