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%%% links
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\hypersetup{%
  pdftitle = {A Handbook of Statistical Analyses Using R},
  pdfsubject = {Book},
  pdfauthor = {Brian S. Everitt and Torsten Hothorn},
  colorlinks = {black},
  linkcolor = {black},
  citecolor = {black},
  urlcolor = {black},
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  linktocpage = {true},
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\begin{document}

%% Title page

\title{A Handbook of Statistical Analyses Using \R{} --- 2nd Edition}

\author{Brian S. Everitt and Torsten Hothorn}

\maketitle
%%\VignetteIndexEntry{Chapter Principal Component Analysis}
\setcounter{chapter}{15}


\SweaveOpts{prefix.string=figures/HSAUR,eps=FALSE,keep.source=TRUE} 

<<setup, echo = FALSE, results = hide>>=
rm(list = ls())
s <- search()[-1]
s <- s[-match(c("package:base", "package:stats", "package:graphics", "package:grDevices",
                "package:utils", "package:datasets", "package:methods", "Autoloads"), s)]
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HSAURpkg <- require("HSAUR2")
if (!HSAURpkg) stop("cannot load package ", sQuote("HSAUR2"))
rm(HSAURpkg)
 ### </FIXME> hm, R-2.4.0 --vanilla seems to need this
a <- Sys.setlocale("LC_ALL", "C")
 ### </FIXME>
book <- TRUE
refs <- cbind(c("AItR", "DAGD", "SI", "CI", "ANOVA", "MLR", "GLM", 
                "DE", "RP", "GAM", "SA", "ALDI", "ALDII", "SIMC", "MA", "PCA", 
                "MDS", "CA"), 1:18)
ch <- function(x) {
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@

\pagestyle{headings}
<<singlebook, echo = FALSE>>=
book <- FALSE
@

\chapter[Principal Component Analysis]{Principal Component Analysis: The Olympic Heptathlon \label{PCA}}

\section{Introduction}


\section{Principal Component Analysis}


\section{Analysis Using \R{}}

To begin it will help to score all seven events in the
same direction, so that `large' values are `good'. We will    
recode the running events to achieve this;
<<PCA-heptathlon-recode, echo = TRUE>>=a
data("heptathlon", package = "HSAUR2")
heptathlon$hurdles <- max(heptathlon$hurdles) - 
    heptathlon$hurdles
heptathlon$run200m <- max(heptathlon$run200m) - 
    heptathlon$run200m
heptathlon$run800m <- max(heptathlon$run800m) - 
    heptathlon$run800m
@

\begin{figure}
\begin{center}
<<PCA-heptathlon-scatter, echo = TRUE, fig = TRUE>>=
score <- which(colnames(heptathlon) == "score")
plot(heptathlon[,-score])
@
\caption{Scatterplot matrix for the \Robject{heptathlon} data (all countries). \label{PCA-heptathlon-scatter}}
\end{center}
\end{figure}

Figure~\ref{PCA-heptathlon-scatter} 
shows a scatterplot matrix of the results from all $25$ competitors for the seven events. Most of the scatterplots in 
the diagram suggest that there is a positive relationship between the results for each pairs of events. The exception are the 
plots involving the javelin event which give little evidence of any relationship between the result for this event and the
results from the other six events; we will suggest possible reasons for this below, but first we will examine the numerical 
values of the between pairs events correlations by applying the 
\Rcmd{cor} function
<<PCA-options65, echo = FALSE>>=
w <- options("width")
options(width = 65)
@
<<PCA-heptathlon-cor, echo = TRUE>>=
round(cor(heptathlon[,-score]), 2)
@
<<PCA-optionsw, echo = FALSE>>=
options(width = w$width)
@
Examination of these numerical values confirms that most pairs of events are positively correlated, some moderately (for 
example, high jump and shot) and others relatively highly (for example, high jump and hurdles). And we see that the 
correlations involving the javelin event are all close to zero. One possible explanation for the latter finding is perhaps 
that training for the other six events does not help much in the javelin because it is essentially a `technical' event. An 
alternative explanation is found if we examine the scatterplot matrix in Figure~\ref{PCA-heptathlon-scatter} 
a little more closely. 
It is very clear in this diagram that for all events except the
javelin there is an outlier, the competitor from Papua New Guinea
(PNG), who is much poorer than the other athletes at these six
events and who finished last in the competition in terms of
points scored. But surprisingly in the 
scatterplots involving the javelin it is this competitor who again 
stands out but because she has the third highest value for the event. 
It might be sensible to look again at both the correlation matrix and the 
scatterplot matrix after removing the competitor from PNG; the relevant \R{} 
code is
<<PCA-heptathlon-PNG, echo = TRUE>>=
heptathlon <- heptathlon[-grep("PNG", rownames(heptathlon)),]
@
Now, we again look at the scatterplot and correlation matrix;
\begin{figure}
\begin{center}
<<PCA-heptathlon-scatter2, echo = TRUE, fig = TRUE>>=
score <- which(colnames(heptathlon) == "score")
plot(heptathlon[,-score])
@
\caption{Scatterplot matrix for the \Robject{heptathlon} data 
         after removing observations of 
         the PNG competitor. \label{PCA-heptathlon-scatter2}}
\end{center}
\end{figure}

<<PCA-options65, echo = FALSE>>=
w <- options("width")
options(width = 65)
@
<<PCA-heptathlon-cor2, echo = TRUE>>=
round(cor(heptathlon[,-score]), 2)
@
<<PCA-optionsw, echo = FALSE>>=
options(width = w$width)
@
The correlations change quite substantially and the
new scatterplot matrix in Figure~\ref{PCA-heptathlon-scatter2} does 
not point us to any further extreme observations. In the remainder
of this chapter we analyse the \Robject{heptathlon} data with
the observations of the competitor from Papua New Guinea removed.

