{"id":5942,"date":"2023-09-08T16:33:01","date_gmt":"2023-09-08T14:33:01","guid":{"rendered":"https:\/\/francestat.com\/?page_id=5942"},"modified":"2024-12-03T10:52:33","modified_gmt":"2024-12-03T09:52:33","slug":"uniwin-pls","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-pls\/","title":{"rendered":"Uniwin – PLS"},"content":{"rendered":"

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UNIWIN - R\u00e9gression PLS<\/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

La m\u00e9thode R\u00e9gression PLS (partial least squares ou moindres carr\u00e9s partiels) est con\u00e7ue pour ajuster un mod\u00e8le statistique reliant un ensemble de variables explicatives X \u00e0 une variable \u00e0 expliquer Y (PLS1) ou \u00e0 plusieurs variables \u00e0 expliquer Y (PLS2).<\/p>\n

La proc\u00e9dure est utile lorsqu\u2019il y a de nombreux X et que le but principal est de pr\u00e9voir simultan\u00e9ment les variables Y.<\/p>\n

Elle est recommand\u00e9e dans le cas o\u00f9 un grand nombre de variables X est utilis\u00e9, lorsqu’il y a de fortes colin\u00e9arit\u00e9s entre ces variables X, lorsque le nombre de variables X est sup\u00e9rieur au nombre d\u2019observations et lorsqu\u2019il y a des donn\u00e9es manquantes.<\/p>\n

La m\u00e9thode PLS est notamment largement utilis\u00e9e par les chimistes et les chimiom\u00e9triciens pour l\u2019\u00e9talonnage en spectrom\u00e9trie.<\/p>\n

Un rapport g\u00e9n\u00e9ral de synth\u00e8se est propos\u00e9 contenant notamment les PRESS, R2, Q2, R2X, R2Y, les poids w et w*, les scores t et u, les poids des variables X et Y, les corr\u00e9lations des variables avec les composantes, les valeurs observ\u00e9es, ajust\u00e9es et r\u00e9sidus du mod\u00e8le, les distances au mod\u00e8le en X et Y, les T2 de Hotelling et les VIP. Les graphiques des R2X, R2Y, Q2, des coefficients standardis\u00e9s, des cercles des corr\u00e9lations, des plans factoriels, des poids des variables, des T2, des distances au mod\u00e8le en X et Y, des VIP, des valeurs observ\u00e9es vs estim\u00e9es et des r\u00e9sidus sont \u00e9galement disponibles.<\/p>\n

La proc\u00e9dure impl\u00e9ment\u00e9e est bas\u00e9e sur le package R \u2018plsdepot\u2019.<\/p>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb6438″ 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

PRESS, RSS, R2, R2 cumul\u00e9, Q2, limite Q2, Q2 cumul\u00e9<\/p>\n<\/td>\n<\/tr>\n

\n

R2X, R2Y, R2X cumul\u00e9, R2Y cumul\u00e9<\/p>\n<\/td>\n<\/tr>\n

\n

Corr\u00e9lations X vs t, Y vs t, X vs u, Y vs u (PLS2)<\/p>\n<\/td>\n<\/tr>\n

\n

Coefficients de r\u00e9gression standardis\u00e9s<\/p>\n<\/td>\n<\/tr>\n

\n

Coefficients de r\u00e9gression non standardis\u00e9s<\/p>\n<\/td>\n<\/tr>\n

\n

Poids w<\/p>\n<\/td>\n<\/tr>\n

\n

Poids w*<\/p>\n<\/td>\n<\/tr>\n

\n

Scores X (t)<\/p>\n<\/td>\n<\/tr>\n

\n

Scores Y (u)<\/p>\n<\/td>\n<\/tr>\n

\n

Poids des variables X et Y<\/p>\n<\/td>\n<\/tr>\n

\n

Corr\u00e9lations variables – composantes<\/p>\n<\/td>\n<\/tr>\n

\n

Valeurs observ\u00e9es, estim\u00e9es et r\u00e9sidus pour chaque Y (jeu d’apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Distances au mod\u00e8le en X<\/p>\n<\/td>\n<\/tr>\n

\n

Distances au mod\u00e8le en Y<\/p>\n<\/td>\n<\/tr>\n

\n

T2 de Hotelling (PLS1)<\/p>\n<\/td>\n<\/tr>\n

\n

VIP (PLS2)<\/p>\n<\/td>\n<\/tr>\n

\n

Valeurs observ\u00e9es, estim\u00e9es et r\u00e9sidus pour chaque Y (jeux de validation et de 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\n\n\n\n\n\n\n\n
\n

