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<<prelim,echo=FALSE,print=FALSE>>=
library(MixAll)
MixAll.version <- packageDescription("MixAll")$Version
MixAll.date <- packageDescription("MixAll")$Date
@

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\title{\pkg{MixAll}: Clustering Mixed data with Missing Values}
\Plaintitle{MixAll: Clustering Mixed data with Missing Values}
\Shorttitle{MixAll: Clustering data}

\Abstract{
  The Clustering project is a part of the \pkg{STK++} library \citep{stk++}
  that can be accessed from \proglang{R} \citep{R:Main} using the \pkg{MixAll}
  package. It is possible to cluster Gaussian, gamma, categorical,
  Poisson, kernel mixture models or a combination of these models in case of
  mixed data. Moreover, if there is missing values in the original
  data set, these missing values will be imputed during the estimation
  process. These imputations can be biased estimators or Monte-Carlo estimators
  of the Maximum A Posteriori (MAP) values depending of the algorithm used.
}

\Keywords{\proglang{R}, \proglang{C++}, \proglang{STK++}, Clustering, missing
values}
\Plainkeywords{R, C++, STK++, Clustering, missing values}


\Address{
  Serge Iovleff\\
  Univ. Lille 1, CNRS U.M.R. 8524, Inria Lille Nord Europe \\
  59655 Villeneuve d'Ascq Cedex, France \\
  E-mail: \email{Serge.Iovleff@stkpp.org} \\
  URL: \url{http://www.stkpp.org}\\
}


% Title Page
\author{Serge Iovleff\\University Lille 1}
\date{now.data}

\begin{document}

\SweaveOpts{concordance=FALSE}
\maketitle

%\tableofcontents

\section{Introduction}
The Clustering project in \proglang{STK++} implements a set of mixture model
allowing to perform clustering on various data set using generative models.
There is five kinds of generative models implemented:
\begin{enumerate}
  \item the diagonal Gaussian mixture models (8 models), see sections
  \ref{subsec:DiagGaussian} and \ref{subsec:clustDiagGaussian},
  \item the gamma mixture models (24 models), see sections
  \ref{subsec:Gamma} and \ref{subsec:clustGamma},
  \item the categorical mixture models (4 models), see sections
  \ref{subsec:Categorical} and \ref{subsec:clustCategorical},
  \item the Poisson mixture models (6 models), see sections
  \ref{subsec:Poisson} and \ref{subsec:clustPoisson},
  \item the kernel mixture models (4 models), see sections
  \ref{subsec:Kernel} and \ref{subsec:clustKernel}.
\end{enumerate}
and a special model called "mixed data" mixture model allowing to cluster
mixed data sets using conditional independance between the different
kinds of data, see sections \ref{subsec:MixedData} and
\ref{subsec:clustMixedData}.

These models and the estimation algorithms can take into account missing
values. It is thus possible to use these models in order to cluster, but
also to complete data set with missing values.

The \pkg{MixAll} package provide an access in \citep{R:Main} to the
\pkg{STK++} \citep*{stk++} \proglang{C++} part of the library dedicated to
clustering.

In this paper we will first give a general introduction about mixture models and
the different algorithms, initialization methods and strategies that can be used
in order to estimate parameters of mixture models
(Section~\ref{sec:MixtureTools}). In Section~\ref{sec:ImplementedModels} we
present the different mixture models implemented in \proglang{STK++} that can
be estimated using MixAll. Finally we give examples of clustering on real data
set in Section~\ref{sec:ClustWithMixAll}.

\section{MixAll Modeling and Estimation Tools}
\label{sec:MixtureTools}

\subsection{Short Introduction to Mixture Models}
Let $\X$ be an arbitrary measurable space and let ${\bx}=\{{\bx}_1,...,{\bx}_n\}$
be $n$ independent vectors in $\X$ such that each ${\bx}_i$ arises from a
probability distribution with density (a mixture model)
\begin{equation}
  f({\bx}_i|\theta) = \sum_{k=1}^K p_k h({\bx}_{i}| \blambda_{k},\balpha)
\end{equation}
where the $p_k$'s are the mixing proportions ($0<p_k<1$ for all $k=1,...,K$ and
$p_1+...+p_K=1$), $h(\cdot| \blambda_{k},\balpha)$ denotes a $d$-dimensional
distribution parameterized by $\blambda_k$ and $\balpha$. The parameters
$\balpha$ do not depend from $k$ and are common to all the components of the
mixture. The vector parameter to be estimated is
$\theta=(p_1,\ldots,p_K,\blambda_1,\ldots,\blambda_K, \balpha)$ and is chosen
to maximize the observed log-likelihood
\begin{equation}
  \label{eq:vraisemblance}
  L(\theta|\bx_1,\ldots,\bx_n)=\sum_{i=1}^n \ln \left(\sum_{k=1}^K p_k h(\bx_i|\blambda_k, \balpha)\right).
\end{equation}
In case there is missing data, that is some $\bx_i$ are splited in observed
values $\bx_i^o$ and missing values $\bx^m_i$, the log-likelihood to maximize
should be the integrated log-likelihood
\begin{equation}
  \label{eq:vraisemblanceIntegrated}
  L(\theta|\bx_1^o,\ldots,\bx_n^o)=
  \sum_{i=1}^n  \int
  \ln \left(\sum_{k=1}^K p_k h(\bx_i^o,\bx_i^m|\blambda_k, \balpha)\right)
  d\bx_i^m.
\end{equation}
In the package \pkg{MixAll}, this quantity is approximated using a Monte-Carlo
estimator by the \code{SEM} or the \code{SemiSEM} algorithms and by a biased
estimator by the \code{EM} or the \code{CEM} algorithms.

It is well known that for a mixture distribution, a sample of indicator vectors
or {\em labels} ${\bz}=\{ {\bz}_1,...,{\bz}_n\}$, with
${\bz}_i=(z_{i1},\ldots,z_{iK})$, $z_{ik}=1$ or 0, according to the fact that
${\bx}_i$ is arising from the $k$th mixture component or not, is associated to
the observed data ${\bx}$. The sample ${\bz}$ is {\em unknown} so that the
maximum likelihood estimation of mixture models is traditionally performed via
the \code{EM} algorithm \cite{Dempster97} or by a stochastic version of
\code{EM} called SEM (see \cite{McLachlanPeel00}), or by a k-means like
algorithm called \code{CEM}. In the \pkg{MixAll} package it is also possible to
use an algorithm called \code{SemiSEM} which is an intermediate between the
\code{EM} and \code{SEM} algorithm. In case there is no missing values,
\code{SemiSEM} and \code{EM} are equivalents (except that the \code{SemiSEM}
algorithm will run all the iterations as it does not stop using a tolerance).

\subsection{Estimation Algorithms}\label{subsec:algorithms}

\subsubsection{EM algorithm}
Starting from an initial arbitrary parameter $\theta^0$, the $m$th iteration of
the \code{EM} algorithm consists of repeating the following I (if there
exists missing values), E  and M steps.
\begin{itemize}
\item {\bf I step:} The missing values $\bx_i^m$ are imputed using the current
MAP value given by the current value $\theta^{m-1}$ of the parameter.

