{"id":1976,"date":"2023-07-24T05:44:05","date_gmt":"2023-07-24T05:44:05","guid":{"rendered":"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/"},"modified":"2023-07-24T05:44:05","modified_gmt":"2023-07-24T05:44:05","slug":"funcao-lm-em-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/","title":{"rendered":"Como usar a fun\u00e7\u00e3o lm() em r para ajustar modelos lineares"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">A fun\u00e7\u00e3o <strong>lm()<\/strong> em R \u00e9 usada para ajustar modelos de regress\u00e3o linear.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Esta fun\u00e7\u00e3o usa a seguinte sintaxe b\u00e1sica:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>lm(f\u00f3rmula, dados,\u2026)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Ouro:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>f\u00f3rmula:<\/strong> A f\u00f3rmula do modelo linear (por exemplo, y ~ x1 + x2)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>dados:<\/strong> o nome do bloco de dados que cont\u00e9m os dados<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">O exemplo a seguir mostra como usar esta fun\u00e7\u00e3o em R para fazer o seguinte:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Ajustar um modelo de regress\u00e3o<\/span><\/li>\n<li> <span style=\"color: #000000;\">Ver resumo de ajuste do modelo de regress\u00e3o<\/span><\/li>\n<li> <span style=\"color: #000000;\">Visualizar gr\u00e1ficos de diagn\u00f3stico de modelo<\/span><\/li>\n<li> <span style=\"color: #000000;\">Trace o modelo de regress\u00e3o ajustado<\/span><\/li>\n<li> <span style=\"color: #000000;\">Fa\u00e7a previs\u00f5es usando o modelo de regress\u00e3o<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>Ajuste o modelo de regress\u00e3o<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">O c\u00f3digo a seguir mostra como usar a fun\u00e7\u00e3o <strong>lm()<\/strong> para ajustar um modelo de regress\u00e3o linear em R:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define data<\/span>\ndf = data. <span style=\"color: #3366ff;\">frame<\/span> (x=c(1, 3, 3, 4, 5, 5, 6, 8, 9, 12),\n                y=c(12, 14, 14, 13, 17, 19, 22, 26, 24, 22))\n\n<span style=\"color: #008080;\">#fit linear regression model using 'x' as predictor and 'y' as response variable<\/span>\nmodel &lt;- lm(y ~ x, data=df)\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Mostrar resumo do modelo de regress\u00e3o<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Podemos ent\u00e3o usar a fun\u00e7\u00e3o <strong>summary()<\/strong> para exibir o resumo do ajuste do modelo de regress\u00e3o:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view summary of regression model<\/span>\nsummary(model)\n\nCall:\nlm(formula = y ~ x, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-4.4793 -0.9772 -0.4772 1.4388 4.6328 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 11.1432 1.9104 5.833 0.00039 ***\nx 1.2780 0.2984 4.284 0.00267 ** \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.929 on 8 degrees of freedom\nMultiple R-squared: 0.6964, Adjusted R-squared: 0.6584 \nF-statistic: 18.35 on 1 and 8 DF, p-value: 0.002675\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Veja como interpretar os valores mais importantes do modelo:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>Estat\u00edstica F<\/strong> = 18,35, <strong>valor p<\/strong> correspondente = 0,002675. Como esse valor de p \u00e9 inferior a 0,05, o modelo como um todo \u00e9 estatisticamente significativo.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>M\u00faltiplo R ao quadrado<\/strong> = 0,6964. Isso nos diz que 69,64% da varia\u00e7\u00e3o na vari\u00e1vel resposta, y, pode ser explicada pela vari\u00e1vel preditora, x.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Coeficiente estimado de x<\/strong> : 1,2780. Isso nos diz que cada aumento unit\u00e1rio adicional em x est\u00e1 associado a um aumento m\u00e9dio de 1,2780 em y.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Podemos ent\u00e3o usar as estimativas dos coeficientes da sa\u00edda para escrever a equa\u00e7\u00e3o de regress\u00e3o estimada:<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = 11,1432 + 1,2780*(x)<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>B\u00f4nus<\/strong> : voc\u00ea pode encontrar um guia completo para interpretar cada valor da sa\u00edda da regress\u00e3o em R <a href=\"https:\/\/statorials.org\/pt\/interpretar-a-saida-da-regressao-em-r\/\" target=\"_blank\" rel=\"noopener\">aqui<\/a> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Visualizar gr\u00e1ficos de diagn\u00f3stico de modelo<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Podemos ent\u00e3o usar a fun\u00e7\u00e3o <strong>plot()<\/strong> para tra\u00e7ar os gr\u00e1ficos de diagn\u00f3stico do modelo de regress\u00e3o:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create diagnostic plots\n<span style=\"color: #000000;\">plot(model)<\/span><\/span><\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-18677 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lm2.png\" alt=\"\" width=\"653\" height=\"649\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">Esses gr\u00e1ficos nos permitem analisar os <a href=\"https:\/\/statorials.org\/pt\/residuo\/\" target=\"_blank\" rel=\"noopener\">res\u00edduos<\/a> do modelo de regress\u00e3o para determinar se o modelo \u00e9 apropriado para uso nos dados.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Consulte <a href=\"https:\/\/statorials.org\/pt\/diagramas-de-diagnostico-em-r\/\" target=\"_blank\" rel=\"noopener\">este tutorial<\/a> para obter uma explica\u00e7\u00e3o completa de como interpretar os gr\u00e1ficos de diagn\u00f3stico de um modelo em R.