## Sunday, February 25, 2018

### Make model diagrams for human comprehension and ease of programming

There's a great new book by Farrell and Lewandowsky, Computational Modeling of Cognition and Behavior (at the publisher, at Amazon.com), that includes some chapters on Bayesian methods. Each chapter includes a little "in vivo" commentary by an outside contributor. My commentary accompanies their chapter regarding JAGS. The commentary is posted here in a succession of three blog posts; this is 3 of 3. (Part 1 is here, and part 2 is here.) Do check out their book!

Make model diagrams for human comprehension and ease of programming

While a JAGS model specification captures the full structure of the model, it can help human beings to have a diagrammatic representation of the model. A diagram can help the viewer achieve a comprehensive overview of the relations between parameters and their meanings with respect to each other and to the data. A good conceptual diagram of a model can also guide writing the JAGS model specification.

For example, Figure 8.13 (below) shows a representation of the normal model used in the previous section. Because of graphical conventions for probability distributions, the data must be shown at the bottom of the diagram. Starting with yi, the diagram shows that the data come from a normal distribution that has parameters μ and σ. Then the top of the diagram illustrates the prior distributions on the parameters.

 Figure 8.13. Diagram of the normal model, in the style of the book, Doing Bayesian Data Analysis (Kruschke, 2015). Scan the diagram from the bottom up, that is, beginning with the data yi at the bottom. Notice that every arrow has a corresponding line of code in the JAGS model specification.
The type of diagram in Figure 8.13 has several helpful attributes. It spatially organizes related parameters in the same distribution. For example, we can see that parameters μ and σ are both participating in the same distribution, and the icon also suggests the μ is for the central tendency and σ is for the scale (standard deviation). Moreover, the diagram completely captures all the structure of the model, showing the form of the prior distribution along with the likelihood function. Indeed, every arrow in the diagram has a corresponding line of code in the JAGS model specification, as shown in the previous post's Listing 8.11 and repeated here for convenience:
 model { for ( i in 1:N ) { y[i] ~ dnorm( mu , 1/sigma^2 ) } mu ~ dunif( -100 , 100 ) sigma ~ dunif( 0 , 100 ) }  (Listing 8.11. Describe data with a normal distribution in JAGS.)
Often when I’m creating a new model, I first sketch out a diagram in the style of Figure 8.13, and after I’m sure I have a coherent structure, then I type the model into JAGS, scanning the diagram from the bottom up.

There is another convention that is sometimes used to illustrate Bayesian models. This convention has historical roots in general treatments of statistical models that specify probabilistic dependencies between parameters such that no dependencies cycle back on themselves. Such structures are called directed acyclic graphs (DAG’s). In particular, the DAG diagrammatic convention was used by the software DoodleBUGS, which was a component of WinBUGS (Spiegelhalter, Thomas, Best, & Lunn, 2003), the precursor to JAGS.

 Figure 8.14. Diagram of the normal model, in the style of conventional graphical models. Shaded node indicates observed (not estimated) values. Plate indicates repetition. Notice that the arrows have no relation to lines of code in the JAGS model specification.
Figure 8.14 shows a DAG diagram for the normal model. The arrows between variables indicate that the data, yi, are dependent on parameter μ and on parameter σ. But the diagram does not indicate whether or not the two parameters participate in the same distribution or come from different distributions. The diagram does not show the prior distributions at all. Importantly, the diagram provides no clue how to express the model in JAGS because there is no relation between the arrows in the diagram and the lines of code in JAGS. Often when DAGs are used for illustration, the diagram will be accompanied by a list of all the equations that specify the model. While the equations provide complete information, the reader must scan back and forth between equations and diagram to make sense of the diagram.

For more disucssion, see p. 197 of Kruschke (2015). It’s repeatedly emphasized for many different models in that book that every arrow in a model diagram (usually) has a corresponding line of code in JAGS. See another comparison of diagrams at this blog post. See tools for creating diagrams at this blog post

(Ironically, the next chapter of Farrell and Lewandowsky's book violates this advice. You can judge for yourself whether you think the DAGs have any useful correspondence to the JAGS code. Despite the use of DAGs, their book is a great resource!)