\item {\bf E step:} The current conditional probabilities that $z_{ik}=1$ for
$i=1,\ldots,n$ and $k=1,\ldots,K$ are computed using the current value
$\theta^{m-1}$ of the parameter:
\begin{equation}\label{eq:condi}
t^m_{ik}=t^m_k(\bx_i|\theta^{m-1})=\frac{ p^{m-1}_k
h(\bx_i|{\blambda^{m-1}_k},\balpha^{m-1})}
{\sum_{l=1}^K  p^{m-1}_l h(\bx_i|\blambda^{m-1}_l,\balpha^{m-1})}.
\end{equation}

\item {\bf M step:} The maximum likelihood estimate $\theta^m$ of $\theta$
is updated using the conditional probabilities $t^m_{ik}$ as conditional
mixing weights. It leads to maximize
\begin{equation} \label{eq:mStepEM}
L(\theta| {\bx}_{1},\ldots,{\bx}_{n}, {\bt}^m)
=\sum_{i=1}^{n}\sum_{k=1}^{K} t_{ik}^m \ln \left [p_{k} h({\bf x}_{i}|\blambda_{k},\balpha)\right],
\end{equation}
where ${\bt}^m=(t_{ik}^m, i=1,\ldots,n, k=1,\ldots,K)$. Updated expression of
mixture proportions are, for $k=1,\ldots,K$,
\begin{equation}
p_k^m=\frac{\sum_{i=1}^n t^m_{ik}}{n}.
\end{equation}
Detailed formula for the updating of the $\blambda_k$'s and $\balpha$ are
depending of the component parameterization and are detailed in
section \ref{sec:ImplementedModels}.
\end{itemize}
The \code{EM} algorithm may converge to a local maximum of the observed data
likelihood function, depending on starting values.

\subsubsection{SEM algorithm}\label{subsubsec:SEM}
The \code{SEM} algorithm is a stochastic version of \code{EM} incorporating
between the E and M steps a restoration of the unknown component labels
$\bz_i$, $i=1,\ldots,n,$ by drawing them at random from their current
conditional distribution. Starting from an initial parameter $\theta^0$, an
iteration of \code{SEM} consists of three steps.
\begin{itemize}
\item {\bf I step:} The missing values are simulated using the current value
$\theta^{m-1}$ of the parameter and current conditional probabilities
$t^{m-1}_{ik}$.

\item {\bf E step:} The conditional probabilities $t^m_{ik}$ $(1 \leq i \leq n,
1 \leq k \leq K)$ are computed for the current value of $\theta^{m-1}$ as in
the E step of \code{EM} algorithm (equation \ref{eq:condi}).

\item {\bf S step:} Generate labels ${\bz}^m=\{ {\bz}^m_1,...,{\bz}^m_n\}$
by assigning each point ${\bx}_i$ at random to one of the mixture
components according to the categorical distribution with parameter
$(t^m_{ik}, 1 \leq k \leq K)$.

\item {\bf M step:} The maximum likelihood estimate of $\theta$ is updated
using the generated labels by maximizing
\begin{equation} \label{eq:mStepSEM}
  L(\theta| {\bx}_{1},\ldots,{\bx}_{n}, {\bt}^m)
    =\sum_{i=1}^{n}\sum_{k=1}^{K} z_{ik}^m \ln \left [p_{k} h({\bf x}_{i}|\blambda_{k},\balpha)\right],
\end{equation}
\end{itemize}

SEM does not converge point wise. It generates a Markov chain whose stationary
distribution is more or less concentrated around the m.l. parameter estimator. A
natural parameter estimate from a \code{SEM} sequence
$\bar{\theta}=(\theta^r)_{r=1,\ldots,R}$ is the mean $\sum_{r=1}^R \theta^r/R$
of the iterates values.

At the end of the algorithm, the missing values will be imputed using the
MAP value given by the averaged estimator $\bar{\theta}$.

\subsubsection{SemiSEM algorithm}\label{subsubsec:SemiSEM}
The \code{SemiSEM} algorithm is a stochastic version of \code{EM} incorporating
a restoration of the missing values $\bx_i^m$, $i=1,\ldots,n$ by drawing them at
random from their current conditional distribution. Starting from an initial
parameter $\theta^0$, an iteration of \code{SemiSEM} consists of three steps.
\begin{itemize}
\item {\bf I step:} The missing values are simulated using the current value
$\theta^{m-1}$ and current conditional probabilities $t^{m-1}_{ik}$ of the
parameter as in the \code{SEM} algorithm.

\item {\bf E step:} The conditional probabilities $t^m_{ik}$ $(1 \leq i \leq n,
1 \leq k \leq K)$ are computed for the current value of $\theta^{m-1}$.

\item {\bf M step:} The maximum likelihood estimate of $\theta$ is updated
by maximizing conditional probabilities $t^m_{ik}$ as conditional
mixing weights as in the \code{EM} algorithm.
\end{itemize}

If there is no missing values, SemiSEM algorithm is equivalent to the EM
algorithm. If there is missing values, SemiSEM does not converge point wise. It
generates a Markov chain whose stationary distribution is more or less
concentrated around the m.l. parameter estimator. A natural parameter estimate
from a \code{SemiSEM} sequence $(\theta^r)_{r=1, \ldots,R}$ is the mean
$\bar{\theta}=\sum_{r=1}^R \theta^r/R$ of the iterates values.

At the end of the algorithm, the missing values are imputed using the
MAP value given by the averaged estimator $\bar{\theta}$.

\subsubsection{CEM algorithm}
This algorithm incorporates a classification step between the E and M steps of EM. Starting
from an initial parameter $\theta^0$, an iteration of \code{CEM} consists of three steps.
\begin{itemize}
\item {\bf I step:} The missing values are imputed using the current MAP
value given by the current value $\theta^{m-1}$and current conditional
probabilities $t^{m-1}_{ik}$ of the parameter as in the \code{EM} algorithm.

\item {\bf E step:} The conditional probabilities $t^m_{ik}$ $(1\leq i \leq n,
1 \leq k \leq K)$ are computed for the current value of $\theta$ as done in the
E step of EM.

\item {\bf C step:} Generate labels ${\bz}^m=\{ {\bz}^m_1,...,{\bz}^m_n\}$ by
assigning each point ${\bx}_i$ to the component maximizing the conditional
probability $(t^m_{ik}, 1 \leq k \leq K)$.

\item {\bf M step:} The maximum likelihood estimate of $\theta$ are computed as
done in the M step of SEM.
\end{itemize}

CEM is a {\em K-means}-like algorithm and contrary to \code{EM}, it converges in
a finite number of iterations. \code{CEM} is not maximizing the observed
log-likelihood $L$ (\ref{eq:vraisemblance}) but is maximizing in
$\theta$ and $\bz_{1},\ldots,\bz_{n}$ the complete data log-likelihood
\begin{equation} \label{cl}
  CL(\theta, {\bf z}_{1},\ldots,{\bf z}_{n}|{\bf
    x}_{1},\ldots,{\bf x}_{n}) = \sum_{i=1}^n\sum_{k=1}^{K}
  z_{ik}\ln[p_{k} h({\bf x}_i|\blambda_k)].
\end{equation}
where the missing component indicator vector $\bz_i$ of each sample point is
included in the data set. As a consequence, \code{CEM} is not expected to
converge to the maximum likelihood estimate of $\theta$ and yields inconsistent
estimates of the parameters especially when the mixture components are
overlapping or are in disparate proportions (see \cite{McLachlanPeel00},
Section 2.21).

\subsubsection{Creating an Algorithm}\label{subsec:CretingAlgo}

All the algorithms (\code{EM}, \code{SEM}, \code{CEM} and \code{SemiSEM}) are
encoded in a \code{S4} class and can be created using the utility function
\code{clusterAlgo}. This function take as input three parameters:
\begin{itemize}
\item \code{algo}: name of the algorithm to define (\code{"EM"},
\code{"SEM"}, \code{"CEM"} or \code{"SemiSEM"}). Default value is \code{"EM"}.
\item \code{nbIteration}: maximal number of iteration to perform. Default value
is 200.
\item \code{epsilon}:  threshold to use in order to stop the iterations
(not used by the \code{SEM} and \code{SemiSEM} algorithms).
\end{itemize}
<< >>=
clusterAlgo()
clusterAlgo(algo="SemiSEM",nbIteration=100,epsilon=1e-08)
@

\subsection{Initialization Methods}\label{subsubsec:init}

All the estimation algorithms need a first value of the parameter $\theta$.
There is three kinds of initialization that can be performed: either by
generating directly random parameters, or by using random classes labels/random
fuzzy classes and estimating $\theta^0$. In order to prevent unlucky
initialization, multiple initialization with a limited number of an algorithm
are performed and the best initialization (in the likelihood sense) is
conserved. This initialization method can appear to be disappointing in a
large dimension setting because the domain parameter to be explored becomes
very large or when the number of mixture components is large \cite{baudry2015}.

