%\VignetteEngine{knitr::knitr}
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\cfoot{Statistical Sleuth in R: Chapter 3}
\rfoot{}

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\newcommand{\cran}{\href{http://www.R-project.org/}{CRAN}}

\title{The Statistical Sleuth in R: \\
Chapter 3}

\author{
Ruobing Zhang \and Kate Aloisio \and Nicholas J. Horton\thanks{Department of Mathematics, Amherst College, nhorton@amherst.edu}
} 

\date{\today}

\begin{document}


\maketitle
\tableofcontents

%\parindent=0pt


<<pvalues, echo=FALSE, message=FALSE>>=
print.pval = function(pval) {
  threshold = 0.0001
    return(ifelse(pval < threshold, paste("p<", sprintf("%.4f", threshold), sep=""),
                ifelse(pval > 0.1, paste("p=",round(pval, 2), sep=""),
                       paste("p=", round(pval, 3), sep=""))))
}
@
<<setup0, include=FALSE, cache=FALSE>>=
require(knitr)
opts_chunk$set(
  dev="pdf",
  fig.path="figures/",
        fig.height=3,
        fig.width=4,
        out.width=".47\\textwidth",
        fig.keep="high",
        fig.show="hold",
        fig.align="center",
        prompt=TRUE,  # show the prompts; but perhaps we should not do this 
        comment=NA    # turn off commenting of ouput (but perhaps we should not do this either
  )
@

<<setup,echo=FALSE,message=FALSE>>=
require(Sleuth2)
require(mosaic)
trellis.par.set(theme=col.mosaic())  # get a better color scheme 
set.seed(123)
# this allows for code formatting inline.  Use \Sexpr{'function(x,y)'}, for exmaple.
knit_hooks$set(inline = function(x){
if (is.numeric(x)) return(knitr:::format_sci(x, 'latex'))
x = as.character(x)
h = knitr:::hilight_source(x, 'latex', list(prompt=FALSE, size='normalsize'))
h = gsub("([_#$%&])", "\\\\\\1", h)
h = gsub('(["\'])', '\\1{}', h)
gsub('^\\\\begin\\{alltt\\}\\s*|\\\\end\\{alltt\\}\\s*$', '', h)
})
showOriginal=FALSE
showNew=TRUE
@ 

\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_mosaic,eval=FALSE>>=
install.packages('mosaic')               # note the quotation marks
@
Once this is installed, it can be loaded by running the command:
<<load_mosaic,eval=FALSE>>=
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_Sleuth2,eval=FALSE>>=
install.packages('Sleuth2')               # note the quotation marks
@
<<load_Sleuth2,eval=FALSE>>=
require(Sleuth2)
@

We also set some options to improve legibility of graphs and output.
<<eval=TRUE>>=
trellis.par.set(theme=col.mosaic())  # get a better color scheme for lattice
options(digits=3, show.signif.stars=FALSE)
@

The specific goal of this document is to demonstrate how to calculate the quantities described in \emph{Sleuth} Chapter 3: A Closer Look at Assumptions using R.

\section{Cloud Seeding to Increase Rainfall}

Does seeding clouds lead to more rainfall? This is the question being addressed by case study 3.1 in the \emph{Sleuth}.

\subsection{Summary statistics and graphical displays (untransformed)}

We begin by reading the data and summarizing the variables.

<<>>=
summary(case0301)
favstats(Rainfall ~ Treatment, data=case0301)
@

A total of \Sexpr{nrow(case0301)} subjects were included in this data: \Sexpr{nrow(subset(case0301, Treatment=="Seeded"))} seeded days  and \Sexpr{nrow(subset(case0301, Treatment=="Unseeded"))} 
unseeded days (Display 3.1, page 57). 

<<fig.height=8, fig.width=8>>=
bwplot(Rainfall ~ Treatment, data=case0301)
@

<<fig.height=8, fig.width=8>>=
densityplot(~Rainfall, groups=Treatment, auto.key=TRUE, data=case0301)
@

According to the boxplot and the density plot, the rainfall from seeded days seems to be larger than unseeded days. Both density curves are highly skewed to the right.

\subsection{Summary statistics and graphical display (transformed)}

The skewness suggests there is a need to apply the logarithmic transformation. The transformed data is shown on page 71 (Display 3.9).

