fix error with TIM models in the manual

manual/manual.tex

@@ -46,7 +46,7 @@
 }
 
 \begin{document}
-\providecommand{\versionnumber}{0.1.10}
+\providecommand{\versionnumber}{0.1.11}
 \title{jModelTest 2 Manual v\versionnumber}
 \author{Diego Darriba, David Posada}
 \date{\today}
@@ -61,14 +61,15 @@ \section{Overview}
 
 jModelTest is a tool to carry out statistical selection of best-fit models of nucleotide substitution. It implements five different model selection strategies: hierarchical and dynamical likelihood ratio tests (hLRT and dLRT), Akaike and Bayesian information criteria (AIC and BIC), and a decision theory method (DT). It also provides estimates of model selection uncertainty, parameter importances and model-averaged parameter estimates, including model-averaged tree topologies. jModelTest 2 includes High Performance Computing (HPC) capabilities and additional features like new strategies for tree optimization, model-averaged phylogenetic trees (both topology and branch lenght), heuristic filtering and automatic logging of user activity.
 
+In 2020, jModelTest was superseded by ModelTest-NG, available at \url{https://github.com/ddarriba/modeltest}.
+
 \subsection{Download}
 
 The main project webpage is located at GitHub: \url{https://github.com/ddarriba/jmodeltest2}.
 
-New distributions of jModelTest will be hosted in GitHub releases and google drive.
+New distributions of jModelTest will be hosted in GitHub releases.
 \begin{itemize}
   \item \url{https://github.com/ddarriba/jmodeltest2/releases}
-  \item \url{https://drive.google.com/folderview?id=0ByrkKOPtF_n_OUs3d0dNcnJPYXM#list}
 \end{itemize}
 
 Please use the jModelTest discussion group for any question: 
@@ -121,7 +122,7 @@ \subsection{Launching the Graphical User Interface}
 The following window will show on the screen:
 
 \begin{center}
-\includegraphics[width=.9\textwidth]{images/main-window}
+\includegraphics[width=.9\textwidth]{images/main-window.pdf}
 \end{center}
 
 \subsection{Menu description}

manual/sec-quickstart.tex

@@ -18,41 +18,41 @@ \subsubsection{Graphical User Interface}
 \item Execute the script for the Graphical User Interface (runjmodeltest-gui.sh). The main jModelTest frame should pop up on the screen:
 
 \begin{center}
-\includegraphics[width=.9\textwidth]{images/main-window}
+\includegraphics[width=.9\textwidth]{images/main-window.pdf}
 \end{center}
 
 \item Load an input alignment file using the {\bf File/Load Alignment} option.
 
 \item Go to {\bf Analysis/Compute Likelihood Scores} and select the candidate models and the options for model optimization (optionally you can set a base topology from a file). Press Enter or the {\bf Compute Likelihoods} button.
 
 \begin{center}
-\includegraphics[width=.6\textwidth]{images/lkl-settings}
+\includegraphics[width=.6\textwidth]{images/lkl-settings.pdf}
 \end{center}
 
 \item Perform statistical selection among the optimized models. For example, we can calculate the Bayesian Information Criterion using {\bf Analysis/Do BIC calculations...} option, or any other. You can find a Criteria comparison in terms of accuracy in the \href{http://www.nature.com/nmeth/journal/v9/n8/extref/nmeth.2109-S1.pdf}{supplementary material} of the \href{http://www.nature.com/nmeth/journal/v9/n8/full/nmeth.2109.html}{jModelTest publication}.
 
 \begin{center}
-\includegraphics[width=.6\textwidth]{images/bic}
+\includegraphics[width=.6\textwidth]{images/bic.pdf}
 \end{center}
 
 The results will be shown in the main console.
 
