{"id":5879,"date":"2023-09-08T15:11:13","date_gmt":"2023-09-08T13:11:13","guid":{"rendered":"https:\/\/francestat.com\/?page_id=5879"},"modified":"2024-12-02T17:47:17","modified_gmt":"2024-12-02T16:47:17","slug":"uniwin-afd","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-afd\/","title":{"rendered":"Uniwin – AFD"},"content":{"rendered":"

[vc_row][vc_column]

<\/div>

UNIWIN - Analyse Factorielle Discriminante<\/h2><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]<\/p>\n

L’Analyse Factorielle Discriminante (AFD) est une m\u00e9thode g\u00e9om\u00e9trique permettant de construire \u00e0 partir d’un ensemble de variables quantitatives et d’une variable qualitative d\u00e9coupant la population en plusieurs groupes (2 ou plus) des fonctions discriminantes qui les s\u00e9parent au mieux dans l\u2019\u00e9chantillon d\u2019apprentissage.<\/p>\n

En fonction des donn\u00e9es et des param\u00e8tres d\u00e9finis par l\u2019utilisateur, l\u2019analyse AFD r\u00e9alise automatiquement les \u00e9tudes de la population d\u2019apprentissage et des \u00e9ventuelles populations de validation et de pr\u00e9vision.<\/p>\n

De fa\u00e7on plus pr\u00e9cise, la m\u00e9thode peut se d\u00e9composer en trois \u00e9tapes. Supposons une population de n individus. D\u00e9coupons cette population en trois sous-populations de tailles n1<\/sub>, n2<\/sub>\u00a0et n3<\/sub>\u00a0avec n1<\/sub>\u00a0+ n2 +<\/sub>\u00a0n3<\/sub>\u00a0= n. Les trois \u00e9tapes sont\u00a0une \u00e9tude initiale sur la population d\u2019apprentissage de taille n1<\/sub>\u00a0puis une \u00e9tude de validation sur la population de validation de taille n2\u00a0<\/sub>et enfin une \u00e9tude prospective sur une population de pr\u00e9vision de taille n3<\/sub>.<\/p>\n

Des tableaux r\u00e9sum\u00e9s et d\u00e9taill\u00e9s des classements sont calcul\u00e9s.<\/p>\n

Le trac\u00e9 de plans factoriels et un rapport g\u00e9n\u00e9ral de synth\u00e8se sont propos\u00e9s.<\/p>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb6415″ img_size=\u00a0\u00bblarge\u00a0\u00bb alignment=\u00a0\u00bbcenter\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]<\/p>\n

Tableaux<\/u><\/span><\/strong><\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
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Centro\u00efdes des groupes et global<\/p>\n<\/td>\n<\/tr>\n

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Matrice des covariances globales<\/p>\n<\/td>\n<\/tr>\n

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Matrice des corr\u00e9lations globales<\/p>\n<\/td>\n<\/tr>\n

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Covariances par groupe<\/p>\n<\/td>\n<\/tr>\n

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Corr\u00e9lations par groupe<\/p>\n<\/td>\n<\/tr>\n

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Covariances intra-groupes<\/p>\n<\/td>\n<\/tr>\n

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Corr\u00e9lations intra-groupes<\/p>\n<\/td>\n<\/tr>\n

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Distances de Mahalanobis, valeurs des Fishers et tests de signification<\/p>\n<\/td>\n<\/tr>\n

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Tableau des inerties<\/p>\n<\/td>\n<\/tr>\n

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Test de Pillai<\/p>\n<\/td>\n<\/tr>\n

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Test de Box<\/p>\n<\/td>\n<\/tr>\n

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Fonctions discriminantes standardis\u00e9es<\/p>\n<\/td>\n<\/tr>\n

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Fonctions discriminantes non standardis\u00e9es<\/p>\n<\/td>\n<\/tr>\n

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R\u00e9sultats pour les variables<\/p>\n<\/td>\n<\/tr>\n

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R\u00e9sultats pour les individus<\/p>\n<\/td>\n<\/tr>\n

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Coordonn\u00e9es des centres des groupes<\/p>\n<\/td>\n<\/tr>\n

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Matrice de confusion (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

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D\u00e9tails du classement (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

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Sensibilit\u00e9s, sp\u00e9cificit\u00e9s (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

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Statistiques pour les groupes observ\u00e9s<\/p>\n<\/td>\n<\/tr>\n

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Statistiques pour les groupes pr\u00e9vus<\/p>\n<\/td>\n<\/tr>\n

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Matrice de confusion (validation)<\/p>\n<\/td>\n<\/tr>\n

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D\u00e9tails du classement (validation)<\/p>\n<\/td>\n<\/tr>\n

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Sensibilit\u00e9s, sp\u00e9cificit\u00e9s (validation)<\/p>\n<\/td>\n<\/tr>\n

