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\author{Tarak Kharrat\\Salford Business School
        \And Georgi N. Boshnakov \\ University of Manchester
        \And Ian McHale\\Salford Business School
        \AND Rose Baker\\Salford Business School
}
\title{Flexible Regression Models for Count Data Based on Renewal Processes: The \pkg{Countr} Package}

%% for pretty printing and a nice hypersummary also set:
\Plainauthor{Tarak Kharrat, Georgi N. Boshnakov, Ian McHale, Rose Baker} %% comma-separated
\Plaintitle{Flexible Regression for Count Data Based on Renewal Processes: The Countr Package} %% without formatting
\Shorttitle{\pkg{Countr}: Flexible Regression Models for Count Data} %% a short title (if necessary)

%% an abstract and keywords
\Abstract{
  %% A new alternative to the standard Poisson regression model for count data is suggested.
  %% This new family of models is based on discrete distributions derived from renewal
  %% processes, \ie  distributions of the number of events by some time $t$, and allows for
  %% the relaxation of the time constant hazard assumption imposed by the Poisson model.
  %% Recently, a new method \citep{baker2016Renewal} for the fast computation of
  %% the count probabilities was derived.
  %% It is based on the repeated convolution of the discretized distribution and
  %% then correction by Richardson extrapolation. Any survival distribution can be
  %% used to describe the inter-arrival times between events, which gives a rich
  %% class of count processes with great flexibility for modelling both
  %% underdispersed and overdispersed data.
  %% For some survival distributions, faster computational methods based on Taylor expansion
  %% and series transformation are also discussed. Renewal count regression models are now
  %% available in \proglang{R} in the function \code{renewal()} from the package
  %% \pkg{Countr}. \code{renewal()} has been designed to mimic the \code{glm()}
  %% interface and standard methods for model diagnosis and prediction are also implemented.
  %% The package functionalities are illustrated using fertility data first analysed
  %% in \citet{winkelmann1995duration}.
  A new alternative to the standard Poisson regression model for count data is
  suggested.  This new family of models is based on discrete distributions
  derived from renewal processes, \ie distributions of the number of events by
  some time $t$. Unlike the Poisson model, these models have, in general,
  time-dependent hazard functions. Any survival distribution can be used to
  describe the inter-arrival times between events, which gives a rich class of
  count processes with great flexibility for modelling both underdispersed and
  overdispersed data.
  The \proglang{R} package \pkg{Countr} provides a function, \code{renewalCount()},
  for fitting renewal count regression models and methods for working with the
  fitted models. The interface is designed to mimic the \code{glm()} interface
  and standard methods for model exploration, diagnosis and prediction are
  implemented. Package \pkg{Countr} implements state-of-the-art recently
  developed methods for fast computation of the count probabilities.
  The package functionalities are illustrated using several datasets.
}
\Keywords{renewal process, duration dependence, count data, Weibull distribution, convolution,
Richardson extrapolation}
\Plainkeywords{renewal process, duration dependence, count data, Weibull distribution, convolution,
Richardson extrapolation} %% without formatting
%% at least one keyword must be supplied

%% publication information
%% NOTE: Typically, this can be left commented and will be filled out by the technical editor
%% \Volume{50}
%% \Issue{9}
%% \Month{June}
%% \Year{2012}
%% \Submitdate{2012-06-04}
%% \Acceptdate{2012-06-04}

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%% in the following format:
\Address{
  Tarak Kharrat\\
  Salford Business School\\
  Maxwell Building, Salford \\
  M5 4WT Greater Manchester, United Kingdom
  E-mail: \email{T.Kharrat@salford.ac.uk}\\

  Georgi N. Boshnakov\\
  School of Mathematics\\
  The University of Manchester\\
  Oxford Road, Manchester M13 9PL, UK\\
  URL: \url{http://www.maths.manchester.ac.uk/~gb/}
}
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\begin{document}

%% include your article here, just as usual
%% Note that you should use the \pkg{}, \proglang{} and \code{} commands.

\section[Introduction]{Introduction}

Modelling a count variable (the number of events occurring in a given time
interval) is a common task in many fields such as econometrics, social sciences,
sports modelling, marketing, physics or actuarial science just to name a few.
The standard approach is to use the Poisson model,
where $Y |x \sim Poisson (\E(Y|x) = \exp(x^\top \beta))$. Here
$Y$ is predicted given covariates with values $x$, using regression coefficients
$\beta$.  This model was built around a one-to-one correspondence between the
count model (Poisson) and the distribution of the inter-arrival times
(exponential). Perhaps this conceptual elegance contributed to its
popularity. With this elegance comes some limitation: the Poisson model
restricts the (conditional) variance to be equal to the (conditional) mean. This
situation is rarely observed in real life data and among the thousands of
alternatives proposed in the literature (see for example
\citet{winkelmann2013econometric} or \citet{cameron2013regression} for a
review), only a few retain the correspondence between the count model and the
timing process. This correspondence is not only a conceptual elegance but also offers the
researcher the flexibility to model the aspect (counting or timing) that is perhaps
known better (from the available data) and to draw conclusions (typically
prediction) using the other. A classic example is the exponential
distribution used in radioactive decay which leads to Poisson count model.
Another very good example in the marketing context can be found in
\citet{McShane2008Count}.

Another limitation of the Poisson model results from the memorylessness property
of the exponential distribution. This property states that the probability of
having an arrival during the next $[t, t + \Delta t]$ time period (where $t > 0$
and $\Delta t > 0$) is independent of when the last arrival occured.  In many
situations, this assumption is not realistic and the history of the process can
be informative about future occurrences.

One way to incorporate the history of the process in the modelling process is to make
the current probability of an occurence depend on the number of previous event
occurences. These models are known as \textit{occurence dependence} and they are
said to display true contagion.
\citet{bittner2007self} gave a discrete time example where the probability of
scoring a goal in soccer in the current unit of time depends on the number of
goals scored previously. The modelling process resulted in a negative binomial
distribution.

Another way to take advantage of the process history is to assume that the time
since the last observed event is informative about the probability of a future
occurence.  Inter-arrival times between events are still assumed to be
independent and identically distributed but the hazard function, defined by
$h(t) = \frac{f(t)}{1 - F(t)}$, where $f(t)$ and $F(t)$ are the density and the
cumulative probability function, is no longer a constant function of time (as
in the exponential case) but is replaced by a time-varying function.
These type of models display \textit{duration dependence} where
negative duration dependence is obtained by a decreasing hazard function (of
time) and positive duration dependence by an increasing hazard function. As
noted by \citet{winkelmann1995duration},
\begin{em}
  "Events are `dependent' in the
  sense that the occurence of at least one event (in contrast to none) up to
  time $t$ influences the occurence in $t + \Delta t$".
\end{em}
This class of models is known as \textit{renewal processes} and will form the
main focus of this paper.

The key quantity when studying renewal processes (and time to event in general)
is the hazard function. Not only does it fully characterize the inter-arrival timing
distribution but it also relates to the type of dispersion observed in the
corresponding count data.  In particular, \citet{winkelmann1995duration}
established that if the hazard function is monotonic, increasing (decreasing)
hazard corresponds to count data with under-dispersion (over-dispersion); the
constant hazard characterizing the exponential distribution corresponds to data
with equi-dispersion.  Therefore, allowing for a more flexible hazard function
results in more flexible counting processes able to accomodate over-dispersed
and under-dispersed, as well as equi-dispersed data.

\citet{winkelmann1995duration} was the first to comment on the usefulness of
renewal process models and derived a count model based on gamma distributed
inter-arrival times.  The choice of the gamma distribution was justified by
computational necessity. In fact, the reproductive property of the gamma
distribution (sums of independent gamma random variables are gamma distributed)
leads to a simple form for the derived gamma count
probability. \citet{McShane2008Count} derived a closed formula for the count
probability of a renewal process based on Weibull inter-arrival times using
series expansion. The same approach has been used by \citet{jose2011count} and
\citet{Jose2013Gumbel} to derive a counting process with Mittag-Leffler and
Gumbel inter-arrival times, respectively.

Despite the attractive properties of count models based on renewal processes,
their use is still limited in practice where Poisson, geometric and negative
binomial are usually preferred. Perhaps the main reason is the lack of available
software to easily fit this new type of models. The development of \pkg{Countr}
\citep{RpackageCountr}, available from the Comprehensive \proglang{R} Archive
Network (CRAN), is meant to fill in this gap and complete the practioners'
toolbox for modeling count data in \proglang{R} \citep{Rcore}.

The \pkg{Countr} package provides a function, \code{renewalCount()}, for fitting
count regression models based on renewal distributions. It offers several
built-in inter-arrival times distributions and supports custom distributions.
The design of the fitting function (\code{renewalCount()}) and the methods that
act on the object returned by it, is meant to mimic the familiar user interface
associated with a number of \proglang{R} modelling functions, especially
\code{glm()} \citep{chambers1992statistical} from package \pkg{stats}
\citep{Rcore}, \code{hurdle()} and \code{zeroinfl()}
\citep{zeileis2008regression} and \code{flexsurvreg()} \citep{flexsurv2016}.

The remainder of this paper is laid out as follows. In Section~\ref{sec:models},
we briefly review the fundamental relationship between a timing process and the
resulting count model, the different computation methods as well as the renewal
regression models considered in \pkg{Countr}.  The package design is discussed
in Section~\ref{sec:design} and a first working example is given in
Section~\ref{sec:quickStart}. A second extended example is analysed in
Section~\ref{sec:extendedExample} and is used to discuss the package
functionality. A strategy to discriminate between models is suggested in
Section~\ref{sec:modCompare} and illustrated with a real dataset. We
conclude and discuss future work in Section~\ref{sec:cl}.

\section{Models}
\label{sec:models}

% subsection{The fundamental relationship between the inter-arrival time process
%   and the resulting count model}

\subsection{Count models and inter-arrival times}
\label{ssec:inter_models}
% We start this section by recalling the fundamental relationship between a timing
% process (in the shape of the distribution of the inter-arrival times) and the
% resulting count model.

The distribution of non-negative integer valued discrete random variables,
count distributions for short, can be used as the distribution of the number of
events in a given time interval, and vice versa. A powerful method to specify
count distributions then can be based on models of the times between the
events.