 \item Take a look at the results table in {\bf Results/Show results table}. Best model is the one with the lowest criterion value (BIC column in the example) and therefore delta = 0.
 
 \begin{center}
-\includegraphics[width=.9\textwidth]{images/results}
+\includegraphics[width=.9\textwidth]{images/results.pdf}
 \end{center}
 
 \item Build a consensus tree from a given selection criteria using {\bf Analysis/Model-averaged phylogeny}:
 
 \begin{center}
-\includegraphics[width=.6\textwidth]{images/consensus}
+\includegraphics[width=.6\textwidth]{images/consensus.pdf}
 \end{center}
 
 \item Finally, you can save the results displayed in the main console using {\bf Edit/Save console}. Alternatively, you can get a formatted HTML document using {\bf Results/Build HTML log}:
 
 \begin{center}
-\includegraphics[width=.9\textwidth]{images/html-log}
+\includegraphics[width=.9\textwidth]{images/html-log.pdf}
 \end{center}
 
 Take a look at Section \ref{sec:gui} for further information.

manual/sec-theory.tex

@@ -39,17 +39,17 @@ \subsection{Models of nucleotide substitution}
 \hline
 TPM3uf & & 5 & unequal & AC=CG;AT=GT;AG=CT & 012012 \\
 \hline
-TIM1 & \citep{Posada-2003} & 3 & equal & AC=GT;AT=CG;AG;CT & 012230 \\
+TIM1ef & \citep{Posada-2003} & 3 & equal & AC=GT;AT=CG;AG;CT & 012230 \\
 \hline
-TIM1uf & \citep{Posada-2003} & 6 & unequal & AC=GT;AT=CG;AG;CT & 012230 \\
+TIM1 & \citep{Posada-2003} & 6 & unequal & AC=GT;AT=CG;AG;CT & 012230 \\
 \hline
-TIM2 & & 3 & equal & AC=AT;CG=GT;AG;CT & 010232 \\
+TIM2ef & & 3 & equal & AC=AT;CG=GT;AG;CT & 010232 \\
 \hline
-TIM2uf & & 6 & unequal & AC=AT;CG=GT;AG;CT & 010232 \\
+TIM2 & & 6 & unequal & AC=AT;CG=GT;AG;CT & 010232 \\
 \hline
-TIM3 & & 3 & equal & AC=CG;AT=GT;AG;CT & 012032 \\
+TIM3ef & & 3 & equal & AC=CG;AT=GT;AG;CT & 012032 \\
 \hline
-TIM3uf & & 6 & unequal & AC=CG;AT=GT;AG;CT & 012032 \\
+TIM3 & & 6 & unequal & AC=CG;AT=GT;AG;CT & 012032 \\
 \hline
 TVMef & \citep{Posada-2003} & 4 & equal & AC;CG;AT;GT;AG=CT & 012314 \\
 \hline
@@ -72,7 +72,7 @@ \subsection{Sequential Likelihood Ratio Tests (sLRT)}
 LRT=2(l_1-l_0)
 \]
 where $l_1$ is the maximum likelihood under the more parameter-rich, complex model (alternative hypothesis) and $l_0$ is the maximum likelihood under the less parameter-rich simple model (null hypothesis).
- When the models compared are nested (the null hypothesis is a special case of the alternative hypothesis) and the null hypothesis is correct, the LRT statistic is asymptotically distributed as a χ2 with q degrees of freedom, where q is the difference in number of free parameters between the two models \citep{Kendall-1979, Goldman-1993b}. Note that, to preserve the nesting of the models, the likelihood scores need to be estimated upon the same tree. When some parameter is fixed at its boundary (p-inv, α), a mixed χ2 is used instead \citep{Ohta-1992, Goldman-2000}. The behavior of the χ2 approximation for the LRT has been investigated with quite a bit of detail \citep{Goldman-1993a, Goldman-1993b, Yang-1995, Whelan-1999, Goldman-2000}.
+ When the models compared are nested (the null hypothesis is a special case of the alternative hypothesis) and the null hypothesis is correct, the LRT statistic is asymptotically distributed as a x2 with q degrees of freedom, where q is the difference in number of free parameters between the two models \citep{Kendall-1979, Goldman-1993b}. Note that, to preserve the nesting of the models, the likelihood scores need to be estimated upon the same tree. When some parameter is fixed at its boundary (p-inv, $\alpha$), a mixed x2 is used instead \citep{Ohta-1992, Goldman-2000}. The behavior of the x2 approximation for the LRT has been investigated with quite a bit of detail \citep{Goldman-1993a, Goldman-1993b, Yang-1995, Whelan-1999, Goldman-2000}.
 
 \subsection{Hierarchical Likelihood Ratio Tests (hLRT)}
 \label{sec:hlrt}