The initialization step is encoded in a \code{S4} class and can be created
using the utility function \code{clusterInit}. This function take as input
four parameters:
\begin{itemize}
\item \code{method}: name of the initialization to perform (\code{"random"},
\code{"class"} or \code{"fuzzy"}). Default value is \code{"class"}
\item \code{nbInit} number of initialization to do.  Default value is 5.
\item \code{algo} name of the algorithm to use during the limited
estimation steps  (see also \ref{subsec:algorithms}). Default value is "EM".
\item \code{nbIteration} maximal number of iteration to perform during the
initialization algorithm.  Default values is 20.
\item \code{epsilon} threshold to use in order to stop the iterations. Default
value: 0.01.
\end{itemize}

<< >>=
clusterInit()
clusterInit(method="random", nbInit= 2, algo="CEM", nbIteration=10,epsilon=1e-04)
@

\subsection{Estimation Strategy}
\label{subsec:Strategy}

A strategy is a way to find a good estimate of the parameters of a mixture model
and to avoid local maxima of the likelihood function. A strategy is an efficient
three steps Search/Run/Select way for maximizing the likelihood:
\begin{enumerate}
\item Build a search method for generating \code{nbShortRun} initial positions.
This is based on the initialization method we describe previously.
\item Run a short algorithm for each initial position.
\item Select the solution providing the best likelihood and launch a long
run algorithm from this solution.
\end{enumerate}

A strategy is encoded in a S4 class and can be created using the utility
function \code{clusterStrategy()}. This function have no mandatory argument but
the default strategy can be tuned. In table~\ref{tab:clusterStrategy} the reader
will find a summary of all the input parameters of the \code{clusterStrategy()}
function.

\begin{table}[H]
\centering
\begin{tabular}{|p{0.3\textwidth}|p{0.65\textwidth}|}
\hline
Input Parameter & Description \\
\hline
\code{nbTry} & Integer defining the number of tries. \code{nbTry}
  must be a positive integer. Default value is 1.\\
\hline
\code{nbInit} & Integer defining the number of initialization to do during the
initialization step. Default is 5. \\
\hline
\code{initAlgo} & String with the estimation algorithm to use in the
initialization step. Possible values are \code{"EM"}, \code{"SEM"},
\code{"CEM"}, \code{"SemiSEM"}. Default value is \code{"EM"}.\\
\hline
\code{nbInitIteration} & Integer defining the maximal number of iteration in
\code{initAlgo} algorithm. \code{nbInitIteration} can be 0. Default value is
20.\\
\hline
\code{initEpsilon} & Real defining the epsilon value for the initial algorithm.
\code{initEpsilon} is not used by the \code{"SEM"} and \code{"SemiSEM"}
algorithms. Default value is 0.01.\\
\hline
\code{nbShortRun} & Integer defining the number of short run to perform
(remember the strategy launch an initialization step before each short run, so
you get nbShortRun*nbInit initialization).
Default value is 5.\\
\hline
\code{shortRunAlgo} & String with the estimation algorithm to use in short
run(s). Possible values are \code{"EM"}, \code{"SEM"}, \code{"CEM"},
\code{"SemiSEM"}. Default value is \code{"EM"}.\\
\hline
\code{nbShortIteration} & Integers defining the maximal number of iterations
in a short run. Default value is 100.\\
\hline
\code{shortEpsilon} & Real defining the epsilon value in a short run. It is
not used if \code{shortRunAlgo} is \code{"SEM"} or \code{"SemiSEM"}. Default
value is 1e-04.\\
\hline
\code{longRunAlgo} & String with the estimation algorithm to use for the long
run. Possible values are \code{"EM"}, \code{"SEM"}, \code{"CEM"} or
\code{"SemiSEM"}. Default value is \code{"EM"}.\\
\hline
\code{nbLongIteration} & Integers defining the maximal number of iterations
in the the long run. Default value is 1000.\\
\hline
\code{longEpsilon} & Real defining the epsilon value in the long run. It is
not used if \code{shortRunAlgo} is \code{"SEM"} or \code{"SemiSEM"}. Default
value is 1e-07.\\
\hline
\end{tabular}
\caption{List of all the input parameters of the  \code{clusterStrategy()} function.}
\label{tab:clusterStrategy}
\end{table}

<< >>=
clusterStrategy()
@
Users have to take care that there will be \code{nbInit} $\times$
\code{nbShortRun} starting points $\theta^0$ during the estimation process. The
default generate randomly fifty times $\theta^0$.

The strategy class is very flexible and allow to tune the estimation process.
There is two defined utility functions for the end-user:
\begin{itemize}
\item the \code{clusterFastStrategy} for impatient users,
\item the \code{clusterSemiSEMStrategy} for user with missing values.
\end{itemize}

For impatient user, the \code{clusterFastStrategy} furnish results very quickly.
The accuracy of the result is not guaranteed if the model is a bit difficult to
estimate.
<< >>=
clusterFastStrategy()
@

The function \code{clusterSemiSEMStrategy} is highly recommended if there is
missing values int the data set. The "SemiSEM" algorithm simulate the missing
values and computes a Monte-Carlo estimator of the $\theta$ parameter during the
iterations allowing to get unbiased estimators.
<< >>=
clusterSemiSEMStrategy()
@

\section{Implemented Mixture Models}
\label{sec:ImplementedModels}


\subsection{Multivariate (diagonal) Gaussian Mixture Models}
\label{subsec:DiagGaussian}
A Gaussian density on $\R$ is a density of the form:
\begin{equation}\label{law::gaussian-density}
f(x;\mu,\sigma) = \frac{1}{\sqrt{2\pi} \sigma} \exp\left\{- \frac{(x-\mu)^2}{2\sigma^2}\right\} \quad \sigma>0.
\end{equation}

A joint diagonal Gaussian density on $\Rd$ is a density of the form:
\begin{equation}\label{law::joint-gaussian-density}
h(\bx;\bmu,\bsigma) = \prod_{j=1}^d f(x^j;\mu^j,\sigma^j) \quad \sigma^j>0.
\end{equation}
The parameters $\bmu=(\mu^1,\ldots,\mu^d)$ are the position parameters and the
parameters $\bsigma=(\sigma^1,\ldots,\sigma^d)$ are the standard-deviation
parameters. Assumptions on the standard-deviation parameters among the variables
and the components lead to define four families of mixture model.

