{"id":1178,"date":"2023-07-27T09:26:41","date_gmt":"2023-07-27T09:26:41","guid":{"rendered":"https:\/\/statorials.org\/pl\/bootstrap-w-r\/"},"modified":"2023-07-27T09:26:41","modified_gmt":"2023-07-27T09:26:41","slug":"bootstrap-w-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/pl\/bootstrap-w-r\/","title":{"rendered":"Jak uruchomi\u0107 bootstrap w r (z przyk\u0142adami)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Metoda \u0142adowania pocz\u0105tkowego<\/strong> to metoda, kt\u00f3rej mo\u017cna u\u017cy\u0107 do oszacowania b\u0142\u0119du standardowego dowolnej <a href=\"https:\/\/statorials.org\/pl\/statystyki-vs-parametry\/\" target=\"_blank\" rel=\"noopener noreferrer\">statystyki<\/a> i ustalenia <a href=\"https:\/\/statorials.org\/pl\/przedzia\u0142y-ufnosci\/\" target=\"_blank\" rel=\"noopener noreferrer\">przedzia\u0142u ufno\u015bci<\/a> dla tej statystyki.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Podstawowy proces \u0142adowania pocz\u0105tkowego jest nast\u0119puj\u0105cy:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Pobierz <em>k<\/em> powt\u00f3rzonych pr\u00f3bek z zamian\u0105 z danego zbioru danych.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Dla ka\u017cdej pr\u00f3bki oblicz interesuj\u0105c\u0105 statystyk\u0119.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Daje to <em>k<\/em> r\u00f3\u017cnych szacunk\u00f3w dla danej statystyki, kt\u00f3re mo\u017cna nast\u0119pnie wykorzysta\u0107 do obliczenia b\u0142\u0119du standardowego statystyki i utworzenia przedzia\u0142u ufno\u015bci dla tej statystyki.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Mo\u017cemy wykona\u0107 bootstrap w R, korzystaj\u0105c z nast\u0119puj\u0105cych funkcji z <a href=\"https:\/\/cran.r-project.org\/web\/packages\/boot\/boot.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">biblioteki bootstrap<\/a> :<\/span><\/p>\n<p> <span style=\"color: #000000;\">1. Wygeneruj pr\u00f3bki bootstrap.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>boot(dane, statystyki, R, \u2026)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Z\u0142oto:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>dane:<\/strong> wektor, macierz lub blok danych<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>statystyka:<\/strong> funkcja generuj\u0105ca statystyk\u0119, kt\u00f3ra ma zosta\u0107 zainicjowana<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Odp.:<\/strong> Liczba powt\u00f3rze\u0144 bootstrapu<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">2. Wygeneruj przedzia\u0142 ufno\u015bci \u0142adowania pocz\u0105tkowego.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>boot.ci (obiekt startowy, konf, typ)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Z\u0142oto:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>bootobject:<\/strong> Obiekt zwracany przez funkcj\u0119 boot().<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>conf:<\/strong> Przedzia\u0142 ufno\u015bci do obliczenia. Warto\u015b\u0107 domy\u015blna to 0,95<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>type:<\/strong> Typ przedzia\u0142u ufno\u015bci do obliczenia. Opcje obejmuj\u0105 \u201estandard\u201d, \u201epodstawowy\u201d, \u201estud\u201d, \u201eperc\u201d, \u201ebca\u201d i \u201eall\u201d &#8211; domy\u015blnie jest to \u201eall\u201d<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Poni\u017csze przyk\u0142ady pokazuj\u0105, jak wykorzysta\u0107 te funkcje w praktyce.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Przyk\u0142ad 1: bootstrap pojedynczej statystyki<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Poni\u017cszy kod pokazuje, jak obliczy\u0107 b\u0142\u0105d standardowy dla <a href=\"https:\/\/statorials.org\/pl\/dobra-wartosc-r-do-kwadratu\/\" target=\"_blank\" rel=\"noopener noreferrer\">kwadratu R<\/a> prostego modelu regresji liniowej:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>set.seed(0)\n<span style=\"color: #993300;\">library<\/span> (boot)\n\n<span style=\"color: #008080;\">#define function to calculate R-squared\n<\/span>rsq_function &lt;- <span style=\"color: #008000;\">function<\/span> (formula, data, indices) {\n  d &lt;- data[indices,] <span style=\"color: #008080;\">#allows boot to select sample<\/span>\n  fit &lt;- lm(formula, data=d) <span style=\"color: #008080;\">#fit regression model<\/span>\n  <span style=\"color: #008000;\">return<\/span> (summary(fit)$r.square) <span style=\"color: #008080;\">#return R-squared of model<\/span>\n}\n<span style=\"color: #008080;\">#perform bootstrapping with 2000 replications\n<\/span>reps &lt;- boot(data=mtcars, statistic=rsq_function, R=2000, formula=mpg~disp)\n\n<span style=\"color: #008080;\">#view results of boostrapping\n<\/span>reps\n\nORDINARY NONPARAMETRIC BOOTSTRAP\n\n\nCall:\nboot(data = mtcars, statistic = rsq_function, R = 2000, formula = mpg ~ \n    available)\n\n\nBootstrap Statistics:\n     original bias