glm.poisson.disp {dispmod}

Overdispersed Poisson log-linear models

Description

This function estimates overdispersed Poisson log-linear models using the approach discussed by Breslow N.E. (1984).

Usage

glm.poisson.disp(object, maxit = 30, verbose = TRUE)

Arguments

object

an object of class "glm" providing a fitted Poisson log-linear regression model; see glm.

maxit

integer giving the maximal number of iterations for the model fitting procedure.

verbose

logical, if TRUE information are printed during each step of the algorithm.

Details

Breslow (1984) proposed an iterative algorithm for fitting overdispersed Poisson log-linear models. The method is similar to that proposed by Williams (1982) for handling overdispersion in logistic regression models (see glm.binomial.disp).

Suppose we observe n independent responses such that

y_i \mid \lambda_i \sim \mathrm{Poisson}(\lambda_in_i)

for i = 1, \ldots, n. The response variable y_i may be an event counts variable observed over a period of time (or in the space) of length n_i, whereas \lambda_i is the rate parameter. Then,

E(y_i \mid \lambda_i) = \mu_i = \lambda_i n_i = \exp(\log(n_i) + \log(\lambda_i))

where \log(n_i) is an offset and \log(\lambda_i) = \beta'x_i expresses the dependence of the Poisson rate parameter on a set of, say p, predictors. If the periods of time are all of the same length, we can set n_i = 1 for all i so the offset is zero.

The Poisson distribution has E(y_i \mid \lambda_i) = V(y_i \mid \lambda_i), but it may happen that the actual variance exceeds the nominal variance under the assumed probability model.

Suppose that \theta_i = \lambda_i n_i is a random variable distributed according to

\theta_i \sim \mathrm{Gamma} (\mu_i, 1/\phi)

where E(\theta_i) = \mu_i and V(\theta_i) = \mu_i^2 \phi. Thus, it can be shown that the unconditional mean and variance of y_i are given by

E(y_i) = \mu_i

and

V(y_i) = \mu_i + \mu_i^2 \phi = \mu_i (1 + \mu_i\phi)

Hence, for \phi > 0 we have overdispersion. It is interesting to note that the same mean and variance arise also if we assume a negative binomial distribution for the response variable.

The method proposed by Breslow uses an iterative algorithm for estimating the dispersion parameter \phi and hence the necessary weights 1/(1 + \mu_i \hat{\phi}) (for details see Breslow, 1984).

Value

The function returns an object of class "glm" with the usual information and the added components:

dispersion

the estimated dispersion parameter.

disp.weights

the final weights used to fit the model.

Note

Based on a similar procedure available in Arc (Cook and Weisberg, http://www.stat.umn.edu/arc)

References

Breslow, N.E. (1984), Extra-Poisson variation in log-linear models, Applied Statistics, 33, 38–44.

See Also

lm, glm, lm.disp, glm.binomial.disp

Examples

## Salmonella TA98 data
data(salmonellaTA98)
salmonellaTA98 <- within(salmonellaTA98, logx10 <- log(x+10))
mod <- glm(y ~ logx10 + x, data = salmonellaTA98, family = poisson(log)) 
summary(mod)

mod.disp <- glm.poisson.disp(mod)
summary(mod.disp)
mod.disp$dispersion

# compute predictions on a grid of x-values...
x0 <- with(salmonellaTA98, seq(min(x), max(x), length=50))
eta0 <- predict(mod, newdata = data.frame(logx10 = log(x0+10), x = x0), se=TRUE)
eta0.disp <- predict(mod.disp, newdata = data.frame(logx10 = log(x0+10), x = x0), se=TRUE)
# ... and plot the mean functions with variability bands
plot(y ~ x, data = salmonellaTA98)
lines(x0, exp(eta0$fit))
lines(x0, exp(eta0$fit+2*eta0$se), lty=2)
lines(x0, exp(eta0$fit-2*eta0$se), lty=2)
lines(x0, exp(eta0.disp$fit), col=3)
lines(x0, exp(eta0.disp$fit+2*eta0.disp$se), lty=2, col=3)
lines(x0, exp(eta0.disp$fit-2*eta0.disp$se), lty=2, col=3)

## Holford's data
data(holford)

mod <- glm(incid ~ offset(log(pop)) + Age + Cohort, data = holford, 
           family = poisson(log)) 
summary(mod)

mod.disp <- glm.poisson.disp(mod)
summary(mod.disp)
mod.disp$dispersion