On this Picostat.com statistics page, you will find information about the epil data set which pertains to Seizure Counts for Epileptics. The epil data set is found in the MASS R package. You can load the epil data set in R by issuing the following command at the console data("epil"). This will load the data into a variable called epil. If R says the epil data set is not found, you can try installing the package by issuing this command install.packages("MASS") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the epil R data set. The size of this file is about 14,894 bytes.
Seizure Counts for Epileptics
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
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.
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)
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")))
summary(glm(y ~ pred + factor(subject) + offset(log(time)),
family = poisson, data = epil3), cor = FALSE)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))
Dataset imported from https://www.r-project.org.