std. error\nt1* 0.7183433 0.002164339 0.06513426<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Z wynik\u00f3w mo\u017cemy zobaczy\u0107:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Szacowany wsp\u00f3\u0142czynnik R-kwadrat dla tego modelu regresji wynosi <strong>0,7183433<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">B\u0142\u0105d standardowy tego oszacowania wynosi <strong>0,06513426<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Mo\u017cemy r\u00f3wnie\u017c szybko wizualizowa\u0107 rozk\u0142ad pr\u00f3bek \u0142adowanych metod\u0105 \u0142adowania pocz\u0105tkowego:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>plot(reps)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11750 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/demarrage1.png\" alt=\"Histogram pr\u00f3bek bootstrapowych w R\" width=\"456\" height=\"437\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">Mo\u017cemy r\u00f3wnie\u017c u\u017cy\u0107 poni\u017cszego kodu, aby obliczy\u0107 95% przedzia\u0142 ufno\u015bci dla szacowanego kwadratu R modelu:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate adjusted bootstrap percentile (BCa) interval\n<\/span>boot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \")\n\nCALL: \nboot.ci(boot.out = reps, type = \"bca\")\n\nIntervals: \nLevel BCa          \n95% (0.5350, 0.8188)  \nCalculations and Intervals on Original Scale<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Z wyniku widzimy, \u017ce bootstrapowy 95% przedzia\u0142 ufno\u015bci dla prawdziwych warto\u015bci R-kwadrat wynosi (0,5350, 0,8188).<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Przyk\u0142ad 2: \u0142adowanie wielu statystyk<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Poni\u017cszy kod pokazuje, jak obliczy\u0107 b\u0142\u0105d standardowy dla ka\u017cdego wsp\u00f3\u0142czynnika w modelu wielokrotnej regresji liniowej:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>set.seed(0)\n<span style=\"color: #993300;\">library<\/span> (boot)\n\n<span style=\"color: #008080;\">#define function to calculate fitted regression coefficients\n<\/span>coef_function &lt;- <span style=\"color: #008000;\">function<\/span> (formula, data, indices) {\n  d &lt;- data[indices,] <span style=\"color: #008080;\">#allows boot to select sample<\/span>\n  fit &lt;- lm(formula, data=d) <span style=\"color: #008080;\">#fit regression model<\/span>\n  <span style=\"color: #008000;\">return<\/span> (coef(fit)) <span style=\"color: #008080;\">#return coefficient estimates of model<\/span>\n}\n\n<span style=\"color: #008080;\">#perform bootstrapping with 2000 replications\n<\/span>reps &lt;- boot(data=mtcars, statistic=coef_function, R=2000, formula=mpg~disp)\n\n<span style=\"color: #008080;\">#view results of boostrapping\n<\/span>reps\n\nORDINARY NONPARAMETRIC BOOTSTRAP\n\n\nCall:\nboot(data = mtcars, statistic = coef_function, R = 2000, formula = mpg ~ \n    available)\n\n\nBootstrap Statistics:\n       original bias std. error\nt1* 29.59985476 -5.058601e-02 1.49354577\nt2* -0.04121512 6.549384e-05 0.00527082<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Z wynik\u00f3w mo\u017cemy zobaczy\u0107:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Oszacowany wsp\u00f3\u0142czynnik wyrazu wolnego modelu wynosi <strong>29,59985476<\/strong> , a b\u0142\u0105d standardowy tego oszacowania wynosi <strong>1,49354577<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Oszacowany wsp\u00f3\u0142czynnik dla zmiennej predykcyjnej <em>disp<\/em> w modelu wynosi <strong>-0,04121512<\/strong> , a b\u0142\u0105d standardowy tego oszacowania wynosi <strong>0,00527082<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Mo\u017cemy r\u00f3wnie\u017c szybko wizualizowa\u0107 rozk\u0142ad pr\u00f3bek \u0142adowanych metod\u0105 bootstrap:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>plot(reps, index=1) <span style=\"color: #008080;\">#intercept of model<\/span>\nplot(reps, index=2) <span style=\"color: #008080;\">#disp predictor variable\n<\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11752 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/demarrage2.png\" alt=\"Bootstrapowanie w R\" width=\"669\" height=\"325\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">Mo\u017cemy r\u00f3wnie\u017c u\u017cy\u0107 poni\u017cszego kodu do obliczenia 95% przedzia\u0142\u00f3w ufno\u015bci dla ka\u017cdego wsp\u00f3\u0142czynnika:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate adjusted bootstrap percentile (BCa) intervals\n<\/span>boot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \", index=1) <span style=\"color: #008080;\">#intercept of model<\/span>\nboot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \", index=2) <span style=\"color: #008080;\">#disp predictor variable\n<\/span>\nCALL: \nboot.ci(boot.out = reps, type = \"bca\", index = 1)\n\nIntervals: \nLevel BCa          \n95% (26.78, 32.66)  \nCalculations and Intervals on Original Scale\nBOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\nBased on 2000 bootstrap replicates\n\nCALL: \nboot.ci(boot.out = reps, type = \"bca\", index = 2)\n\nIntervals: \nLevel BCa          \n95% (-0.0520, -0.0312)  \nCalculations and Intervals on Original Scale<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Z wynik\u00f3w wida\u0107, \u017ce bootstrapowe 95% przedzia\u0142y ufno\u015bci dla wsp\u00f3\u0142czynnik\u00f3w modelu s\u0105 nast\u0119puj\u0105ce:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">IC dla przechwytywania: (26,78, 32,66)<\/span><\/li>\n<li> <span style=\"color: #000000;\">CI dla <em>disp<\/em> : (-.0520, -.0312)<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>Dodatkowe zasoby<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/pl\/prosta-regresja-liniowa-w-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">Jak wykona\u0107 prost\u0105 regresj\u0119 liniow\u0105 w R<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/wielokrotna-regresja-liniowa-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">Jak wykona\u0107 wielokrotn\u0105 regresj\u0119 liniow\u0105 w R<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/przedzia\u0142y-ufnosci\/\" target=\"_blank\" rel=\"noopener noreferrer\">Wprowadzenie do przedzia\u0142\u00f3w ufno\u015bci<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Metoda \u0142adowania pocz\u0105tkowego to metoda, kt\u00f3rej mo\u017cna u\u017cy\u0107 do oszacowania b\u0142\u0119du standardowego dowolnej statystyki i ustalenia przedzia\u0142u ufno\u015bci dla tej statystyki. Podstawowy proces \u0142adowania pocz\u0105tkowego jest nast\u0119puj\u0105cy: Pobierz k powt\u00f3rzonych pr\u00f3bek z zamian\u0105 z danego zbioru danych. Dla ka\u017cdej pr\u00f3bki oblicz interesuj\u0105c\u0105 statystyk\u0119. Daje to k r\u00f3\u017cnych szacunk\u00f3w dla danej statystyki, kt\u00f3re mo\u017cna nast\u0119pnie wykorzysta\u0107 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-1178","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Jak Bootstrapping w R (z przyk\u0142adami) - Statologia<\/title>\n<meta name=\"description\" content=\"W tym samouczku wyja\u015bniono, jak rozpocz\u0105\u0107 \u0142adowanie w j\u0119zyku R, podaj\u0105c kilka przyk\u0142ad\u00f3w.\" \/>\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\/pl\/bootstrap-w-r\/\" \/>\n<meta property=\"og:locale\" content=\"pl_PL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Jak Bootstrapping w R (z przyk\u0142adami) - Statologia\" \/>\n<meta property=\"og:description\" content=\"W tym samouczku wyja\u015bniono, jak rozpocz\u0105\u0107 \u0142adowanie w j\u0119zyku R, podaj\u0105c kilka przyk\u0142ad\u00f3w.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pl\/bootstrap-w-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T09:26:41+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/demarrage1.png\" \/>\n<meta name=\"author\" content=\"Benjamin Anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Napisane przez\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Szacowany czas czytania\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minuty\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pl\/bootstrap-w-r\/\",\"url\":\"https:\/\/statorials.org\/pl\/bootstrap-w-r\/\",\"name\":\"Jak Bootstrapping w R (z przyk\u0142adami) - Statologia\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pl\/#website\"},\"datePublished\":\"2023-07-27T09:26:41+00:00\",\"dateModified\":\"2023-07-27T09:26:41+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\"},\"description\":\"W tym samouczku wyja\u015bniono, jak rozpocz\u0105\u0107 \u0142adowanie w j\u0119zyku R, podaj\u0105c kilka przyk\u0142ad\u00f3w.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pl\/bootstrap-w-r\/#breadcrumb\"},\"inLanguage\":\"pl-PL\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pl\/bootstrap-w-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pl\/bootstrap-w-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Dom\",\"item\":\"https:\/\/statorials.org\/pl\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Jak uruchomi\u0107 bootstrap w r (z przyk\u0142adami)\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/pl\/#website\",\"url\":\"https:\/\/statorials.org\/pl\/\",\"name\":\"Statorials\",\"description\":\"Tw\u00f3j przewodnik po kompetencjach statystycznych!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/pl\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"pl-PL\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\",\"name\":\"Benjamin Anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pl-PL\",\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin Anderson\"},\"description\":\"Cze\u015b\u0107, jestem Benjamin i jestem emerytowanym profesorem statystyki, kt\u00f3ry zosta\u0142 oddanym nauczycielem Statorials. 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