{"id":10349,"date":"2019-11-10T11:28:48","date_gmt":"2019-11-10T09:28:48","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?post_type=dt_lessons&#038;p=10349"},"modified":"2019-11-12T02:55:35","modified_gmt":"2019-11-12T00:55:35","slug":"graphique-de-concordance-dans-r","status":"publish","type":"dt_lessons","link":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/graphique-de-concordance-dans-r\/","title":{"rendered":"Graphique de Concordance dans R"},"content":{"rendered":"<div id=\"rdoc\">\n<p>Pr\u00e9c\u00e9demment, nous avons d\u00e9crit de nombreuses m\u00e9triques statistiques, telles que le Kappa de Cohen @ref(cohen-s-kappa) et le Kappa pond\u00e9r\u00e9 @ref (weighted-kappa), pour \u00e9valuer l\u2019accord ou la concordance entre deux \u00e9valuateurs (juges, observateurs, cliniciens) ou deux m\u00e9thodes de mesure.<\/p>\n<p>Ce chapitre d\u00e9crit le <strong>graphique de concordance<\/strong> <span class=\"citation\">(S. I. Bangdiwala 1985)<\/span>, qui fournit une solution pour visualiser la force de l\u2019accord entre deux m\u00e9thodes mesurant \u00e0 l\u2019\u00e9chelle ordinale. Par exemple, le graphique de concordance peut \u00eatre utilis\u00e9 pour comparer visuellement deux m\u00e9thodes de diagnostic ou de classification. Il est \u00e0 noter que le graphique de concordance est g\u00e9n\u00e9ralement recommand\u00e9e pour les variables cat\u00e9gorielles ordinales.<\/p>\n<p>Vous apprendrez:<\/p>\n<ul>\n<li><strong>Les bases de la construction d\u2019un graphique de concordance<\/strong><\/li>\n<li><strong>Codes R pour la cr\u00e9ation d\u2019un tableau de concordance<\/strong><\/li>\n<\/ul>\n<p>Contents:<\/p>\n<div id=\"TOC\">\n<ul>\n<li><a href=\"#prerequis\">Pr\u00e9requis<\/a><\/li>\n<li><a href=\"#construction-dun-graphique-de-concordance\">Construction d\u2019un graphique de concordance<\/a><\/li>\n<li><a href=\"#exemple-de-donnees\">Exemple de donn\u00e9es<\/a><\/li>\n<li><a href=\"#creez-un-graphique-de-concordance-dans-r\">Cr\u00e9ez un graphique de concordance dans R<\/a><\/li>\n<li><a href=\"#resume\">R\u00e9sum\u00e9<\/a><\/li>\n<li><a href=\"#references\">References<\/a><\/li>\n<\/ul>\n<\/div>\n<div class='dt-sc-hr-invisible-medium  '><\/div>\n<div class='dt-sc-ico-content type1'><div class='custom-icon' ><a href='https:\/\/www.datanovia.com\/en\/fr\/produit\/concordance-inter-juges-lessentiel-guide-pratique-dans-r\/' target='_blank'><span class='fa fa-book'><\/span><\/a><\/div><h4><a href='https:\/\/www.datanovia.com\/en\/fr\/produit\/concordance-inter-juges-lessentiel-guide-pratique-dans-r\/' target='_blank'> Livre associ\u00e9 <\/a><\/h4>Concordance Inter-Juges: L'Essentiel - Guide Pratique dans R<\/div>\n<div class='dt-sc-hr-invisible-medium  '><\/div>\n<div id=\"prerequis\" class=\"section level2\">\n<h2>Pr\u00e9requis<\/h2>\n<p>Assurez-vous d\u2019avoir install\u00e9 le package <code>vcd<\/code> pour visualiser les donn\u00e9es cat\u00e9gorielles.<\/p>\n<pre class=\"r\"><code>install.packages(\"vcd\")<\/code><\/pre>\n<\/div>\n<div id=\"construction-dun-graphique-de-concordance\" class=\"section level2\">\n<h2>Construction d\u2019un graphique de concordance<\/h2>\n<p><strong>Structure des donn\u00e9es<\/strong>. Consid\u00e9rons le tableau de contingence k x k suivant, o\u00f9 k est le nombre de cat\u00e9gories:<\/p>\n<pre><code>##           rater2\r\n## rater1     Level.1 Level.2 Level... Level.k Total\r\n##   Level.1  n11     n12     ...      n1k     n1+  \r\n##   Level.2  n21     n22     ...      n2k     n2+  \r\n##   Level... ...     ...     ...      ...     ...  \r\n##   Level.k  nk1     nk2     ...      nkk     nk+  \r\n##   Total    n+1     n+2     ...      n+k     N<\/code><\/pre>\n<p><strong>Terminologies<\/strong>:<\/p>\n<ul>\n<li>La colonne \u201cTotal\u201d (<code>n1+, n2+, ...., nk+<\/code>) indique la somme de chaque ligne, connue sous le nom de <strong>marges des lignes<\/strong> ou <strong>effectifs marginaux<\/strong>. Ici, la somme totale d\u2019une ligne donn\u00e9e <code>i<\/code> est nomm\u00e9e <code>ni+<\/code>.