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| CushnyPeebles   | R Documentation   |
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Cushny-Peebles Data: Soporific Effects of Scopolamine Derivatives
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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 <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.

See Also
~~~~~~~~

``sleep`` for an alternative form of this data set.

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 Ns

    CPlong <- 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)