Consider a stochastic process starting at time $t=0$ which produces a sequence
of events.  Let $\tau_{1}$ be the time of the first event and, in general,
$\tau_k$ be the time between the $(k-1)$th and the $k$th event,
$k \in \mathbb{N}$. The $\tau_k$'s are known as \textit{inter-arrival times} or
\textit{waiting times}.  The arrival time of the $m$th event is
\begin{equation*}
  a_m = \sum_{\substack{k=1}}^m \tau_k, \qquad m=1,2,\dots
  ,
\end{equation*}
with cumulative probability function $F_m(t) = \Prob(a_{m} < t)$.

Let $N_t = N(t)$ denote the total number of events in $[0, t)$.  For any
fixed~$t$ (the observation horizon), $N_t$ is the count variable we wish to
model. We have $\Prob(N_{t} \geq m) = F_{m}(t)$ and $\Prob(N_{t} < m) = 1 - F_{m}(t)$,
since $N_t \geq m$ if and only if the $m$th event occurs before time~$t$.
Moreover, the probability, $P_m(t)$, for exactly $m$ events before time $t$ is
\begin{align}
 \label{eq:rela}
  P_m(t)
    %% & =  \Prob(N_t < m + 1) - \Prob(N_t < m)  \nonumber \\
    &\equiv \Prob(N_t = m)   \nonumber \\
    &= \Prob(N_t \geq m) - \Prob(N_t \geq m+1)  \nonumber \\
    &= F_m(t) - F_{m + 1}(t)
\end{align}
For fixed $t$, Equation~(\ref{eq:rela}) shows how a count distribution,
$\{P_{m}(t), \ m=0,1,\ldots\}$, can be obtained from
$\{F_m(t), \ m=0,1,\ldots\}$, which in turn can be specified flexibly by the
inter-arrival distributions.

More specifically, let $\{\tau_k\}_{k\in N}$ be independent and identically
distributed (iid) random variables with common density $f(\tau)$. In this case
the process is called a \textit{renewal process} \citep[see][for a formal
definition]{feller1970} and Equation~(\ref{eq:rela}) can be used to derive
the following recursive relationship:
\begin{align}
  P_{m+1}(t) & =  \int_0^t F_m(t-u)\dee F(u) - \int_0^t F_{m+1}(t-u)\dee F(u) \nonumber \\
            & =   \int_0^t P_m(t-u)\dee F(u),
              \qquad \text{for $m=1,2,\ldots$,}
\label{eq:conv}
\end{align}
where $P_0(u)=S(u)=1-F(u)$ (a survival function).  Equation~(\ref{eq:conv}) can
be understood intuitively: the probability of exactly $m+1$ events occurring by
time $t$ is the probability that the first event occurs at time $0 \le u < t$,
and that exactly $m$ events occur in the remaining time interval, integrated
over all times $u$.  $P_1(t), \dots, P_m(t)$ can be generated in turn by
evaluating this integral.

\subsection{Count probability computation methods}
\label{ssec:ProbabilityComp}
\subsubsection{Convolution methods}
To compute the integral defined in Equation~(\ref{eq:conv}), one can adapt
composite midpoint rule (\eg \citet[section 4.1.4]{press}):
\begin{equation}
\int_0^{Nh} f(x)\dee x = h\sum_{j=1}^N f\{(j-1/2)h\} + O(h^2),
\label{eq:int_romberg}
\end{equation}

where there are $N$ steps with stepsize $h$, and $Nh=t$. Note that the integrand
\ie $f$ is not evaluated at the limits of the integral (open rule). Furthermore,
if we define $g(u) = P_m(t-u)$ for some values of the count $m$ and the time $t$
(fixed) and $F(t)$ the CDF of the inter-arrival times distribution, the previous
integral can be seen as the sum of $N$ integrals of the form:
\[
\int_{(j-1)h}^{jh} g(u)\dee F(u) \simeq g\{(j-1/2)h\}(F\{jh\}-F\{(j-1)h\}),
\]
In order to reach $P_m(t)$, the previous computation requires all the previous
$m$ probabilities to be available. The algorithm starts by initialising a (local)
$q$ array to contain the $P_0$ at the midpoints $h/2\cdots (N-1/2)h$, sets up
another local array to contain $F\{jh\}-F\{(j-1)h\}$, and carries out the
convolutions.  At the end of this step, the array $q[~]$, initially containing
$P_0$, will be overwritten to contain $P_1$. These steps are repeated until the
desired probability is obtained. This method was named the \textit{direct}
method in \pkg{Countr} and it has been shown in \citet[Section 3]{baker2016Renewal}
to have a  $O(mN^2)$ complexity.

The \textit{direct} method computes all probabilities up to the $m$th, which is
slow if we need only the $m$th probability.  It can be improved so that
computing time is $O(\ln(m)N^2)$ instead of $O(mN^2)$, using the addition chain
method (an adaptation of the method used by compilers for fast computation of
integer powers of a variable with the minimum number of multiplications). We
label this method \textit{naive} method in \pkg{Countr} and refer readers to
\citet[Appendix A]{baker2016Renewal} for computation details.

A more efficient method to directly compute the $m$th probability is based on
\citet{depril} algorithm (and hence is called the \textit{dePril} method in
\pkg{Countr}). It has been shown to have a $O(N^2)$ complexity \citep[Section
4]{baker2016Renewal} and hence is the recommended (and the default) method in
\pkg{Countr}. Readers interested in the computation details are referred to
Depril's paper and \citet[Section 4]{baker2016Renewal}. Here we simply describe
the main idea of the algorithm. Let $q_i$ be the value of probability density
function of the survival distribution evaluated at points $t_i \ge 0$ where
$q_0 > 0$. Then the probability of $m$ events is $f_N^{(m)}$, the $m$-fold
convolution of $q$, given by
\[f_0^{(m)}=q_0^m,\]
and for $N > 0$  by the recursion

\begin{equation}
  f_N^{(m)}=q_0^{-1}\sum_{j=1}^N (\frac{(m+1)j}{N}-1)f_{N-j}^{(m)}q_j.
  \label{eq:depril}
\end{equation}
This algorithm when applied to our case requires three arrays: one to hold the
survival function, one for the probability mass $q$, and one work array to hold
$f$. To apply this method to continuous distributions like the Weibull, we first
discretised the distribution, so that $q_j=F((j+1)h)-F(jh)$.
\subsubsection{Improvement by Richardson extrapolation}
The integration rule described in Equation~(\ref{eq:int_romberg}) generates
approximations of order $O(h^2)$. Richardson extrapolation can be used to
progressively remove errors of order $h^2$, $h^4$ etc. Clearly, if an estimate
$S_1=S+\gamma h^\delta$ and $S_2=S+\gamma (h/2)^\delta$, where $S_1$ and $S_2$
are the approximations with $N$ and $2N$ steps respectively and $S$ is the true
value, we can remove the error and estimate $S$ as
\begin{equation}
S_3=(2^\delta S_2-S_1)/(2^\delta-1)
\label{eq:rich}.
\end{equation}
Subsequently, higher-order errors can be removed in the same way until the
required accuracy is attained. Romberg integration can also be done with the
extended-midpoint rule (\eg \citet{press}). The situation for convolutions is
less straightforward, but a satisfactory solution was derived in \citet[Appendix
B]{baker2016Renewal}. Clearly, the Richardson extrapolation has the appealing
property of improving the accuracy, without necessitating a large value of $N$
and consequent slow computation. Therefore, for all the built-in distributions,
the default behaviour in \pkg{Countr} is to apply the Richardson extrapolation.
Whenever it is possible, users are advised to activate the extrapolation option.

\subsubsection{The special case of the Weibull distribution}
In \pkg{Countr}, methods inspired from \citet{McShane2008Count} have been
implemented when the inter-arrival times are Weibull distributed. In this case,
the exponential in the Weibull density can be expanded out and series
transformation techniques can be used to speed up convergence. Two algorithms
are available: a matrix approach using a specified number of terms and a series
accelerated method based on the Euler and van-Wijngaarden transformations
\citep[Chapter~5]{press2007numerical} controlled by a number of iterations and a
convergence tolerance parameters.

\subsubsection{Naming conventions}
We use the term \emph{count distribution} or \emph{renewal count distribution}
for the distribution of $N_{t}$ and qualify it with the name of the
inter-arrival distribution for a particular distribution of the inter-arrival
times. For example, \emph{Weibull count distribution} refers to the count model
arising from a renewal process with inter-arrival times having a Weibull
distribution.
\subsection{Renewal regression models}

The regression models fitted by \pkg{Countr} are in the spirit of the
generalised linear models \citep{mccullagh1989generalized} and consist of two
main components: a conditional distribution of the response variable (given the
covariates, if any) and one or more linear equations relating parameters to
covariates, possibly via link functions.

More formally, let $\boldsymbol{Y}$ be the response variable of interest,
$\boldsymbol{x}$ a vector of covariates and $\countdist$ a renewal count
distribution. We assume that
\begin{equation}
  \label{eq:generalSpec}
  \boldsymbol{Y}|\boldsymbol{x} \sim \countdist(\boldsymbol{\theta}),
\end{equation}
where $\boldsymbol{\theta}$ is the vector of the parameters of $\countdist$.

One or more parameters of the distribution may depend linearly
on covariates via link functions. The equation for the $k$th parameter then is:
\begin{equation}
  \label{eq:linkFct}
  g_{k}(\theta_{k}) = \boldsymbol{x}^{\top} \boldsymbol{\beta}_{k}
  ,
\end{equation}
where $g_{k}$ is the link function for the $k$th parameter, $\boldsymbol{x}$ the
covariates and $\boldsymbol{\beta}_{k}$ the corresponding vector of regression
parameters. Typically, covariates are related to a location parameter but it is
helpful in some applications to be able to let other parameters depend on
covariates.

We call these models \emph{renewal regression models}.  Note that, in general,
the renewal distributions are not from the exponential family. For comparison,
in standard generalised linear models (glm) the distribution is
taken from the exponential family of distributions and the mean, transformed by
a link function, is a linear combination of the covariates.


