{"id":4440,"date":"2023-07-11T02:36:29","date_gmt":"2023-07-11T02:36:29","guid":{"rendered":"https:\/\/statorials.org\/pt\/r-previsao-de-regressao-logistica\/"},"modified":"2023-07-11T02:36:29","modified_gmt":"2023-07-11T02:36:29","slug":"r-previsao-de-regressao-logistica","status":"publish","type":"post","link":"https:\/\/statorials.org\/pt\/r-previsao-de-regressao-logistica\/","title":{"rendered":"Como usar predict() com modelo de regress\u00e3o log\u00edstica em r"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Depois de ajustarmos um <a href=\"https:\/\/statorials.org\/pt\/regressao-logistica-1\/\" target=\"_blank\" rel=\"noopener\">modelo de regress\u00e3o log\u00edstica<\/a> em R, podemos usar a fun\u00e7\u00e3o <strong>prever()<\/strong> para prever o valor da resposta de uma nova observa\u00e7\u00e3o que o modelo nunca viu antes.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Esta fun\u00e7\u00e3o usa a seguinte sintaxe:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>prever (objeto, novos dados, tipo = \u201cresposta\u201d)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Ouro:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>objeto:<\/strong> O nome do modelo de regress\u00e3o log\u00edstica<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>newdata:<\/strong> O nome do novo quadro de dados para fazer previs\u00f5es<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>type:<\/strong> O tipo de previs\u00e3o a ser feita<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">O exemplo a seguir mostra como usar esta fun\u00e7\u00e3o na pr\u00e1tica.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Exemplo: Usando Predict() com um modelo de regress\u00e3o log\u00edstica em R<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Para este exemplo, usaremos o conjunto de dados R integrado chamado <a href=\"https:\/\/statorials.org\/pt\/conjunto-de-dados-mtcars-r\/\" target=\"_blank\" rel=\"noopener\">mtcars<\/a> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view first six rows of <em>mtcars<\/em> dataset<\/span>\nhead(mtcars)\n\n                   mpg cyl disp hp drat wt qsec vs am gear carb\nMazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\nMazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\nDatsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\nHornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\nHornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\nValiant 18.1 6 225 105 2.76 3,460 20.22 1 0 3 1<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Ajustaremos o seguinte modelo de regress\u00e3o log\u00edstica no qual utilizamos as vari\u00e1veis <strong>disp<\/strong> e <strong>hp<\/strong> para prever a vari\u00e1vel resposta <strong>am<\/strong> (tipo de transmiss\u00e3o do carro: 0 = autom\u00e1tica, 1 = manual):<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit logistic regression model<\/span>\nmodel &lt;- glm(am ~ disp + hp, data=mtcars, family=binomial)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = am ~ disp + hp, family = binomial, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-1.9665 -0.3090 -0.0017 0.3934 1.3682  \n\nCoefficients:\n            Estimate Std. Error z value Pr(&gt;|z|)  \n(Intercept) 1.40342 1.36757 1.026 0.3048  \navailable -0.09518 0.04800 -1.983 0.0474 *\nhp 0.12170 0.06777 1.796 0.0725 .\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 43,230 on 31 degrees of freedom\nResidual deviance: 16,713 on 29 degrees of freedom\nAIC: 22,713\n\nNumber of Fisher Scoring iterations: 8\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Podemos ent\u00e3o criar um novo quadro de dados contendo informa\u00e7\u00f5es sobre oito carros que o modelo nunca viu antes e usar a fun\u00e7\u00e3o <strong>prever()<\/strong> para prever a probabilidade de um carro novo ter uma transmiss\u00e3o autom\u00e1tica (am=0) ou uma transmiss\u00e3o manual ( sou =1):<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new data frame\n<span style=\"color: #000000;\">newdata = data. <span style=\"color: #3366ff;\">frame<\/span> (disp=c(200, 180, 160, 140, 120, 120, 100, 160),\n                     hp=c(100, 90, 108, 90, 80, 90, 80, 90),\n                     am=c(0, 0, 0, 1, 0, 1, 1, 1))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>newdata\n\n<span style=\"color: #008080;\">#use model to predict value of am for all new cars\n<\/span>newdata$am_prob &lt;- predict(model, newdata, type=\" <span style=\"color: #ff0000;\">response<\/span> \")\n\n<span style=\"color: #008080;\">#view updated data frame\n<\/span>newdata\n\n  disp hp am am_prob\n1 200 100 0 0.004225640\n2 180 90 0 0.008361069\n3 160 108 0 0.335916069\n4 140 90 1 0.275162866\n5 120 80 0 0.429961894\n6 120 90 1 0.718090728\n7 100 80 1 0.835013994\n8 160 90 1 0.053546152<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Veja como interpretar o resultado:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">A probabilidade de o carro 1 ter transmiss\u00e3o manual \u00e9 <strong>0,004<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">A probabilidade de o carro 2 ter transmiss\u00e3o manual \u00e9 <strong>0,008<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">A probabilidade de o carro 3 ter transmiss\u00e3o manual \u00e9 de <strong>0,336<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">E assim por diante.