{"id":868,"date":"2023-07-28T11:53:32","date_gmt":"2023-07-28T11:53:32","guid":{"rendered":"https:\/\/statorials.org\/pt\/como-calcular-vive-em-python\/"},"modified":"2023-07-28T11:53:32","modified_gmt":"2023-07-28T11:53:32","slug":"como-calcular-vive-em-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/pt\/como-calcular-vive-em-python\/","title":{"rendered":"Como calcular vif em python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/pt\/regressao-multicolinearidade\/\" target=\"_blank\" rel=\"noopener\">A multicolinearidade<\/a> na an\u00e1lise de regress\u00e3o ocorre quando duas ou mais vari\u00e1veis explicativas s\u00e3o altamente correlacionadas entre si, de modo que n\u00e3o fornecem informa\u00e7\u00f5es \u00fanicas ou independentes no modelo de regress\u00e3o.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Se o grau de correla\u00e7\u00e3o entre as vari\u00e1veis for alto o suficiente, isso pode causar problemas no ajuste e na interpreta\u00e7\u00e3o do modelo de regress\u00e3o.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Uma forma de detectar a multicolinearidade \u00e9 usar uma m\u00e9trica conhecida como <strong>fator de infla\u00e7\u00e3o de vari\u00e2ncia (VIF)<\/strong> , que mede a correla\u00e7\u00e3o e a for\u00e7a da correla\u00e7\u00e3o entre vari\u00e1veis explicativas em um <a href=\"https:\/\/statorials.org\/pt\/regressao-linear-python\/\" target=\"_blank\" rel=\"noopener\">modelo de regress\u00e3o<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Este tutorial explica como calcular VIF em Python.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Exemplo: Calcular VIF em Python<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Para este exemplo, usaremos um conjunto de dados que descreve os atributos de 10 jogadores de basquete:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n<span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create dataset<\/span>\ndf = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n<span style=\"color: #008080;\">#view dataset\n<\/span>df\n\n\trating points assists rebounds\n0 90 25 5 11\n1 85 20 7 8\n2 82 14 7 10\n3 88 16 8 6\n4 94 27 5 6\n5 90 20 7 9\n6 76 12 6 6\n7 75 15 9 10\n8 87 14 9 10\n9 86 19 5 7<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Suponha que queiramos ajustar um modelo de regress\u00e3o linear m\u00faltipla usando pontua\u00e7\u00e3o como vari\u00e1vel de resposta e pontos, assist\u00eancias e rebotes como vari\u00e1veis explicativas.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Para calcular o VIF para cada vari\u00e1vel explicativa do modelo, podemos usar a <a href=\"https:\/\/www.statsmodels.org\/stable\/generated\/statsmodels.stats.outliers_influence.variance_inflation_factor.html\" target=\"_blank\" rel=\"noopener\">fun\u00e7\u00e3o variance_inflation_factor()<\/a> da biblioteca statsmodels:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">from<\/span> patsy <span style=\"color: #107d3f;\">import<\/span> damatrices\n<span style=\"color: #107d3f;\">from<\/span> statsmodels.stats.outliers_influence <span style=\"color: #107d3f;\">import<\/span> variance_inflation_factor\n\n<span style=\"color: #008080;\">#find design matrix for linear regression model using 'rating' as response variable<\/span> \ny, X = dmatrices('rating ~ points+assists+rebounds', data=df, return_type='dataframe')\n\n<span style=\"color: #008080;\">#calculate VIF for each explanatory variable<\/span>\nvivid = pd.DataFrame()\nvive['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\nvivid['variable'] = X.columns\n\n<span style=\"color: #008080;\">#view VIF for each explanatory variable<\/span> \nlively\n\n\t       Variable VIF\n0 101.258171 Intercept\n1 1.763977 points\n2 1.959104 assists\n3 1.175030 rebounds<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Podemos observar os valores VIF para cada uma das vari\u00e1veis explicativas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>pontos:<\/strong> 1,76<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>assist\u00eancias:<\/strong> 1,96<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>rebotes:<\/strong> 1,18<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><em><strong>Nota:<\/strong> Ignore o VIF para \u201cIntercept\u201d no modelo, pois este valor n\u00e3o \u00e9 relevante.