R Dataset / Package geepack / seizure
On this Rdata statistics page, you will find information about the seizure data set which pertains to Epiliptic Seizures. The seizure data set is found in the geepack R package. You can load the seizure data set in R by issuing the following command at the console data("seizure"). This will load the data into a variable called seizure. If R says the seizure data set is not found, you can try installing the package by issuing this command install.packages("geepack") and then attempt to reload the data with the library() command. 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 seizure R data set. The size of this file is about 1,035 bytes.
Epiliptic Seizures
Description
The seizure
data frame has 59 rows and 7 columns. The dataset has the number of epiliptic seizures in each of four twoweek intervals, and in a baseline eightweek inverval, for treatment and control groups with a total of 59 individuals.
Usage
data(seizure)
Format
This data frame contains the following columns:
 y1

the number of epiliptic seizures in the 1st 2week interval
 y2

the number of epiliptic seizures in the 2nd 2week interval
 y3

the number of epiliptic seizures in the 3rd 2week interval
 y4

the number of epiliptic seizures in the 4th 2week interval
 trt

an indicator of treatment
 base

the number of epilitic seizures in a baseline 8week interval
 age

a numeric vector of subject age
Source
Thall, P.F. and Vail S.C. (1990) Some covariance models for longitudinal count data with overdispersion. Biometrics 46: 657–671.
References
Diggle, P.J., Liang, K.Y., and Zeger, S.L. (1994) Analysis of Longitudinal Data. Clarendon Press.
Examples
data(seizure) ## Diggle, Liang, and Zeger (1994) pp166168, compare Table 8.10 seiz.l < reshape(seizure, varying=list(c("base","y1", "y2", "y3", "y4")), v.names="y", times=0:4, direction="long") seiz.l < seiz.l[order(seiz.l$id, seiz.l$time),] seiz.l$t < ifelse(seiz.l$time == 0, 8, 2) seiz.l$x < ifelse(seiz.l$time == 0, 0, 1) m1 < geese(y ~ offset(log(t)) + x + trt + x:trt, id = id, data=seiz.l, corstr="exch", family=poisson) summary(m1) m2 < geese(y ~ offset(log(t)) + x + trt + x:trt, id = id, data = seiz.l, subset = id!=49, corstr = "exch", family=poisson) summary(m2)## Thall and Vail (1990) seiz.l < reshape(seizure, varying=list(c("y1","y2","y3","y4")), v.names="y", direction="long") seiz.l < seiz.l[order(seiz.l$id, seiz.l$time),] seiz.l$lbase < log(seiz.l$base / 4) seiz.l$lage < log(seiz.l$age) seiz.l$v4 < ifelse(seiz.l$time == 4, 1, 0) m3 < geese(y ~ lbase + trt + lbase:trt + lage + v4, sformula = ~ as.factor(time)  1, id = id, data = seiz.l, corstr = "exchangeable", family=poisson) ## compare to Model 13 in Table 4, noticeable difference summary(m3)## set up a design matrix for the correlation z < model.matrix(~ age, data = seizure)# data is not seiz.l ## just to illustrate the scale link and correlation link m4 < geese(y ~ lbase + trt + lbase:trt + lage + v4, sformula = ~ as.factor(time)1, id = id, data = seiz.l, corstr = "ar1", family = poisson, zcor = z, cor.link = "fisherz", sca.link = "log") summary(m4)
Dataset imported from https://www.rproject.org.