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\ihead[]{Examples}
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\newcommand{\cc}{\texttt}
\newcommand{\xmin}{x_{\min}}

<<echo=FALSE, message=FALSE>>=
library(knitr)
library(poweRlaw)
options(replace.assign=FALSE)

opts_chunk$set(fig.path='knitr_figure_examples/graphics-', 
               cache.path='knitr_cache_examples/', 
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@
% 
\begin{document}
\date{Last updated: \today}  
\title{The poweRlaw package: Examples}
\author{Colin S. Gillespie}
\maketitle

<<echo=FALSE, results='hide', message=FALSE, warning=FALSE, error=FALSE>>=
if(!file.exists("blackouts.txt"))
  download.file("http://goo.gl/BsqnP", destfile="blackouts.txt")
@

\section{Discrete data: Moby Dick}

The Moby Dick dataset contains the frequency of unique words in the novel Moby Dick by Herman Melville. This data set can be downloaded from
\begin{center}
\url{http://tuvalu.santafe.edu/~aaronc/powerlaws/data.htm}
\end{center}
\noindent or loaded directly
<<dis_data>>=
library("poweRlaw")
data("moby")
@
\noindent To fit a discrete power law to this data\footnote{The object \texttt{moby} is a simple R vector.}, we use the \texttt{displ} constructor
<<cache=TRUE>>=
m_pl = displ$new(moby)
@
\noindent The resulting object, \cc{m\_pl}, is a \cc{displ}\footnote{\cc{displ}: discrete power law.} object. It also inherits the \cc{discrete\_distribution} class. After creating the \cc{displ} object, a typical first step would be to infer model parameters.\footnote{When the \cc{displ} object is first created, the default parameter values are \cc{NULL} and $\xmin$ is set to the minimum $x$-value.} We can estimate the lower threshold, via
<<cache=TRUE>>=
est = estimate_xmin(m_pl)
m_pl$setXmin(est)
@
\noindent For a given value $\xmin$, the scaling parameter is estimated by numerically optimising the log-likelihood. The optimiser is \textit{initialised} using the analytical MLE
\[
\hat \alpha \simeq 1 + n \left[\sum_{i=1}^n \log \left(\frac{x_i}{\xmin -0.5}\right)\right]^{-1} \;.
\]
\noindent This yields a threshold estimate of $\xmin=\Sexpr{est$xmin}$ and scaling parameter $\alpha=\Sexpr{signif(est$pars, 3)}$, which matches results found in \citet{clauset2009}. 

Alternatively, we could perform a parameter scan for each value of $\xmin$
<<eval=FALSE>>=
estimate_xmin(m_pl, pars=seq(1.8, 2.3, 0.1))
@

To fit a discrete log-normal distribution, we follow a similar procedure, except
we begin by creating a \cc{dislnorm} object\footnote{\cc{dislnorm}: discrete log
normal object}

<<warning=FALSE, eval=FALSE>>=
m_ln = dislnorm$new(moby)
est = estimate_xmin(m_ln)
@
<<echo=FALSE>>=
if(file.exists("examples1.rds")) {
  load("examples1.rds")
} else {
  m_ln = dislnorm$new(moby)
  est = estimate_xmin(m_ln)
  est_ln = est
  est_ln$pars = signif(est_ln$pars, 3)
  m_ln$setXmin(est)
  m_pois = dispois$new(moby)
  est = estimate_xmin(m_pois)
  m_pois$setXmin(est)
  save(m_pois, m_ln, est_ln, file="examples1.rds")
}
@

\noindent which yields a lower threshold of $\xmin=\Sexpr{est_ln$xmin}$ and parameters $(\Sexpr{est_ln$pars[1]},$ $\Sexpr{est_ln$pars[2]})$. A similar procedure is applied to fit the Poisson distribution; we create a distribution object using \cc{dispois}, then fit as before.