\subsubsection{The inter-arrival distribution}

Table~\ref{tab:countdist} gives count distributions available in \pkg{Countr}
and the corresponding inter-arrival time distributions from which they are
obtained.
\begin{table}[!htbp]
  \centering
  \begin{tabular}{ll||llllll}
    Count          & $P(Y=k)$ & Inter-arrival  & pdf $f(t)$& Parameters & & \\
    distribution   &          & distribution   &     &            & & \\ \hline

    Poisson        & $\frac{\lambda^{k}}{k!}\exp^{-\lambda} $ & Exponential   & $ \lambda \exp^{-\lambda t}$ & $\lambda$    & \\[2pt] \hline

    Weibull-count  & NSCF & Weibull       & $ \lambda \beta t ^{\beta -1} \exp^{-\lambda t^\beta}$ & $ \lambda, \beta $   \\
    Gamma-count    & NSCF & Gamma         & $\lambda^k t^{k-1} \exp^{-\lambda t}/\Gamma(k)$  & $ \lambda, k $ \\
    Gengamma-count & NSCF & Gen. gamma    &  see Equation~(\ref{eq:genGamPre}) &
    $\mu, \sigma, q$\\
    burr-count & NSCF & burr    &   $\frac{\frac{kc}{\alpha} (\frac{t}{\alpha})^{c-1}}{\Big(1 + (\frac{t}{\alpha})^c     \Big)^{k+1}} $&
    $\alpha, c, k$\\

  \end{tabular}
  \caption{Built-in count distributions in package \pkg{Countr} and the interarrival time
    distributions generating them. The Poisson distribution is given for reference (it can be
    fitted using base \proglang{R}'s \code{glm()}). NSCF stands for \emph{no
      simple closed form}. $\Gamma(k)$ is the gamma function and the burr-count uses
    the Burr type \RNum{12} parameterization \citep{tadikamalla1980look}.}
  \label{tab:countdist}
\end{table}

The Poisson distribution is the only one with a simple closed form
expression. The other distributions provide alternatives, which extend the range
of data that can be modelled with count regression models. For example, they can
accommodate over- and under-dispersion. Also, the systematic way in which these
count distributions are derived may give advantageous insight in some cases.

It is also noteworthy that both the Weibull and gamma count models nest the
basic Poisson model. In fact, setting $\beta = 1$ in the Weibull case or $k=1$
in the Gamma case leads to the exponential distribution. Another interesting
distribution that could be used with the convolution method is the generalised
gamma first introduced by
\citet{stacy1962generalization}. \citet{prentice1974log} proposed an alternative
parametrization which is preferred for computation. In the
\citet{prentice1974log} parametrization, the distribution has three parameters
$(\mu, \sigma, q)$, and its survival function is given by:
\begin{equation}
  \label{eq:genGamPre}
  S(t) =
  \begin{cases}
     1 - I(\gamma, u) \ & \   \text{if q} > 0  \\
     1 - \Phi(z) \ & \ \text{if q} = 0
\end{cases}
\end{equation}

where $ I(\gamma, u) = \int_0^u x ^{\gamma - 1} \exp(-x) / \Gamma (\gamma) $ is the
regularised incomplete gamma function (the  gamma distribution function with
shape $ \gamma $ and scale $1$), $ \Phi$ is the standard normal distribution function,
$ u = \gamma \exp(|q|z), z = (\log(t) - \mu) / \sigma $, and $\gamma = 1 / q^2$.
This distribution includes the Weibull (when $ q = 1$), gamma (when $q = \sigma$) and
log-normal (when $ q = 0$) as special cases.

The default links (the functions $g_{k}()$ in equation~\eqref{eq:linkFct})
associated with the built-in distributions are given in
Table~\ref{tab:distparam}.

\begin{table}[ht]
  \centering
  \begin{tabular}{l|l|ll|ll|ll|ll|ll}
    Count          & dist & Par.~1  & Link & Par.~2 & Link & Par.~3 & Link    \\
    distribution   &    &     &      &        &      &        &        \\ \hline

    Weibull-count  & \code{weibull} & \code{scale}  & log  & \code{shape} & log
                                                           & &
                                                              \\
    Gamma-count    & \code{gamma} & \code{rate}   & log  & \code{shape} & log  & &        \\
    Gengamma-count & \code{gengamma} & \code{mu}    & log  & \code{sigma} & log  & \code{Q}        & I()    \\
    Burr-count     & \code{burr} & \code{scale} & log  & \code{shape1}& log  & \code{shape2}  & log  &
  \end{tabular}
  \caption{Parameters and default link functions for the built-in count
    distributions in package \pkg{Countr}.I() stands for the identity function.}
  \label{tab:distparam}
\end{table}


As discussed before, count models arising from renewal processes provide very
flexible families of distributions. Perhaps the simplest way to use them is to
simply ignore their connections to renewal theory. Several models can be tried
and users can discriminate between models using the following strategy:
\begin{itemize}
\item when models are nested, a likelihood ratio test (LR) statistic can be
  used.  This is possible because renewal-count models are fully parametric and
  in this case the LR statistic has the usual $\chi^2(p)$ distribution, where
  $p$ is the difference in the number of parameters in the model.
\item when models are not nested, one can compare information
criteria such as the Akaike information criterion (AIC) or the Bayesian
information criterion (BIC) to choose the model that provides the best fit to the data.
\end{itemize}
This strategy is illustrated in Section~\ref{sec:modCompare}.

In some applications however, the researcher may have some information about the
inter-arrival time process which can lead to a particular choice of model. For
example, assume that a researcher is interested in modelling the number of
occurences by some time horizon $t$. He has data on the observed count for a
number, $n$, of individuals, together with a set of individual covariates
$\boldsymbol{x}_i, i =1, \dots,n$. If data on time to first event are also
available, the researcher can fit a parametric hazard model using package
\pkg{flexsurv} \citep{flexsurv2016}, choose the parametric model that presents
the best fit and use the associated renewal count family to model his data. This
approach has been used in \citet[Chapter 4]{TarakPhd}.

\subsubsection{Parameters estimation}

Parameter estimation is performed by maximum likelihood (ML). Define the
log-likelihood $\mathcal{L} = \sum_{i=1}^{n} ln P_{y_i}(t|\boldsymbol{x}_i,
\boldsymbol{\beta}_i)$, where $\boldsymbol{\beta}$ is the vector of parameters.
The ML estimator $\boldsymbol{\hat{\beta}}$ is the solution of the first-order
conditions,
\begin{equation}
     \frac{\partial \mathcal{L}}{\partial \boldsymbol{\beta}} = \sum_{i=1}^{n} \frac{\partial ln P_i}{\partial \boldsymbol{\beta}} = 0
     ,
\end{equation}
where $P_i = P_{y_i}(t|\mathbf{x}_i, \boldsymbol{\beta}_i)$ and $\frac{\partial
\mathcal{L}}{\partial \boldsymbol{\beta}}$ is a $q \times 1$ vector.

Let $\boldsymbol{\beta}_0$ be the \textit{true} value of $\boldsymbol{\beta}$.
Using ML theory, we obtain
$\boldsymbol{\hat{\beta}} \xrightarrow{p} \boldsymbol{\beta}_0$ and
\begin{equation}
     \sqrt{n}(\boldsymbol{\hat{\beta}}_{ML} - \boldsymbol{\beta}_0) \xrightarrow{d} \mathcal{N}[\mathbf{0}, \mathbf{V}^{-1}]
     ,
\end{equation}
where the $q \times q$ matrix $\mathbf{V}$ matrix is defined as
\begin{equation}
     \mathbf{V} = - \lim_{n \rightarrow \infty} \frac{1}{n} \E
     \bigg[ \sum_{i=1}^n \frac{\partial^2 \ln P_i }{\partial \boldsymbol{\beta} \partial \boldsymbol{\beta}'}|_{\boldsymbol{\beta}_0}
     \bigg]
     .
     \label{eq:var_covar}
\end{equation}

To use this result, we need a consistent estimator of the variance matrix
$\mathbf{V}$. Many options are available: the one implemented in \pkg{Countr} is
known as the \textit{Hessian estimator} and simply evaluates
Equation~(\ref{eq:var_covar}) at $\boldsymbol{\hat{\beta}}$ without taking
expectation and limit.


\subsubsection{Goodness-of-fit}

For fully parametric models such as Poisson or renewal-count, a crude diagnosis
is to compare the fitted probabilities with observed frequencies. Things are
better understood with a formula. Define the count variable $y_i, i=1, \dots, n$,
where $n$ is the total number of individuals and let $m = max(y_i)$. We denote
by $\bar{p}_j$ the observed frequencies (the fraction of the sample where $y =
j$) and let $\hat{p}_j, j = 1, \dots, m$, be the fitted frequencies. For example, in
the Poisson model, $\hat{p}_j = \frac{1}{n} \sum_{i=1}^{n} \exp{(-\hat{\mu}_i^j) /
j!}$.

To start with, one can compare $\bar{p}_j$ to $\hat{p}_j$ for specific values of
the count variable $j$ to gain some insight about the range of counts where the
model has a tendency to over or under predict or to allow a visual inspection of
the predictive performance of competing models. This computation can be done in
\pkg{Countr} by a call to the function \code{compareToGLM()} which can take a
fitted Poisson and (optionally) a negative binomial model and compare them to a
number of fitted \code{renewal} models passed as additional arguments. The
function returns a table with $\bar{p}_j$ (\code{Actual}) and the estimates
$\hat{p}_j$ induced by the different models. The contribution to the Pearson
statistic of each cell, defined as
$\sum_{j=1}^J n\frac{(\bar{p}_j - \hat{p}_j)^2}{\bar{p}_j}$, is computed, as
well. The result can be visualised by a call to \code{frequency_plot()}, see Section~\ref{sec:quickStart}.

Visualising the result can be a good starting point but without a formal test,
it is hard to conclude if $\hat{p}_j$ is a \textit{good approximation} to $\bar{p}_j$
and hence if the model fits the data well. \citet[Section 5.3.4]{cameron2013regression})
suggest a formal \textit{chi-square goodness-of-fit test} which is a generalisation of the Pearson's \textit{chi-square test} and controls
for estimation error in $\hat{p}_j$. The test is a conditional moment test. It
has been implemented in \pkg{Countr} in the \code{chiSq_gof()} method using the gradient
version which is justified for renewal models as they are fully parametric and
parameters estimation is based on maximum-likelihood.

Applications of the above  tests are given in the following sections.