## Saturday, February 24, 2018

### Compose JAGS model statements for human readability

There's a great new book by Farrell and Lewandowsky, Computational Modeling of Cognition and Behavior (at the publisher, at Amazon.com), that includes some chapters on Bayesian methods. Each chapter includes a little "in vivo" commentary by an outside contributor. My commentary accompanies their chapter regarding JAGS. The commentary is posted here in a succession of three blog posts; this is 2 of 3. (Part 1 is here.) Do check out their book!

Compose JAGS model statements for human readability

All mathematical models are designed to describe structure in data. Logically, to comprehend a model, we must first know what the data are that the model is supposed to describe. We begin with describing how the data are probabilistically distributed according to some likelihood function. The likelihood function has parameters, which typically describe some trend or relation in the data. The parameters might be expressed in terms of higher-level parameters. Finally, the parameters have uncertainty, expressed as prior distributions on the parameters. The JAGS model-specification language lets us write models in this logical and comprehensible way: Start with the data, write the likelihood function, then write any dependencies among parameters, and finish with the prior distribution on the parameters. This makes it easy to write the model, and, importantly, easy for readers of the model specification to make sense of the model.

For example, consider a JAGS model specification for describing a set of data with a normal distribution (cf. Listing 8.3 [in Farrell and Lewandowsky's book]):
 model { for ( i in 1:N ) { y[i] ~ dnorm( mu , 1/sigma^2 ) } mu ~ dunif( -100 , 100 ) sigma ~ dunif( 0 , 100 ) }  (Listing 8.11. Describe data with a normal distribution in JAGS.)
The model specification (above) is easy to comprehend sequentially in reading order.

JAGS does not execute the lines of the model specification as if they were procedural R commands, but instead JAGS examines the overall model statement for structural consistency. The three lines in the model specification (above) could be put in any order and JAGS would not care. For example, JAGS would also allow the following:
 model { sigma ~ dunif( 0 , 100 ) mu ~ dunif( -100 , 100 ) for ( i in 1:N ) { y[i] ~ dnorm( mu , 1/sigma^2 ) } }  (Listing 8.12. Alternative JAGS description of a normal distribution.)
In terms of information content, it does not matter if you say “the knee bone’s connected to the thigh bone, and the thigh bone’s connected to the hip bone,” or instead say “the thigh bone’s connected to the hip bone, and the knee bone’s connected to the thigh bone.”

But for human readers trying to comprehend the statements, order does matter. Especially for complicated models with unfamiliar or arbitrary parameter names, it can be very difficult to understand model specifications that begin by specifying priors on parameters before specifying what distributions those parameters play a role in, and what the relation of the data to the parameters is. Therefore, be kind to your readers, and to your future self who will look back on your code months later. Specify JAGS models starting with the data likelihood then working through the parameters and their priors. These ideas are expressed with more examples on p. 199 and p. 414 of Kruschke (2015).

## Friday, February 23, 2018

### Run MCMC to achieve effective sample size (ESS) of 10,000

There's a great new book by Farrell and Lewandowsky, Computational Modeling of Cognition and Behavior (at the publisher, at Amazon.com), that includes some chapters on Bayesian methods. Each chapter includes a little "in vivo" commentary by an outside contributor. My commentary accompanies their chapter regarding JAGS. The commentary is posted here in a succession of three blog posts; this is 1 of 3. Do check out their book!

Run MCMC to achieve effective sample size (ESS) of 10,000

Bayesian analysis of complex models is possible only by virtue of modern software that takes an abstract model specification and returns a representation of the posterior distribution. In software that uses Markov-chain Monte Carlo (MCMC) methods, such as JAGS, the representation is inherently noisy. The random noise from MCMC tends to cancel out as the chain gets longer and longer. But different aspects of the posterior distribution are differently affected by noise. A relatively stable aspect is the median value of the chain. The median tends to stabilize relatively quickly, that is, with relatively shorter chains, because the median is usually in a high-density region of the posterior and the value of the median does not depend on the distance to outliers (unlike the mean). But other crucial aspects of the posterior distribution tend to need longer chains to achieve stable values.