The data CDF and lines of best fit can be easily plotted
<<fig.keep='none', cache=TRUE>>=
plot(m_pl)
lines(m_pl, col=2)
lines(m_ln, col=3)
lines(m_pois, col=4)
@
\begin{figure}[t]
\centering
<<echo=FALSE, cache=TRUE>>=
plot(m_pl, xlab="x", ylab="CDF", 
     panel.first=grid(col="grey80"), 
     pch=21, bg=1)

lines(m_pl, col=2, lwd=2)
lines(m_ln, col=3, lwd=2)
lines(m_pois, col=4, lwd=2)
@
\caption{Data CDF of the Moby Dick data set. The fitted power law (green line), log-normal (red line) and poisson (blue) distributions are also given.}\label{F1a}
\end{figure}
\noindent to obtain figure \ref{F1a}. It clear that the Poisson distribution is not appropriate for this data set. However, the log-normal and power law distribution both provide reasonable fits to the data.


\subsection{Parameter uncertainty}

<<par_uncertainty, echo=FALSE>>=
data(bootstrap_moby)
bs = bootstrap_moby
@

To get a handle on the uncertainty in the parameter estimates, we use a
bootstrapping procedure, via the \texttt{bootstrap} function. This procedure can
be applied to any distribution object.\footnote{For example,
\mbox{bootstrap(m\_ln)}.} Furthermore, the bootstrap procedure can utilize
multiple CPU cores to speed up inference.\footnote{The output of this
bootstrapping procedure can be obtained via \cc{data(bootstrap\_moby)}.}
<<eval=FALSE, tidy=FALSE>>=
## 5000 bootstraps using two cores
bs = bootstrap(m_pl, no_of_sims=5000, threads=2)
@
\noindent By default, the \cc{bootstrap} function will use the maximum likelihood estimate to estimate the parameter and check all values of $\xmin$. When possible $\xmin$ values are large, then it is recommend that the search space is reduced. For example, this function call
<<eval=FALSE>>=
bootstrap(m_pl, xmins = seq(2, 20, 2))
@
\noindent will only calculate the Kolmogorov-Smirnoff statistics at values of $\xmin$ equal to
\[
2, 4, 6, \ldots, 20\;.
\]
A similar argument exists for the parameters.\footnote{For single parameter models, \cc{pars} should be a vector. For the log-normal distribution, \cc{pars} should be a matrix of values.}

The bootstrap function, returns \cc{bs\_xmin} object that has three components:
\begin{enumerate}
\item The goodness of fit statistic obtained from the Kolmogorov-Smirnoff test. This value should correspond to the value obtained from the \mbox{\cc{estimate\_xmin}} function.
\item A data frame containing the results for the bootstrap procedure. 
\item The average simulation time, in seconds, for a single bootstrap.
\end{enumerate}
The boostrap results can be explored in a variety way. First we can estimate the standard deviation of the parameter uncertainty, i.e.

\begin{figure}[t]
\centering
<<echo=FALSE, fig.width=8, fig.height=8, out.width='0.7\\textwidth'>>=
plot(bs)
@
\caption{Results from the standard bootstrap procedure (for the power law model) using the Moby Dick data set: \mbox{\texttt{bootstrap(m\_pl)}}. The top row shows the mean estimate of parameters $\xmin$ and $\alpha$. The bottom row shows the estimate of standard deviation for each parameter. The dashed-lines give approximate 95\% confidence intervals. After 5,000 iterations, the standard deviation of $\xmin$ and $\alpha$ is estimated to be 2.1 and 0.03 respectively.}\label{F1b}
\end{figure}
<<>>=
sd(bs$bootstraps[,2])
sd(bs$bootstraps[,3])
@
\noindent Alternatively, we can visualise the results using the \cc{plot} function:
<<fig.keep='none'>>=
## trim=0.1 only displays the final 90% of iterations 
plot(bs, trim=0.1)
@
\noindent to obtain figure \ref{F1b}. This top row of graphics in figure \ref{F1b} give a 95\% confidence interval for the mean estimate of the parameters. The bottom row of graphics give a 95\% confidence for the standard deviation of the parameters. The parameter \texttt{trim} in the \texttt{plot} function controls the percentage of samples displayed.\footnote{When \cc{trim=0}, all iterations are displayed.} When \texttt{trim=0.1}, we only display the final 90\% of data. 
\begin{figure}[t]
\centering
<<echo=FALSE, fig.width=6, fig.height=3, cache=TRUE, out.width='0.8\\textwidth'>>=
par(mfrow=c(1, 2))
hist(bs$bootstraps[,2], xlab=expression(x[min]), ylim=c(0, 1600), 
     xlim=c(0, 20), main=NULL, breaks="fd")
grid()
hist(bs$bootstraps[,3], xlab=expression(alpha), 
     ylim=c(0, 500), xlim=c(1.80, 2.05), main=NULL, breaks="fd")
grid()
@
\caption{Characterising uncertainty in parameter values. (a) $\xmin$ uncertainty (standard deviation 2) (b) $\alpha$ uncertainty (std dev. 0.03)}\label{F1c}
\end{figure}