Let us write a multidimensional Gaussian mixture model in the from \verb+Gaussian_s*+
with \verb+s*+, the different ways to parameterize the standard-deviation
parameters of a Gaussian mixture:
\begin{itemize}
\item \verb+sjk+ means that we have one standard-deviation parameter for each
variable in each component,
\item \verb+sk+ means that the standard-deviation parameters are the same for
all the variables inside a component,
\item \verb+sj+ means that the standard-deviation parameters are different for
each variable but are equals between the components,
\item and finally \verb+s+ means that the standard-deviation parameters are all
equals.
\end{itemize}

The \verb+gaussian_pk_sjk+ model is the most general model and has a density
function of the form
\begin{equation}\label{eq:f_sjk}
  f({\bx}|\theta) = \sum_{k=1}^K p_k
  \prod_{j=1}^d g(x^j_{i}| \mu^j_{k}, \sigma^j_{k}).
\end{equation}
On the other side, the \verb+gaussian_p_s+ model is the most parsimonious model
and has a density function of the form
\begin{equation}\label{eq:f_s}
  f({\bx}|\theta) = \sum_{k=1}^K \frac{1}{K}
  \prod_{j=1}^d g(x^j_{i}| \mu^j_{k}, \sigma).
\end{equation}

It is possible to get a vector with all Gaussian mixture model names using the
\code{clusterDiagGaussianNames} function.

<< >>=
clusterDiagGaussianNames()
clusterDiagGaussianNames("all", "equal", "free")
clusterValidDiagGaussianNames(c("gaussian_pk_sjk","gaussian_p_ljk"))
@

\subsection{Multivariate categorical Mixture Models}
\label{subsec:Categorical}

A Categorical probability distribution on a finite space
$\mathcal{X} = \{1,\ldots,L\}$ is a probability distribution of the form:
\begin{equation}\label{law::categorical}
P(x=l) = p_l \quad p_l>0,\, l\in \mathcal{X},
\end{equation}
with the constraint $p_1+\ldots+p_L = 1.$

A joint Categorical probability distribution on $\Xd$ is a probability
distribution of the form:
\begin{equation}\label{law::joint-categorical-probability}
P(\bx=(x_1,\ldots,x_d)) = \prod_{j=1}^d p^j_{x_j}
\end{equation}
The parameters $\bp=(p^1,\ldots,p^d)$ are the probabilities of the possibles
outcomes. Assumptions on the probabilities among the variables and the
components lead to define two families of mixture model.

It is possible to get a vector with all Gaussian model names using the
\code{clusterDiagGaussianNames} function.

It is possible to get a vector with all categorical mixture model names using
the \code{clusterCategoricalNames} function.
<< >>=
clusterCategoricalNames()
clusterCategoricalNames("all", "equal")
clusterValidCategoricalNames(c("categorical_pk_pjk","categorical_p_pk"))
@

\subsection{Multivariate Poisson Mixture Models}
\label{subsec:Poisson}

A Poisson probability distribution is a probability over $\N$ of the form
\begin{equation}\label{law::poisson-density}
p(k;\lambda) = \frac{ \lambda^k}{k!} e^{-\lambda}  \quad \lambda>0.
\end{equation}

A joint Poisson probability on $\Nd$ is a probability distribution of the form
\begin{equation}\label{law::joint-poisson-density}
h(\bx;\blambda) = \prod_{j=1}^d p(x^j;\lambda^j)  \quad \lambda^j>0.
\end{equation}
The parameters $\blambda=(\lambda^1,\ldots,\lambda^d)$ are the mean
parameters. Assumptions on the mean among the variables and the
components lead to define three families of mixture model.

The \verb+poisson_pk_ljk+ is the most general Poisson model and has a
probability distribution of the form
\begin{equation}
 f({\bx}|\theta) = \sum_{k=1}^K p_k  \prod_{j=1}^d h(x^j;\lambda^j_k).
\end{equation}
The \verb+poisson_p_lk+ is the most parsimonious Poisson model and has a
probability distribution of the form
\begin{equation}
 f({\bx}|\theta) = \sum_{k=1}^K \frac{1}{K} \prod_{j=1}^d h(x^j;\lambda_k).
\end{equation}
The \verb+poisson_pk_ljlk+ is an intermediary model for the number of parameters
and has a density of the form
\begin{equation}
 f({\bx}|\theta) = \sum_{k=1}^K p_k \prod_{j=1}^d h(x^j;\lambda_j\lambda_k).
\end{equation}

It is possible to get a vector with all Poisson mixture model names using
the \code{clusterPoissonNames} function.
<< >>=
clusterPoissonNames()
clusterPoissonNames("all","proportional")
clusterValidPoissonNames(c("poisson_pk_ljk","poisson_p_ljlk"))
@

\subsection{Multivariate Gamma Mixture Models}
\label{subsec:Gamma}

A gamma density on $\R_+$ is a density of the form:
\begin{equation}\label{law::gamma-density}
g(x;a,b) = \frac{ \left(x\right)^{a-1} e^{-x/b}}{\Gamma(a) \left(b\right)^{a}} \quad a>0, \quad b>0.
\end{equation}
A joint gamma density on $\Rd_+$ is a density of the form:
\begin{equation}\label{law::joint-gamma-density}
h(\bx;\ba,\bb) = \prod_{j=1}^d g(x^j;a^j,b^j) \quad a^j>0, \quad b^j>0.
\end{equation}
The parameters $\ba=(a^1,\ldots,a^d)$ are the shape parameters and the
parameters $\bb=(b^1,\ldots,b^d)$ are the scale parameters. Assumptions on the
scale and shape parameters among the variables and the components lead to
define twelve families of mixture model. Let us write a multidimensional gamma
mixture model in the form \verb+gamma_a*_b*+ with \verb+a*+ (resp. \verb+b*+),
the different ways to parameterize the shape (resp. scale) parameters of a
gamma mixture:
\begin{itemize}
\item \verb+ajk+ (resp. \verb+bjk+) means that we have one shape (resp. scale)
parameter for each variable and for each component,
\item \verb+ak+ (resp. \verb+bk+) means that the shape (resp. scale) parameters
are the same for all the variables inside a component,
\item \verb+aj+ (resp. \verb+bj+) means that the shape (resp. scale) parameters
are different for each variable but are equals between the components,
\item and finally \verb+a+ (resp. \verb+b+) means that the shape (resp. scale)
parameters are the same for all the variables and all the components.
\end{itemize}

The models we can build in this way are summarized in the table
\ref{tab:gammamodels}, in parenthesis we give the number of parameters of each
models.
\begin{table}
\begin{center}
\begin{tabular}{lllll}
           &  ajk                 &  ak          &  aj           &  a \\
bjk & \begin{tabular}[t]{@{}c@{}} \verb+gamma_ajk_bjk+ \\ (2dK) \end{tabular}
    & \begin{tabular}[t]{@{}c@{}} \verb+gamma_ak_bjk+  \\ (dK + K)  \end{tabular}
    & \begin{tabular}[t]{@{}c@{}} \verb+gamma_aj_bjk+  \\ (dK+d) \end{tabular}
    & \begin{tabular}[t]{@{}c@{}} \verb+gamma_a_bjk+   \\ (dK+1) \end{tabular}
    \\
bk  & \begin{tabular}[t]{@{}c@{}} \verb+gamma_ajk_bk+ \\  (dK+K)  \end{tabular}
    & \begin{tabular}[t]{@{}c@{}} \verb+gamma_ak_bk+ \\ (2K)      \end{tabular}
    & \begin{tabular}[t]{@{}c@{}} \verb+gamma_aj_bk+ \\ (K+d)  \end{tabular}
    & \begin{tabular}[t]{@{}c@{}} \verb+gamma_a_bk+ \\ (K+1)  \end{tabular}
    \\
bj  & \begin{tabular}[t]{@{}c@{}} \verb+gamma_ajk_bj+  \\ (dK+d) \end{tabular}
    & \begin{tabular}[t]{@{}c@{}} \verb+gamma_ak_bj+  \\(K+d)    \end{tabular} & NA  & NA  \\
b   & \begin{tabular}[t]{@{}c@{}} \verb+gamma_ajk_b+  \\  (dK+1) \end{tabular}
    & \begin{tabular}[t]{@{}c@{}} \verb+gamma_ak_b+  \\ (K+1)     \end{tabular} & NA & NA \\
\end{tabular}
\end{center}
\caption{The twelve multidimensional gamma mixture models. In parenthesis the
number of parameters of each model.}
\label{tab:gammamodels}
\end{table}

