R Dataset / Package HistData / CushnyPeeblesN

On this R-data statistics page, you will find information about the CushnyPeeblesN data set which pertains to Cushny-Peebles Data: Soporific Effects of Scopolamine Derivatives. The CushnyPeeblesN data set is found in the HistData R package. You can load the CushnyPeeblesN data set in R by issuing the following command at the console data("CushnyPeeblesN"). This will load the data into a variable called CushnyPeeblesN. If R says the CushnyPeeblesN data set is not found, you can try installing the package by issuing this command install.packages("HistData") 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 CushnyPeeblesN R data set. The size of this file is about 141 bytes.

Cushny-Peebles Data: Soporific Effects of Scopolamine Derivatives

Description

Cushny and Peebles (1905) studied the effects of hydrobromides related to scopolamine and atropine in producing sleep. The sleep of mental patients was measured without hypnotic (Control) and after treatment with one of three drugs: L. hyoscyamine hydrobromide (L_hyoscyamine), L. hyoscine hydrobromide (L_hyoscyine), and a mixture (racemic) form, DL_hyoscine, called atropine. The L (levo) and D (detro) form of a given molecule are optical isomers (mirror images).

The drugs were given on alternate evenings, and the hours of sleep were compared with the intervening control night. Each of the drugs was tested in this manner a varying number of times in each subject. The average number of hours of sleep for each treatment is the response.

Student (1908) used these data to illustrate the paired-sample t-test in small samples, testing the hypothesis that the mean difference between a given drug and the control condition was zero. This data set became well known when used by Fisher (1925). Both Student and Fisher had problems labeling the drugs correctly (see Senn & Richardson (1994)), and consequently came to wrong conclusions.

But as well, the sample sizes (number of nights) for each mean differed widely, ranging from 3-9, and this was not taken into account in their analyses. To allow weighted analyses, the number of observations for each mean is contained in the data frame CushnyPeeblesN.

Usage

data(CushnyPeebles)
data(CushnyPeeblesN)

Format

CushnyPeebles: A data frame with 11 observations on the following 4 variables.

Control: a numeric vector: mean hours of sleep
L_hyoscyamine: a numeric vector: mean hours of sleep
L_hyoscine: a numeric vector: mean hours of sleep
D_hyoscine: a numeric vector: mean hours of sleep

CushnyPeeblesN: A data frame with 11 observations on the following 4 variables.

Control: a numeric vector: number of observations
L_hyoscyamine: a numeric vector: number of observations
L_hyoscine: a numeric vector: number of observations
DL_hyoscine: a numeric vector: number of observations

Details

The last patient (11) has no Control observations, and so is often excluded in analyses or other versions of this data set.

Source

Cushny, A. R., and Peebles, A. R. (1905), "The Action of Optical Isomers. II: Hyoscines," Journal of Physiology, 32, 501-510.

Senn, Stephen, Data from Cushny and Peebles, http://www.senns.demon.co.uk/Data/Cushny.xls

References

Fisher, R. A. (1925), Statistical Methods for Research Workers, Edinburgh and London: Oliver & Boyd.

Student (1908), "The Probable Error of a Mean," Biometrika, 6, 1-25.

Senn, S.J. and Richardson, W. (1994), "The first t-test", Statistics in Medicine, 13, 785-803.

Examples

data(CushnyPeebles)
# quick looks at the data
plot(CushnyPeebles)
boxplot(CushnyPeebles, ylab="Hours of Sleep", xlab="Treatment")##########################
# Repeated measures MANOVA
require(car)CPmod <- lm(cbind(Control, L_hyoscyamine, L_hyoscine, DL_hyoscine) ~ 1, data=CushnyPeebles)# Assign within-S factor and contrasts
Treatment <- factor(colnames(CushnyPeebles), levels=colnames(CushnyPeebles))
contrasts(Treatment) <- matrix(
c(-3, 1, 1, 1,
 0,-2, 1, 1,
 0, 0,-1, 1), ncol=3)
colnames(contrasts(Treatment)) <- c("Control.Drug", "L.DL", "L_hy.DL_hy")Treats <- data.frame(Treatment)
(CPaov <- Anova(CPmod, idata=Treats, idesign= ~Treatment))
summary(CPaov, univariate=FALSE)if (require(heplots)) {
heplot(CPmod, idata=Treats, idesign= ~Treatment, iterm="Treatment", 
xlab="Control vs Drugs", ylab="L vs DL drug")
pairs(CPmod, idata=Treats, idesign= ~Treatment, iterm="Treatment")
}################################
# reshape to long format, add NsCPlong <- stack(CushnyPeebles)[,2:1]
colnames(CPlong) <- c("treatment", "sleep")
CPN <- stack(CushnyPeeblesN)
CPlong <- data.frame(patient=rep(1:11,4), CPlong, n=CPN$values)
str(CPlong)

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

Attachments: csv, json

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