Diagramme des R2X cumul\u00e9s<\/p>\n<\/td>\n<\/tr>\n

\n

Diagramme des R2Y cumul\u00e9s (PLS2)<\/p>\n<\/td>\n<\/tr>\n

\n

Diagramme des Q2 cumul\u00e9s<\/p>\n<\/td>\n<\/tr>\n

\n

Diagramme des coefficients standardis\u00e9s<\/p>\n<\/td>\n<\/tr>\n

\n

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

\n

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

\n

Plan factoriel des observations (t vs t)<\/p>\n<\/td>\n<\/tr>\n

\n

Plan factoriel des observations (u vs t)<\/p>\n<\/td>\n<\/tr>\n

\n

Plan factoriel des observations (u vs u)<\/p>\n<\/td>\n<\/tr>\n

\n

Diagramme des poids des variables X et Y<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des T2 de Hotelling (PLS1)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des distances au mod\u00e8le en X<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des distances au mod\u00e8le en Y<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des VIP (PLS2)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique observ\u00e9s vs estim\u00e9s (jeu d’apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des r\u00e9sidus (jeu d’apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique observ\u00e9s vs estim\u00e9s (jeu de validation)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des r\u00e9sidus (jeu 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:https%3A%2F%2Ffrancestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FR%25e9gression%2520PLS.pdf|title:UNIWIN%20-%20PLS\u00a0\u00bb][\/vc_column][\/vc_row]<\/p>\n<\/div>","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] La m\u00e9thode R\u00e9gression PLS (partial least squares ou moindres carr\u00e9s partiels) est con\u00e7ue pour ajuster un mod\u00e8le statistique reliant un ensemble de variables explicatives X \u00e0 une variable \u00e0 expliquer Y (PLS1) ou \u00e0 plusieurs variables \u00e0 expliquer Y (PLS2). La proc\u00e9dure est utile lorsqu\u2019il y a de nombreux X et que le but…<\/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-5942","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - PLS - 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-pls\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - PLS - 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] La m\u00e9thode R\u00e9gression PLS (partial least squares ou moindres carr\u00e9s partiels) est con\u00e7ue pour ajuster un mod\u00e8le statistique reliant un ensemble de variables explicatives X \u00e0 une variable \u00e0 expliquer Y (PLS1) ou \u00e0 plusieurs variables \u00e0 expliquer Y (PLS2). La proc\u00e9dure est utile lorsqu\u2019il y a de nombreux X et que le but…\" \/>\n<meta property=\"og:url\" content=\"https:\/\/francestat.com\/index.php\/uniwin-pls\/\" \/>\n<meta property=\"og:site_name\" content=\"FRANCESTAT\" \/>\n<meta property=\"article:modified_time\" content=\"2024-12-03T09:52:33+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-pls\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-pls\\\/\",\"name\":\"Uniwin - PLS - FRANCESTAT\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\"},\"datePublished\":\"2023-09-08T14:33:01+00:00\",\"dateModified\":\"2024-12-03T09:52:33+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-pls\\\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-pls\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-pls\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Accueil\",\"item\":\"https:\\\/\\\/francestat.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Uniwin – PLS\"}]},{\"@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 - PLS - 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-pls\/","og_locale":"fr_FR","og_type":"article","og_title":"Uniwin - PLS - 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] La m\u00e9thode R\u00e9gression PLS (partial least squares ou moindres carr\u00e9s partiels) est con\u00e7ue pour ajuster un mod\u00e8le statistique reliant un ensemble de variables explicatives X \u00e0 une variable \u00e0 expliquer Y (PLS1) ou \u00e0 plusieurs variables \u00e0 expliquer Y (PLS2). La proc\u00e9dure est utile lorsqu\u2019il y a de nombreux X et que le but…","og_url":"https:\/\/francestat.com\/index.php\/uniwin-pls\/","og_site_name":"FRANCESTAT","article_modified_time":"2024-12-03T09:52:33+00:00","twitter_card":"summary_large_image","twitter_misc":{"Dur\u00e9e de lecture estim\u00e9e":"3 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/francestat.com\/index.php\/uniwin-pls\/","url":"https:\/\/francestat.com\/index.php\/uniwin-pls\/","name":"Uniwin - PLS - FRANCESTAT","isPartOf":{"@id":"https:\/\/francestat.com\/#website"},"datePublished":"2023-09-08T14:33:01+00:00","dateModified":"2024-12-03T09:52:33+00:00","breadcrumb":{"@id":"https:\/\/francestat.com\/index.php\/uniwin-pls\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/francestat.com\/index.php\/uniwin-pls\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/francestat.com\/index.php\/uniwin-pls\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Accueil","item":"https:\/\/francestat.com\/"},{"@type":"ListItem","position":2,"name":"Uniwin – PLS"}]},{"@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\/"}}]}},"_links":{"self":[{"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/pages\/5942","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/comments?post=5942"}],"version-history":[{"count":12,"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/pages\/5942\/revisions"}],"predecessor-version":[{"id":6566,"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/pages\/5942\/revisions\/6566"}],"wp:attachment":[{"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/media?parent=5942"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}