To model count data and contingency tables often Poisson regression is used. Poisson regression models belong to the generalized linear models family (GLM).

Since GLMs are commonly used R has already built-in functionality to estimate GLMs. Specifically the glm function from the stats package, withpoisson family and log link can be used to estimate a Poisson model.

The following poisson regression example is from the glm manual page and based on Dobson (1990).

options(width = 10000)
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson(link = "log"))
round(coef(glm.D93), 4)
## (Intercept)    outcome2    outcome3  treatment2  treatment3
##      3.0445     -0.4543     -0.2930      0.0000      0.0000

Making use of maximum likelihood estimation the logistic regression model can also be estimated in ROI. Here either a conic solver or a general purpose solver can be used. The conic solvers have the advantages that they are specifically designed to find the global optimum and are (often) faster.

# Log-likelihood

The maximum likelihood estiamte can be obtained be solving the following optimzation problem. $$$\underset{\beta}{\text{maximize}} ~~ \sum_{i = 1}^n y_i ~ log(\lambda_i) - \lambda_i ~~ \text{where} ~~ \lambda_i = exp(X_{i*} \beta)$$$

# Estimation

Sys.setenv(ROI_LOAD_PLUGINS = FALSE)
library(ROI)
library(ROI.plugin.nloptr)
library(ROI.plugin.ecos)
X <- model.matrix(glm.D93)
y <- counts

# General purpose solver

log_likelihood <- function(beta) {
xb <- drop(X %*% beta)
sum(y * xb - exp(xb))
}

op_gps <- OP(F_objective(log_likelihood, n =  ncol(X)), maximum = TRUE,
bounds = V_bound(ld = -Inf, nobj = ncol(X)))
s1 <- ROI_solve(op_gps, "nloptr.lbfgs", start = rnorm(ncol(X)))
round(solution(s1), 4)
## [1]  3.0445 -0.4543 -0.2930  0.0000  0.0000

# Conic solver

This problem can also be estimated by making use of conic optimization.

library(slam)
poisson_regression <- function(y, X) {
m <- nrow(X); n <- ncol(X)
i <- 3 * seq_len(m) - 2
op <- OP(c(-(y %*% X), rep.int(1, m)))
stm <- simple_triplet_matrix
A <- cbind(stm(rep(i, n), rep(seq_len(n), each = m), -drop(X), 3 * m, n),
stm(i + 2, seq_len(m), rep.int(-1, m), 3 * m, m))
rhs <- rep(c(0, 1, 0), m)
cones <- K_expp(m)
constraints(op) <- C_constraint(A, cones, rhs)
bounds(op) <- V_bound(ld = -Inf, nobj = ncol(A))
op
}

op <- poisson_regression(y, X)
s <- ROI_solve(op, solver = "ecos")
round(head(solution(s), n = NCOL(X)), 4)
## [1]  3.0445 -0.4543 -0.2930  0.0000  0.0000

# References

• Dobson, A. J. (1990) An Introduction to Generalized Linear Models. London: Chapman and Hall.