Jonathan Olmsted — written Oct 1, 2014 — source

Users new to the Rcpp family of functionality are often impressed with the performance gains that can be realized, but struggle to see how to approach their own computational problems. Many of the most impressive performance gains are demonstrated with seemingly advanced statistical methods, advanced C++–related constructs, or both. Even when users are able to understand how various demonstrated features operate in isolation, examples may implement too many at once to seem accessible.

The point of this Gallery article is to offer an example application that performs well (thanks to the Rcpp family) but has reduced statistical and programming overhead for some users. In addition, rather than simply presenting the final product, the development process is explicitly documented and narrated.

As an example, we will consider estimating the parameters the standard Probit regression model given by

\[y_i^* = x_i'\beta + \epsilon_i\]where \(x_i\) and \(\beta\) are length \(K\) vectors and the presence of an “intercept” term is absorbed into \(x_i\) if desired.

The analyst only has access to a censored version of \(y_i^*\), namely \(y_i\) where the subscript \(i\) denotes the \(i\) th observation.

As is common, the censoring is assumed to generate \(y_i = 1\) if \(y_i^* \geq 0\) and \(y_i = 0\) otherwise. When we assume \(\epsilon_i \sim N(0, 1)\), the problem is just the Probit regression model loved by all.

To make this concrete, consider a model of voter turnout using the dataset provided by the Zelig R package.

race age educate income vote 1 white 60 14 3.3458 1 2 white 51 10 1.8561 0 3 white 24 12 0.6304 0 4 white 38 8 3.4183 1 5 white 25 12 2.7852 1 6 white 67 12 2.3866 1

[1] 2000 5

Our goal will be to estimate the parameters associated with the variables
*income*, *educate*, and *age*. Since there is nothing special about this
dataset, standard methods work perfectly well.

Call: glm(formula = vote ~ income + educate + age, family = binomial(link = "probit"), data = turnout) Coefficients: (Intercept) income educate age -1.6824 0.0994 0.1067 0.0169 Degrees of Freedom: 1999 Total (i.e. Null); 1996 Residual Null Deviance: 2270 Residual Deviance: 2030 AIC: 2040

Using `fit0`

as our baseline, the question is how can we recover these estimates
with an Rcpp-based approach. One answer is implement the EM-algorithm in C++
snippets that can be processed into R-level functions; that’s what we will
do. (Think of this as a Probit regression analog to
the linear regression example — but with fewer features.)

For those unfamiliar with the EM algorithm, consider the Wikipedia article and a denser set of Swarthmore lecture notes.

The intuition behind this approach begins by noticing that if mother nature revealed the \(y_i^*\) values, we would simply have a linear regression problem and focus on

\[\widehat{\beta} = (X'X)^{-1} X'y^*\]where the meaning of the matrix notation is assumed.

Because mother nature is not so kind, we have to *impute* the \(y_i^*\)
values. For a given guess of \(\widehat{\beta}\), due to our distributional
assumptions about \(\epsilon_i\) we know that

and

\[E\left[y_i^* \big\vert y_i=0, \widehat{\beta}\right] = \mu_i - \frac{\phi(-\mu_i)}{\Phi(-\mu_I)}\]where \(\mu_i = x_i'\hat{\beta}\).

By iterating through these two steps we can eventually recover the desired parameter estimates:

- impute/augment \(y_i^*\) values
- estimate \(\widehat{\beta}\) given the data augmentation

To demonstrate implementation of the EM algorithm for a Probit regression model using Rcpp-provided functionality we consider a series of steps.

These are:

- Attempt 1: Main Structure
- Attempt 2: EM with Mistaken Augmentation
- Attempt 3: EM with Correct Augmentation
- Attempt 4: EM with Correct Augmentation in Parallel

These steps are **not** chosen because each produces useful output (from the
perspective of parameters estimation), but because they mirror milestones in a
development process that benefits new users: only small changes are made at a
time.

To begin, we prepare our R-level data for passage to our eventual C++-based functions.

The first milestone will be to mock up a function `em1`

that is exported to
create an R-level function of the same name. The key features here are that we
have defined the function to

- accept arguments corresponding to likely inputs
- create containers for the to-be-computed values,
- outline the main loop of the code for the EM iterations, and
- return various values of interest in a list

Users new to the Rcpp process will benefit from return `List`

objects in the
beginning. They allow you rapidly return new and different values to the R-level
for inspection.

