R Dataset / Package MASS / epil

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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<iframe src="https://embed.picostat.com/r-dataset-package-mass-epil.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-19894.csv 14.54 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the epil data set which pertains to Seizure Counts for Epileptics. The epil data set is found in the MASS R package. You can load the epil data set in R by issuing the following command at the console data("epil"). This will load the data into a variable called epil. If R says the epil data set is not found, you can try installing the package by issuing this command install.packages("MASS") 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 epil R data set. The size of this file is about 14,894 bytes.

Documentation

Seizure Counts for Epileptics

Description

Thall and Vail (1990) give a data set on two-week seizure counts for 59 epileptics. The number of seizures was recorded for a baseline period of 8 weeks, and then patients were randomly assigned to a treatment group or a control group. Counts were then recorded for four successive two-week periods. The subject's age is the only covariate.

Usage

epil

Format

This data frame has 236 rows and the following 9 columns:

y

the count for the 2-week period.

trt

treatment, "placebo" or "progabide".

base

the counts in the baseline 8-week period.

age

subject's age, in years.

V4

0/1 indicator variable of period 4.

subject

subject number, 1 to 59.

period

period, 1 to 4.

lbase

log-counts for the baseline period, centred to have zero mean.

lage

log-ages, centred to have zero mean.

Source

Thall, P. F. and Vail, S. C. (1990) Some covariance models for longitudinal count data with over-dispersion. Biometrics 46, 657–671.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer.

Examples

summary(glm(y ~ lbase*trt + lage + V4, family = poisson,
            data = epil), cor = FALSE)
epil2 <- epil[epil$period == 1, ]
epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]
epil["time"] <- 1; epil2["time"] <- 4
epil2 <- rbind(epil, epil2)
epil2$pred <- unclass(epil2$trt) * (epil2$period > 0)
epil2$subject <- factor(epil2$subject)
epil3 <- aggregate(epil2, list(epil2$subject, epil2$period > 0),
   function(x) if(is.numeric(x)) sum(x) else x[1])
epil3$pred <- factor(epil3$pred,
   labels = c("base", "placebo", "drug"))contrasts(epil3$pred) <- structure(contr.sdif(3),
    dimnames = list(NULL, c("placebo-base", "drug-placebo")))
summary(glm(y ~ pred + factor(subject) + offset(log(time)),
            family = poisson, data = epil3), cor = FALSE)summary(glmmPQL(y ~ lbase*trt + lage + V4,
                random = ~ 1 | subject,
                family = poisson, data = epil))
summary(glmmPQL(y ~ pred, random = ~1 | subject,
                family = poisson, data = epil3))
--

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

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