epil {MASS} | R Documentation |
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.
epil
This data frame has 236 rows and the following 9 columns:
y
the count for the 2-week period.
trt
treatment, "placebo"
or "progabide"
.
base
the counts in the baseline 8-week period.
age
subject's age, in years.
V4
0/1
indicator variable of period 4.
subject
subject number, 1 to 59.
period
period, 1 to 4.
lbase
log-counts for the baseline period, centred to have zero mean.
lage
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.
summary(glm(y ~ lbase*trt + lage + V4, family = poisson,
data = epil), cor = FALSE)
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[1])
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), cor = 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))