\section{Package design}
\label{sec:design}

The \pkg{Countr} package is available from CRAN
\url{https://cran.r-project.org/package=Countr} and can be installed using the
standard \proglang{R} tools.

The main function in \pkg{Countr} is \code{renewalCount()}. It fits renewal
regression models for count data using maximum likelihood. Several built-in
count distributions are provided. The distributions are parameterised in terms
of the corresponding inter-arrival times, see
Table~\ref{tab:countdist}. The Poisson distribution is given in the table for
reference and can be fitted using base \proglang{R}'s \code{glm()}).
User-defined distributions are also supported.


The \code{renewalCount()} function returns the fitted model as an object from S3 class
\code{"renewal"}.  The standard interface to the modelling functions is maintained,
as much as possible.  In particular, methods for \code{summary()}, \code{predict()},
\code{confint()}, \code{coef()} and similar functions are available, see also
Table~\ref{tab:methods}.

The \pkg{Countr} package also exports functions for the computation of the
probabilities associated with several renewal count models. The probability
computations are rather intensive and are mostly implemented in \proglang{C++}
with the help of the \pkg{RcppArmadillo} \citep{RcppArmadillo2014} package.  Several
methods are provided offering various degrees of trade-off between speed and
accuracy, see Section~\ref{ssec:ProbabilityComp}.


Renewal regression models are fitted with the function \code{renewalCount()}. It
has been designed to mimic the GLM functionality in \proglang{R}. In fact, users
familiar with \code{glm()} should recognize several common arguments in
\code{renewalCount()}'s interface:
% Note: this should never be run!
<<eval=FALSE>>=
renewalCount(formula, data, subset, na.action, weights, offset,
  dist = c("custom", "weibull", "weibullgam", "gamma", "gengamma", "burr"),
  anc = NULL, convPars = NULL, link = NULL, time = 1.0,
  control = renewal.control(...), customPars = NULL,
  seriesPars = NULL, weiMethod = NULL,
  computeHessian = TRUE, model = TRUE, y = TRUE, x = FALSE, ...)
@
The first line contains the standard model-frame specifications, while
arguments \code{computeHessian}, \code{model}, \code{x} and \code{y} are boolean
flags indicating whether the returned object should contain the
variance-covariance matrix, the model frame, the model matrix and the response,
respectively. All default to \code{TRUE}. If the variance-covariance matrix is
suppressed it is not computed at all.  The remaining arguments are specific to
the renewal regression model.

The minimum required inputs are \code{formula} (an \proglang{R} formula),
\code{data} (a data frame) and \code{dist} (a character string).  Argument
\code{formula} describes the model, \code{data} contains the values of the
response and the covariates, while \code{dist} specifies the desired count model
distribution.

The fitting process is based on maximum likelihood using optimization routines
implemented in package \pkg{optimx} \citep{optimx2014}.  Users can customize
different aspects of the fitting process and control what is returned but if the
minimum inputs are provided the routine will work just fine.
We give more details in the following sections.
Additional guidance can be found in the package documentation and the vignettes.



% \subsection{}
% \section{Comparing the Poisson and weibull models}
\section{Quick start - an example without covariates}
\label{sec:quickStart}

The examples in this and later sections assume that the package is made
available in the current session via
<<>>=
library("Countr")
@
We also need \code{dplyr} \citep{dplyr2016} and
\code{xtable} \citep{xtable2016}, which
provide usefull facilities for data manipulation and presentation:
<<load-aux-pkg>>=
library(dplyr)
library(xtable)
@

The purpose of this section is to give users a general sense of the package,
including the components, what they do and some basic usage. For this purpose,
we use the \emph{football} dataset shipped with \texttt{Countr} which contains
the final scores of the 1104 matches played in the English Premier League from
season 2009/2010 to season 2016/2017 (380 matches per season). The game data and
home and away team names are also provided. The data were collected from
\url{http://www.football-data.co.uk/englandm.php} and slightly formatted and
simplified.

As discussed in length in \citet[Chapter 4]{TarakPhd} and more briefly in
\citet{boshnakov2017bivariate}, the main issue with the Poisson model when
modelling the goals scored by a team in football is that the hazard function
(the instant probability of scoring) remains constant for every time unit
(minutes say in football).  However, empirical studies showed that this is
rather questionable. In particular, goals are more likely to be scored at the
end of each half because of players' tiredness, see for example
\citet[Figure~1]{dixon1998birth}.

Renewal-count distributions give the flexibility to drop the constant intensity
assumption by selecting non-exponential interval-arrival distributions.
%How to use this flexibility is beyond the scope of this paper.
One strategy to select this distribution is discussed in \citet[Chapter
4]{TarakPhd}.  Here we simply say that the weibull density seemed to provide the
best fit and will be used in this example.

We focus on the goals scored by the away team:
<<>>=
data(football)
table(football$awayTeamGoals)
@
Our aim here is not to conduct an extensive analysis of the data but to
highlight the improvement introduced by the weibull-count model compared to
Poisson.

We fit the Poisson model using \texttt{glm()} with the family
argument set to \texttt{poisson}. For the weibull-count model we use
\texttt{renewalCount()} with \texttt{dist = "weibull"}. Both models are
intercept only (no covariates specified).
<<eval=FALSE>>=
away_poiss <- glm(formula = awayTeamGoals ~ 1, family = poisson,
  data = football)
away_wei <- renewalCount(formula = awayTeamGoals ~ 1, data = football,
  dist = "weibull", computeHessian = FALSE,
  control = renewal.control(trace = 0))
@

We start by investigating the distribution of goals and the associated fitted
probabilities induced by the two models. The away team rarely scores more than 4
goals and hence we decided to aggregate counts of 5 and larger.
<<>>=
breaks_ <- 0:5
pears <- compareToGLM(poisson_model = away_poiss, breaks = breaks_,
  weibull = away_wei)
@
Figure~\ref{fig:football:freq} shows the observed relative frequencies together with the
predictions from the two models.
\begin{figure}[!htbp]
  \centering
<<fig=TRUE>>=
library(dplyr)
frequency_plot(pears$Counts, pears$Actual,
  dplyr::select(pears, contains("_predicted")),
  colours = c("grey", "blue", "green", "black"))
@

  \caption{Comparison of Weibull and Poisson models for football data.
    The predicted frequencies from the Weibull model (green) match the observed
    frequencies (grey) better than those from the Poisson model (blue).
  }
  \label{fig:football:freq}
\end{figure}


As expected, the most likely outcome for away goals is 0, 1 and to some
extent~2. Eyeballing Figure~\ref{fig:football:freq}, the weibull-count model is
a clear improvement over the Poisson model.
%% Even of the other count values, it seems that the weibull-count
%% model still slightly outperforms the Poisson alternative.

%% @Georgi: We have an issue with the placement of the lr code block. I am sure
%% the referee will complain about it.
%%
%% @tarak: this is for the final edit - it may disappear by then or the problem may pop up
%% elsewhere.
We can now verify these findings with formal statistical tests. As discussed
before, the two models are nested and the likelihood ratio test can be used to
discriminate between them:
%  The warning:
%    'In modelUpdate(objects[[i - 1]], objects[[i]]) :
%       original model was of class "glm", updated model is of class "renewal"'
%  is issued by lrtest()
<<>>=
lr <- lmtest::lrtest(away_poiss, away_wei)
lr
@
The likelihood ratio test's null hypothesis (both models present equal fits) is
rejected at any reasonable confidence level, thus confirming that the Weibull
addition induced by the shape parameter does improve the fitting.

Finally, formal chi-square goodness-of-fit tests implemented in
\texttt{Countr::chiSq\_gof()} can be used to judge how well the models describe
the data. Here are the results for the Weibull-count and Poisson models:
<<>>=
gof_wei <- chiSq_gof(away_wei, breaks = breaks_)
gof_pois <- chiSq_gof(away_poiss, breaks = breaks_)
rbind(Poisson = gof_pois, "Weibull-count" = gof_wei)
@

The results from the two tests are printed together for convenience. The null
hypothesis that the Weibull model is adequate cannot be rejected (p-value
\Sexpr{format.pval(gof_wei$"Pr(>Chisq)")}), % $
supporting the claim that the Weibull-count model describes the data well.  On
the other hand, the hypothesis that the Poisson model is adequate is confidently
rejected (p-value~\Sexpr{format(gof_pois$"Pr(>Chisq)", digits = 3)}). % $


\section{An extended example with under-dispersion}
\label{sec:extendedExample}

We illustrate the usage of \pkg{Countr} with
the fertility data, first described in \citet[Section 5]{winkelmann1995duration}
and re-analyzed by \cite{McShane2008Count} and \citet{baker2016Renewal}.
The \code{fertility} dataset contains information about a sample of 1,243 women who
were over 44 years old in 1985 and answered the questions of the German
Socio-Economic Panel. The responses are arranged in a data frame with one row
for each mother and \Sexpr{dim(fertility)[2]} columns\footnote{%
  The dataset is equivalent to the earlier references but,
  for convenience, we have renamed the variables and replaced the dummy variables with
  factors.
}, coded as follows:

% ferNames <- c( "children", "german", "years_school", "voc_train", "university", "Religion",
%                "year_birth", "rural", "age_marriage" )
% origFerNames <- c( "GERMAN", "EDU", "VOC", "UNI", "CATH", "PROT", "MUSL", "RURAL",
%                "YEAR_OF_B", "AGEMARR", "Y" )
%
%  [1,] "Y"         "children"
%  [2,] "GERMAN"    "german"
%  [3,] "EDU"       "years_school"
%  [4,] "VOC"       "voc_train"
%  [5,] "UNI"       "university"
%  [6,] "Religion"  "Religion"  ("CATH", "PROT", "MUSL" and other)
%  [7,] "YEAR_OF_B" "year_birth"
%  [8,] "RURAL"     "rural"
%  [9,] "AGEMARR"   "age_marriage"

\begin{itemize}

\item \code{children}           % \code{Y}
  --- number of children.

\item \code{german}     % old: \code{GERMAN}
  --- German nationality, a factor variable with levels
        \code{yes} and \code{no}.

\item \code{years_school}        % \code{EDU}
  --- general education, measured as years of schooling.

\item \code{voc_train}, \code{university} % \code{VOC}, \code{UNI}
  --- post-secondary education:
          vocational training (\code{voc_train}) and
          university (\code{university}), factor variables with levels
          \code{yes} and \code{no}.