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Classement pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Graphiques<\/u><\/span><\/strong><\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n
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Diagramme des inerties<\/p>\n<\/td>\n<\/tr>\n

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Cercle factoriel des corr\u00e9lations des variables (points)<\/p>\n<\/td>\n<\/tr>\n

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Cercle factoriel des corr\u00e9lations des variables (points + lignes)<\/p>\n<\/td>\n<\/tr>\n

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Plan factoriel du jeu d’apprentissage<\/p>\n<\/td>\n<\/tr>\n

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Plan factoriel du jeu de validation<\/p>\n<\/td>\n<\/tr>\n

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Plan factoriel du jeu de pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n

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Plan factoriel des jeux d’apprentissage et de validation<\/p>\n<\/td>\n<\/tr>\n

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Plan factoriel des jeux d’apprentissage et de pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n

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Graphiques des matrices de confusion pour les jeux d’apprentissage et de validation<\/p>\n<\/td>\n<\/tr>\n

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Courbes ROC pour les jeux d’apprentissage et de validation<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_btn title=\u00a0\u00bbConsulter la documentation compl\u00e8te\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:http%3A%2F%2Fwww.francestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FAnalyse%20factorielle%20discriminante.pdf|title:UNIWIN%20-%20AFD\u00a0\u00bb][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text] L’Analyse Factorielle Discriminante (AFD) est une m\u00e9thode g\u00e9om\u00e9trique permettant de construire \u00e0 partir d’un ensemble de variables quantitatives et d’une variable qualitative d\u00e9coupant la population en plusieurs groupes (2 ou plus) des fonctions discriminantes qui les s\u00e9parent au mieux dans l\u2019\u00e9chantillon d\u2019apprentissage. En fonction des donn\u00e9es et des param\u00e8tres d\u00e9finis par l\u2019utilisateur, l\u2019analyse AFD…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-5879","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - AFD - FRANCESTAT<\/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:\/\/francestat.com\/index.php\/uniwin-afd\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - AFD - FRANCESTAT\" \/>\n<meta property=\"og:description\" content=\"[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text] L’Analyse Factorielle Discriminante (AFD) est une m\u00e9thode g\u00e9om\u00e9trique permettant de construire \u00e0 partir d’un ensemble de variables quantitatives et d’une variable qualitative d\u00e9coupant la population en plusieurs groupes (2 ou plus) des fonctions discriminantes qui les s\u00e9parent au mieux dans l\u2019\u00e9chantillon d\u2019apprentissage. En fonction des donn\u00e9es et des param\u00e8tres d\u00e9finis par l\u2019utilisateur, l\u2019analyse AFD…\" \/>\n<meta property=\"og:url\" content=\"https:\/\/francestat.com\/index.php\/uniwin-afd\/\" \/>\n<meta property=\"og:site_name\" content=\"FRANCESTAT\" \/>\n<meta property=\"article:modified_time\" content=\"2024-12-02T16:47:17+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-afd\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-afd\\\/\",\"name\":\"Uniwin - AFD - FRANCESTAT\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\"},\"datePublished\":\"2023-09-08T13:11:13+00:00\",\"dateModified\":\"2024-12-02T16:47:17+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-afd\\\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-afd\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-afd\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Accueil\",\"item\":\"https:\\\/\\\/francestat.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Uniwin – AFD\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\",\"url\":\"https:\\\/\\\/francestat.com\\\/\",\"name\":\"FRANCESTAT\",\"description\":\"Logiciels, formations et services statistiques\",\"publisher\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/francestat.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#organization\",\"name\":\"FRANCESTAT\",\"url\":\"https:\\\/\\\/francestat.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/wp-content\\\/uploads\\\/2018\\\/05\\\/logo_horizontal_small_francestat.jpg\",\"contentUrl\":\"https:\\\/\\\/francestat.com\\\/wp-content\\\/uploads\\\/2018\\\/05\\\/logo_horizontal_small_francestat.jpg\",\"width\":155,\"height\":51,\"caption\":\"FRANCESTAT\"},\"image\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Uniwin - AFD - FRANCESTAT","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/francestat.com\/index.php\/uniwin-afd\/","og_locale":"fr_FR","og_type":"article","og_title":"Uniwin - AFD - FRANCESTAT","og_description":"[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text] L’Analyse Factorielle Discriminante (AFD) est une m\u00e9thode g\u00e9om\u00e9trique permettant de construire \u00e0 partir d’un ensemble de variables quantitatives et d’une variable qualitative d\u00e9coupant la population en plusieurs groupes (2 ou plus) des fonctions discriminantes qui les s\u00e9parent au mieux dans l\u2019\u00e9chantillon d\u2019apprentissage. 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