R Dataset / Package geepack / respdis

On this R-data statistics page, you will find information about the respdis data set which pertains to Clustered Ordinal Respiratory Disorder. The respdis data set is found in the geepack R package. You can load the respdis data set in R by issuing the following command at the console data("respdis"). This will load the data into a variable called respdis. If R says the respdis 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 respdis R data set. The size of this file is about 1,136 bytes.

Clustered Ordinal Respiratory Disorder

Description

The respdis data frame has 111 rows and 3 columns. The study described in Miller et. al. (1993) is a randomized clinical trial of a new treatment of respiratory disorder. The study was conducted in 111 patients who were randomly assigned to one of two treatments (active, placebo). At each of four visits during the follow-up period, the response status of each patients was classified on an ordinal scale.

Usage

data(respdis)

Format

This data frame contains the following columns:

y1, y2, y3, y4: ordered factor measured at 4 visits for the response with levels, 1 < 2 < 3, 1 = poor, 2 = good, and 3 = excellent
trt: a factor for treatment with levels, 1 = active, 0 = placebo.

References

Miller, M.E., David, C.S., and Landis, R.J. (1993) The analysis of longitudinal polytomous data: Generalized estimating equation and connections with weighted least squares, Biometrics 49: 1033-1048.

Examples

data(respdis)
resp.l <- reshape(respdis, varying = list(c("y1", "y2", "y3", "y4")),
v.names = "resp", direction = "long")
resp.l <- resp.l[order(resp.l$id, resp.l$time),]
fit <- ordgee(ordered(resp) ~ trt, id = id, data = resp.l, int.const = FALSE)
summary(fit)z <- model.matrix( ~ trt - 1, data = respdis)
ind <- rep(1:111, 4*3/2 * 2^2)
zmat <- z[ind,,drop=FALSE]
fit <- ordgee(ordered(resp) ~ trt, id = id, data = resp.l, int.const = FALSE,
z = zmat, corstr = "exchangeable")
summary(fit)

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

Attachments: csv, json

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