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Trace o modelo de regress\u00e3o ajustado<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Podemos usar a fun\u00e7\u00e3o <strong>abline()<\/strong> para tra\u00e7ar o modelo de regress\u00e3o ajustado:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create scatterplot of raw data<\/span>\nplot(df$x, df$y, col=' <span style=\"color: #ff0000;\">red<\/span> ', main=' <span style=\"color: #ff0000;\">Summary of Regression Model<\/span> ', xlab=' <span style=\"color: #ff0000;\">x<\/span> ', ylab=' <span style=\"color: #ff0000;\">y<\/span> ')\n\n<span style=\"color: #008080;\">#add fitted regression line\n<span style=\"color: #000000;\">abline(model)\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-18678\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lm3.png\" alt=\"plotar lm() em R\" width=\"448\" height=\"442\" srcset=\"\" sizes=\"auto, \"><\/p>\n<h3> <strong>Use o modelo de regress\u00e3o para fazer previs\u00f5es<\/strong><\/h3>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Podemos usar a fun\u00e7\u00e3o <strong>predizer()<\/strong> para prever o valor da resposta para uma nova observa\u00e7\u00e3o:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#define new observation\n<\/span>new &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (x=c(5))\n\n<span style=\"color: #008080;\">#use the fitted model to predict the value for the new observation\n<\/span>predict(model, newdata = new)\n\n      1 \n17.5332<\/span>\n<\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">O modelo prev\u00ea que esta nova observa\u00e7\u00e3o ter\u00e1 um valor de resposta de <strong>17,5332<\/strong> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Recursos adicionais<\/strong><\/span><\/h3>\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-passo-a-passo-r\/\" target=\"_blank\" rel=\"noopener\">Como realizar a regress\u00e3o stepwise em R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A fun\u00e7\u00e3o lm() em R \u00e9 usada para ajustar modelos de regress\u00e3o linear. Esta fun\u00e7\u00e3o usa a seguinte sintaxe b\u00e1sica: lm(f\u00f3rmula, dados,\u2026) Ouro: f\u00f3rmula: A f\u00f3rmula do modelo linear (por exemplo, y ~ x1 + x2) dados: o nome do bloco de dados que cont\u00e9m os dados O exemplo a seguir mostra como usar esta [&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-1976","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 a fun\u00e7\u00e3o lm() em R para ajustar modelos lineares - Statorials<\/title>\n<meta name=\"description\" content=\"Este tutorial explica como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos de regress\u00e3o linear, com v\u00e1rios exemplos.\" \/>\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\/funcao-lm-em-r\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos lineares - Statorials\" \/>\n<meta property=\"og:description\" content=\"Este tutorial explica como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos de regress\u00e3o linear, com v\u00e1rios exemplos.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-24T05:44:05+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lm2.png\" \/>\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\/funcao-lm-em-r\/\",\"url\":\"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/\",\"name\":\"Como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos lineares - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pt\/#website\"},\"datePublished\":\"2023-07-24T05:44:05+00:00\",\"dateModified\":\"2023-07-24T05:44:05+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666\"},\"description\":\"Este tutorial explica como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos de regress\u00e3o linear, com v\u00e1rios exemplos.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/#breadcrumb\"},\"inLanguage\":\"pt-PT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Lar\",\"item\":\"https:\/\/statorials.org\/pt\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Como usar a fun\u00e7\u00e3o lm() em r para ajustar modelos lineares\"}]},{\"@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. Com vasta experi\u00eancia e conhecimento na \u00e1rea de estat\u00edstica, estou empenhado em compartilhar meu conhecimento para capacitar os alunos por meio de Statorials. Saber mais\",\"sameAs\":[\"https:\/\/statorials.org\/pt\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos lineares - Statorials","description":"Este tutorial explica como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos de regress\u00e3o linear, com v\u00e1rios exemplos.","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:\/\/statorials.org\/pt\/funcao-lm-em-r\/","og_locale":"pt_PT","og_type":"article","og_title":"Como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos lineares - Statorials","og_description":"Este tutorial explica como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos de regress\u00e3o linear, com v\u00e1rios exemplos.","og_url":"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/","og_site_name":"Statorials","article_published_time":"2023-07-24T05:44:05+00:00","og_image":[{"url":"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lm2.png"}],"author":"Dr. benjamim anderson","twitter_card":"summary_large_image","twitter_misc":{"Escrito por":"Dr. benjamim anderson","Tempo estimado de leitura":"3 minutos"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/","url":"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/","name":"Como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos lineares - Statorials","isPartOf":{"@id":"https:\/\/statorials.org\/pt\/#website"},"datePublished":"2023-07-24T05:44:05+00:00","dateModified":"2023-07-24T05:44:05+00:00","author":{"@id":"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666"},"description":"Este tutorial explica como usar a fun\u00e7\u00e3o lm() em R para ajustar modelos de regress\u00e3o linear, com v\u00e1rios exemplos.","breadcrumb":{"@id":"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/#breadcrumb"},"inLanguage":"pt-PT","potentialAction":[{"@type":"ReadAction","target":["https:\/\/statorials.org\/pt\/funcao-lm-em-r\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/statorials.org\/pt\/funcao-lm-em-r\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Lar","item":"https:\/\/statorials.org\/pt\/"},{"@type":"ListItem","position":2,"name":"Como usar a fun\u00e7\u00e3o lm() em r para ajustar modelos lineares"}]},{"@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. Com vasta experi\u00eancia e conhecimento na \u00e1rea de estat\u00edstica, estou empenhado em compartilhar meu conhecimento para capacitar os alunos por meio de Statorials. Saber mais","sameAs":["https:\/\/statorials.org\/pt"]}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"_links":{"self":[{"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/posts\/1976","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/comments?post=1976"}],"version-history":[{"count":0,"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/posts\/1976\/revisions"}],"wp:attachment":[{"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/media?parent=1976"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/categories?post=1976"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/tags?post=1976"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}