<<PCA-options65, echo = FALSE>>=
w <- options("digits")
options(digits = 4)
@

Because the results for the seven heptathlon events are on different
scales we shall extract the principal components from the correlation matrix.
A principal component analysis of the data can be applied
using the \Rcmd{prcomp} function with the \Rcmd{scale} argument
set to \Robject{TRUE} to ensure the analysis is carried out on the correlation
matrix. The result is a list
containing the coefficients defining each component 
(sometimes referred to as \stress{loadings}), 
\index{Loadings}
the principal component scores, etc. The required
code is (omitting the \Robject{score} variable)
<<PCA-heptathlon-pca, echo = TRUE>>=
heptathlon_pca <- prcomp(heptathlon[, -score], scale = TRUE)
print(heptathlon_pca)
@
The \Rcmd{summary} method can be used for further inspection of the details:
<<PCA-heptathlon-summary, echo = TRUE>>=
summary(heptathlon_pca)
@
<<PCA-optionsw, echo = FALSE>>=
options(digits = w$digits)
@
The linear combination for the first principal component is
<<PCA-heptathlon-a1, echo = TRUE>>=
a1 <- heptathlon_pca$rotation[,1]
a1
@
We see that the hurdles and long jump competitions receive the highest weight
but the javelin result is less important.
For computing the first principal component, the data need to be rescaled
appropriately. The center and the scaling used by \Rcmd{prcomp} internally
can be extracted from the \Robject{heptathlon\_pca}
via
<<PCA-heptathlon-scaling, echo = TRUE>>=
center <- heptathlon_pca$center
scale <- heptathlon_pca$scale
@
Now, we can apply the \Rcmd{scale} function to the data and multiply with
the loadings matrix in order to compute the first principal component score 
for each competitor
<<PCA-heptathlon-s1, echo = TRUE>>=
hm <- as.matrix(heptathlon[,-score])
drop(scale(hm, center = center, scale = scale) %*% 
     heptathlon_pca$rotation[,1])
@
or, more conveniently, by extracting the first from all precomputed 
principal components
<<PCA-heptathlon-s1, echo = TRUE>>=
predict(heptathlon_pca)[,1]
@

\begin{figure}
\begin{center}
<<PCA-heptathlon-pca-plot, echo = TRUE, fig = TRUE>>=
plot(heptathlon_pca)
@
\caption{Barplot of the variances explained by the principal components.
         (with observations for PNG removed). \label{PCA-heptathlon-pca-plot}}
\end{center}
\end{figure}

<<PCA-heptathlon-sdev, echo = FALSE, results = hide>>=
sdev <- heptathlon_pca$sdev
prop12 <- round(sum(sdev[1:2]^2)/sum(sdev^2)*100, 0)
@
The first two components account for $\Sexpr{prop12}\%$ of the variance. 
A barplot of each component's variance (see %%'
Figure~\ref{PCA-heptathlon-pca-plot}) shows how the first two components dominate.
A plot of the data in the space of the first two principal components, with the points
labelled by the name of the corresponding competitor, can be produced as shown
with Figure~\ref{PCA-heptathlon-biplot}. In addition, the first two loadings for
the events are given in a second coordinate system, also illustrating the special
role of the javelin event. This graphical representation is known as 
\stress{biplot} \citep{HSAUR:Gabriel1971}. \index{Biplot}
A biplot is a graphical representation of the information in an 
$n \times p$ data matrix. The ``bi'' is a reflection that the technique 
produces a diagram that gives variance and covariance information about 
the variables and information about generalised distances 
between individuals. The coordinates used to produce the biplot can all 
be obtained directly from the principal components analysis of the 
covariance matrix of the data and so the plots can be viewed as an 
alternative representation of the results of such an analysis. Full 
details of the technical details of the biplot are given in 
\cite{HSAUR:Gabriel1981} and in \cite{HSAUR:GowerHand1996}.  
Here we simply construct the biplot for the heptathlon data (without PNG);
the result is shown in Figure~\ref{PCA-heptathlon-biplot}.
The plot clearly shows that the winner of the gold medal, Jackie Joyner-Kersee,
accumulates the majority of her points from the three events
long jump, hurdles, and 200m.

\begin{figure}
\begin{center}
<<PCA-heptathlon-biplot, eval = FALSE>>=
biplot(heptathlon_pca, col = c("gray", "black"))
@
<<PCA-heptathlon-biplot, echo = FALSE, fig = TRUE>>=
tmp <- heptathlon[, -score]
rownames(tmp) <- abbreviate(gsub(" \\(.*", "", rownames(tmp)))
biplot(prcomp(tmp, scale = TRUE), col = c("black", "lightgray"), xlim =
c(-0.5, 0.7))
@
\caption{Biplot of the (scaled) first two principal components
         (with observations for PNG removed).
         \label{PCA-heptathlon-biplot}}
\end{center}
\end{figure}

The correlation between the score
given to each athlete by the standard scoring system used for
the heptathlon and the first principal component score can be   
found from
<<PCA-scorecor, echo = TRUE>>=
cor(heptathlon$score, heptathlon_pca$x[,1])
@
This implies that the first principal component is in good agreement with
the score assigned to the athletes by official Olympic rules; a scatterplot
of the official score and the first principal component is given in 
Figure~\ref{PCA-heptathlonscore}.

\begin{figure}
\begin{center}
<<PCA-heptathlonscore, echo = TRUE, fig = TRUE>>=
plot(heptathlon$score, heptathlon_pca$x[,1])
@
\caption{Scatterplot of the score assigned to each athlete in 1988 and the
first principal component. \label{PCA-heptathlonscore}}
\end{center}
\end{figure}


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\end{document}
