Commit 762805dc by Eric Coissac

A version of the manuscript that compile according to the two formats

parent 48e5710c
......@@ -9,17 +9,35 @@
\copyrightyear{2015} \pubyear{2015}%
\access{Advance Access Publication Date: Day Month Year}%
\appnotes{Manuscript Category}%
\newcommand\authorname[2]{\author[#1]{#2}}
\newcommand\maintitle[2]{\title[#1]{#2}}
\else
\documentclass{article}%
\newcommand\sfb{}
\newcommand\firstpage[1]{}
\newcommand\subtitle[1]{}
\newcommand\history[1]{}
\newcommand\editor[1]{}
\newcommand\address[1]{\date{#1}}
\newcommand\corresp[1]{}
\newcommand\authorname[2]{\author{#2}}
\newcommand\maintitle[2]{\title{#2}}
\usepackage{hyperref}
\usepackage{natbib}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{xcolor}
\usepackage{graphicx}
\usepackage{lineno}
\newenvironment{methods}{}{}
\newenvironment{knitrout}{}{}
\definecolor{fgcolor}{rgb}{0.345, 0.345, 0.345}
\newcommand\maxwidth{\textwidth}
\fi
\usepackage{amsmath}
\usepackage{multirow}
\DeclareMathOperator{\rpearson}{R}
\DeclareMathOperator{\ir}{IR}
\DeclareMathOperator{\covls}{CovLs}
......@@ -45,11 +63,12 @@
\subtitle{Data and text mining}
\title[A modified $Rls$]{Assessing the shared variation among high-dimensional data matrices: a modified version of the Procrustean correlation coefficient}
\author[E. Coissac \textit{et~al}.]{E. Coissac\,$^{\text{\sfb 1,}*}$, Co-Author\,$^{\text{\sfb 2}}$ and C. Gonindard-Melodelima\,$^{\text{\sfb 1}}$}
\maintitle{A modified $Rls$}{Assessing the shared variation among high-dimensional data matrices: a modified version of the Procrustean correlation coefficient}
\authorname{E. Coissac \textit{et~al}.}{E. Coissac\,$^{\text{\sfb 1,}*}$, Co-Author\,$^{\text{\sfb 2}}$ and C. Gonindard-Melodelima\,$^{\text{\sfb 1}}$}
\address{$^{\text{\sf 1}}$Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LECA, Grenoble, F-38000, France \\
$^{\text{\sf 2}}$Department, Institution, City, Post Code,
Country.}
$^{\text{\sf 2}}$Department, Institution, City, Post Code, Country.}
\corresp{$^\ast$To whom correspondence should be addressed.}
......@@ -57,6 +76,10 @@ Country.}
\editor{Associate Editor: XXXXXXX}
\if 1\mode
\maketitle
\fi
\abstract{\textbf{Motivation:} Text Text Text Text Text Text Text Text Text Text Text Text Text
Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text
Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text
......@@ -70,7 +93,11 @@ Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text\\
\textbf{Supplementary information:} Supplementary data are available at \textit{Bioinformatics}
online.}
\if 0\mode
\maketitle
\else
\linenumbers
\fi
<<initialisation, message=FALSE, warning=FALSE, include=FALSE>>=
knitr::opts_chunk$set(cache.extra = R.version)
......@@ -1270,8 +1297,6 @@ The main advantage of $\irls$ over other matrix correlation coefficients is that
The second advantage of $\irls$ is that its definition implies that the variance/co-variance matrix of a set of matrices is positive-definite. That allows for estimating partial correlation coefficients matrix by inverting the variance/co-variance matrix. The effect of the correction is less strong on such partial coefficients than on full correlation, but the partial coefficients that should theoretically be estimated to zero seem to be better identified after the correction.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% please remove the " % " symbol from \centerline{\includegraphics{fig01.eps}}
......