The \verb+gamma_ajk_bjk+ model is the most general and have a density function of the form
\begin{equation}\label{eq:gamma_ajk_bjk}
  f({\bx}_i|\theta) = \sum_{k=1}^K p_k \prod_{j=1}^d g(x^j_{i}| a^j_{k},b^j_{k}).
\end{equation}
All the other models can be derived from this model by dropping the indexes in
$j$ and/or $k$ from the expression (\ref{eq:gamma_ajk_bjk}). For example the
mixture model $\verb+gamma_aj_bk+$ has a density function of the form
\begin{equation}\label{eq:gamma_aj_bk}
  f({\bx}_i|\theta) = \sum_{k=1}^K p_k \prod_{j=1}^d g(x^j_{i}| a^j,b^{k}).
\end{equation}

It is possible to get a vector with all gamma mixture model names using
the \code{clusterGammaNames} function.
<< >>=
clusterGammaNames()
clusterGammaNames("all", "equal","free","free","all")
clusterValidGammaNames(c("gamma_pk_aj_bk","gamma_p_ajk_bjk"))
@

\subsection{Gaussian Kernel Mixture Models}
\label{subsec:Kernel}

Gaussian Kernel Mixture models are a generalization of the Kernel k-means method
\cite{ShaweTaylor2004}.

Let us consider a pair of random variables $(X,Z)$ with values in a measurable space
$(\mathcal{X}\times\{1\ldots K\},\mathcal{B}_X\otimes\mathcal{P}(\{1...K\}))$.
The random variate $Z$ is a categorical random variate and Conditionally to
$Z$, the distribution of $X$ is a probability measure $\nu_k$ on $\mathcal{X}$.
\begin{equation*}
P(X\in A| Z=k) = \nu_k(A).
\end{equation*}
The marginal distribution of $X$ is a \emph{mixing} distribution such that
\begin{equation*}
P(X\in A) = \sum_{k=1}^K p_k \nu_k(A),\; A\in \mathcal{B}_X.
\end{equation*}


Let $\mathcal{H}$ denote a reproducing Kernel Hilbert Space (RKHS) and let
$\phi$ be a feature map from $\mathcal{X}$ to $\mathcal{H}$. The reproducing
property of $\mathcal{H}$ ensure us assure there exists a kernel $k$ positive definite
on $\X\times\X$ such that
\begin{equation*}
\scalprod{\phi(x)}{\phi(y)} = k(x,y),\; \forall (x,y)\in\X^2.
\end{equation*}

It is easily verified that the the image by $\phi$ of the probability distribution of $X$
is also a mixing distribution. Let us denote by $\mu$ this distribution and by
$\mu_k$ the conditional probability distribution of the random variate $\phi(X)$
conditionally to $Z=k$.

In a kernel mixture model, we assume the following hypothesis about $\mu$:

\noindent\textbf{G}: \textit{(Gaussianity) $\mu_k$ is well approximated by an
isotropic finite Gaussian measure on $\mathcal{H}$ with mean $m_k$ and
covariance matrix $\sigma_k\, I_{d}$}.

The $\log$-likelihood to maximize in $\theta$ is thus
\begin{equation}
\label{eq:kmmLikelihood}
l(x_1,\ldots,x_n;\theta) = \prod_{i=1}^n
\sum_{k=1}^K p_k \left(\frac{1}{\sqrt{2\pi}\sigma_k}\right)^{d}
\exp\left\{-\frac{\|\phi(x_i)-m_k\|^2}{2\sigma_k^2} \right\}.
\end{equation}
with $\theta=\left((p_k)_{k=1}^K,(m_k)_{k=1}^K,(\sigma_k)_{k=1}^K\right)$.
This model is called a Kernel Mixture Model (KMM).

The dimension $d$ is an hyper-parameter fixed by the user. Assumptions about the
variance lead to four models. The \verb+kernelGaussian_sk+ model is the most general
and have a density function given in (\ref{eq:kmmLikelihood}), the
\verb+kernelGaussian_s+ model assume that all variances are equals.

It is possible to get a vector with all (Gaussian) kernel mixture model names
using \code{clusterKernelNames} function.

<< >>=
clusterKernelNames()
clusterValidKernelNames(c("kernelGaussian_pk_sk","kernelGaussian_pk_s"))
@

There is threee kernels availables: "gaussian", "exponential" and "polynomial"
\begin{itemize}
\item The Gaussian Kernel is a kernel of the form
\begin{equation*}
k(x,y) = \exp\left(- \frac{\|x-y\|^2}{h} \right)
\end{equation*}
where $h$ represents the bandwidth of the kernel (default value 1).
\item  The Exponential Kernel is a kernel of the form
\begin{equation*}
k(x,y) = \exp\left(- \frac{\|x-y\|}{h} \right)
\end{equation*}
where $h$ represents the bandwidth of the kernel (default value 1).
\item The Polynomial Kernel is a kernel of the form
\begin{equation*}
k(x,y) = \left(<x-y>+c\right)^d
\end{equation*}
where $c$  represents the shift of the kernel (default value 0)
and $d$ represents the degree (defaut value 1).
\end{itemize}

\subsection{Mixed Data Mixture Models}
\label{subsec:MixedData}

Mixed Data mixture models are special models allowing to cluster
mixed data sets assuming conditional independency. More precisely, assume
that the observation space is of the form $ \X = \X_1 \times \X_2 \times\ldots\X_L$.
Then it is assumed that each ${\bx}_i$ arises from a mixture probability
distribution with density
\begin{equation}
 f({\bx}_i=({\bx}_{1i}, {\bx}_{2i},\ldots {\bx}_{Li})|\theta)
 = \sum_{k=1}^K p_k \prod_{l=1}^L h^l({\bx}_{li}| \blambda_{lk},\balpha_l).
\end{equation}
The density functions (or probability distribution functions) $h^l(.|
\blambda_{lk},\balpha_l)$ can be any implemented model (Gaussian, Poisson,...).

\section{Clustering with MixAll}
\label{sec:ClustWithMixAll}

Cluster analysis can be performed with the functions
\begin{enumerate}
\item \code{clusterDiagGaussian} for diagonal Gaussian mixture models,
\item \code{clusterCategorical} for Categorical mixture models,
\item \code{clusterPoisson} for Poisson mixture models,
\item \code{clusterGamma} for gamma mixture models,
\item \code{clusterKernel} for kernel mixture models,
\item \code{clusterMixedData} for MixedData mixture models.
\end{enumerate}

These functions have a common set of parameters with default values given
in the table~\ref{tab:clusterCommon}.

\begin{table}[H]
\centering
\begin{tabular}{|p{0.3\textwidth}|p{0.65\textwidth}|}
\hline
Input Parameter & Description \\
\hline
\code{nbCluster} & Numeric. A vector with the number of clusters to try. Default
is 2.\\
\hline
\code{strategy} & A \code{Strategy} object containing the strategy to run.
Call \code{clusterStrategy()} (see \ref{subsec:Strategy}) method by default. \\
\hline
\code{criterion} & A string defining the model selection criterion to use.
The  best model is the one with the lowest criterion value.
Possible values: \code{"AIC"}, \code{"BIC"}, \code{"ICL"}. Default is
\code{"ICL"}.\\
\hline
\code{nbCore} & An integer defining the number of processor to use. Default
is 1, 0 for all cores. \\
\hline
\end{tabular}
\caption{List of common parameters of the clustering functions.}
\label{tab:clusterCommon}
\end{table}

\subsection{Clustering Multivariate (diagonal) Gaussian Mixture Models}
\label{subsec:clustDiagGaussian}

Multivariate Gaussian data (without correlations) can be clustered using the
\code{clusterDiagGaussian} function.