\item \code{Religion}
  --- factor variable with levels \code{Catholic}, \code{Muslim},
                                  \code{Protestant}, and \code{Other}.

\item \code{rural}
  --- rural, a factor variable with levels \code{yes} and \code{no}.

\item \code{year_birth}
  --- year of birth,
\item \code{age_marriage}
  --- age at marriage.
\end{itemize}


The dataset is available when \pkg{Countr} is attached. It can also be loaded
independently using \code{data()}, e.g.
<<>>=
data("fertility", package = "Countr")
@

The motivation to use the fertility data is twofold. First, we wanted to analyse
a dataset with under-dispersion, where by construction the Poisson model and the
natural extensions, such as negative binomial, fail to capture this aspect in
the data. Second, we wanted to give the users the software to reproduce the
results discussed in the reference papers \citet{winkelmann1995duration} and
\citet{McShane2008Count}.

The first few rows of the data are shown in Table~\ref{tbl:data}.
<<head-fertility,echo=FALSE,results=tex>>=
dataCaption <- "First few rows of fertility data."
print(xtable(head(fertility), caption = dataCaption, label = "tbl:data"),
      rotate.colnames = TRUE)
@ %def
The response variable considered here is the number of children per woman
(\code{children}).  The average number of children observed in this sample is
\Sexpr{format(mean(fertility$children), digits=4)} %$
and variance of \Sexpr{format(var(fertility$children), digits=4)}, %$
so there is a hint for under-dispersion in the response variable's marginal
distribution. A frequency table of this variable is shown in Table~\ref{tbl:freq}.
<<children-table,echo=FALSE,results=tex>>=
freqtable <-
    count_table(count = fertility$children, breaks = 0:9, formatChar = TRUE)
print(xtable(freqtable, caption = "Fertility data: Frequency distribution of column \\texttt{children}.",
             label = "tbl:freq"))
@ %def

There are
\Sexpr{ ncol(fertility) -1 }
possible explanatory variables:
\Sexpr{ sum(sapply(select(fertility, -children), is.numeric)) }
numeric and
\Sexpr{ sum(sapply(select(fertility, -children), is.factor)) }
categorical (factors).
Tables~\ref{tbl:frecfac} and \ref{tbl:frecnum} show summaries of these variables.

% This computes separate summaries for the numeric and non-numeric variables:
<<echo=FALSE>>=
nam_fac <- sapply(fertility, function(x) !is.numeric(x))
fert_factor <- summary(fertility[ , nam_fac])
fert_num <- t(sapply(fertility[ , !nam_fac], summary)) # summary(fertility[ , !nam_fac])
@ %def

% The results are shown in Tables~\ref{tbl:frecfac} and \ref{tbl:frecnum}.
<<covariates-table,echo=FALSE,results=tex>>=
print(xtable(fert_factor, caption = "Summary of the factor variables", label = "tbl:frecfac"))
@ %def


<<covariates-table-num,echo=FALSE,results=tex>>=
print(xtable(fert_num, caption = "Summary of the numeric explanatory variables",
                       label = "tbl:frecnum"))
@ %def


\subsubsection{Specifying the count distribution}
\label{sec:spec-distr}

The count distribution is selected by specifying the distribution of the
inter-arrival times. \pkg{Countr} currently provides the four built-in
distributions discussed in Table~\ref{tab:countdist}. Besides, another
distribution (\code{dist = "weibullgam"}) has been implemented. It is known as
the weibull-gamma (or compound weibull) and is obtained from weibull by letting
the parameter $\lambda$ have a gamma distribution. The Weibull-gamma count
distribution has Weibull-gamma for its interarrival times. This model has been
derived in details in \citet{McShane2008Count}. This model can be seen as a
means to model heterogeneity of individuals' inter-arrival times, the same way
the negative binomial extends the Poisson model.
We found this model to be numerically unstable and it should be used with care
\citep[see also the discussion in ][Section 7.4]{baker2016Renewal}.

For the \code{renewalCount()} function and other functions in the package that
provide a choice, the desired inter-arrival distribution is specified by the
argument \code{dist} as a character string, which should have one of the values
reported in Table~\ref{tab:distparam}. Inter-arrival distributions defined by
the user are also supported and specified by \code{dist = "custom"}, see
Section~\ref{ssec:custom-dist}.

\subsubsection{Specifying covariates}
\label{sec:spec-covar}

Covariates can be introduced using familiar \proglang{R} formula syntax.  In the
examples with the \code{fertility} dataset we will use the following formula:
<<>>=
regModel <- children ~ german + years_school + voc_train + university +
  Religion + rural + year_birth + age_marriage
@

When supplied as argument \code{formula} in a call to \code{renewalCount()}
the left-hand side of the formula specifies the response variable. The
right-hand side gives the covariates for the linear relationship to the
(possibly transformed by a link function) corresponding parameter of the count
distribution.

Different links
can be specified using the \code{link} argument of \code{renewalCount()}.  It
should be a named list, where the link for each parameter of the distribution is
given as a character string.  For example, the \code{log} link can be
specified for the shape and scale parameters of the Weibull distribution as
follows:
<<>>=
link_weibull <- list(scale = "log", shape = "log")
@
Possible options for the link function are \code{"log"}, \code{"cauchit"},
\code{"cloglog"}, \code{"probit"}, \code{"logit"} and \code{"identity"} (default
for user defined distributions).


\subsubsection{Fitting built-in models}
\label{sec:fitting-models-1}

The fitting function \code{renewalCount()} has many arguments but
when fitting models with the built-in count distributions
it is usually sufficient to specify the model, the data, and possibly
initial values, leaving the remaining settings to their default values.
For example, the gamma model of \citet{winkelmann1995duration} can be fitted as
follows:
<<eval=FALSE>>=
gamModel <- renewalCount(formula = regModel, data = fertility,
  dist = "gamma", control = renewal.control(trace = 0) )
@
The setting \code{trace = 0} in this and other examples prevents the optimising
function from printing during the optimisation step.

% To fit a count distribution without covariates, put the number~1 in the
% right-hand side of the formula. This fits a Weibull count distribution to
% \code{children}:
% %% @tarak: this was:
% %%
% %% weiCountA <- renewalCount(formula = children ~ 1, data = fertility, dist = "weibull",
% %%   weiMethod = "series_acc", control = renewal.control(trace = 0) )
% %%
% %% I removed weiMethod = "series_acc" since it seems confusing without explanation.
% %% (and also the reader may wonder why this was not done with covariates too)
% <<eval=FALSE>>=
% weiCountA <- renewalCount(formula = children ~ 1, data = fertility,
%   dist = "weibull", control = renewal.control(trace = 0) )
% @
Almost any aspect of the computation can be customized in \code{renewalCount()} and
options are provided to give the user control over the computation of the
initial values, the numerical optimization algorithm, the method for computing
the count probability and the returned values, among others.

\subsubsection{User defined initial values}
\label{ssec:InitialVals}

As usual in non-linear optimisation, for best results informed initial values
should be provided whenever possible.

One strategy is to fit a Poisson glm model and use its parameter estimates as
starting values for the linear predictor of the location parameter. Since the
models fitted by \pkg{Countr} contain additional parameters, these need to be
set suitably. For example, the Weibull count model reduces to the Poisson if the
shape parameter is equal to one, so this is a natural initial value for this
parameter. More generally, for a count distribution which generalizes the
Poisson model, values of the parameters that reduce the distribution to Poisson
are often suitable starting points.  Some theoretical results support this
procedure, see for example \citet[Section~3.2]{cameron2013regression}.

The above strategy is adopted by \code{renewalCount()} when no initial values
are provided. However, when fitting a model with more than one linear predictor,
initial values are required from the user.

The initial values are passed to \code{renewalCount()} as a named numeric
vector.  For this, the names of the coefficients are needed. They have the form
\code{par_covname}, where \code{covname} is the name of a covariate and
\code{par} is the name of the distribution parameter, such as \code{shape} to
which the covariate is linked.  Intercepts are named \code{par_}. The names of
the distribution parameters can be found by a call to \code{getParNames()}.
For example, this shows that the parameters of the Weibul distribution are named
\code{"scale"} and \code{"shape"}:
<<>>=
getParNames("weibull")
@

A suitably named vector can be obtained by extracting the coefficients of an
existing model and, if necessary, changing their values. The convenience
function \code{renewalCoef(object, target)} eliminates most of the tedious work
by going even further --- it takes \code{object}, usually a fitted model,
extracts coefficients, and renames them for use with the parameter or
distribution specified by target, see the examples below. If a suitable model is
not available, the function \code{renewalNames()} can be used to get a character
vector of names of parameters. It can be used in two ways.  The first,
\code{renewalNames(object, target)}, has similar semantics to
\code{renewalCoef()}. The second is \code{renewalNames(object,...)}, where
\code{"..."} are the same argument that would be used in a call to
\code{renewalCount()}. In this case \code{renewalNames()} returns the names of
the coefficients of the model that would be produced if \code{renewalCount()}
was called with the same parameters. For example, changing \code{renewalCount}
to \code{renewalNames} in the code used to obtain \code{gamModel}, we get:
<<>>=
renewalNames(regModel, data = fertility, dist = "gamma")
@


We illustrate below the preparation of initial values for the Weibull count
model of \citet{McShane2008Count}.  This is a model with one linear predictor,
as in the Poisson model. An example for the case with more linear predictors is
given later along with the discussion of regression on ancilliary parameters.

