R Dataset / Package geepack / seizure

On this R-data 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 two-week intervals, and in a baseline eight-week 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 2-week interval
y2: the number of epiliptic seizures in the 2nd 2-week interval
y3: the number of epiliptic seizures in the 3rd 2-week interval
y4: the number of epiliptic seizures in the 4th 2-week interval
trt: an indicator of treatment
base: the number of epilitic seizures in a baseline 8-week 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) pp166-168, 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.r-project.org.

Attachments: csv, json

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