This function has one mandatory argument: a \code{matrix} or \code{data.frame}
$\bx$. In Table~\ref{tab:clusterDiagGaussian} the reader will find a summary of
all the specific input parameters of this function with its default value.
\begin{table}[H]
\centering
\begin{tabular}{|p{0.3\textwidth}|p{0.65\textwidth}|}
\hline
Input Parameter & Description \\
\hline
\code{data} & Matrix or data frame\\
\hline
\code{models} & A \code{vector} object defining the list of models to
estimate.  Call \code{clusterDiagGaussianNames()} by default
(see \ref{subsec:DiagGaussian}).\\
\hline
\end{tabular}
\caption{List of all the specific parameters of the
\code{clusterDiagGaussian} function.}
\label{tab:clusterDiagGaussian}
\end{table}

We illustrate this function with the well known geyser data set
(\cite{azzalini1990},\cite{hardle1991}).
<<fig=TRUE>>=
data(geyser);
x = as.matrix(geyser); n <- nrow(x); p <- ncol(x);
# add missing values at random
indexes  <- matrix(c(round(runif(10,1,n)), round(runif(10,1,p))), ncol=2);
x[indexes] <- NA;
model <- clusterDiagGaussian(data=x, nbCluster=3, strategy = clusterFastStrategy())
summary(model)
missingValues(model)
plot(model)
@

\subsection{Clustering Multivariate categorical Mixture Models}
\label{subsec:clustCategorical}

Categorical (nominal) data can be clustered using the \code{clusterCategorical}
function.

This function has one mandatory argument: a data.frame or matrix $\bx$. The
matrix $\bx$ can contain characters (nominal values), these characters will be
mapped as integer using the \code{factor} function.

In Table~\ref{tab:clusterCategorical} the reader will find a summary of all
the specific input parameters of this function with its default value.
\begin{table}[H]
\centering
\begin{tabular}{|p{0.3\textwidth}|p{0.65\textwidth}|}
\hline
Input Parameter & Description \\
\hline
\code{data} & Matrix or data frame\\
\hline
\code{models} & A \code{vector} defining the models to estimate. Call
\code{clusterCatgoricalNames()} by default (see \ref{subsec:Categorical}).\\
\hline
\end{tabular}
\caption{List of all the specific parameters of the
\code{clusterCategorical} function.}
\label{tab:clusterCategorical}
\end{table}

We illustrate this function with the birds data set.

<<fig=TRUE>>=
data(birds)
x = as.matrix(birds);  n <- nrow(x); p <- ncol(x);
indexes  <- matrix(c(round(runif(10,1,n)), round(runif(10,1,p))), ncol=2);
x[indexes] <- NA;
model <- clusterCategorical(data=x, nbCluster=2)
summary(model)
missingValues(model)
plot(model)
@

Categorical mixture models are plotted using the \emph{logistic latent
representation}.

\subsection{Clustering Multivariate gamma Mixture Models}
\label{subsec:clustGamma}

Gamma data can be clustered using the \code{clusterGamma} function.

This function has one mandatory argument: a data.frame or matrix $\bx$.

In Table~\ref{tab:clusterGamma} the reader will find a summary of all
the specific input parameters of this function with its default value.
\begin{table}[H]
\centering
\begin{tabular}{|p{0.3\textwidth}|p{0.65\textwidth}|}
\hline
Input Parameter & Description \\
\hline
\code{data} & Matrix or data frame\\
\hline
\code{models} & A \code{vector} defining the models to estimate. Call
\code{clusterGammaNames()} by default (see \ref{subsec:Gamma}).\\
\hline
\end{tabular}
\caption{List of all the specific parameters of the
\code{clusterGamma} function.}
\label{tab:clusterGamma}
\end{table}

<<fig=TRUE>>=
data(geyser);
x = as.matrix(geyser); n <- nrow(x); p <- ncol(x);
indexes  <- matrix(c(round(runif(10,1,n)), round(runif(10,1,p))), ncol=2);
x[indexes] <- NA;
model <- clusterGamma(data=x, nbCluster=3, strategy = clusterFastStrategy())
summary(model)
missingValues(model)
plot(model)
@

\subsection{Clustering Multivariate Poisson Models}
\label{subsec:clustPoisson}

Poisson data (count data) can be clustered using the \code{clusterPoisson}
function.

This function has one mandatory argument: a data.frame or matrix $\bx$.

In Table~\ref{tab:clusterPoisson} the reader will find a summary of all
the specific input parameters of this function with its default value.
\begin{table}[H]
\centering
\begin{tabular}{|p{0.3\textwidth}|p{0.65\textwidth}|}
\hline
Input Parameter & Description \\
\hline
\code{data} & Matrix or data frame\\
\hline
\code{models} & A \code{vector} defining the models to estimate. Call
\code{clusterPoissonNames()} by default (see \ref{subsec:Poisson}).\\
\hline
\end{tabular}
\caption{List of all the specific parameters of the
\code{clusterPoisson} function.}
\label{tab:clusterPoisson}
\end{table}


<<fig=TRUE>>=
data(DebTrivedi)
dt <- DebTrivedi[1:500, c(1, 6,8, 15)]
model <- clusterPoisson( data=dt, nbCluster=3, strategy = clusterFastStrategy())
summary(model)
missingValues(model)
plot(model)
@

\subsection{Clustering Kernel Mixture Models}
\label{subsec:clustKernel}

Data can be clustered using the \code{clusterKernel} function.

This function has one mandatory argument: a data.frame or matrix $\bx$.

In Table~\ref{tab:clusterKernel} the reader will find a summary of all
the specific input parameters of this function with its default value.
\begin{table}[H]
\centering
\begin{tabular}{|p{0.3\textwidth}|p{0.65\textwidth}|}
\hline
Input Parameter & Description \\
\hline
\code{data} & Matrix or data frame\\
\hline
\code{models} & A \code{vector} defining the models to estimate. Call
\code{clusterKernelNames()} by default (see \ref{subsec:Kernel}).\\
\hline
\code{kernelName} & A \code{string} defining the kernel to use. Use
a "gaussian" kernel by default (Possible values are "gaussian", "polynomial"
or "exponential").\\
\hline
\code{kernelParameters} & A vector with the kernel parameter value(s).
Default value is $1$.\\
\hline
\end{tabular}
\caption{List of all the specific parameters of the \code{clusterKernel}
function.}
\label{tab:clusterKernel}
\end{table}


<<fig=TRUE>>=
data(bullsEye)
model <- clusterKernel( data=bullsEye[,1:2], nbCluster=2, models = "kernelGaussian_pk_s", strategy = clusterFastStrategy())
summary(model)
plot(model)
@
\subsection{Clustering Mixed data sets}
\label{subsec:clustMixedData}

Mixed data sets can be clustered using the \code{clusterMixedData}
function. The original mixed data set has to be splited in multiple
homogeneous data sets and each one associated to a mixture model name.

In Table~\ref{tab:clusterMixedData} the reader will find a summary of all
the specific input parameters of this function with its default value.
\begin{table}[H]
\centering
\begin{tabular}{|p{0.3\textwidth}|p{0.65\textwidth}|}
\hline
Input Parameter & Description \\
\hline
\code{data} & A \code{list} containing the homogeneous data sets (matrices
and/or data.frames). All the data sets must have the same number of rows. \\
\hline
\code{models} & A \code{vector} of character and of same length than data
containing the model names to fit to each data set (see \ref{subsec:MixedData}).\\
\hline
\end{tabular}
\caption{List of all the specific parameters of the \code{clusterMixedData}
function.}
\label{tab:clusterMixedData}
\end{table}

<<fig=TRUE>>=
data(HeartDisease.cat)
data(HeartDisease.cont)
ldata = list(HeartDisease.cat,HeartDisease.cont);
lnames = c("categorical_pk_pjk","gaussian_pk_sjk")
model <- clusterMixedData(ldata, lnames, nbCluster=3, strategy = clusterFastStrategy())
summary(model)
missingValues(model)
plot(model)
@

MixedData mixture models are plotted using the \emph{logistic latent
representation}.