As discussed above, we fit a Poisson model:
<<eval=FALSE>>=
IV <- glm(regModel, family = poisson(), data = fertility)
@
<<>>=
coef(IV)
@
We then rename the coefficients of model \code{IV} to link them to the first
parameter, \code{scale}, of the Weibull distribution:
%% <<>>=
%% pars <- getParNames("weibull")
%% pars
%% par1Values <- coef(IV)
%% names(par1Values) <- paste(pars[1], names(par1Values), sep = "_")
%% names(par1Values) <- gsub('\\(Intercept\\)', "", names(par1Values))
%% par2Value <- 1.0
%% names(par2Value) <- paste("shape", "", sep = "_")
%%
%% start <- c(par1Values, par2Value)
%% @
%% check with: identical(par1ValuesA, par1ValuesA)
<<>>=
startW <- renewalCoef(IV, target = "scale")
@
The Poisson model is a particular case of the Weibull model with shape parameter equal
to one, which we use as a natural initial value for this parameter and append it to
\code{startW} to complete it. Note that the regression is done on \code{log()}
scale for both the shape and scale parameters as explained above.
<<<>>=
startW <- c(startW, "shape_" = log(1))
startW
@

Finally, we fit the model, The initial values are passed to
\code{renewalCount()} through the \code{renewal.control()} routine that will run
a sanity check before passing them to the optimizer:

<<eval=FALSE>>=
weiModel <- renewalCount(formula = regModel, data = fertility,
  dist = "weibull", control = renewal.control(trace = 0, start = startW))
@
This model is further discussed in Section~\ref{sec:function-renewal}.



\subsubsection{Customizing the optimization routine}

As mentioned above, \code{renewalCount()} maximizes the log-likelihood of the
desired model by a call to \code{optimx()} from package
\pkg{optimx} \citep{optimx2014}. The default is to use \code{method = "nlminb"}
with a maximum of 1000 iterations. Users can change this again through the
\code{renewal.control()} routine. Any other option accepted by \code{optimx()}
can also be passed in \code{renewal.control()}, e.g.,
<<eval=FALSE>>=
weiModelA <- renewalCount(formula = regModel, data = fertility,
  dist = "weibull",
  control = renewal.control(trace = 0, method = "L-BFGS-B"))
@

It is also possible to experiment with more than one optimisation algorithm in a
single call to \code{renewalCount()}. The result with the highest value of the
objective function (largest log-likelihood) will be preferred.  This is
illustrated below:
<<eval=FALSE>>=
weiModel_many <- renewalCount(formula = regModel, data = fertility,
  dist = "weibull", control = renewal.control(trace = 0,
  method = c("nlminb", "Nelder-Mead", "BFGS")))
@

Field \code{optim} of the result contains a data frame giving, for each method,
the estimates of the parameters, the run time (\code{xtimes}), the value of
the log-likelihood evaluated at the estimates (\code{value}) and other
quantities from the optimisation routine. In our example:
<<>>=
t(weiModel_many$optim)
@

The algorithms are arranged in decreasing order of the likelihood.
Here the three algorithms found a similar value of the log-likelihood but the
\code{"Nelder-Mead"} did not converge. Although substantially slower, the
\code{"nlminb"} was preferred because it gave a sligltly higher likelihood
(\Sexpr{format(weiModel_many$optim$value[1], digits=7)}, %$$
for \code{"nlminb"} compared to
\Sexpr{format(weiModel_many$optim$value[2], digits=7)}, %$$
for the \code{"BFGS"} algorithm).




\subsubsection{Regression models on the ancillary parameters}

So far we have given examples of regression models in which the parameter
regressed on is the location parameter, or more precisely, the
first parameter of the count distribution. \pkg{Countr} offers the possiblity
to specify covariates on the ``ancillary'' parameters (the ones that determine
the shape, the variance or other higher moments) via argument
\code{anc}. \code{anc} is a named list containing formulas describing regression
equations for the desired ancillary parameters.  Only the right-hand sides of
the formulas in argument \code{arg} are used. This setup is modelled on
(and is compatible with)
package \code{flexsurv} \citep[function \code{flexsurvreg()}]{flexsurv2016}.


In the ancillary regression case, starting values have to be provided.  We show
below an example of fitting such a model for the generalized gamma inter-arrival
times with covariates applied to all the distribution parameters.  The names of
the distribution parameters in this case are \code{"mu"} (location),
\code{"sigma"} and \code{"Q"} (see Table~\ref{tab:distparam}).  In this example
we will use the same formula, \code{regModel}, for the three parameters of the
distribution, so we prepare a list for the ancilliary parameters:
<<>>=
anc <- list(sigma = regModel, Q = regModel)
@

As discussed earlier, we obtain informative initial values for the location parameter from a
Poisson model. Here we can use the previously model \code{IV}.  We also set the intercepts
for (log) \code{"sigma"} and \code{"Q"} to one and the remaining regression coefficients to
zero. With the help of \code{renewalCoef()} all this can be done with a couple of lines:
<<>>=
startA <- renewalCoef(IV, target = "gengamma")
startA[c("Q_", "sigma_")] <- c(1, log(1))
startA
@
The above illustrates the use of the name of a count distribution for argument
\code{target}. In that case \code{renewalCoef()} assumes that the same formula is used for
all parameters.

%% This was before renewalCoef:
%%
%% <<eval=FALSE>>=
%% mu <- coef(IV)
%% names(mu) <- paste0("mu_", names(mu))
%%
%% sigma <- Q <- c(1, rep(0, 10))
%% names(sigma) <- gsub("mu_", "sigma_", names(mu))
%% names(Q) <- gsub("mu_", "Q_", names(mu))
%%
%% start <- c(mu, sigma, Q)
%% names(start) <- gsub('\\(Intercept\\)', "", names(start))
%%
%% anc <- list(sigma = regModel, Q = regModel)
%%
%% gengamModel_ext0 <- renewalCount(formula = regModel, data = fertility,
%%   dist = "gengamma", anc = anc,
%%   control = renewal.control(start = start, trace = 0),
%%   computeHessian = FALSE)
%% @

We can now fit the model:
<<eval=FALSE>>=
gengamModel_ext0 <- renewalCount(formula = regModel, data = fertility,
  dist = "gengamma", anc = anc,
  control = renewal.control(start = startA, trace = 0),
  computeHessian = FALSE)
@

If the optimisation didn't converge or for other reasons, we may need to fit the
model again.  Here we want to fit the same model using the coefficients of the
previous fit as initial values, so we can directly use
\code{gengamModel_ext0}. We also request the \code{spg} algorithm \citep{BB2009}
for this second iteration:
<<eval=FALSE>>=
startB <- coef(gengamModel_ext0)
gengamModel_ext <- renewalCount(formula = regModel, data = fertility,
  dist = "gengamma", anc = anc,
  control = renewal.control(method = "spg", start = startB, trace = 0),
  computeHessian = FALSE )
@


Users can make sure that the optimization did converge by investigating the
convergence status flag in object \code{gengamModel_ext}:
<<>>=
gengamModel_ext$converged
@
% @Tarak - a thought for us:
%  the above illustrates what I discovered in other examples - that optim() algorithm
%  doesn't converge for some indecipherable reason, but spg declares convergence even if, as
%  here, the parameters hardly change.

\subsubsection{Custom inter-arrival distributions}
\label{ssec:custom-dist}

Instead of using the built-in distributions in \code{Countr},
users can also specify their own inter-arrival parametric distributions.
For this to work, the following information is required:
\begin{itemize}
\item names of the distribution parameters, a character vector.
    % \code{parNames}:

\item survival distribution function, a function with signature
  \code{function(tt, distP)}, where \code{tt} is a vector of class
  \code{"numeric"} of non-negative values
  % where the survival function is to be evaluated
  and \code{distP} gives the values of the distribution parameters as a named
  list.

\item the name(s) of the link function(s); different link functions may be used
  for the different parameters. If not specified, \code{identity} will be used.
  % \ie a linear model will be fitted to the f=distribution parameter.
\end{itemize}

The Weibull inter-arrival distribution is one of the built-in distributions but as an
illustrative example here is how it could be specified as a custom distribution:
<<>>=
parNames <- c("scale", "shape")
sWei <- function(tt, distP) exp( -distP[["scale"]] * tt ^ distP[["shape"]])
link <- list(scale = "log", shape = "log")
@
Here \code{parNames} defines the names of the parameters, \code{"sWei"} computes the survival
distribution function and \code{link} requests \code{log} link for both parameters
(a common choice for positive parameters).

Initial values are very desirable for user defined distributions and are
computed as discussed in Section~\ref{ssec:InitialVals}. We are going to fit the
same model, so we can use the initial values \code{startW} defined in that
section.
%% <<>>=
%% pars <- coef(IV)
%% names(pars) <- gsub('\\(Intercept\\)', "", paste0("scale_", names(pars)) )
%% start <- c(pars, shape_ = 1)
%% control_custom <- renewal.control(start = start, trace = 0)
%% @
%%
<<>>=
control_custom <- renewal.control(start = startW, trace = 0)
@

For custom inter-arrival distributions, convolution based methods are the only
option. If the user is willing to speed up the computation using a Richardson
correction scheme, the appropriate correction function that computes the
correction coefficients must be passed. As argued by \citet[Section
3.5]{baker2016Renewal}, the appropriate values for the Weibull case are
$(2, \alpha)$, where $\alpha$ is the shape parameter. This can be communicated
to \code{renewalCount()} by defining a function whose argument is a named list
of the distribution parameters, as in:
<<>>=
.getExtrapol <- function(distP) {
    c(2, distP[["shape"]])
}
@

%Once all the inputs are ready, \code{renewalCount()} can be called in the usual way
%with argument \code{dist} set to \code{"custom"}.

The names of the parameters, the survival function and the extrapolation parameters are
passed to \code{renewalCount()} through argument \code{customPars}.  In our example these are
\code{parNames}, \code{sWei} and \code{.getExtrapol}, respectively.  This illustrates the
syntax for preparing the list:
<<>>=
customPars <- list(parNames = parNames, survivalFct = sWei,
  extrapolFct = .getExtrapol)
@

There is also an argument for the links. A model with our custom specified distribution can
now be fitted with:
<<eval=FALSE>>=
weiModelCust <- renewalCount(formula = regModel, data = fertility,
  dist = "custom", link = link, control = control_custom,
  customPars = customPars, computeHessian = FALSE)
@
Note that the computations in this example can be much slower than for the equivalent
built-in distribution (that is why the hessian computation has been turned off),
since the crucial parts of the latter are implemented
in~\proglang{C++}. Therefore, we recommend using built-in distributions as
much as possible and simply consider custom inter-arrival distributions for
exploratory purpose where long computation time is not an issue.