No preview for this file type
......@@ -59,17 +59,35 @@
\copyrightyear{2015} \pubyear{2015}%
\access{Advance Access Publication Date: Day Month Year}%
\appnotes{Manuscript Category}%
\newcommand\authorname[2]{\author[#1]{#2}}
\newcommand\maintitle[2]{\title[#1]{#2}}
\else
\documentclass{article}%
\newcommand\sfb{}
\newcommand\firstpage[1]{}
\newcommand\subtitle[1]{}
\newcommand\history[1]{}
\newcommand\editor[1]{}
\newcommand\address[1]{\date{#1}}
\newcommand\corresp[1]{}
\newcommand\authorname[2]{\author{#2}}
\newcommand\maintitle[2]{\title{#2}}
\usepackage{hyperref}
\usepackage{natbib}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{xcolor}
\usepackage{graphicx}
\usepackage{lineno}
\newenvironment{methods}{}{}
\newenvironment{knitrout}{}{}
\definecolor{fgcolor}{rgb}{0.345, 0.345, 0.345}
\newcommand\maxwidth{\textwidth}
\fi
\usepackage{amsmath}
\usepackage{multirow}
\DeclareMathOperator{\rpearson}{R}
\DeclareMathOperator{\ir}{IR}
\DeclareMathOperator{\covls}{CovLs}
......@@ -95,11 +113,12 @@
\subtitle{Data and text mining}
\title[A modified $Rls$]{Assessing the shared variation among high-dimensional data matrices: a modified version of the Procrustean correlation coefficient}
\author[E. Coissac \textit{et~al}.]{E. Coissac\,$^{\text{\sfb 1,}*}$, Co-Author\,$^{\text{\sfb 2}}$ and C. Gonindard-Melodelima\,$^{\text{\sfb 1}}$}
\maintitle{A modified $Rls$}{Assessing the shared variation among high-dimensional data matrices: a modified version of the Procrustean correlation coefficient}
\authorname{E. Coissac \textit{et~al}.}{E. Coissac\,$^{\text{\sfb 1,}*}$, Co-Author\,$^{\text{\sfb 2}}$ and C. Gonindard-Melodelima\,$^{\text{\sfb 1}}$}
\address{$^{\text{\sf 1}}$Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LECA, Grenoble, F-38000, France \\
$^{\text{\sf 2}}$Department, Institution, City, Post Code,
Country.}
$^{\text{\sf 2}}$Department, Institution, City, Post Code, Country.}
\corresp{$^\ast$To whom correspondence should be addressed.}
......@@ -107,6 +126,10 @@ Country.}
\editor{Associate Editor: XXXXXXX}
\if 1\mode
\maketitle
\fi
\abstract{\textbf{Motivation:} Text Text Text Text Text Text Text Text Text Text Text Text Text
Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text
Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text
......@@ -120,7 +143,11 @@ Text Text Text Text Text Text Text Text Text Text Text Text Text Text Text\\
\textbf{Supplementary information:} Supplementary data are available at \textit{Bioinformatics}
online.}
\if 0\mode
\maketitle
\else
\linenumbers
\fi
......@@ -353,7 +380,7 @@ To evaluate relative power of the three considered tests, pairs of to random mat
\begin{table}[!t]
\processtable{Estimation of $\overline{\rcovls(\X,\Y)}$ according to the number of random matrices (k) aligned.\label{tab:mrcovls}}{
% latex table generated in R 3.5.2 by xtable 1.8-4 package
% Mon Sep 2 14:59:46 2019
% Tue Sep 3 11:26:56 2019
\begin{tabular}{rrrrrrr}
\hline
& & \multicolumn{2}{c}{normal} & & \multicolumn{2}{c}{exponential}\\ \cline{3-4} \cline{6-7}p & k &\multicolumn{1}{c}{mean} & \multicolumn{1}{c}{sd} & \multicolumn{1}{c}{ } &\multicolumn{1}{c}{mean} & \multicolumn{1}{c}{sd}\\\hline\multirow{3}{*}{10} & 10 & 0.5746 & $1.3687 \times 10^{-2}$ & & 0.5705 & $1.1714 \times 10^{-2}$ \\
......@@ -455,7 +482,7 @@ whatever the $p$ tested (Table~\ref{tab:alpha_pvalue}). This ensure that the pro
of the distribution of $P_{values}$ correlation test to $\mathcal{U}(0,1)$
under the null hypothesis.\label{tab:alpha_pvalue}} {
% latex table generated in R 3.5.2 by xtable 1.8-4 package
% Mon Sep 2 14:59:50 2019
% Tue Sep 3 11:26:59 2019
\begin{tabular*}{0.98\linewidth}{@{\extracolsep{\fill}}crrr}
\hline
& \multicolumn{3}{c}{Cramer-Von Mises p.value} \\
......@@ -477,7 +504,7 @@ Power of the $CovLs$ test based on the estimation of $\overline{RCovLs(X,Y)}$ is
\begin{table}[!t]
\processtable{Power estimation of the procruste tests for two low level of shared variations $5\%$ and $10\%$.\label{tab:power}} {
% latex table generated in R 3.5.2 by xtable 1.8-4 package
% Mon Sep 2 14:59:50 2019
% Tue Sep 3 11:26:59 2019
\begin{tabular}{lcrrrrrrrrr}
\hline
& $R^2$ & \multicolumn{4}{c}{5\%} & &\multicolumn{4}{c}{10\%} \\
......@@ -516,8 +543,6 @@ The main advantage of $\irls$ over other matrix correlation coefficients is that
The second advantage of $\irls$ is that its definition implies that the variance/co-variance matrix of a set of matrices is positive-definite. That allows for estimating partial correlation coefficients matrix by inverting the variance/co-variance matrix. The effect of the correction is less strong on such partial coefficients than on full correlation, but the partial coefficients that should theoretically be estimated to zero seem to be better identified after the correction.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% please remove the " % " symbol from \centerline{\includegraphics{fig01.eps}}
......
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