\bibliography{MixAll}

\appendix

\section{M step computation for the Gaussian models}

For all the M Step, the mean is updated using the following formula
$$
{\bmu}_k = \frac{1}{t_{.k}} \sum_{i=1}^n t_{ik} \bx_i,
$$
with $t_{.k} = \sum_{i=1}^n t_{ik}$, for $k=1,\ldots,K$.

\subsection{M Step of the gaussian sjk model}

Using the equation (\ref{eq:mStepEM}) and dropping the constant, we obtain that
we have to maximize in $\bsigma = (\sigma^j_k)^2$, for $j=1,\ldots,d$ and
$k=1,\ldots,K$ the expression
\begin{equation} \label{eq:gaussian_sjk}
l(\bsigma | {\bx}_{1},\ldots,{\bx}_{n}, {\bt}^m) = \sum_{i=1}^{n} \sum_{k=1}^{K} t_{ik}^m
     \sum_{j=1}^d \left[ - \frac{1}{(\sigma^j_k)^2} (x_i^j - \hat{\mu}_j^k)^2 - \log((\sigma^j_k)^2) \right].
\end{equation}

For this model, the variance is updated using the formula:
$$
(\hat{\sigma}^j_k)^2 = \frac{1}{t_{.k}} \sum_{i=1}^n t_{ik} (x^j_i-\hat{\mu}^j_k)^2.
$$

\subsection{M Step of the gaussian sk model}

For this model, the variance is updated using the formula:
$$
(\hat{\sigma}_k)^2 = \frac{1}{d t_{.k}} \sum_{j=1}^d\sum_{i=1}^n t_{ik} (x^j_i-\hat{\mu}^j_k)^2.
$$

\subsection{M Step of the gaussian sj model}

For this model, the variance is updated using the formula:
$$
(\hat{\sigma}^j)^2 =  t_{ik} (x^j_i-\mu^j_k)^2.
$$

\subsection{M Step of the gaussian s model}

For this model, the variance is updated using the formula:
$$
\hat{\sigma}^2 = \frac{1}{nd} \sum_{i=1}^n \sum_{k=1}^K t_{ik} \|\bx_i-\bmu_k\|^2.
$$

\section{M step computation for the Gamma models}
In this section, given the array $\bt$ of conditional probabilities, we will write
$t_{.k} = \sum_{i=1}^n t_{ik}$, for $k=1,\ldots,K$ and will denote
$$
\bar{x}^j_k = \frac{1}{t_{.k}} \sum_{i=1}^n t_{ik} x^j_i,
$$
the $k$-th pondered mean of the $j$-th observation, and by
$$
(\overline{\log(x)})^j_k = \frac{1}{t_{.k}} \sum_{i=1}^n t_{ik} \log(x^j_i),
$$
the $k$-th pondered $\log$-mean of the $j$-th observation.

Replacing $h$ by its expression in the equation (\ref{eq:mStepEM}) and summing
in $i$, the M-step for the twelve gamma mixture models defined in table
 (\ref{tab:gammamodels}) is equivalent to maximize the following expression
\begin{equation}\label{eq:mStep_A_B}
l(A, B) = \sum_{k=1}^K \sum_{j=1}^d t_{.k}\left( A(\overline{\log(x)})^j_k - \frac{\bar{x}^j_k}{B} - \log(\Gamma(A)) - A \log(B) \right),
\end{equation}
with $A\in \{a, a^j, a_k, a^j_k\}$ and $B\in \{b, b^j, b_k, b^j_k\}$.

We now describe the various derivatives and for each models explicit the maximum
likelihood equations to solve. Taking the derivative with respect to $B$:
\begin{itemize}
\item If $B = b^j_k$ then
$$
\frac{dl}{db^j_k} = t_{.k}  \left(\frac{\bar{x}^j_k}{b^2} - \frac{A}{b}\right)
\mbox{ and thus }
\hat{b}^j_k = \frac{\bar{x}^j_k}{A}$$
\item If $B = b_{k}$ then
$$
\frac{dl}{db_k} = t_{.k}  \sum_{j=1}^d \left(\frac{\bar{x}^j_k}{b_k^2} - \frac{A}{b_k}\right)
\mbox{ and thus }
\hat{b}_k = \frac{\sum_{j=1}^d  \bar{x}^j_k}{\sum_{j=1}^d A}$$
\item If $B = b^j$ then
$$
\frac{dl}{db^j} = \sum_{k=1}^K t_{.k}  \left(\frac{\bar{x}^j_k}{(b^{j})^2} - \frac{A}{b^j}\right)
\mbox{ and thus }
\hat{b}^j = \frac{\sum_{k=1}^K t_{.k} \bar{x}^j_k}{\sum_{k=1}^K t_{.k} A}$$
\item If $B = b$ then
$$
\frac{dl}{db} = \sum_{k=1}^K \sum_{j=1}^d t_{.k}  \left(\frac{\bar{x}^j_k}{b^2} - \frac{A}{b}\right)
\mbox{ and thus }
\hat{b} = \frac{\sum_{k=1}^K \sum_{j=1}^d t_{.k} \bar{x}^j_k}{\sum_{k=1}^K \sum_{j=1}^d t_{.k} A}$$
\end{itemize}

