The LOGISTIC Procedure


For a correctly specified model, the Pearson chi-square statistic and the deviance, divided by their degrees of freedom, should be approximately equal to one. When their values are much larger than one, the assumption of binomial variability might not be valid and the data are said to exhibit overdispersion. Underdispersion, which results in the ratios being less than one, occurs less often in practice.

When fitting a model, there are several problems that can cause the goodness-of-fit statistics to exceed their degrees of freedom. Among these are such problems as outliers in the data, using the wrong link function, omitting important terms from the model, and needing to transform some predictors. These problems should be eliminated before proceeding to use the following methods to correct for overdispersion.

Rescaling the Covariance Matrix

One way of correcting overdispersion is to multiply the covariance matrix by a dispersion parameter. This method assumes that the sample sizes in each subpopulation are approximately equal. You can supply the value of the dispersion parameter directly, or you can estimate the dispersion parameter based on either the Pearson chi-square statistic or the deviance for the fitted model.

The Pearson chi-square statistic and the deviance are given by


where is the number of subpopulation profiles, is the number of response levels, is the total weight (sum of the product of the frequencies and the weights) associated with th level responses in the th profile, , and is the fitted probability for the th level at the th profile. Each of these chi-square statistics has degrees of freedom, where is the number of parameters estimated. The dispersion parameter is estimated by


In order for the Pearson statistic and the deviance to be distributed as chi-square, there must be sufficient replication within the subpopulations. When this is not true, the data are sparse, and the p-values for these statistics are not valid and should be ignored. Similarly, these statistics, divided by their degrees of freedom, cannot serve as indicators of overdispersion. A large difference between the Pearson statistic and the deviance provides some evidence that the data are too sparse to use either statistic.

You can use the AGGREGATE (or AGGREGATE=) option to define the subpopulation profiles. If you do not specify this option, each observation is regarded as coming from a separate subpopulation. For events/trials syntax, each observation represents Bernoulli trials, where is the value of the trials variable; for single-trial syntax, each observation represents a single trial. Without the AGGREGATE (or AGGREGATE=) option, the Pearson chi-square statistic and the deviance are calculated only for events/trials syntax.

Note that the parameter estimates are not changed by this method. However, their standard errors are adjusted for overdispersion, affecting their significance tests.

Williams’ Method

Suppose that the data consist of binomial observations. For the th observation, let be the observed proportion and let be the associated vector of explanatory variables. Suppose that the response probability for the th observation is a random variable with mean and variance


where is the probability of the event, and is a nonnegative but otherwise unknown scale parameter. Then the mean and variance of are


Williams (1982) estimates the unknown parameter by equating the value of Pearson’s chi-square statistic for the full model to its approximate expected value. Suppose is the weight associated with the th observation. The Pearson chi-square statistic is given by


Let be the first derivative of the link function . The approximate expected value of is


where and is the variance of the linear predictor . The scale parameter is estimated by the following iterative procedure.

At the start, let and let be approximated by , . If you apply these weights and approximated probabilities to and and then equate them, an initial estimate of is


where is the total number of parameters. The initial estimates of the weights become . After a weighted fit of the model, the and are recalculated, and so is . Then a revised estimate of is given by


The iterative procedure is repeated until is very close to its degrees of freedom.

Once has been estimated by under the full model, weights of can be used to fit models that have fewer terms than the full model. See Example 53.10 for an illustration.

Note: If the WEIGHT statement is specified with the NORMALIZE option, then the initial values are set to the normalized weights, and the weights resulting from Williams’ method will not add up to the actual sample size. However, the estimated covariance matrix of the parameter estimates remains invariant to the scale of the WEIGHT variable.