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\cfoot{Statistical Sleuth in R: Chapter 10}
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\newcommand{\cran}{\href{http://www.R-project.org/}{CRAN}}
\title{The Statistical Sleuth in R: \\
Chapter 10}
\author{
Kate Aloisio \and Ruobing Zhang \and Nicholas J. Horton\thanks{Department of Mathematics, Amherst College, nhorton@amherst.edu}
}
\date{\today}
\begin{document}
\maketitle
\tableofcontents
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<>=
require(Sleuth2)
require(mosaic)
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@
\section{Introduction}
This document is intended to help describe how to undertake analyses introduced as examples in the Second Edition of the \emph{Statistical Sleuth} (2002) by Fred Ramsey and Dan Schafer.
More information about the book can be found at \url{http://www.proaxis.com/~panorama/home.htm}.
This
file as well as the associated \pkg{knitr} reproducible analysis source file can be found at
\url{http://www.amherst.edu/~nhorton/sleuth}.
This work leverages initiatives undertaken by Project MOSAIC (\url{http://www.mosaic-web.org}), an NSF-funded effort to improve the teaching of statistics, calculus, science and computing in the undergraduate curriculum. In particular, we utilize the
\pkg{mosaic} package, which was written to simplify the use of R for introductory statistics courses. A short summary of the R needed to teach introductory statistics can be found in the mosaic package vignette (\url{http://cran.r-project.org/web/packages/mosaic/vignettes/MinimalR.pdf}).
To use a package within R, it must be installed (one time), and loaded (each session). The package can be installed using the following command:
<>=
install.packages('mosaic') # note the quotation marks
@
Once this is installed, it can be loaded by running the command:
<>=
require(mosaic)
@
This
needs to be done once per session.
In addition the data files for the \emph{Sleuth} case studies can be accessed by installing the \pkg{Sleuth2} package.
<>=
install.packages('Sleuth2') # note the quotation marks
@
<>=
require(Sleuth2)
@
We also set some options to improve legibility of graphs and output.
<>=
trellis.par.set(theme=col.mosaic()) # get a better color scheme for lattice
options(digits=3)
@
The specific goal of this document is to demonstrate how to calculate the quantities described in Chapter 10: Inferential Tools for Multiple Regression using R.
\section{Galileo's data on the motion of falling bodies}
Galileo investigated the relationship between height and horizontal distance. This is the question addressed in case study 10.1 in the \emph{Sleuth}.
\subsection{Data coding, summary statistics and graphical display}
We begin by reading the data and summarizing the variables.
<<>>=
summary(case1001)
favstats(~ Distance, data=case1001)
@
There we a total of \Sexpr{nrow(case1001)} trials of Galileo's experiment. For each trial, he recorded the initial height and then measured the horizontal distance as shown in Display 10.1 (page 268).
We can start to explore this relationship by creating a scatterplot of Galileo's horizontal distances versus initial heights. The following graph is akin to Display 10.2 (page 269).
<<>>=
xyplot(Distance ~ Height, data=case1001)
@
\subsection{Models}
The first model that we created is a cubic model as interpreted on page 269 and summarized in Display 10.13
(page 286).
<<>>=
lm1 = lm(Distance ~ Height+I(Height^2)+I(Height^3), data=case1001); summary(lm1)
@
We next decrease the polynomial for \emph{Height} by one degree to obtain a quadratic model as interpreted on page 269 and summarized in Display 10.7 (page 277). This model is used for most of the following results.
<<>>=
lm2 = lm(Distance ~ Height+I(Height^2), data=case1001); summary(lm2)
@
We can fit an equivalent model (with different parametrization) using the {\tt poly()} function:
<<>>=
lmpoly = lm(Distance ~ poly(Height, 2), data=case1001); summary(lmpoly)
@
The following figure presents the predicted values from the quadratic model and the original data points akin to Display 10.2 (page 269).
<<>>=
case1001 = transform(case1001, pred = predict(lm2))
xyplot(pred+Distance ~ Height, auto.key=TRUE, data=case1001)
@
To obtain the expected values of $\hat{\mu}\left(\mathrm{Distance}|\mathrm{Height} = 0 \right)$
and $\hat{\mu}\left(\mathrm{Distance}|\mathrm{Height} = 250\right)$, we used the {\tt predict()} command with the quadratic model as shown in Display 10.7 (page 277).