<\/li>\n<li>La ligne \u201cTotal\u201d (<code>n+1, n+2, ...., n+k<\/code>) indique la somme de chaque colonne, appel\u00e9e <strong>marges des colonnes<\/strong>. Ici, la somme totale d\u2019une colonne donn\u00e9e <code>i<\/code> est nom\u00e9e <code>n+i<\/code><\/li>\n<li>N est la somme totale de toutes les cellules du tableau<\/li>\n<\/ul>\n<p><strong>Graphique de concordance<\/strong>. Le graphique de concordance est une repr\u00e9sentation visuelle d\u2019un tableau de contingence k \u00d7 k carr\u00e9. Il est construit avec les \u00e9tapes suivantes <span class=\"citation\">(S. Bangdiwala and Shankar 2013, <span class=\"citation\">Friendly, Meyer, and Zeileis (2015)<\/span>)<\/span>:<\/p>\n<ol style=\"list-style-type: decimal;\">\n<li>Dessiner un carr\u00e9 N \u00d7 N, o\u00f9 N est la taille totale de l\u2019\u00e9chantillon<\/li>\n<li>Dessiner k rectangles, de dimensions marges totaux des lignes x colonnes, plac\u00e9s \u00e0 l\u2019int\u00e9rieur du carr\u00e9 N \u00d7 N<\/li>\n<li>Dessiner k carr\u00e9s noirs de dimensions bas\u00e9es sur les fr\u00e9quences diagonales des cellules (<code>nii<\/code>), plac\u00e9s \u00e0 l\u2019int\u00e9rieur du rectangle correspondant. Ces carr\u00e9s noirs repr\u00e9sentent l\u2019accord observ\u00e9.<\/li>\n<li>Les zones \u201cd\u2019accord partiel\u201d peuvent \u00eatre plac\u00e9es de la m\u00eame mani\u00e8re \u00e0 l\u2019int\u00e9rieur des rectangles, avec une couleur grise pour les cellules plus \u00e9loign\u00e9es des cellules diagonales.<\/li>\n<\/ol>\n<\/div>\n<div id=\"exemple-de-donnees\" class=\"section level2\">\n<h2>Exemple de donn\u00e9es<\/h2>\n<p>Nous utiliserons le jeu de donn\u00e9es de d\u00e9monstration sur l\u2019anxi\u00e9t\u00e9 o\u00f9 deux m\u00e9decins cliniciens classent 50 personnes en 4 niveaux d\u2019anxi\u00e9t\u00e9 ordonn\u00e9s : \u201cnormal\u201d (pas d\u2019anxi\u00e9t\u00e9), \u201cmod\u00e9r\u00e9\u201d, \u201c\u00e9lev\u00e9\u201d, \u201ctr\u00e8s \u00e9lev\u00e9\u201d.<\/p>\n<p>Les donn\u00e9es sont organis\u00e9es dans le tableau de contingence 3x3 suivant:<\/p>\n<pre class=\"r\"><code>anxiety &lt;- as.table(\r\n  rbind(\r\n    c(11, 3, 1, 0), c(1, 9, 0, 1),\r\n    c(0, 1, 10, 0 ), c(1, 2, 0, 10)\r\n  )\r\n)\r\ndimnames(anxiety) &lt;- list(\r\n  Doctor1 = c(\"Normal\", \"Moderate\", \"High\", \"Very high\"),\r\n  Doctor2 = c(\"Normal\", \"Moderate\", \"High\", \"Very high\")\r\n)\r\nanxiety<\/code><\/pre>\n<pre><code>##            Doctor2\r\n## Doctor1     Normal Moderate High Very high\r\n##   Normal        11        3    1         0\r\n##   Moderate       1        9    0         1\r\n##   High           0        1   10         0\r\n##   Very high      1        2    0        10<\/code><\/pre>\n<div class=\"warning\">\n<p>Notez que les niveaux des facteurs doivent \u00eatre dans l\u2019ordre correct, sinon les r\u00e9sultats seront erron\u00e9s.<\/p>\n<\/div>\n<\/div>\n<div id=\"creez-un-graphique-de-concordance-dans-r\" class=\"section level2\">\n<h2>Cr\u00e9ez un graphique de concordance dans R<\/h2>\n<p>Nous voulons comparer visuellement la concordance des diagnostics des deux m\u00e9decins.