We can also construct histograms. 
<<fig.keep='none'>>=
hist(bs$bootstraps[,2])
hist(bs$bootstraps[,3]) 
@
\noindent to get figure \ref{F1c}. 

A similar bootstrap analysis can be obtained for the log-normal distribution
<<eval=FALSE>>=
bs1 = bootstrap(m_ln)
@
\noindent in this case we would obtain uncertainty estimates for both of the log-normal parameters.

<<echo=FALSE>>=
data(bootstrap_p_moby)
bs_p = bootstrap_p_moby
@
\begin{figure}[t]
\centering
<<echo=FALSE, fig.width=6, fig.height=4, cache=TRUE, out.width='\\textwidth'>>=
plot(bs_p)
@
\caption{Results from the bootstrap procedure (for the power law model) using the Moby Dick data set: \mbox{\texttt{bootstrap\_p(m\_pl)}}. The top row shows the mean estimate of parameters $\xmin$,  $\alpha$ and the $p\,$-value. The bottom row shows the estimate of standard deviation for each parameter. The dashed-lines give approximate 95\% confidence intervals.}\label{F1d}
\end{figure}

\subsection{Testing the power law hypothesis}

Since it is possible to fit a power law distribution to \textit{any} data set, it is appropriate to test whether the observed data set actually follows a power law. \citet{clauset2009} suggest that this hypothesis is tested using a goodness-of-fit test, via a bootstrapping procedure. This test generates a $p\,$-value that can be used to quantify the plausibility of the hypothesis. If the $p\,$-value is large, than any difference between the empirical data and the model can be explained with statistical fluctuations. If $p \simeq 0$, then the model does not provide a plausible fit to the data and another distribution may be more appropriate. In this scenario, 
\begin{align*}
&H_0: \mbox{data is generated from a power law distribution.}\\
&H_1: \mbox{data is not generated from a power law distribution.}
\end{align*}
\noindent To test these hypothesis, we use the \texttt{bootstrap\_p} function
<<testing_pl, eval=FALSE>>=
bs_p = bootstrap_p(m_pl)
@
\noindent The point estimate of the $p\,$-value is one of the elements of the
\texttt{bs\_p} object\footnote{Also given is the average time of a single
bootstrap: \mbox{\texttt{bs\_p\$sim\_time} = \Sexpr{signif(bs_p$sim_time, 3)}}
seconds.}
<<>>=
bs_p$p
@
\noindent Alternatively we can plot the results

<<fig.keep='none', cache=TRUE>>=
plot(bs_p)
@

\noindent to obtain figure \ref{F1d}. The graph in the top right hand corner
gives the cumulative estimate of the $p\,$-value; the final value of the purple
line corresponds to \texttt{bs\_p\$p}. Also given are approximate 95\%
confidence intervals.

\subsection{Comparing distributions}

A second approach to test the power law hypothesis is a direct comparison of two
models.\footnote{While the bootstrap method is useful, it is computationally
intensive and will be unsuitable for most models.} A standard technique is to
use Vuong's test, which a likelihood ratio test for model selection using the
Kullback-Leibler criteria. The test statistic, $R$, is the ratio of the
log-likelihoods of the data between the two competing models. The sign of $R$
indicates which model is \textit{better}. Since the value of $R$ is obviously
subject to error, we use the method proposed by \cite{Vuong1989}.