\subsection{Working with the fitted models}
\label{sec:function-renewal}

The function \code{renewalCount()} produces an \code{S3} object from class
\code{"renewal"}.  Methods for a number of \proglang{R} functions are provided,
so that objects from class
\code{"renewal"} can be manipulated and explored in a familiar way.
%% Currently, methods for the following generics are available:
%% <<>>=
%% methods(class = "renewal")
%% @
Table~\ref{tab:methods} lists generic functions from base \proglang{R} with
methods for objects from class \code{"renewal"}.  Only functions with explicitly
defined renewal methods are listed. The default methods for generics without
such methods may also work, when they access properties of objects via calls to
functions such as \code{coef} to collect the information they need.

\begin{table}[!htbp]
  \centering
  \begin{tabular}{llll}
    % addBootSampleObject
    % chiSq_gof
    % chiSq_pearson
    \code{coef()   }  & \code{fitted()      }  & \code{df.residual() }  & \code{print()   } \\
    \code{confint()}  & \code{predict()     }  & \code{extractAIC()  }  & \code{summary() } \\
    \code{vcov()   }  & \code{residuals()   }  & \code{df.residual() }  &                   \\
                      & \code{model.matrix()}  & \code{logLik()      }  &                   \\
                      & \code{nobs()        }  &
    % residuals_plot
    % se.coef()
  \end{tabular}
  \caption{Generic functions from base \proglang{R} with methods for objects
    from class \code{"renewal"}.}
  \label{tab:methods}
\end{table}

\subsubsection{Model fit}

The usual \code{summary()} method can be used to check the coefficients'
estimates together with their standard errors (computed using numerical estimates
of the gradient and Hessian) and Wald test statistics. Here is a summary of
Winkelmann's model fitted above:
<<>>=
summary(gamModel)
@
The results are exactly the same as the ones in
\citet[Table~1]{winkelmann1995duration}\footnote{Note that the regression model
  defined in \citet[Equation (17)]{winkelmann1995duration} is slightly different
  and hence the constant value defined in Table 1 is related to our estimated
  \code{rate\_} parameter by $ exp(Constant) * \alpha = exp(scale\_) $.}.
Similarly, the results for \code{weiModel} below coincide with those given by
\citet[Table 2]{McShane2008Count}\footnote{The value of $\lambda$ in
\citet[Table 2]{McShane2008Count} is the exponential of the value of \code{scale\_}.
The same applies for the value of \code{shape\_}.
Also note that the standard errors in their table are obtained by the
bootstrap procedure with 100 replicates.}:
<<>>=
summary(weiModel)
@

Not surprisingly, the summary shows that religion has a major impact on the
number of children, as are being a German and vocational training. On the other
hand, university education seems not significant (p-value 0.2577), even though
the effect (the value, $-0.1815$, of the coefficient) is almost the same as for
vocational training. Similarly, years of schooling seems not significant. These
variables are highly confounded, so such conclusions should not be taken at face
value.


\subsubsection{Bootstrap standard errors}

Standard errors and other quantities computed from the Hessian are based on
assumptions, which are often difficult to check. Non-parametric bootstrap is
often a useful alternative.  The bootstrap computations in \pkg{Countr} are
based on function \code{boot()} from the package with the same name
\citep{boot2017}.  Bootstrap confidence intervals rely on additional methods
implemented in \code{confint.boot()} from package \code{car} \citep{car2011}.

Bootstrap standard erros and confidence intervals can be computed by setting
\code{type = "boot"} and specifying the desired number of bootstrap samples with
argument \code{R}.  \citet[Section 2.6.4]{cameron2013regression} observe that
$400$ bootstrap samples are often enough. This can be slow, so
here we give an example with $R = 5$ replicates for illustration:
<<eval=FALSE>>=
se_boot <- se.coef(object = weiModel, type =  "boot", R = 5)
confint_boot <- confint(object = weiModel, type = "boot", R = 5)
@

For \code{confint()}, the type of bootstrap confidence interval can be specified with argument
\code{bootType}, which can be one of
  \code{"norm"} (normal approximation, the default),
  \code{basic} (basic bootstrap),
  \code{percent} (percentile method) and
  \code{"bca"} (bias-corrected and accelerated method), see
\citet[Chapter~5]{davison1997bootstrap}
for details on the methods.

In the above example, the computation of \code{se_boot} and \code{confint_boot}
involves separately generated bootstrap samples. This can be slow with realistic
number of bootstrap samples. Also, it may be desirable to do such related
calculations on the same bootstrap sample. This can be achieved by creating a bootstrap
sample separately, using \code{addBootSampleObject()}, which stores the bootstrap sample in
component \code{"boot"} of the supplied model object. Renewal methods for functions like \code{confint}
use the bootstrap sample if they find it there.
For example, we could\footnote{This and the following two R commands are not presented as R
  code since the parallel processing options are system dependent.}
do the following:
\begin{verbatim}
weiWithBoot <- addBootSampleObject(weiModel, R = 400, parallel = "multicore",
  ncpus = 14)
\end{verbatim}
We use arguments \code{parallel} and \code{ncplus} to speed up the computations
by using parallel processing facilities provided by \code{boot::boot}, see
\code{help(boot::boot)}.
Now these computations would use the stored bootstrap sample in \code{weiWithBoot}:
\begin{verbatim}
se_boot <- se.coef(object = weiWithBoot, type = "boot")
confint_boot <- confint(object = weiWithBoot, type = "boot")
\end{verbatim}
To use another bootstrap sample, set component \code{"boot"} of
\code{weiWithBoot} to \code{NULL} or remove it.
Vignette \code{vignette("VarianceCovariance", package="Countr")}
gives further examples and details.

%@Georgi: some text can be added from the var-covariance vignette: in
%particular, we need to mention that a parallel option is available. We can
%eventually refer to the vignette.
%
%@tarak: done.

% \subsubsection{Comparing fitted models}
% %% @georgi: this need to be changed and adapted to what we did in the fertility vignette
% %% @tarak: could you do this?
% In the previous section, we fitted three models to the fertility data. One way
% to select the ``best'' model is to use information criteria such as AIC or BIC.
% For completeness, we also include the Poisson model (previously fitted for
% use as initial values) and add it to the list of models to be compared:
% <<>>=
% poissModel <- IV
% @
% <<>>=
% mat <- cbind(
%   logLik = c(logLik(poissModel),
%              logLik(gamModel),
%              logLik(weiModel),
%              logLik(gengamModel_ext)),
%   nPars = c(length(coef(poissModel)),
%             length(coef(gamModel)),
%             length(coef(weiModel)),
%             length(coef(gengamModel_ext))),
%   AIC = c(AIC(poissModel),
%           AIC(gamModel),
%           AIC(weiModel),
%           AIC(gengamModel_ext)),
%   BIC = c(BIC(poissModel),
%           BIC(gamModel),
%           BIC(weiModel),
%           BIC(gengamModel_ext))
%   )

% rownames(mat) <- c("Pois", "gam", "wei", "gengam_ext")
% print(mat)
% @

% The generalized gamma model has the largest log-likelihood and is favoured by
% AIC.  On the other hand, BIC, which penalizes the number of parameters more
% strongly than AIC, favors the less parsimonious Weibull model. Note also that
% for this dataset, the three renewal regression models clearly outperform the
% Poisson one.


\subsubsection{Prediction}

Predictions from the fitted model are obtained by calling \code{predict()}.  Two
types of prediction are available: predicting a given count (response) value
(\code{type = "prob"}), i.e., the probability of observing a specific value
of the count variable  (the value of \code{Y} in our \code{data.frame})
given the (individual) covariates
or predicting the expected value (\code{type = "response"}).

The procedure is illustrated for the first few individuals in the
\code{fertility} data:

<<>>=
newData <- head(fertility)
@
<<eval=FALSE>>=
predNew.response <- predict(weiModel, newdata = newData, type = "response",
  se.fit = TRUE)
predNew.prob <- predict(weiModel, newdata = newData, type = "prob",
  se.fit = TRUE)
@

<<echo=FALSE,print=FALSE>>=
options(digits = 5)
@

<<>>=
predtable <- data.frame(newData$children, predNew.prob$values,
  predNew.response$values)
names(predtable) <- c("Y", "P(Y=y|x)", "E(Y|x)")
predtable
@
The covariates are not printed here since they were shown previously in
Table~\ref{tbl:data}.

<<echo=FALSE,print=FALSE>>=
options(digits = 3)
@


To conclude this section, we verify that the results produced by the
built-in model and the user defined Weibull model are identical (up to rounding
errors):
<<>>=
cbind(builtIn = coef(weiModel), user = coef(weiModelCust))
@

\section{Model selection and comparison}
\label{sec:modCompare}

In the previous section several models were fitted to the fertility
data. It is natural to ask the question: Which model presents the best fit to the
data and hence which one should be preferred?  A strategy has been described in
Section~\ref{ssec:inter_models} and is inspired from the demand for medical care
example detailed in \citet[Section 6.3]{cameron2013regression}. It is
illustrated here with a real data example.

