# Sampling Importance Resampling (SIR) and social revolution.

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## Motivation

The purpose of this gallery post is several fold:

- to demonstrate the use of the new and improved C++-level
implementation of
**R**’s`sample()`

function (see here) - to demonstrate the Gallery’s new support for images in contributed posts
- to demonstrate the usefulness of SIR for updating posterior beliefs given a sample from an arbitrary prior distribution

## Application: Foreign Threats and Social Revolution

The application in this post uses an example from Jackman’s *Bayesian
Analysis for the Social Sciences* (page 72) which now has a 30-year
history in the Political Science (See Jackman for more
references). The focus is on the extent to which the probability of
revolution varies with facing a foreign threat or not. Facing a
foreign threat is measured by “defeated …” or “not defeated …”
over a span of 20 years. The countries come from in Latin
America. During this period of time, there are only three revolutions:
Bolivia (1952), Mexico (1910), and Nicaragua (1979).

Revolution | No Revolution | |
---|---|---|

Defeated and invaded or lost territory |
1 | 7 |

Not defeated for 20 years |
2 | 74 |

The goal is to learn about the true, unobservable probabilities of revolution given a recent defeat or the absence of one. That is, we care about

and

And, beyond that, we care about whether and differ.

These data are assumed to arise from a Binomial process, where the likelihood of the probability parameter value, , is

where is the total number of revolutions and non-revolutions and is the number of revolutions. The MLE for this model is just the sample proportion, so a Frequentist statistician would be wondering whether was sufficiently larger than to be unlikely to have happened by chance alone (given the null hypothesis that the two proportions were identical).

A Bayesian statistician could approach the question a bit more directly and compute the probability that To do this, we first need samples from the posterior distribution of and . In this post, we will get these samples via Sampling Importance Resampling.

## Sampling Importance Resampling

Sampling Importance Resampling allows us to sample from the posterior distribution, where

by resampling from a series of draws from the prior, . Denote one of those draws from the prior distribution, , as . Then draw from the prior sample is drawn with replacement into the posterior sample with probability

### Generating Samples from the Prior Distributions

We begin by drawing many samples from a series of prior
distributions. Although using a prior Beta prior distribution on the
parameter admits a closed-form solution, the point here is
to demonstrate a simulation based approach. On the other hand, a Gamma
prior distribution over is very much **not conjugate** and
simulation is the best approach.

In particular, we will consider our posterior beliefs about the different in probabilities under five different prior distributions.

dfPriorInfo <- data.frame(id = 1:5, dist = c("beta", "beta", "gamma", "beta", "beta"), par1 = c(1, 1, 3, 10, .5), par2 = c(1, 5, 20, 10, .5), stringsAsFactors = FALSE) dfPriorInfo id dist par1 par2 1 1 beta 1.0 1.0 2 2 beta 1.0 5.0 3 3 gamma 3.0 20.0 4 4 beta 10.0 10.0 5 5 beta 0.5 0.5

Using the data frame `dfPriorInfo`

and the `plyr`

package, we will
draw a total of 20,000 values from *each* of the prior
distributions. This can be done in any number of ways and is
completely independent of using **Rcpp** for the SIR magic.

library("plyr") MC1 <- 20000 dfPriors <- ddply(dfPriorInfo, "id", .fun = (function(X) data.frame(draws = (do.call(paste("r", X$dist, sep = ""), list(MC1, X$par1, X$par2))))))

However, we can confirm that our draws are as we expect and that we have the right number of them (5 * 20k = 100k).

head(dfPriors) id draws 1 1 0.7124225 2 1 0.5910231 3 1 0.0595327 4 1 0.4718945 5 1 0.4485650 6 1 0.0431667 dim(dfPriors) [1] 100000 2

### Re-Sampling from the Prior

Now, we write a C++ snippet that will create our R-level function to
generate a sample of `D`

values from the prior draws (`prdraws`

) given
their likelihood after the data (i.e., number of success – `nsucc`

,
number of failures – `nfail`

).

The most important feature to mention here is the use of some new and
improved extensions which effectively provide an equivalent,
performant mirror of **R**’s `sample()`

function at the
C++-level. **Important: as of the time of the writing of this post
these features were not on CRAN, only on github.**

The return value of this function is a length `D`

vector of draws from
the posterior distribution given the draws from the prior distribution
where the likelihood is used as a filtering weight.

# include <RcppArmadilloExtensions/sample.h> # include <RcppArmadilloExtensions/fixprob.h> // [[Rcpp::depends(RcppArmadillo)]] using namespace Rcpp ; // [[Rcpp::export()]] NumericVector samplePost (const NumericVector prdraws, const int D, const int nsucc, const int nfail) { int N = prdraws.size(); NumericVector wts(N); for (int n = 0 ; n < N ; n++) { wts(n) = pow(prdraws(n), nsucc) * pow(1 - prdraws(n), nfail); } RcppArmadillo::FixProb(wts, N, true); NumericVector podraws = RcppArmadillo::sample(prdraws, D, true, wts); return(podraws); }

To use the `samplePost()`

function, we create the **R** representation
of the data as follows.

nS <- c(1, 2) # successes nF <- c(7, 74) # failures

As a simple example, consider drawing a posterior sample of size 30 for the “defeated case” from discrete prior distribution with equal weight on the values of .125 (the MLE), .127, and .8. We see there is a mixture of .125 and .127 values, but no .8 values. values of .8 were simply to unlikely (given the likelihood) to be resampled from the prior.

table(samplePost(c(.125, .127, .8), 30, nS[1], nF[1])) 0.125 0.127 9 21

Again making use of the **plyr** package, we construct samples of size
20,000 for both and under each of the 5
prior distribution samples. These posterior draws are stored in the
data frame `dfPost`

.

MC2 <- 20000 f1 <- function(X) { draws <- X$draws t1 <- samplePost(draws, MC2, nS[1], nF[1]) t2 <- samplePost(draws, MC2, nS[2], nF[2]) return(data.frame(theta1 = t1, theta2 = t2)) } dfPost <- ddply(dfPriors, "id", f1) head(dfPost) id theta1 theta2 1 1 0.3067334 0.0130865 2 1 0.1421879 0.0420830 3 1 0.3218130 0.0634511 4 1 0.0739756 0.0363466 5 1 0.1065267 0.0460336 6 1 0.0961749 0.0440790 dim(dfPost) [1] 100000 3

## Summarizing Posterior Inferences

Here, we are visualizing the posterior draws for the quantity of interest — the difference in probabilities of revolution. These posterior draws are grouped according to the prior distribution used. A test of whether revolution is more likely given a foreign threat is operationalized by the probability that is positive. This probability for each distribution is shown in white. For all choices of the prior here, the probability that “foreign threat matters” exceeds .90.

The full posterior distribution of is shown for each of the five priors in blue. A solid, white vertical band indicates “no effect”. In all cases. the majority of the mass is clearly to the right of this band.

Recall that the priors are, themselves, over the individual revolution probabilities, and . The general shape of each of these prior distributions of the parameter is shown in a grey box by the white line. For example, is actually a uniform distribution over the parameter space, . On the other hand, has most of its mass at the two tails.

At least across these specifications of the prior distributions on , the conclusion that “foreign threats matter” finds a good deal of support. What is interesting about this application is that despite these distributions over the difference in probabilities, the p-value associated with Fisher’s Exact Test for 2 x 2 tables is just .262.

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