In particular, a crucial aspect of a parameter distribution is its width. Narrower distributions connote more certainty in the estimate of the parameter. A very useful indicator of the width of a distribution is its 95% highest density interval (HDI). Parameter values within the 95% HDI have higher probability density than parameter values outside the HDI, and the parameter values inside the 95% HDI have a total probability of 95%. An example of an HDI is illustrated in Figure 1.

 Figure 1. Example of a 95% highest density interval (HDI). On the axes of the graph, θ denotes a parameter in the model, and p(θ|D) denotes the posterior distribution of that parameter. The limits of the HDI are marked by the ends of the double-headed arrow. Any value of θ within the HDI has higher probability density than any value outside the HDI. The mass within the 95% HDI, shaded by gray in the figure, is 95%.
Because the limits of an HDI are usually in the low-density tails of the distribution, there are relatively few steps in the MCMC chain near the limits. Therefore it takes a long chain to generate sufficiently many representative values of the parameter to stabilize the estimate of the HDI limits.

How long of a chain is needed to produce stable estimates of the 95% HDI? One useful heuristic answer is 10,000 independent steps. The rationale for the heuristic is explained in Section 7.5.2 of Kruschke (2015). Note that the requirement is 10,000 independent steps. Unfortunately, most MCMC chains are strongly autocorrelated, meaning that successive steps are near each other, and are not independent. Therefore we need a measure of chain length that takes into account the autocorrelation of the chain. Such a measure is called the effective sample size (ESS), for which a formal definition is provided in Section 7.5.2 of Kruschke (2015).

ESS is computed in R by the effectiveSize function (which is in the coda package, which in turn is part of the rjags package for JAGS). For example, suppose we have generated an MCMC chain using the rjags function, coda.samples, and the resulting object is called mcmcfin. Then we can find the ESS of the parameters by typing effectiveSize(mcmcfin).

It is crucial to realize that (i) the ESS will usually be much less than the number of steps in the MCMC chain, and (ii) every parameter in a multi-parameter model has a different ESS. Some parameters might have large ESS while others have small ESS. Moreover, combinations of parameters, such as a difference of two means, can have quite different ESS than the separate parameters. Therefore it is important to check the ESS of every parameter of interest, and the ESS of any interesting parameter combinations.

Stay tuned for Parts 2 and 3...

# Psychonomic Bulletin & Review

Scroll down on the page linked here for the articles in the special issue on Bayesian data analysis. The list of links includes these:

#### Articles

Bayesian data analysis for newcomers [<-clickable link]
John K. Kruschke and Torrin M. Liddell
The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective [<-clickable link]
John K. Kruschke and Torrin M. Liddell

Starting February 19, there will be a series of commentaries on the articles. I will update the blog when more information is available.

## Sunday, January 28, 2018

### Don't treat ordinal data as metric -- update of movie ratings

In a previous post I applied a Bayesian ordered-probit model to movie ratings and showed how the results differ from treating the data as if they were metric. The metric model used frequentist t tests (because that's what most applied researchers would do). In this post, I re-analyze that data as if they metric but using a Bayesian model that has the same hierarchical structure as the Bayesian hierarchical ordered-probit model I used before. Here we can compare ordered-probit to metric treatments with all else held constant. Spoiler: Same conclusions, of course. The ordered-probit model fits the data much better than the metric model. Don't treat ordinal data as metric.

Please see the previous post for details about the data and the models. In particular, note that there is hierarchical structure on the standard deviations across movies, but not on the means across movies. In other words, there is no direct shrinkage on the means.

Here is a repeat of the data, from 30 movies, fit by the ordered-probit model:
The pink bars (above) are frequency histograms of the data from each movie; the blue dots (with vertical blue whiskers) are the posterior predictions and 95% HDIs for the ordered-probit model. A very good fit, all in all.

Here is some news. The data fit by the metric model. Here a 1-star rating is considered to be a score of 1.0 on a metric scale, a 2-star rating is considered to be a score of 2.0 on a metric scale, and so on. Each histogram is fit by a normal distribution (as is assumed by t tests, ANOVA, etc.). The result:
The superimposed blue curves (above) are a smattering from the posterior distribution. Not a very good fit to the data distributions.

More news. The means of the ordered-probit model plotted against the means of the metric model, with 95% HDI's:
You can see (above) that the rank ordering of the movies is quite different for the two models!

