{"id":1257,"date":"2023-07-27T02:53:55","date_gmt":"2023-07-27T02:53:55","guid":{"rendered":"https:\/\/statorials.org\/tr\/rde-olabilirlik-orani-testi\/"},"modified":"2023-07-27T02:53:55","modified_gmt":"2023-07-27T02:53:55","slug":"rde-olabilirlik-orani-testi","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/rde-olabilirlik-orani-testi\/","title":{"rendered":"R&#39;de olabilirlik oran\u0131 testi nas\u0131l yap\u0131l\u0131r"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Olabilirlik oran\u0131 testi,<\/strong> iki i\u00e7 i\u00e7e regresyon modelinin uyum iyili\u011fini kar\u015f\u0131la\u015ft\u0131r\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/tr\/ic-ice-gecmis-model\/\" target=\"_blank\" rel=\"noopener\">Yuvalanm\u0131\u015f bir model,<\/a> genel regresyon modelinde \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkenlerin bir alt k\u00fcmesini i\u00e7eren bir modeldir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u00d6rne\u011fin, d\u00f6rt \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkene sahip a\u015fa\u011f\u0131daki regresyon modeline sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n<p> <span style=\"color: #000000;\">Y = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> x <sub>1<\/sub> + \u03b2 <sub>2<\/sub> x <sub>2<\/sub> + \u03b2 <sub>3<\/sub> x <sub>3<\/sub> + \u03b2 <sub>4<\/sub> x <sub>4<\/sub> + \u03b5<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0130\u00e7 i\u00e7e ge\u00e7mi\u015f bir modelin bir \u00f6rne\u011fi, orijinal tahmin de\u011fi\u015fkenlerinden yaln\u0131zca ikisini i\u00e7eren a\u015fa\u011f\u0131daki model olabilir:<\/span><\/p>\n<p> <span style=\"color: #000000;\">Y = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> x <sub>1<\/sub> + \u03b2 <sub>2<\/sub> x <sub>2<\/sub> + \u03b5<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu iki modelin \u00f6nemli \u00f6l\u00e7\u00fcde farkl\u0131 olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in a\u015fa\u011f\u0131daki bo\u015f ve alternatif hipotezleri kullanan bir olas\u0131l\u0131k oran\u0131 testi yapabiliriz:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>H <sub>0<\/sub> :<\/strong> Tam model ve i\u00e7 i\u00e7e ge\u00e7mi\u015f model verilere e\u015fit derecede uyum sa\u011flar. Bu nedenle <strong>i\u00e7 i\u00e7e modeli kullanmal\u0131s\u0131n\u0131z<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>H <sub>A<\/sub> :<\/strong> Tam model, verilere i\u00e7 i\u00e7e ge\u00e7mi\u015f modelden \u00f6nemli \u00f6l\u00e7\u00fcde daha iyi uyuyor. Bu nedenle <strong>\u015fablonun tamam\u0131n\u0131 kullanman\u0131z<\/strong> gerekir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Testin p de\u011feri belirli bir anlaml\u0131l\u0131k d\u00fczeyinin (\u00f6rne\u011fin 0,05) alt\u0131ndaysa, s\u0131f\u0131r hipotezini reddedebilir ve tam modelin \u00f6nemli \u00f6l\u00e7\u00fcde daha iyi bir uyum sa\u011flad\u0131\u011f\u0131 sonucuna varabiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki \u00f6rnek, R&#8217;de olabilirlik oran\u0131 testinin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini g\u00f6sterir.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u00d6rnek: R&#8217;de olas\u0131l\u0131k oran\u0131 testi<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki kod, yerle\u015fik <strong>mtcars<\/strong> veri k\u00fcmesindeki verileri kullanarak a\u015fa\u011f\u0131daki iki regresyon modelinin R&#8217;ye nas\u0131l s\u0131\u011fd\u0131r\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Tam model:<\/strong> mpg = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> mevcut + \u03b2 <sub>2<\/sub> karbonhidrat + \u03b2 <sub>3<\/sub> hp + \u03b2 <sub>4<\/sub> silindir<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Model:<\/strong> mpg = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> mevcut + \u03b2 <sub>2<\/sub> karbonhidrat<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu iki model \u00fczerinde olabilirlik oran\u0131 testi ger\u00e7ekle\u015ftirmek i\u00e7in <strong>lmtest<\/strong> paketinin <strong>lrtest()<\/strong> fonksiyonunu kullanaca\u011f\u0131z:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #993300;\">library<\/span> (lmtest)<\/span>\n\n#fit full model<\/span>\nmodel_full &lt;- lm(mpg ~ disp + carb + hp + cyl, data = mtcars)\n\n<span style=\"color: #008080;\">#fit reduced model\n<\/span>model_reduced &lt;- lm(mpg ~ disp + carb, data = mtcars)\n\n<span style=\"color: #008080;\">#perform likelihood ratio test for differences in models\n<\/span>lrtest(model_full, model_reduced)\n\nLikelihood ratio test\n\nModel 1: mpg ~ disp + carb + hp + cyl\nModel 2: mpg ~ available + carb\n  #Df LogLik Df Chisq Pr(&gt;Chisq)\n1 6 -77.558                     \n2 4 -78.603 -2 2.0902 0.3517<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Sonu\u00e7tan ki-kare test istatisti\u011finin <strong>2,0902<\/strong> ve buna kar\u015f\u0131l\u0131k gelen p de\u011ferinin <strong>0,3517<\/strong> oldu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu p de\u011feri 0,05&#8217;ten k\u00fc\u00e7\u00fck olmad\u0131\u011f\u0131ndan s\u0131f\u0131r hipotezini reddetmede ba\u015far\u0131s\u0131z olaca\u011f\u0131z.