<<>>=
predict(lm2, interval="confidence", data.frame(Height=c(0, 250)))
@
We can also verify the above confidence interval calculations with the following code:
<<>>=
355.1+c(-1, 1)*6.62*qt(.975, 4)
@
To verify numbers on page 279, an interval for the predicted values , we used the following code:
<<>>=
predict(lm2, interval="predict", data.frame(Height=c(0, 250)))
@
Lastly, we display the ANOVA table for the quadratic model that is interpreted on page 283 (Display 10.11).
<<>>=
anova(lm2)
@
\section{Echolocation in bats}
How do bats make their way about in the dark? Echolocation requires a lot of energy. Does it depend on mass and species? This is the question addressed in case study 10.2 in the \emph{Sleuth}.
\subsection{Data coding, summary statistics and graphical display}
We begin by reading the data, performing transformations where necessary and summarizing the variables.
<<>>=
case1002 = transform(case1002, logmass = log(Mass))
case1002 = transform(case1002, logenergy = log(Energy))
summary(case1002)
favstats(Mass ~ Type, data=case1002)
favstats(Energy ~ Type, data=case1002)
@
A total of \Sexpr{nrow(case1002)} flying vertebrates were included in this study. There were \Sexpr{nrow(subset(case1002, Type=="echolocating bats"))} echolocating bats, \Sexpr{nrow(subset(case1002, Type=="non-echolocating bats"))} non-echolocating bats, and \Sexpr{nrow(subset(case1002, Type=="non-echolocating birds"))} non-echolocating birds. For each subject their \emph{mass} and \emph{flight energy expenditure} were recorded as shown in Display 10.3
(page 270).
We can next observe the pattern between log(energy expenditure) as a function of log(body mass) for each group with a scatterplot. The following figure is akin to Display 10.4 (page 271).
<>=
xyplot(Energy ~ Mass, group=Type, scales=list(y=list(log=TRUE),
x=list(log=TRUE)), auto.key=TRUE, data=case1002)
@
\subsection{Multiple regression}
We first evaluate a multiple regression model for log(energy expenditure) given type of species and log(body mass) as defined on page 272 and shown in Display 10.6 (page 273).
<<>>=
lm1 = lm(logenergy ~ logmass+Type, data=case1002); summary(lm1)
@
Next, we calculated confidence intervals for the coefficients which are interpreted on page 274.
<<>>=
confint(lm1)
exp(confint(lm1))
@
Since the significance of a model depends on which variables are included, the \emph{Sleuth} proposes two other models, one only looking at the type of flying animal and the other allows the three groups to have different straight-line regressions with \emph{mass}. These two models are displayed below and discussed on pages 274--275.
<<>>=
summary(lm(logenergy ~ Type, data=case1002))
summary(lm(logenergy ~ Type * logmass, data=case1002))
@
To construct the confidence bands discussed on page 277 and shown in Display 10.9 (page 279) we used the following code:
<<>>=
pred = predict(lm1, se.fit=TRUE, newdata=data.frame(Type=c("non-echolocating birds", "non-echolocating birds"), logmass=c(log(100), log(400))))
pred.fit = pred$fit[1]; pred.fit
pred.se = pred$se.fit[1]; pred.se
multiplier = sqrt(4*qf(.95, 4, 16)); multiplier
lower = exp(pred.fit-pred.se*multiplier); lower
upper = exp(pred.fit+pred.se*multiplier); upper
# for the other reference points
pred2 = predict(lm1, se.fit=TRUE, newdata=data.frame(Type=c("non-echolocating bats", "non-echolocating bats"), logmass=c(log(100), log(400))))
pred3 = predict(lm1, se.fit=TRUE, newdata=data.frame(Type=c("echolocating bats", "echolocating bats"), logmass=c(log(100), log(400))))
table10.9 = rbind(c("Intercept estimate", "Standard error"), round(cbind(pred2$fit, pred2$se.fit), 4), round(cbind(pred3$fit, pred3$se.fit), 4)); table10.9
@
Next we can assess the model by evaluating the extra sums of squares $F$-test for testing the equality of intercepts in the parallel regression lines model as shown in Display 10.10 (page 282).
<<>>=
lm2 = lm(logenergy ~ logmass, data=case1002)
anova(lm2, lm1)
@
We can also compare the full model with interaction terms and the reduced model (without interaction terms) with the extra sum of squares $F$-test as described in Display 10.12 (page 284).
<<>>=
lm3 = lm(logenergy ~ logmass*Type, data=case1002)
anova(lm3, lm1)
@
Another way to test the equality of the groups is by using linear combinations which we can attain using the {\tt estimable()} command as follows. These results can be found on page 276 and 289.
<<>>=
require(gmodels)
estimable(lm1, c(0, 0, -1, 1))
@
\end{document}