<\/em><\/span><\/p>\n<h2> <strong>Como interpretar valores VIF<\/strong><\/h2>\n<p> <span style=\"color: #000000;\">O valor VIF come\u00e7a em 1 e n\u00e3o tem limite superior. Uma regra geral para interpretar VIFs \u00e9:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Um valor 1 indica que n\u00e3o h\u00e1 correla\u00e7\u00e3o entre uma determinada vari\u00e1vel explicativa e qualquer outra vari\u00e1vel explicativa no modelo.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Um valor entre 1 e 5 indica uma correla\u00e7\u00e3o moderada entre uma determinada vari\u00e1vel explicativa e outras vari\u00e1veis explicativas no modelo, mas muitas vezes n\u00e3o \u00e9 suficientemente grave para exigir aten\u00e7\u00e3o especial.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Um valor superior a 5 indica uma correla\u00e7\u00e3o potencialmente grave entre uma determinada vari\u00e1vel explicativa e outras vari\u00e1veis explicativas do modelo. Nesse caso, as estimativas dos coeficientes e os valores p nos resultados da regress\u00e3o provavelmente n\u00e3o s\u00e3o confi\u00e1veis.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Como cada um dos valores VIF das vari\u00e1veis explicativas em nosso modelo de regress\u00e3o \u00e9 fechado em 1, a multicolinearidade n\u00e3o \u00e9 um problema em nosso exemplo.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A multicolinearidade na an\u00e1lise de regress\u00e3o ocorre quando duas ou mais vari\u00e1veis explicativas s\u00e3o altamente correlacionadas entre si, de modo que n\u00e3o fornecem informa\u00e7\u00f5es \u00fanicas ou independentes no modelo de regress\u00e3o. Se o grau de correla\u00e7\u00e3o entre as vari\u00e1veis for alto o suficiente, isso pode causar problemas no ajuste e na interpreta\u00e7\u00e3o do modelo de [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-868","post","type-post","status-publish","format-standard","hentry","category-guia"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Como calcular VIF em Python \u2013 Estatologia<\/title>\n<meta name=\"description\" content=\"Uma explica\u00e7\u00e3o simples sobre como calcular VIF (Variance Inflation Factor) em Python.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/pt\/como-calcular-vive-em-python\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Como calcular VIF em Python \u2013 Estatologia\" \/>\n<meta property=\"og:description\" content=\"Uma explica\u00e7\u00e3o simples sobre como calcular VIF (Variance Inflation Factor) em Python.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pt\/como-calcular-vive-em-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-28T11:53:32+00:00\" \/>\n<meta name=\"author\" content=\"Dr. benjamim anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"Dr. benjamim anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tempo estimado de leitura\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pt\/como-calcular-vive-em-python\/\",\"url\":\"https:\/\/statorials.org\/pt\/como-calcular-vive-em-python\/\",\"name\":\"Como calcular VIF em Python \u2013 Estatologia\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pt\/#website\"},\"datePublished\":\"2023-07-28T11:53:32+00:00\",\"dateModified\":\"2023-07-28T11:53:32+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666\"},\"description\":\"Uma explica\u00e7\u00e3o simples sobre como calcular VIF (Variance Inflation Factor) em Python.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pt\/como-calcular-vive-em-python\/#breadcrumb\"},\"inLanguage\":\"pt-PT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pt\/como-calcular-vive-em-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pt\/como-calcular-vive-em-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Lar\",\"item\":\"https:\/\/statorials.org\/pt\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Como calcular vif em python\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/pt\/#website\",\"url\":\"https:\/\/statorials.org\/pt\/\",\"name\":\"Statorials\",\"description\":\"O seu guia para a literacia estat\u00edstica!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/pt\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"pt-PT\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666\",\"name\":\"Dr. benjamim anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-PT\",\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/pt\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/pt\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Dr. benjamim anderson\"},\"description\":\"Ol\u00e1, sou Benjamin, um professor aposentado de estat\u00edstica que se tornou professor dedicado na Statorials. 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