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu, tam modelin ve i\u00e7 i\u00e7e ge\u00e7mi\u015f modelin verilere e\u015fit derecede iyi uydu\u011fu anlam\u0131na gelir. Bu nedenle i\u00e7 i\u00e7e modeli kullanmal\u0131y\u0131z \u00e7\u00fcnk\u00fc tam modeldeki ek yorday\u0131c\u0131 de\u011fi\u015fkenler uyumda \u00f6nemli bir iyile\u015fme sa\u011flamamaktad\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Daha sonra, tek bir yorday\u0131c\u0131 de\u011fi\u015fkene sahip bir modelin, her iki yorday\u0131c\u0131ya sahip bir modelden \u00f6nemli \u00f6l\u00e7\u00fcde farkl\u0131 olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in ba\u015fka bir olabilirlik oran\u0131 testi yapabiliriz:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #993300;\">library<\/span> (lmtest)<\/span>\n\n#fit full model<\/span>\nmodel_full &lt;- lm(mpg ~ disp + carb, data = mtcars)\n\n<span style=\"color: #008080;\">#fit reduced model\n<\/span>model_reduced &lt;- lm(mpg ~ disp, data = mtcars)\n\n<span style=\"color: #008080;\">#perform likelihood ratio test for differences in models\n<\/span>lrtest(model_full, model_reduced)\n\nLikelihood ratio test\n\nModel 1: mpg ~ available + carb\nModel 2: mpg ~ available\n  #Df LogLik Df Chisq Pr(&gt;Chisq)   \n1 4 -78.603                        \n2 3 -82.105 -1 7.0034 0.008136 **\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Sonu\u00e7tan olabilirlik oran\u0131 testinin p de\u011ferinin <strong>0,008136<\/strong> oldu\u011funu g\u00f6rebiliriz. Bu say\u0131 0,05&#8217;ten k\u00fc\u00e7\u00fck oldu\u011fundan s\u0131f\u0131r hipotezini reddederiz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">B\u00f6ylece, iki yorday\u0131c\u0131 modelin, tek yorday\u0131c\u0131 modele g\u00f6re uyum a\u00e7\u0131s\u0131ndan \u00f6nemli bir iyile\u015fme sa\u011flad\u0131\u011f\u0131 sonucuna varabiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Yani son modelimiz \u015fu \u015fekilde olacakt\u0131r:<\/span><\/p>\n<p> <span style=\"color: #000000;\">mpg = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> mevcut + \u03b2 <sub>2<\/sub> karbonhidrat<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Ek kaynaklar<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/tr\/rde-basit-dogrusal-regresyon\/\" target=\"_blank\" rel=\"noopener noreferrer\">R&#8217;de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a><br \/> <a href=\"https:\/\/statorials.org\/tr\/coklu-dogrusal-regresyon-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">R&#8217;de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a><br \/> <a href=\"https:\/\/statorials.org\/tr\/rdeki-anlam-kodlari\/\" target=\"_blank\" rel=\"noopener noreferrer\">R&#8217;deki anlam kodlar\u0131 nas\u0131l yorumlan\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Olabilirlik oran\u0131 testi, iki i\u00e7 i\u00e7e regresyon modelinin uyum iyili\u011fini kar\u015f\u0131la\u015ft\u0131r\u0131r. Yuvalanm\u0131\u015f bir model, genel regresyon modelinde \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkenlerin bir alt k\u00fcmesini i\u00e7eren bir modeldir. \u00d6rne\u011fin, d\u00f6rt \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkene sahip a\u015fa\u011f\u0131daki regresyon modeline sahip oldu\u011fumuzu varsayal\u0131m: Y = \u03b2 0 + \u03b2 1 x 1 + \u03b2 2 x 2 + \u03b2 3 x 3 [&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-1257","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R - Statorials&#039;de olabilirlik oran\u0131 testi nas\u0131l yap\u0131l\u0131r<\/title>\n<meta name=\"description\" content=\"Bu e\u011fitimde R&#039;de olas\u0131l\u0131k oran\u0131 testinin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi bir \u00f6rnekle a\u00e7\u0131klanmaktad\u0131r.\" \/>\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\/tr\/rde-olabilirlik-orani-testi\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"R - 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