# R Dataset / Package geepack / seizure

Attachment | Size |
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dataset-66108.csv | 1.01 KB |

## 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)

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Dataset imported from https://www.r-project.org.

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