Taking now the derivative with respect to $A$:
\begin{enumerate}
\item If $A = a^j_k$, then
$$
\frac{dl}{da^j_k} = t_{.k} \left( (\overline{\log(x)})^j_k -  \log(B)\right) - t_{.k}\Psi(a^j_k).
$$
and thus
\begin{itemize}
\item if $B=b^j_k$ (model \verb+gamma_ajk_bjk+)
\begin{equation}\label{eq:mStep_ajk_bjk}
\left\lbrace
\begin{array}{lcl}
  \Psi(\hat{a}^j_k) & = & (\overline{\log(x)})^j_k  - \log(\hat{b}^j_k) \\
  \hat{b}^j_k & = & \frac{\bar{x}^j_k}{\hat{a}^j_k},
\end{array}
\right.
\end{equation}
\item  if $B=b_k$  (model \verb+gamma_ajk_bk+)
\begin{equation}\label{eq:mStep_ajk_bk}
\left\lbrace
\begin{array}{lcl}
  \Psi(\hat{a}^j_k) & = & (\overline{\log(x)})^j_k  - \log(\hat{b}_k) \\
  \hat{b}_k         & = & \frac{\sum_{j=1}^d  \bar{x}^j_k}{\sum_{j=1}^d a^j_k}
\end{array}
\right.
\end{equation}
\item  if $B=b^j$  (model \verb+gamma_ajk_bj+)
\begin{equation}\label{eq:mStep_ajk_bj}
\left\lbrace
\begin{array}{lcl}
  \Psi(\hat{a}^j_k) & = & (\overline{\log(x)})^j_k  - \log(\hat{b}^j) \\
  \hat{b}^j         & = & \frac{\sum_{k=1}^K t_{.k} \bar{x}^j_k}{\sum_{k=1}^K t_{.k} a^j_k}
\end{array}
\right.
\end{equation}
\item if $B=b$  (model \verb+gamma_ajk_b+)
\begin{equation}\label{eq:mStep_ajk_b}
\left\lbrace
\begin{array}{lcl}
  \Psi(\hat{a}^j_k) & = & (\overline{\log(x)})^j_k  - \log(\hat{b}) \\
  \hat{b}           & = & \frac{\sum_{j=1}^d\sum_{k=1}^K t_{.k} \bar{x}^j_k}{\sum_{j=1}^d\sum_{k=1}^K t_{.k} a^j_k}
\end{array}
\right.
\end{equation}
\end{itemize}
\item If $A = a_{k}$, then
$$
\frac{dl}{da_k} = t_{.k} \sum_{j=1}^d \left( (\overline{\log(x)})^j_k - \log(B)\right) - t_{.k}d\Psi(a_k).
$$
and thus
\begin{itemize}
\item if $B=b^j_k$ (model \verb+gamma_ak_bjk+)
\begin{equation}\label{eq:mStep_ak_bjk}
\left\lbrace
\begin{array}{lcl}
\Psi(\hat{a}_k) & = & \frac{1}{d} \sum_{j=1}^d \left( (\overline{\log(x)})^j_k - \log(\hat{b}^j_k) \right) \\
    \hat{b}^j_k & = & \frac{\bar{x}^j_k}{\hat{a}_k},
\end{array}
\right.
\end{equation}
\item  if $B=b_k$  (model \verb+gamma_ak_bk+)
\begin{equation}\label{eq:mStep_ak_bk}
\left\lbrace
\begin{array}{lcl}
\Psi(\hat{a}_k) & = & \frac{1}{d} \sum_{j=1}^d \left( (\overline{\log(x)})^j_k - \log(\hat{b}_k) \right) \\
  \hat{b}_k     & = & \frac{\sum_{j=1}^d  \bar{x}^j_k}{d a_k}
\end{array}
\right.
\end{equation}
\item  if $B=b^j$  (model \verb+gamma_ak_bj+)
\begin{equation}\label{eq:mStep_ak_bj}
\left\lbrace
\begin{array}{lcl}
\Psi(\hat{a}_k) & = & \frac{1}{d} \sum_{j=1}^d \left( (\overline{\log(x)})^j_k - \log(\hat{b}^j) \right) \\
  \hat{b}^j     & = & \frac{\sum_{k=1}^K t_{.k} \bar{x}^j_k}{\sum_{k=1}^K t_{.k} a_k}
\end{array}
\right.
\end{equation}
\item if $B=b$  (model \verb+gamma_ak_b+)
\begin{equation}\label{eq:mStep_ak_b}
\left\lbrace
\begin{array}{lcl}
\Psi(\hat{a}_k) & = & \frac{1}{d} \sum_{j=1}^d \left( (\overline{\log(x)})^j_k - \log(\hat{b}) \right) \\
  \hat{b}       & = & \frac{\sum_{j=1}^d\sum_{k=1}^K t_{.k} \bar{x}^j_k}{d\sum_{k=1}^K t_{.k} a_k}
\end{array}
\right.
\end{equation}
\end{itemize}

\item If $A = a^j$, then
$$
\frac{dl}{da^j} = \sum_{k=1}^K t_{.k} \left((\overline{\log(x)})^j_k - \log(B)\right) - n\Psi(a^j).
$$
and thus
\begin{itemize}
\item if $B=b^j_k$ (model \verb+gamma_aj_bjk+)
\begin{equation}\label{eq:mStep_aj_bjk}
\left\lbrace
\begin{array}{lcl}
\Psi(\hat{a}^j) & = & \frac{1}{n} \sum_{k=1}^K  t_{.k} \left( (\overline{\log(x)})^j_k - \log(\hat{b}^j_k) \right) \\
    \hat{b}^j_k & = & \frac{\bar{x}^j_k}{\hat{a}^j},
\end{array}
\right.
\end{equation}
\item  if $B=b_k$  (model \verb+gamma_aj_bk+)
\begin{equation}\label{eq:mStep_aj_bk}
\left\lbrace
\begin{array}{lcl}
\Psi(\hat{a}^j) & = & \frac{1}{n} \sum_{k=1}^K  t_{.k} \left( (\overline{\log(x)})^j_k - \log(\hat{b}_k) \right) \\
  \hat{b}_k     & = & \frac{\sum_{j=1}^d  \bar{x}^j_k}{\sum_{j=1}^d \hat{a}^j}
\end{array}
\right.
\end{equation}
\end{itemize}
\item If $A = a$, then
$$
\frac{dl}{da} = \sum_{k=1}^K \sum_{j=1}^d t_{.k} \left((\overline{\log(x)})^j_k - \log(B)\right) - nd\Psi(a).
$$
and thus
\begin{itemize}
\item if $B=b^j_k$ (model \verb+gamma_a_bjk+)
\begin{equation}\label{eq:mStep_a_bjk}
\left\lbrace
\begin{array}{lcl}
\Psi(\hat{a}) & = & \frac{1}{nd} \sum_{j=1}^d \sum_{k=1}^K  t_{.k} \left( (\overline{\log(x)})^j_k - \log(\hat{b}^j_k) \right) \\
  \hat{b}^j_k & = & \frac{\bar{x}^j_k}{\hat{a}},
\end{array}
\right.
\end{equation}
\item  if $B=b_k$  (model \verb+gamma_a_bk+)
\begin{equation}\label{eq:mStep_a_bk}
\left\lbrace
\begin{array}{lcl}
\Psi(\hat{a}) & = & \frac{1}{nd} \sum_{j=1}^d \sum_{k=1}^K  t_{.k} \left( (\overline{\log(x)})^j_k - \log(\hat{b}_k) \right) \\
  \hat{b}_k   & = & \frac{\sum_{j=1}^d  \bar{x}^j_k}{d \hat{a}}
\end{array}
\right.
\end{equation}
\end{itemize}
\end{enumerate}

In the next sections, we will describe for some models the way to estimate $A$ and $B$
when $A = a^j_k$.

\subsection{First algorithm for the M Step of the gamma models}
Among the twelve models, we can find six models from whom it is possible to estimate in
a single pass of the Brent's method the value of $A$ and then to estimate the value of $B$.
For example for the \verb+gamma_ajk_bjk+ model, using (\ref{eq:mStep_ajk_bjk}) gives
$\hat{a}^j_k$ solution in $a$ of the following equation
\begin{equation}\label{eq:mStep_ajk_bjk_zero}
  (\overline{\log(x)})^j_k - \Psi(a) - \log(\bar{x}^j_k) + \log(a) =0
\end{equation}
whom solution can be found using Brent's method \cite{Brent1973}.

Having found the estimator of the $a^j_k$, the estimator of $b^j_k$ can be computed.

\subsection{Second algorithm for the M Step of the gamma models}
For the other models we have to iterate in order to find the ML estimators.
For example for the \verb+gamma_ajk_bj+ model, the set of non-linear equations
(\ref{eq:mStep_ajk_bj}) can be solved using an iterative algorithm:
\begin{itemize}
\item {\bf Initialization:} Compute an initial estimator of the $\ba_k$, $k=1,\ldots K$ and $\bb$
using moment estimators.
\item {\bf Repeat until convergence :}
\begin{itemize}
\item {\bf a step:} For fixed $b^j$ solve for each $a^j_k$, the equation:
\begin{equation*}
  \Psi(a) - (\overline{\log(x)})^j_k + \log(b^j) = 0.
\end{equation*}
\item {\bf b step:} Update $b^j$ using equation (\ref{eq:mStep_ajk_bj}).
\end{itemize}
\end{itemize}

This algorithm minimize alternatively the log-likelihood in $\ba_k$, $k=1,\ldots n$ and in
$\bb$ and converge in few iterations.

\end{document}