To compare two distributions, each distribution must have the same lower
threshold. So we first set the log normal distribution object to have the same
$\xmin$ as the power law object
<<comp_dist, cache=TRUE>>=
m_ln$setXmin(m_pl$getXmin())
@
\noindent Next we estimate the parameters for this particular value of $\xmin$:
<<cache=TRUE>>=
est = estimate_pars(m_ln)
m_ln$setPars(est)
@
\noindent Then we can compare distributions
<<cache=TRUE>>=
comp = compare_distributions(m_pl, m_ln)
@
\noindent This comparison gives a $p$-value of \Sexpr{signif(comp[["p_two_sided"]], 4)}.
This $p\,$-value corresponds to the $p$-value on page 29 of the
\citeauthor{clauset2009} paper (the paper gives 0.69).

Overall these results suggest that one model can't be favoured over the other. 

\subsection{Investigating the lower bound}

The estimate of the scaling parameter, $\alpha$, is typically highly correlated
with the threshold limit, $\xmin$. This relationship can be easily investigated
with the \cc{poweRlaw} package. First, we create a vector of thresholds to scan
<<>>=
xmins = seq(1, 1001, 5)
@

<<est_scan, echo=FALSE, cache=TRUE>>=
est_scan = 0*xmins
for(i in seq_along(xmins)){
  m_pl$setXmin(xmins[i])
  est_scan[i] = estimate_pars(m_pl)$pars
}
@

\begin{figure}[t]
\centering
<<echo=FALSE, cache=TRUE>>=
plot(xmins, est_scan, type="s", 
     panel.first=grid(), 
     xlab=expression(x[min]), ylab=expression(alpha), 
     ylim=c(1.6, 2.8), col=1)
abline(h=1.95, col=2, lty=2)
@
\caption{Estimated parameter values conditional on the threshold, $\xmin$. The
horizontal line corresponds to $\alpha=1.95$.}\label{F1e}
\end{figure}
\noindent then a vector to store the results
<<est_scan, echo=1, eval=FALSE>>=
@
\noindent Next, we loop over the $\xmin$ values and estimate the parameter value
conditional on the $\xmin$ value
<<est_scan, echo=2:4, eval=FALSE>>=
@
\noindent The results are plotted figure \ref{F1e}. For this data set, as the
lower threshold increases, so does the point estimate of $\alpha$.

\clearpage

\section{Continuous data: electrical blackouts}

In this example, we will investigate the numbers of customers affected in
electrical blackouts in the United States between 1984 and 2002 (see
\cite{newman2005} for further details). The data set can be downloaded from
Clauset's website
\begin{center}
\url{http://tuvalu.santafe.edu/~aaronc/powerlaws/data/blackouts.txt}
\end{center}
and loaded into R in the usual way
<<>>=
blackouts = read.table("blackouts.txt")
@
\noindent Although the \texttt{blackouts} data set is discrete, since the values are large it makes sense to treat the data as continuous. Continuous power law objects take vectors as inputs, so
<<cache=TRUE>>=
m_bl = conpl$new(blackouts$V1)
@
\noindent then we estimate the lower-bound via
<<cache=TRUE>>=
est = estimate_xmin(m_bl)
@
\noindent This gives a point estimate of $\xmin=\Sexpr{est$xmin}$. We can then update the distribution object
<<cache=TRUE>>=
m_bl$setXmin(est)
@
\noindent and plot the data with line of best fit
<<fig.keep='none', cache=TRUE>>=
plot(m_bl)
lines(m_bl, col=2, lwd=2)
@
\noindent to get figure \ref{F2a}. To fit a discrete log-normal distribution we follow a similar procedure
<<m_bl_ln, echo=FALSE, cache=TRUE>>=
m_bl_ln = conlnorm$new(blackouts$V1)
est = estimate_xmin(m_bl_ln)
m_bl_ln$setXmin(est)
@


\begin{figure}[b]
\centering
<<echo=FALSE, fig.width=4, fig.height=4>>=
plot(m_bl, pch=21, bg=1, 
     panel.first=grid(col="grey80"), 
     xlab="Blackouts", ylab="CDF")
lines(m_bl, col=2, lwd=3)
lines(m_bl_ln, col=3, lwd=3)
@

\caption{CDF plot of the blackout dataset with line of best fit. Since the minimum value of $x$ is large, we fit a continuous power law as this is more it efficient. The power law fit is the green line, the discrete log-normal is the red line.}\label{F2a}
\end{figure}
<<m_bl_ln, eval=FALSE>>=
@
\noindent and add the line of best fit to the plot via
<<eval=FALSE>>=
lines(m_bl_ln, col=3, lwd=2)
@
\noindent It is clear from figure \ref{F2a} that the log-normal distribution provides a better fit to this data set.