<<>>=
case0301 = transform(case0301, lograin=log(Rainfall))
favstats(lograin ~ Treatment, data=case0301)
@

<<fig.height=8, fig.width=8>>=
bwplot(lograin ~ Treatment, data=case0301)
@

<<fig.height=8, fig.width=8>>=
densityplot(~lograin, groups=Treatment, auto.key=TRUE, data=case0301)
@

The log transformation reduces skewness of these two distributions.

\subsection{Inferential procedures (two-sample t-test)}

 
<<>>=
t.test(Rainfall ~ Treatment, var.equal=FALSE, data=case0301)
t.test(Rainfall ~ Treatment, var.equal=TRUE, data=case0301)
@

The following corresponds to the calculations on page 71.

<<>>=
summary(lm(lograin ~ Treatment, data=case0301))
ttestlog = t.test(lograin ~ Treatment, data=case0301); ttestlog
@

The two-sided $p$-value is $p=0.014$ and the 95\% confidence interval is between \Sexpr{round(ttestlog$conf.int[1], 2)} and \Sexpr{round(ttestlog$conf.int[2], 2)}.

\subsection{Interpretation of log model}

The following code is used to calculate the ``Summary of Statistical Findings" on page 57. First, we want to calculate the multiplier.

<<>>=
obslogdiff = -diff(mean(lograin ~ Treatment, data=case0301)); obslogdiff
multiplier = exp(obslogdiff); multiplier
@

Next we can calculate the 95\% confidence interval for the multiplier.
<<>>=
ttestlog$conf.int
exp(ttestlog$conf.int)
@

\section{Effects of Agent Orange on Troops in Vietnam}

Is dioxin concentration related to veteran status? This is the question being addressed by case study 3.2 in the \emph{Sleuth}.

\subsection{Summary statistics and graphical display}

We begin by reading the data and summarizing the variables.

<<>>=
summary(case0302)
favstats(Dioxin ~ Veteran, data=case0302)
@

A total of \Sexpr{nrow(case0302)} veterans were included in this data: \Sexpr{nrow(subset(case0302, Veteran=="Vietnam"))} served in Vietnam during 1967 and 1968  and \Sexpr{nrow(subset(case0302, Veteran=="Other"))} served in US or Germany during 1965 and 1971.

<<fig.height=8, fig.width=8>>=
bwplot(Veteran ~ Dioxin, data=case0302)
@

<<fig.height=8, fig.width=8>>=
densityplot(~Dioxin, groups=Veteran, auto.key=TRUE, data=case0302)
@

Both distributions are highly skewed to the right.


\subsection{Inferential procedures (two-sample t-test)}

The following code is used to calculate the ``Summary of Statistical Findings" on page 60.
<<>>=
t.test(Dioxin ~ Veteran, var.equal=TRUE, alternative="less", data=case0302)
t.test(Dioxin ~ Veteran, var.equal=TRUE, data=case0302)$conf.int
@

So the one-sided $p$-value from a two-sample $t$-test is \Sexpr{pval(t.test(Dioxin ~ Veteran, var.equal=TRUE, alternative="less", data=case0302))}. The 95\% confidence interval is (\Sexpr{round(t.test(Dioxin ~ Veteran, var.equal=TRUE, data=case0302)$conf.int[1], 2)}, \Sexpr{round(t.test(Dioxin ~ Veteran, var.equal=TRUE, data=case0302)$conf.int[2], 2)}).

\subsection{Removing outliers}

We will remove two extreme observations from the data.  First we remove observation 646 and perform a $t$-test (Display 3.7, page 67).   

<<>>=
case0302.2 = case0302[-c(646), ]
t.test(Dioxin ~ Veteran, alternative="less", data=case0302.2)
@

Next we remove observations 645 and 646 and perform a $t$-test.

<<>>=
dim(case0302)
case0302.3 = case0302[-c(645, 646), ]
dim(case0302.3)
t.test(Dioxin ~ Veteran, alternative="less", data=case0302.3)
@

Notice that after removing these outliers, the $p$-value and the confidence interval have changed but the 
substantive conclusion is unchanged.


\end{document}
