# Implementing an EM Algorithm for Probit Regressions

September 30, 2014
By

(This article was first published on Rcpp Gallery, and kindly contributed to R-bloggers)

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.

## Motivating Example: Probit Regression

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

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.

``````library("Zelig")
data("turnout")
```   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
```
``dim(turnout)``
```[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.

``````fit0 <- glm(vote ~ income + educate + age,
data = turnout,
family = binomial(link = "probit")
)

fit0``````
```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.)

### EM Algorithm: Intuition

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

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

where $mu_i = x_i'hat{beta}$.

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

1. impute/augment $y_i^*$ values
2. estimate $widehat{beta}$ given the data augmentation

## Our Implementations

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

These are:

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.

``````mY <- matrix(turnout\$vote)
mX <- cbind(1,
turnout\$income,
turnout\$educate,
turnout\$age
)``````

### Attempt 1: Main Structure

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.

``````# include

using namespace Rcpp ;

// [[Rcpp::export()]]
List em1 (const arma::mat y,
const arma::mat X,
const int maxit = 10
) {
// inputs
const int N = y.n_rows ;
const int K = X.n_cols ;

// containers
arma::mat beta(K, 1) ;
beta.fill(0.0) ; // initialize betas to 1
arma::mat eystar(N, 1) ;
eystar.fill(0) ;

// algorithm
for (int it = 0 ; it < maxit ; it++) { // EM iterations
// NEXT STEP: implement algorithm
}

// returns
List ret ;
ret["N"] = N ;
ret["K"] = K ;
ret["beta"] = beta ;
ret["eystar"] = eystar ;
return(ret) ;
}``````

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.

``````fit1 <- em1(y = mY,
X = mX,
maxit = 20
)

fit1\$beta``````
```     [,1]
[1,]    0
[2,]    0
[3,]    0
[4,]    0
```
``head(fit1\$eystar)``
```     [,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.

### Attempt 2: EM with Mistaken Augmentation

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).

``````# include

using namespace Rcpp ;

// [[Rcpp::export()]]
List em2 (const arma::mat y,
const arma::mat X,
const int maxit = 10
) {
// inputs
const int N = y.n_rows ;
const int K = X.n_cols ;

// containers
arma::mat beta(K, 1) ;
beta.fill(0.0) ; // initialize betas to 0
arma::mat eystar(N, 1) ;
eystar.fill(0) ;

// algorithm
for (int it = 0 ; it < maxit ; it++) {
arma::mat mu = X * beta ;
// augmentation step
for (int n = 0 ; n < N ; n++) {
if (y(n, 0) == 1) { // y = 1
// NEXT STEP: fix augmentation
eystar(n, 0) = 1 ;
}
if (y(n, 0) == 0) { // y = 0
// NEXT STEP: fix augmentation
eystar(n, 0) = -1 ;
}
}
// maximization step
beta = (X.t() * X).i() * X.t() * eystar ;
}

// returns
List ret ;
ret["N"] = N ;
ret["K"] = K ;
ret["beta"] = beta ;
ret["eystar"] = eystar ;
return(ret) ;
}``````

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.

``````fit2 <- em2(y = mY,
X = mX,
maxit = 20
)

fit2\$beta``````
```          [,1]
[1,] -0.816273
[2,]  0.046065
[3,]  0.059481
[4,]  0.009085
```
``head(fit2\$eystar)``
```     [,1]
[1,]    1
[2,]   -1
[3,]   -1
[4,]    1
[5,]    1
[6,]    1
```