\clearpage

\section{Multiple data sets: the American-Indian war}

In a recent paper, \citeauthor{Bohorquez2009} investigated insurgent attacks in
Afghanistan, Iraq, Colombia, and Peru. Each time, the data resembled power laws.
\citeauthor{Friedman2013} used the power law nature of casualties to infer
under-reporting in the American-Indian war. Briefly, by fitting a power law
distribution to the observed process, the latent, unobserved casualties can be
inferred (\cite{Friedman2013}).

The number of casualties observed in the American-Indian War can be obtained via
<<>>=
data("native_american")
data("us_american")
@
\noindent Each data set is a data frame with two columns. The first column is number of casualties recorded, the second the conflict date
<<>>=
head(native_american, 3)
@
\noindent The records span around one hundred years, 1776 -- 1890. The data is plotted in figure \ref{F3a}. 
\begin{figure}[!b]
\centering
<<echo=FALSE, fig.width=6, fig.height=4, out.width="0.8\\textwidth">>=
plot(native_american$Date, native_american$Cas, 
     log="y", pch=21, bg=1, 
     ylim=c(1, 2000), 
     cex=0.5, panel.first=grid(col="grey70"), 
     xlab="Date", ylab="#Casualties")
points(us_american$Date, us_american$Cas, 
       pch=24, bg=2, cex=0.5)
@
\caption{Casualty record for the Indian-American war, 1776 -- 1890. Native
Americans casualties (purple circles) and US Americans casualties (green
triangles). Data taken from \citet{Friedman2013}.}\label{F3a}
\end{figure}

It is straightforward to fit a discrete power law to this data set. First, we
create discrete power law objects
<<cache=TRUE>>=
m_na = displ$new(native_american$Cas)
m_us = displ$new(us_american$Cas)
@
\noindent then we estimate $\xmin$ for each data set
<<cache=TRUE>>=
est_na = estimate_xmin(m_na, pars=seq(1.5, 2.5, 0.01))
est_us = estimate_xmin(m_us, pars=seq(1.5, 2.5, 0.01))
@
\noindent and update the power law objects
<<cache=TRUE>>=
m_na$setXmin(est_na)
m_us$setXmin(est_us)
@
\noindent The resulting fitted distributions can be plotted on the same figure
<<fig.keep='none', cache=TRUE, tidy=FALSE>>=
plot(m_na)
lines(m_na)
## Don't create a new plot, just store the output
d = plot(m_us, draw=FALSE)
points(d$x, d$y, col=2)
lines(m_us, col=2)
@
\begin{figure}[t]
<<echo=FALSE>>=
plot(m_na, bg=1, pch=21, cex=0.5,  
     panel.first=grid(col="grey70"), 
     xlab="#Casualties", ylab="CDF")
lines(m_na, lwd=2, col=1)

d = plot(m_us, draw=FALSE)
points(d$x, d$y, bg=2, pch=24, cex=0.5)
lines(m_us, lwd=2, col=2)
@
\caption{Plots of the CDFs for the Native American and US American casualties.
The lines of best fit are also given.}\label{F3b}
\end{figure}
\noindent The result is given in figure \ref{F3b}. The tails of the
distributions appear to follow a power law. This is consistent with the
expectation that smaller-scale engagements are less likely to be recorded.
However, for larger scale engagements, it is very likely that a record is made.

<<clean-up, include=FALSE>>=
# R compiles all vignettes in the same session, which can be bad
rm(list = ls(all = TRUE))
@

\bibliography{poweRlaw}
\bibliographystyle{plainnat}

\end{document}