Case in point (and more news):
You can see (above) that the ordered-probit puts movie 10 well above movie 26, but treating the data as metric yields the opposite conclusion. Which conclusion is more appropriate? Clearly the ordered-probit describes the data much more accurately than treating the data as metric. Watch movie 10 before movie 26.

And don't be a free rider on the rating system. If you use the rating system, give a rating.

## Monday, December 25, 2017

### Which movie is rated better? (Don't treat ordinal ratings as metric)

When deciding what movie to watch online, have you ever considered the star ratings provided by previous viewers? For example, Amazon.com has a 5-star rating system, in which reviewers can give a movie an ordinal rating from 1 star to 5 stars. Here are frequency histograms of 30 movies listed under "drama":

 Frequency histograms of star ratings from 30 movies (shown as pink bars). Posterior predictions of an ordered-probit model are shown by blue dots with blue vertical segments indicating the 95% HDIs.

Usually people analyze rating data as if the data were metric, that is, people pretend that 1 star is 1.0 on a metric scale, and 2 stars is 2.0 on the metric scale, and 3 stars is 3.0, and so forth. But this is not appropriate because all we know about the star ratings is their order, not their interval separation. The ordinal data should instead be described with an ordinal model. For more background, see Chapter 23 of DBDA2E, and this manuscript.

Here I used an ordered-probit model to describe the data from the 30 movies. I assumed the same response thresholds across the movies because the response scale is presented to everyone the same way, for all movies; this is a typical assumption. Each movie was given its own latent mean (mu) and standard deviation (sigma). I put no hierarchical structure on the means, as I didn't want the means of small-N movies to be badly shrunken toward enormous-N movies. But I did put hierarchical structure on the standard deviations, because I wanted some constraint on the sigma's of movies that show extreme ceiling effects in their data; it turns out the sigma's were estimated to vary quite a lot anyway.

Below is a graph of the resulting latent means (mu's) of the movies plotted against the means of their ordinal ratings treated as metric:
 Each point is a movie. Vertical axis is posterior mean (mu) of ordered probit model, with 95% HDI displayed as blue segment. Horizontal axis is mean of the star ratings treated as metric values.
In the scatter plot above, notice the many non-monotonicities; that is, as the means of ordinal-as-metric values increase along the horizontal axis, the latent mu's do not consistently increase on the vertical axis. In other words, the latent mu's are telling a different story than the ordinal-as-metric means.

Two movies with nearly equal ordinal-as-metric means, but with very different latent means in the ordered-probit model:

 Upper row shows ordered-probit fit; lower row shows t test with unequal variances. Notice the blue dots from the ordered-probit model fit the data much better than the blue normal distributions of the ordinal-as-metric model. (Case 19 is Ekaterina: The rise of Catherine the Great, and Case 26 is John Grisham's The Rainmaker.)
Do we conclude that the movies (above) are rated about the same, or that movie 19 is rated much better than movie 26? I think we have to conclude that movie 19 is rated much better than movie 26 because the ordered-probit model is a much better description of the data.

Two movies with ordinal-as-metric means that are significantly different in one direction but the latent means in the ordered-probit model are quite different in the opposite direction:

 Upper row shows ordered-probit fit; lower row shows t test with unequal variances. Notice the blue dots from the ordered-probit model fit the data much better than the blue normal distributions of the ordinal-as-metric model. (Case 10 is Miss Sloane, and Case 26 is John Grisham's The Rainmaker.)
Do we conclude that movie 26 is rated better than movie 10, or the other way around? I think we have to conclude that movie 10 is rated better than movie 26 because the ordered probit model is a much better description of the data.

This isn't (only) about movies: The point is that ordinal data from any source should not be treated as metric. Pretending that a rating of "1" is numeric 1.0, and rating "2" is 2.0, and rating "3" is 3.0, and so forth, is usually nonsensical because it's assuming metric information in the data that simply is not there. Treating the data as normally-distributed metric values is often a terrible description of the data. Instead, use an ordinal model for ordinal data. The ordinal model will describe the data better, and sometimes yield rather different implications than the ordinal-as-metric description.

For more information, see Chapter 23 of DBDA2E, and this manuscript titled, "Analyzing ordinal data with metric models: What could possibly go wrong?"