{"id":5923,"date":"2023-09-08T16:06:54","date_gmt":"2023-09-08T14:06:54","guid":{"rendered":"https:\/\/francestat.com\/?page_id=5923"},"modified":"2024-07-13T10:24:44","modified_gmt":"2024-07-13T08:24:44","slug":"uniwin-simca","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-simca\/","title":{"rendered":"Uniwin – SIMCA"},"content":{"rendered":"

[vc_row][vc_column]

<\/div>

UNIWIN - Classement par la m\u00e9thode SIMCA<\/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 SIMCA (Soft Independent Modeling of Class Analogy) est une technique de classement propos\u00e9e par Svante Wold dans les ann\u00e9es 1970.<\/p>\n

Cette m\u00e9thode supervis\u00e9e est bas\u00e9e sur l\u2019analyse en composantes principales (ACP). Pour chaque classe, une ACP est r\u00e9alis\u00e9e en utilisant uniquement les observations de cette classe.<\/p>\n

Les diff\u00e9rents mod\u00e8les obtenus pour chacune des classes peuvent ainsi avoir des nombres de composantes diff\u00e9rents.<\/p>\n

Ces mod\u00e8les permettent de pr\u00e9voir l\u2019appartenance ou non d\u2019une observation du jeu d\u2019apprentissage ou de pr\u00e9vision \u00e0 une classe pr\u00e9d\u00e9finie mais \u00e9galement de d\u00e9terminer si une observation appartient \u00e0 plusieurs classes (recouvrement) ou \u00e0 aucune classe (possible point aberrant ou nouvelle classe).<\/p>\n

Cette proc\u00e9dure est bas\u00e9e sur le package R ‘mdatools’.<\/p>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb6431″ img_size=\u00a0\u00bb1366×768″ 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

Facteur de classement<\/p>\n<\/td>\n<\/tr>\n

\n

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

\n

Poids des variables<\/p>\n<\/td>\n<\/tr>\n

\n

Scores des observations<\/p>\n<\/td>\n<\/tr>\n

\n

R\u00e9sum\u00e9 du classement (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

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

\n

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

\n

Distances entre les mod\u00e8les<\/p>\n<\/td>\n<\/tr>\n

\n

Pouvoirs discriminants des variables<\/p>\n<\/td>\n<\/tr>\n

\n

D\u00e9tails du 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

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

\n

Poids des variables<\/p>\n<\/td>\n<\/tr>\n

\n

Scores des observations<\/p>\n<\/td>\n<\/tr>\n

\n

Distances entre les mod\u00e8les<\/p>\n<\/td>\n<\/tr>\n

\n

Pouvoirs discriminants des variables<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique de la matrice de confusion (apprnetissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique de Cooman<\/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%2Fwww.francestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FM%25e9thode%2520SIMCA.pdf|title:UNIWIN%20-%20SIMCA\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] La m\u00e9thode SIMCA (Soft Independent Modeling of Class Analogy) est une technique de classement propos\u00e9e par Svante Wold dans les ann\u00e9es 1970. Cette m\u00e9thode supervis\u00e9e est bas\u00e9e sur l\u2019analyse en composantes principales (ACP). Pour chaque classe, une ACP est r\u00e9alis\u00e9e en utilisant uniquement les observations de cette classe. Les diff\u00e9rents mod\u00e8les obtenus pour chacune…<\/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-5923","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - SIMCA - 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-simca\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - SIMCA - 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 SIMCA (Soft Independent Modeling of Class Analogy) est une technique de classement propos\u00e9e par Svante Wold dans les ann\u00e9es 1970. Cette m\u00e9thode supervis\u00e9e est bas\u00e9e sur l\u2019analyse en composantes principales (ACP). Pour chaque classe, une ACP est r\u00e9alis\u00e9e en utilisant uniquement les observations de cette classe. 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Les diff\u00e9rents mod\u00e8les obtenus pour chacune…","og_url":"https:\/\/francestat.com\/index.php\/uniwin-simca\/","og_site_name":"FRANCESTAT","article_modified_time":"2024-07-13T08:24:44+00:00","twitter_card":"summary_large_image","twitter_misc":{"Dur\u00e9e de lecture estim\u00e9e":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/francestat.com\/index.php\/uniwin-simca\/","url":"https:\/\/francestat.com\/index.php\/uniwin-simca\/","name":"Uniwin - SIMCA - FRANCESTAT","isPartOf":{"@id":"https:\/\/francestat.com\/#website"},"datePublished":"2023-09-08T14:06:54+00:00","dateModified":"2024-07-13T08:24:44+00:00","breadcrumb":{"@id":"https:\/\/francestat.com\/index.php\/uniwin-simca\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/francestat.com\/index.php\/uniwin-simca\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/francestat.com\/index.php\/uniwin-simca\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Accueil","item":"https:\/\/francestat.com\/"},{"@type":"ListItem","position":2,"name":"Uniwin – SIMCA"}]},{"@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\/5923","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=5923"}],"version-history":[{"count":16,"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/pages\/5923\/revisions"}],"predecessor-version":[{"id":6434,"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/pages\/5923\/revisions\/6434"}],"wp:attachment":[{"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/media?parent=5923"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}