# R Dataset / Package geepack / seizure

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On this Picostat.com 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. 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## DescriptionThe ## Usagedata(seizure) ## FormatThis 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
## SourceThall, P.F. and Vail S.C. (1990)
Some covariance models for longitudinal count data with
overdispersion. ## ReferencesDiggle, P.J., Liang, K.Y., and Zeger, S.L. (1994) Analysis of Longitudinal Data. Clarendon Press. ## Examplesdata(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. |

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dataset-66108.csv | 1.01 KB |