<\/p>\n<pre class=\"r\"><code>par(mar = c(4, 2, 2,2))\r\nlibrary(vcd)\r\n# Cr\u00e9er le graphique\r\np &lt;- agreementplot(anxiety)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/inter-rater-reliability\/figures\/205-inter-rater-agreement-chart-agreement-plot-1.png\" width=\"432\" \/><\/p>\n<pre class=\"r\"><code># Afficher les statistiques sur la force de l'accord de Bangdiwala\r\nunlist(p)[1 : 2]<\/code><\/pre>\n<pre><code>##          Bangdiwala Bangdiwala_Weighted \r\n##               0.644               0.795<\/code><\/pre>\n<p>L\u00e9gende du graphique:<\/p>\n<ul>\n<li>Carr\u00e9 noir : accord exact observ\u00e9<\/li>\n<li>Carr\u00e9 gris : accord partiel en incluant une contribution pond\u00e9r\u00e9e des cellules hors diagonale. Par d\u00e9faut, le poids w = 1 est attribu\u00e9 \u00e0 toutes les cellules diagonales (accord exact) ; un poids quadratique (<code>1 - 1\/(k - 1)^2<\/code>) est attribu\u00e9 aux d\u00e9saccords d\u2019un cran (ou aux cellules hors diagonale d\u2019un cran, accord partiel). Si vous ne voulez pas montrer un accord partiel, sp\u00e9cifiez l\u2019argument <code>weights<\/code> as follow: <code>agreementplot(anxiety, weights = 1)<\/code><\/li>\n<\/ul>\n<div class=\"warning\">\n<p>Notez que,<\/p>\n<ul>\n<li>L\u2019image visuelle est affect\u00e9e si l\u2019ordre des cat\u00e9gories est permut\u00e9, et donc l\u2019utilisation du graphique de concordance n\u2019est recommand\u00e9e que pour les variables ordinales (Bangdiwala et al., 2013).<\/li>\n<li>Dans le cas d\u2019une concordance parfaite, les k rectangles d\u00e9termin\u00e9s par les totaux marginaux sont tous des carr\u00e9s parfaits et les carr\u00e9s fonc\u00e9s d\u00e9termin\u00e9s par les entr\u00e9es des cellules diagonales sont exactement \u00e9gaux aux rectangles, produisant une valeur statistique B de 1 (Bangdiwala et al., 2013).<\/li>\n<li>Une concordance moindre est visualis\u00e9e en comparant la surface des carr\u00e9s noircis et la surface des rectangles (Bangdiwala et al., 2013).<\/li>\n<li>Il peut arriver qu\u2019un \u00e9valuateur ait tendance \u00e0 classer syst\u00e9matiquement le sujet dans des cat\u00e9gories sup\u00e9rieures ou inf\u00e9rieures \u00e0 l\u2019autre, peut-\u00eatre en raison de l\u2019utilisation de seuils plus stricts pour les limites entre cat\u00e9gories adjacentes (Friendly et al., 2015). Ce biais des observateurs ou des \u00e9valuateurs est visualis\u00e9 en examinant la d\u00e9viation des carr\u00e9s fonc\u00e9s par rapport \u00e0 la ligne diagonale de 45\u00b0 \u00e0 l\u2019int\u00e9rieur du carr\u00e9 N x N plus grand (Bangdiwala et al., 2013 ; Friendly et al., 2015).<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div id=\"resume\" class=\"section level2\">\n<h2>R\u00e9sum\u00e9<\/h2>\n<p>Cet article d\u00e9crit comment cr\u00e9er un graphique de concordance dans R.<\/p>\n<\/div>\n<div id=\"references\" class=\"section level2 unnumbered\">\n<h2>References<\/h2>\n<div id=\"refs\" class=\"references\">\n<div id=\"ref-Bangdiwala1985\">\n<p>Bangdiwala, Shrikant I. 1985. \u201cA Graphical Test for Observer Agreement.\u201d <em>Amsterdam: International Statistical Institute<\/em>, 307\u20138.<\/p>\n<\/div>\n<div id=\"ref-Bangdiwala2013\">\n<p>Bangdiwala, Shrikant, and Viswanathan Shankar. 2013. \u201cThe Agreement Chart.\u201d <em>BMC Medical Research Methodology<\/em> 13 (July): 97. doi:<a href=\"https:\/\/doi.org\/10.1186\/1471-2288-13-97\">10.1186\/1471-2288-13-97<\/a>.<\/p>\n<\/div>\n<div id=\"ref-Friendly2015\">\n<p>Friendly, Michael, D. Meyer, and A. Zeileis. 2015. <em>Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data<\/em>. 1st ed. Chapman; Hall\/CRC.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cet article d\u00e9crit comment cr\u00e9er un diagramme d&rsquo;accord dans R.<\/p>\n","protected":false},"author":1,"featured_media":10350,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","class_list":["post-10349","dt_lessons","type-dt_lessons","status-publish","has-post-thumbnail","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Graphique de Concordance dans R: Meuilleure R\u00e9f\u00e9rence - Datanovia<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.datanovia.com\/en\/fr\/lessons\/graphique-de-concordance-dans-r\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Graphique de Concordance dans R: Meuilleure R\u00e9f\u00e9rence - 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