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\lhead{\sf Mixed-effects models workshop}\chead{\sf Exercises \#3}
\rhead{\sf 2009-07-02 (p. \thepage)}
\lfoot{\sf Models with simple, scalar random effects}\cfoot{}\rfoot{}
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\begin{document}
Install the MEMSS package that contains data sets
from the book "Mixed-effects Models in S and S-PLUS" that I wrote with
Jos\'{e} Pinheiro.
\begin{enumerate}
\item Check the structure of the \code{Rail} data and the
documentation for it. It is a simple, one-factor, balanced design,
similar to the \code{Dyestuff} data.
\begin{enumerate}
\item Plot the data in a form that seems appropriate - perhaps a
dotplot by \code{Rail} sorted by increasing \code{travel} time
joining the means of the travel times. Note that you probably
don't want to use a comparative boxplot because there are only
three observations per rail and you don't want to ``summarize''
three observations with five summary statistics.
\item Fit a model with \code{travel} as the response and a simple,
scalar random-effects term for the variable \code{Rail}. Use the
REML criterion, which is the default. Check the dotplot (i.e. the
``caterpillar'' plot) for the random effects.
\item Refit the model using maximum likelihood. Check the parameter
estimates and, in the case of the fixed-effects parameter, its
standard error. In what ways have the parameter estimates changed?
In particular, which parameter estimates have not changed?
\end{enumerate}
\item Consider the \code{ergostool} data; check its stucture and
summary. The stool types are fixed, the subjects in the experiment
are chosen at random. The structure of the data are similar to the
\code{Penicillin} data (unreplicated, completely crossed) but here
we will consider the \code{Type} factor to have fixed levels.
\begin{enumerate}
\item Plot the data. You may want to consider a plot similar to
that of the \code{Penicillin} data shown on the slides.
\item Fit a mixed-effects model to these data with fixed-effects for
\code{Type} and a simple, scalar random effect for each level of
\code{Subject}. A suitable formula could be
<>=
effort ~ 1 + Type + (1|Subject)
@
What does the \code{(Intercept)} coefficient represent? What do the
other fixed-effects terms represent? Change the formula to
<>=
effort ~ 0 + Type + (1|Subject)
@
which suppresses the intercept term. What do the fixed-effects parameters represent now?
\item Fit a model with random effects for \code{Type} and for
\code{Subject}. Check the fixed-effects parameter (the
\code{(Intercept)} and the conditional means of the random effects.
Check the caterpillar plot for the random-effects for the
\code{Type} factor. How do the random effects compare to the
corresponding fixed-effects parameters from the previous part in
precision and position?
\end{enumerate}
\item The \code{MathAchieve} data are similar to the \code{classroom}
data but with school identifiers only. The variable \code{MEANSES}
is the mean socio-economic status for students in the school.
\begin{enumerate}
\item Check that \code{MEANSES} is defined consistently within a
school. That is, check that each school has exactly one value for
\code{MEANSES}.
\item Determine the distribution of the number of students per
school. You can use \code{xtabs} to get a count of the number of
students in each school, then summarize that distribution, perhaps
by a densityplot. Do you think you will be able to estimate
school effects reasonably precisely?
\item Fit a model with fixed effects for \code{Sex}, \code{Minority}
and \code{MEANSES} and simple scalar random effects for
\code{School}. Compare the magnitudes of the estimates for the
\code{Sex} and \code{Minority} terms to the shifts that you might
observe in, say, comparative density plots.
\item Check the caterpillar plot of the random effects for school.
Are there schools that seem clearly better or clearly worse than
the typical school?
\item Fit a model with the previous fixed-effects term plus a
Sex/Minority interaction term. A suitable formula would be
<>=
MathAch ~ Sex * Minority + MEANSES + (1|School)
@
Is the interaction term significant?
\item (Optional) Other school-level covariates are given in the data
frame \code{MathAchSchool}. Consider how you could merge these with
the current variables (see \code{?merge}). Examine these variables
both graphically and by fitting and comparing models.
\end{enumerate}
\item Consider the \code{RatPupWeight} data. Check its structure and
a summary.
\begin{enumerate}
\item Plot the data using an xyplot of the weight versus litter size
with scatterplot smoother lines according to sex. Do you think
there will be significant effects on weight due to the litter size
and to the sex of the pup?
\item Consider the pattern with respect to litter size. Replot
versus the logarithm of the litter size. Does that enhance the
linearity of the plot?
\item Plot weight versus litter size or logarithm of litter size,
whichever seems reasonable to you with groups according to the
treatment. Do you expect significant effects due to treatment?
\item Fit a mixed-effects model with random effects according to
litter and with whatever fixed-effects terms you consider
appropriate. Expand or contract the model as you see fit.
\item Notice the outlier --- one rat pup was unusually low weight.
Investigate the effect on your conclusions of omitting this
observation.
\end{enumerate}
\item Consider the \code{Wheat} data. Check its structure, summary and documentation.
\begin{enumerate}
\item Plot the \code{DryMatter} by \code{Moisture} with groups for
fertilizer. Try a log transformation of the \code{DryMatter}.
Does that help to stabilize the patterns?
\item Fit and analyze a mixed-effects model with random effects for
\code{Tray}.
\end{enumerate}
\end{enumerate}
\end{document}
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