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tamb\u00e9m podemos usar a fun\u00e7\u00e3o <strong>table()<\/strong> para criar uma matriz de confus\u00e3o que exibe os valores am reais versus os valores previstos pelo modelo:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create vector that contains 0 or 1 depending on predicted value of am\n<span style=\"color: #000000;\">am_pred = rep(0, dim(newdata)[1])\nam_pred[newdata$am_prob &gt; .5] = 1\n\n<span style=\"color: #008080;\">#create confusion matrix\n<\/span>table(am_pred, newdata$am)\n\nam_pred 0 1\n      0 4 2\n      1 0 2\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Finalmente, podemos usar a fun\u00e7\u00e3o <strong>Mean()<\/strong> para calcular a porcentagem de observa\u00e7\u00f5es no novo banco de dados para as quais o modelo previu corretamente o valor de <strong>am<\/strong> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate percentage of observations the model correctly predicted response value for\n<span style=\"color: #000000;\">mean(am_pred == newdata$am)\n\n[1] 0.75\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Podemos ver que o modelo previu corretamente o valor <strong>am<\/strong> para <strong>75%<\/strong> dos carros no novo banco de dados.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Recursos adicionais<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Os tutoriais a seguir explicam como realizar outras tarefas comuns em R:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/pt\/regressao-linear-simples-em-r\/\" target=\"_blank\" rel=\"noopener\">Como realizar regress\u00e3o linear simples em R<\/a><br \/> <a href=\"https:\/\/statorials.org\/pt\/regressao-linear-multipla-r\/\" target=\"_blank\" rel=\"noopener\">Como realizar regress\u00e3o linear m\u00faltipla em R<\/a><br \/> <a href=\"https:\/\/statorials.org\/pt\/regressao-polinomial-r\/\" target=\"_blank\" rel=\"noopener\">Como realizar regress\u00e3o polinomial em R<\/a><br \/> <a href=\"https:\/\/statorials.org\/pt\/intervalo-de-previsao-r\/\" target=\"_blank\" rel=\"noopener\">Como criar um intervalo de previs\u00e3o em R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Depois de ajustarmos um modelo de regress\u00e3o log\u00edstica em R, podemos usar a fun\u00e7\u00e3o prever() para prever o valor da resposta de uma nova observa\u00e7\u00e3o que o modelo nunca viu antes. Esta fun\u00e7\u00e3o usa a seguinte sintaxe: prever (objeto, novos dados, tipo = \u201cresposta\u201d) Ouro: objeto: O nome do modelo de regress\u00e3o log\u00edstica newdata: O [&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-4440","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 usar predizer() com o modelo de regress\u00e3o log\u00edstica em R - Estatologia<\/title>\n<meta name=\"description\" content=\"Este tutorial explica como fazer previs\u00f5es sobre novos dados usando um modelo de regress\u00e3o log\u00edstica em R, com um exemplo.\" \/>\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\/r-previsao-de-regressao-logistica\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Como usar predizer() com o modelo de regress\u00e3o log\u00edstica em R - Estatologia\" \/>\n<meta property=\"og:description\" content=\"Este tutorial explica como fazer previs\u00f5es sobre novos dados usando um modelo de regress\u00e3o log\u00edstica em R, com um exemplo.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pt\/r-previsao-de-regressao-logistica\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-11T02:36:29+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=\"3 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pt\/r-previsao-de-regressao-logistica\/\",\"url\":\"https:\/\/statorials.org\/pt\/r-previsao-de-regressao-logistica\/\",\"name\":\"Como usar predizer() com o modelo de regress\u00e3o log\u00edstica em R - Estatologia\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pt\/#website\"},\"datePublished\":\"2023-07-11T02:36:29+00:00\",\"dateModified\":\"2023-07-11T02:36:29+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666\"},\"description\":\"Este tutorial explica como fazer previs\u00f5es sobre novos dados usando um modelo de regress\u00e3o log\u00edstica em R, com um exemplo.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pt\/r-previsao-de-regressao-logistica\/#breadcrumb\"},\"inLanguage\":\"pt-PT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pt\/r-previsao-de-regressao-logistica\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pt\/r-previsao-de-regressao-logistica\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Lar\",\"item\":\"https:\/\/statorials.org\/pt\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Como usar predict() com modelo de regress\u00e3o log\u00edstica em r\"}]},{\"@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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