We know that this code does not produce estimates of anything. Indeed, that is
by design. Neither the `beta`

nor `eystar`

elements of the returned `list`

are
ever updated after they are initialized to 0.

However, we can see that much of the administrative work for a working implementation is complete.

[,1] [1,] 0 [2,] 0 [3,] 0 [4,] 0

[,1] [1,] 0 [2,] 0 [3,] 0 [4,] 0 [5,] 0 [6,] 0

Having verified that input data structures and output data structures are “working” as expected, we turn to updating the \(y_i^*\) values.

Updates to the \(y_i^*\) values are different depending on whether \(y_i=1\) or \(y_i=0\). Rather than worrying about correctly imputing the unobserved propensities, we will use dummy values of 1 and -1 as placeholders. Instead, the focus is on building on the necessary conditional structure of the code and looping through the update step for every observation.

Additionally, at the end of each imputation step (the *E* in *EM*) we update the
\(\beta\) estimate with the least squares estimate (the *M* in *EM*).

This code, like that in Attempt 1, is syntactically fine. But, as we know, the
update step is very wrong. However, we can see that the updates are happening as
we’d expect and we see non-zero returns for the `beta`

element and the `eystar`

element.

[,1] [1,] -0.816273 [2,] 0.046065 [3,] 0.059481 [4,] 0.009085

[,1] [1,] 1 [2,] -1 [3,] -1 [4,] 1 [5,] 1 [6,] 1

With the final logical structure of the code built out, we will now correct the
data augmentation. Specifically, we replace the assignment of 1 and -1 with the
expectation of the unobservable values \(y_i^*\). Rather than muddy our EM
function (`em3()`

) with further arithmetic, we sample call the C++ level
functions `f()`

and `g()`

which were included prior to our definition of
`em3()`

.

But, since these are just utility functions needed internally by `em3()`

, they
are not tagged to be exported (via `// [[Rcpp::export()]]`

) to the R level.

As it stands, this is a correct implementation (although there is room for improvement).

[,1] [1,] 1.3910 [2,] -0.6599 [3,] -0.7743 [4,] 0.8563 [5,] 0.9160 [6,] 1.2677

Second, notice that this output is identical to the parameter estimates (the
object `fit0`

) from our R level call to the `glm()`

function.

[,1] [1,] -1.68241 [2,] 0.09936 [3,] 0.10667 [4,] 0.01692

Call: glm(formula = vote ~ income + educate + age, family = binomial(link = "probit"), data = turnout) Coefficients: (Intercept) income educate age -1.6824 0.0994 0.1067 0.0169 Degrees of Freedom: 1999 Total (i.e. Null); 1996 Residual Null Deviance: 2270 Residual Deviance: 2030 AIC: 2040

With a functional implementation complete as `em3()`

, we know turn to the second
order concern: *performance*. The time required to evaluate our function can be
reduced from the perspective of a user sitting at a computer with idle cores.

Although the small size of these data don’t necessitate parallelization, the *E*
step is a natural candidate for being parallelized. Here, the parallelization
relies on OpenMP. See here for other examples of combining
Rcpp and OpenMP or here for a different approach.

Aside from some additional compiler flags, the changes to our new implementation
in `em4()`

are minimal. They are:

- include the additional header
- mark the
`for`

loop for parallelization with a`#pragma`

This change should not (and does not) result in any change to the calculations being done. However, if our algorithm involved random number generation, great care would need to be taken to ensure our results were reproducible.

[1] TRUE

Finally, we can confirm that our parallelization was “successful”. Again, because there is really no need to parallelize this code, performance gains are modest. But, that it indeed runs faster is clear.

Unit: milliseconds expr min lq mean median uq max neval cld seq 32.94 33.01 33.04 33.03 33.07 33.25 20 b par 11.16 11.20 11.35 11.26 11.29 13.16 20 a

The purpose of this lengthy gallery post is neither to demonstrate new functionality nor the computational feasibility of cutting-edge algorithms. Rather, it is to explicitly walk through a development process similar that which new users can benefit from using while using a very common statistical problem, Probit regression.

Tweet- Benchmarking Rcpp code with RcppClock — Zach DeBruine
- Simulation Smoother using RcppArmadillo — Tomasz Woźniak
- Constructing a Sparse Matrix Class in Rcpp — Zach DeBruine and Dirk Eddelbuettel