Thall and Vail (1990) give a data set on two-week seizure counts for 59 epileptics. The number of seizures was recorded for a baseline period of 8 weeks, and then patients were randomly assigned to a treatment group or a control group. Counts were then recorded for four successive two-week periods. The subject's age is the only covariate.
This data frame has 236 rows and the following 9 columns:
the count for the 2-week period.
the counts in the baseline 8-week period.
subject's age, in years.
0/1 indicator variable of period 4.
subject number, 1 to 59.
period, 1 to 4.
log-counts for the baseline period, centred to have zero mean.
log-ages, centred to have zero mean.
Thall, P. F. and Vail, S. C. (1990) Some covariance models for longitudinal count data with over-dispersion. Biometrics 46, 657–671.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer.
## IGNORE_RDIFF_BEGIN summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), correlation = FALSE) ## IGNORE_RDIFF_END epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"] epil["time"] <- 1; epil2["time"] <- 4 epil2 <- rbind(epil, epil2) epil2$pred <- unclass(epil2$trt) * (epil2$period > 0) epil2$subject <- factor(epil2$subject) epil3 <- aggregate(epil2, list(epil2$subject, epil2$period > 0), function(x) if(is.numeric(x)) sum(x) else x) epil3$pred <- factor(epil3$pred, labels = c("base", "placebo", "drug")) contrasts(epil3$pred) <- structure(contr.sdif(3), dimnames = list(NULL, c("placebo-base", "drug-placebo"))) ## IGNORE_RDIFF_BEGIN summary(glm(y ~ pred + factor(subject) + offset(log(time)), family = poisson, data = epil3), correlation = FALSE) ## IGNORE_RDIFF_END summary(glmmPQL(y ~ lbase*trt + lage + V4, random = ~ 1 | subject, family = poisson, data = epil)) summary(glmmPQL(y ~ pred, random = ~1 | subject, family = poisson, data = epil3))
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