The dataset used in this example is the \texttt{quine} data from package
\texttt{MASS} \citep{Venables20102MASS}, first analysed by
\citet{aitkin1978analysis}.
<<load-data>>=
data(quine, package = "MASS")
@
The dataset gives the number of days absent from school (\texttt{Days}) of
\Sexpr{ nrow(quine) } children in a particular school year. A number of
explanatory variables are available describing the children's ethnic background
(\texttt{Eth}), sex (\texttt{Sex}), age (\texttt{Age}) and learner status
(\texttt{Lrn}).  The count variable \texttt{Days} is characterised by large
\emph{overdispersion} --- the variance is more than
16 times larger the mean, \Sexpr{formatC(var(quine$Days))} versus
\Sexpr{formatC(mean(quine$Days))}. Table~\ref{tab:quine:days} gives an idea about its
distribution. The entries in the table were calculated as follows:
<<children-table,results=tex>>=
breaks_ <- c(0, 1, 3, 5:7, 9, 12, 15, 17, 23, 27, 32)
freqtable <-
  count_table(count = quine$Days, breaks = breaks_, formatChar = TRUE)
@

\begin{table}[!htbp]
\centering
\begin{tabular}{lrrrrrrrrr}
<<echo=FALSE,results=tex>>=
##   print(xtable(freqtable,
##   caption = "quine data: Frequency distribution of column \\texttt{Days}.",
##   tabular.environment = "longtable", label = "tbl:freq"))

 print(xtable(freqtable[ , 1:7]), floating = FALSE, only.contents = TRUE)
 cat("\n\\\\[5pt]\n")
 print(xtable(freqtable[ , -(1:7)]), floating = FALSE, only.contents = TRUE)

@
\end{tabular}
\caption{quine data: Frequency distribution of column \texttt{Days}.}
\label{tab:quine:days}
\end{table}

Given the extreme over-dispersion observed in the \texttt{quine} data, we do not
expect the Poisson model to perform well. Nevertheless, we can still use it as a
starting point and treat it as a benchmark (any model worse than Poisson should
be strongly rejected). We also consider the negative binomial (as implemented in
\texttt{MASS:glm.nb()}) and 3 renewal-count models based on weibull, gamma and
generalised-gamma inter-arrival times. This gives a total of five models to
choose from. The following code fits the 5 models:
<<models,eval=FALSE>>=
quine_form <- as.formula(Days ~ Eth + Sex + Age + Lrn)
pois <- glm(quine_form, family = poisson(), data = quine)
nb <- MASS::glm.nb(quine_form, data = quine)

## various renewal models
wei <- renewalCount(formula = quine_form, data = quine, dist = "weibull",
  computeHessian = FALSE, weiMethod = "conv_dePril",
  control = renewal.control(trace = 0))

gam <- renewalCount(formula = quine_form, data = quine, dist = "gamma",
  computeHessian = FALSE, control = renewal.control(trace = 0))

gengam <- renewalCount(formula = quine_form, data = quine, dist = "gengamma",
  computeHessian = FALSE, control = renewal.control(trace = 0))
@

The models considered here are fully parametric. Therefore, a
straightforward method to discriminate between them is a likelihood
ratio (LR) test. This is possible when models are nested and in this case the LR
statistic has the usual \(\chi^2(p)\) distribution, where \(p\) is the difference in
the number of parameters in the models. Here we compare all
renewal-count models against Poisson, negative-binomial against Poisson,
weibull-count against generalised-gamma and gamma against the generalised-gamma.

For non-nested models, the standard approach is to use information criteria such
as the Akaike information criterion (AIC) and the Bayesian information criterion
(BIC). This method can be applied to discriminate between weibull and gamma
renewal count models, and between these two models and the negative binomial.

Therefore, a possible strategy can be summarised as follows:
\begin{itemize}
\item Use the LR test to compare Poisson with negative binomial.
\item Use the LR test to compare Poisson with weibull-count.
\item Use the LR test to compare Poisson with gamma-count.
\item Use the LR test to compare Poisson with generalised-gamma-count.
\item Use the LR test to compare weibull-count with generalised-gamma-count.
\item Use the LR test to compare gamma-count with generalised-gamma-count.
\item Use information criteria to compare gamma-count with weibull-count.
\item Use information criteria to compare weibull-count to negative binomial.
\end{itemize}

Here is the code for these tests \footnote{The \code{suppressWarnings()} command
is used to avoid printing a message to complain about the model objects being
from different class.}:
<<lr-test-poisson>>=
library(lmtest)
pois_nb <- lrtest(pois, nb)
pois_wei <- suppressWarnings(lrtest(pois, wei))
pois_gam <- suppressWarnings(lrtest(pois, gam))
pois_gengam <- suppressWarnings(lrtest(pois, gengam))
pois_res <- data.frame("Alternative model" =
  c("negative-binomial", "weibull", "gamma", "generalised-gamma"),
  Chisq = c(pois_nb$Chisq[2], pois_wei$Chisq[2],
            pois_gam$Chisq[2], pois_gengam$Chisq[2]),
  Df = c(1, 1, 1, 2),
  Critical_value = c(rep(qchisq(0.95, 1), 3), qchisq(0.95, 2)),
  stringsAsFactors = FALSE)
@
The results are summarised in Table~\ref{tab:lr_pois}.
As observed in Table~\ref{tab:lr_pois}, the LR test rejects the null
hypothesis and all the alternative models are preferred to Poisson. This is due to
the large over-dispersion.
<<echo=FALSE,results=tex>>=
print(xtable(pois_res, caption = "LR results against Poisson model. Each row compares an alternative model vs the Poisson model. All alternatives are preferable to Poisson.",
             label = "tab:lr_pois"))
@

\par
Next, we carry out LR test to discriminate between the renewal count
models (see Table~\ref{tab:lr_gengam} for the results):
<<lr-test-renewal>>=
gengam_wei <- lrtest(wei, gengam)
gengam_gam <- lrtest(gam, gengam)
gengam_res <- data.frame(Model = c("weibull", "gamma"),
  Chisq = c(gengam_wei$Chisq[2], gengam_gam$Chisq[2]), Df = 1,
  Critical_value = rep(qchisq(0.95, 1), 2), stringsAsFactors = FALSE)
@

<<echo=FALSE,results=tex>>=
print(xtable(gengam_res, caption = "LR results against generalised-gamma model",
             label = "tab:lr_gengam"))
@ %def
The results in Table~\ref{tab:lr_gengam} suggest that the weibull and gamma
models should be preferred to the generalised gamma model.

Finally, we use information criteria to choose the best model among the weibull
and gamma renewal models and the negative binomial:
<<IC-models>>=
ic <- data.frame(Model = c("weibull", "gamma", "negative-binomial"),
  AIC = c(AIC(wei), AIC(gam), AIC(nb)),
  BIC = c(BIC(wei), BIC(gam), BIC(nb)), stringsAsFactors = FALSE)
@
<<echo=FALSE,results=tex>>=
print(xtable(ic, caption = "Information criteria results",
             label = "tab:ic_models"))
@
It is worth noting here that all models have the same degree of freedom and
hence comparing information criteria is equivalent to comparing the
likelihoods. According to Table~\ref{tab:ic_models}, the gamma renewal model is
slightly preferred to the weibull model.

We conclude this analysis by running a formal chi-square goodness of fit test
\citep[Section 5.3.4]{cameron2013regression} to the results of the previously
selected model.
<<go-f>>=
gof <- chiSq_gof(gam, breaks = breaks_)
gof
@

The null hypothesis cannot be rejected at standard confidence levels and we
conclude that the selected model presents a good fit to the data. Users can
check that the same test yields similar results for the weibull and negative
binomial models but comfortably rejects the null hypothesis for the Poisson and
generalised gamma models. These results confirm what we observed before.


%% \section{Additional details}
%% \label{sec:closingRem}


%% \subsection{Count probability computation methods}
%% \label{ssec:ProbabilityComp}

%% In order to compute the different count probabilities defined in
%% equation~\eqref{eq:conv}, two families of algorithms have been
%% implemented in \pkg{Countr}:

%% \paragraph*{Taylor series expansion:}
%% this is specific to the Weibull renewal process.  Following the method of
%% \citet{McShane2008Count}, the exponential in the Weibull density can be expanded
%% out and series transformation techniques can be used to speed up
%% convergence. Two algorithms have been implemented: a matrix approach
%% (\code{series\_mat}) using \code{series\_terms} terms and a series accelerated
%% method based on the Euler and van-Wijngaarden transformations
%% \citep[Chapter~5]{press2007numerical} controlled by \code{series\_acc\_niter}
%% number of iterations and a convergence tolerance \code{series\_acc\_eps}.

%% \paragraph*{Convolution methods:}
%% as developed and described in \citet{baker2016Renewal}, three algorithms are available:
%% the naive convolution that computes all the probabilities up to
%% the desired one (\code{conv\_naive}), the direct convolution that computes a
%% reduced number of convolutions to produce the result (\code{conv\_direct}) and the
%% dePril's convolution method (\code{conv\_dePril}). The number of discretization
%% steps can be controlled by setting \code{conv\_steps}. By default, the built-in
%% distributions apply Richardson correction (\code{conv\_extrap = TRUE}).


%% \subsection{Naming conventions}
%% The names of the more technical functions in the package are somewhat verbose.
%% We use the following conventions. The names of the renewal count models are
%% formed by concatenating the name of the inter-arrival distribution and the word
%% `Count'.  Functions that accept the inter-arrival distribution as a parameter
%% simply contain the word `Count'.  Following the conventions
%% from base \proglang{R}, names of functions that compute densities (actually
%% probabilities, since the distributions are discrete) start with `d'. Functions
%% with suffix \verb|_bi| (short for `built-in') do computations for any of the
%% built-in models, the particular one being chosen by argument \code{dist}.
%% Functions with suffix \verb|_user| accept a user specified distribution for the
%% inter-arrival times. See the documentation of the individual functions for
%% further details.


\section{Conclusion and Future Work}
\label{sec:cl}

Count regression models derived from renewal processes are a flexible class of
models that extends the Poisson model and allows the use of inter-arrival times
distributions that are more flexible than the exponential. The \pkg{Countr}
package implements this class of models using standard \proglang{R} framework
(very similar to \code{glm()}) and hence allows users familiar with the
generalized linear models to experience a more flexible class of models with
minimum additional effort. Usual methods for model fitting, goodness of fit
diagnosis and prediction are also provided.

\pkg{Countr} currently contains four built-in distributions for which the
computations are optimised by programming crucial parts in \proglang{C++} and
choosing taylored parameters for optimisation functions.
Although users can
define their own inter-arrival times distribution, this may result in long
computation times as demonstrated in Section~\ref{ssec:custom-dist}. In future
versions of the package, a larger choice of survival distributions will be
available.

Renewal regression models can be extended in many directions. One of them is to
allow the time to the first event to have a different distribution from the
inter-arrival times for later events. This gives rise to a type of hurdle model
that we call ``modified renewal processes''. This family of models is being
studied by the authors and an experimental version is shipped with \pkg{Countr}.
Another direction in which the \pkg{Countr} package can be extended is by
allowing multivariate (and especially bivariate) models to be fitted.  A Copula
\citep[Section 8.5]{cameron2013regression} can be used to take into account
dependence between the count marginals. Such models will also be included in
future versions of \pkg{Countr}.

\section{Acknowledgments}
\label{sec:acknowledgments}

We would like to thank two anonymous reviewers for their thorough comments and
constructive suggestions, which helped us to improve the quality of the
software and the manuscript.


\bibliography{REFERENCES,football,quine,refresponse,var_covar}

<<echo=FALSE,print=FALSE>>=
options(op) # restore options
if(resaveResults)
    save.image(file = resultsFile)
@


\end{document}