### Attempt 3: EM with Correct Augmentation

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).

``````# include

using namespace Rcpp ;

double f (double mu) {
double val = ((R::dnorm(-mu, 0, 1, false)) /
(1 - R::pnorm(-mu, 0, 1, true, false))
) ;
return(val) ;
}

double g (double mu) {
double val = ((R::dnorm(-mu, 0, 1, false)) /
(R::pnorm(-mu, 0, 1, true, false))
) ;
return(val) ;
}

// [[Rcpp::export()]]
List em3 (const arma::mat y,
const arma::mat X,
const int maxit = 10
) {
// inputs
const int N = y.n_rows ;
const int K = X.n_cols ;

// containers
arma::mat beta(K, 1) ;
beta.fill(0.0) ; // initialize betas to 0
arma::mat eystar(N, 1) ;
eystar.fill(0) ;

// algorithm
for (int it = 0 ; it < maxit ; it++) {
arma::mat mu = X * beta ;
// augmentation step
// NEXT STEP: parallelize augmentation step
for (int n = 0 ; n < N ; n++) {
if (y(n, 0) == 1) { // y = 1
eystar(n, 0) = mu(n, 0) + f(mu(n, 0)) ;
}
if (y(n, 0) == 0) { // y = 0
eystar(n, 0) = mu(n, 0) - g(mu(n, 0)) ;
}
}
// maximization step
beta = (X.t() * X).i() * X.t() * eystar ;
}

// returns
List ret ;
ret["N"] = N ;
ret["K"] = K ;
ret["beta"] = beta ;
ret["eystar"] = eystar ;
return(ret) ;
}``````
``````fit3 <- em3(y = mY,
X = mX,
maxit = 100
)``````
``head(fit3\$eystar)``
```        [,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.

``fit3\$beta``
```         [,1]
[1,] -1.68241
[2,]  0.09936
[3,]  0.10667
[4,]  0.01692
```
``fit0``
```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
```

### Attempt 4: EM with Correct Augmentation in Parallel

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.

``````Sys.setenv("PKG_CXXFLAGS" = "-fopenmp")
Sys.setenv("PKG_LIBS" = "-fopenmp")``````

Aside from some additional compiler flags, the changes to our new implementation
in `em4()` are minimal. They are:

• mark the `for` loop for parallelization with a `#pragma`
``````# include
# include

using namespace Rcpp ;

double f (double mu) {
double val = ((R::dnorm(-mu, 0, 1, false)) /
(1 - R::pnorm(-mu, 0, 1, true, false))
) ;
return(val) ;
}

double g (double mu) {
double val = ((R::dnorm(-mu, 0, 1, false)) /
(R::pnorm(-mu, 0, 1, true, false))
) ;
return(val) ;
}

// [[Rcpp::export()]]
List em4 (const arma::mat y,
const arma::mat X,
const int maxit = 10,
const int nthr = 1
) {
// inputs
const int N = y.n_rows ;
const int K = X.n_cols ;

// containers
arma::mat beta(K, 1) ;
beta.fill(0.0) ; // initialize betas to 0
arma::mat eystar(N, 1) ;
eystar.fill(0) ;

// algorithm
for (int it = 0 ; it < maxit ; it++) {
arma::mat mu = X * beta ;
// augmentation step
#pragma omp parallel for
for (int n = 0 ; n < N ; n++) {
if (y(n, 0) == 1) { // y = 1
eystar(n, 0) = mu(n, 0) + f(mu(n, 0)) ;
}
if (y(n, 0) == 0) { // y = 0
eystar(n, 0) = mu(n, 0) - g(mu(n, 0)) ;
}
}
// maximization step
beta = (X.t() * X).i() * X.t() * eystar ;
}

// returns
List ret ;
ret["N"] = N ;
ret["K"] = K ;
ret["beta"] = beta ;
ret["eystar"] = eystar ;
return(ret) ;
}``````

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.

``````fit4 <- em4(y = mY,
X = mX,
maxit = 100
)

identical(fit4\$beta, fit3\$beta)``````
```[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.

``````library("microbenchmark")

microbenchmark(seq = (em3(y = mY,
X = mX,
maxit = 100
)
),
par = (em4(y = mY,
X = mX,
maxit = 100,
nthr = 4
)
),
times = 20
)``````
```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
```

## Wrap-Up

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.

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