{"id":1012,"date":"2023-07-28T00:10:59","date_gmt":"2023-07-28T00:10:59","guid":{"rendered":"https:\/\/statorials.org\/tr\/tukey-testi-r\/"},"modified":"2023-07-28T00:10:59","modified_gmt":"2023-07-28T00:10:59","slug":"tukey-testi-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/tukey-testi-r\/","title":{"rendered":"R'de tukey testi nas\u0131l yap\u0131l\u0131r?"},"content":{"rendered":"

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\u00dc\u00e7 veya daha fazla ba\u011f\u0131ms\u0131z grubun ortalamalar\u0131 aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in tek y\u00f6nl\u00fc ANOVA<\/a> kullan\u0131l\u0131r.<\/span><\/p>\n

ANOVA tablosunun genel p de\u011feri belirli bir anlaml\u0131l\u0131k d\u00fczeyinin alt\u0131ndaysa, grup ortalamalar\u0131ndan en az birinin di\u011ferlerinden farkl\u0131 oldu\u011funu s\u00f6ylemek i\u00e7in yeterli kan\u0131t\u0131m\u0131z var demektir.<\/span><\/p>\n

Ancak bu bize hangi<\/em> gruplar\u0131n birbirinden farkl\u0131 oldu\u011funu s\u00f6ylemez. Bu bize basit\u00e7e t\u00fcm grup ortalamalar\u0131n\u0131n e\u015fit olmad\u0131\u011f\u0131n\u0131 s\u00f6yler. Hangi gruplar\u0131n birbirinden farkl\u0131 oldu\u011funu tam olarak bilmek i\u00e7in post hoc test<\/a> yapmam\u0131z gerekir.<\/span><\/p>\n

En s\u0131k kullan\u0131lan post hoc testlerden biri, aile baz\u0131nda hata oran\u0131n\u0131<\/a> kontrol ederken her grubun ortalamalar\u0131 aras\u0131nda ikili kar\u015f\u0131la\u015ft\u0131rmalar yapmam\u0131za olanak tan\u0131yan Tukey testidir<\/strong> .<\/span><\/p>\n

Bu e\u011fitimde R’de Tukey testinin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n

Not:<\/strong> \u00c7al\u0131\u015fman\u0131zdaki gruplardan herhangi biri kontrol grubu olarak kabul ediliyorsa bunun yerine post-hoc test olarak Dunnett testini<\/a> kullanmal\u0131s\u0131n\u0131z.<\/em><\/span><\/p>\n

\u00d6rnek: R’de Tukey testi<\/strong><\/span><\/h3>\n

Ad\u0131m 1: ANOVA modelini yerle\u015ftirin.<\/strong><\/span><\/p>\n

A\u015fa\u011f\u0131daki kod, \u00fc\u00e7 grupla (A, B ve C) sahte bir veri k\u00fcmesinin nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 ve her grubun ortalama de\u011ferlerinin e\u015fit olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in verilere tek y\u00f6nl\u00fc bir ANOVA modelinin nas\u0131l yerle\u015ftirilece\u011fini g\u00f6sterir:<\/span><\/p>\n

 #make this example reproducible<\/span>\nset.seed(0)\n\n#create data<\/span>\ndata <- data.frame(group = rep<\/span> (c(\"A\", \"B\", \"C\"), each<\/span> = 30),\n                   values = c(runif(30, 0, 3),\n                                   runif(30, 0, 5),\n                                   runif(30, 1, 7)))\n\n#view first six rows of data<\/span>\nhead(data)\n\n  group values\n1 A 2.6900916\n2 A 0.7965260\n3 A 1.1163717\n4 A 1.7185601\n5 A 2.7246234\n6 A 0.6050458\n\n#fit one-way ANOVA model<\/span>\nmodel <- aov<\/span> (values~group, data=data)\n\n#view the model output\n<\/span>summary(model)\n\n            Df Sum Sq Mean Sq F value Pr(>F)    \ngroup 2 98.93 49.46 30.83 7.55e-11 ***\nResiduals 87 139.57 1.60                     \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n
 ANOVA tablosundan genel p de\u011ferinin 7,55e-11<\/strong> oldu\u011funu g\u00f6rebiliriz. Bu say\u0131 0,05’ten k\u00fc\u00e7\u00fck oldu\u011fundan her gruptaki ortalama de\u011ferlerin e\u015fit olmad\u0131\u011f\u0131n\u0131 s\u00f6yleyebilecek yeterli kan\u0131t\u0131m\u0131z var. Yani tam olarak hangi grup ortalamalar\u0131n\u0131n farkl\u0131 oldu\u011funu belirlemek i\u00e7in Tukey testini yapabiliriz.<\/span><\/p>\n
 Ad\u0131m 2: Tukey testini ger\u00e7ekle\u015ftirin.<\/strong><\/span><\/p>\n
 A\u015fa\u011f\u0131daki kod, Tukey testini ger\u00e7ekle\u015ftirmek i\u00e7in TukeyHSD()<\/strong> i\u015flevinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n
 #perform Tukey's Test\n<\/span>TukeyHSD(model, conf.level= .95<\/span> ) \n\n  Tukey multiple comparisons of means\n    95% family-wise confidence level\n\nFit: aov(formula = values ~ group, data = data)\n\n$group\n         diff lwr upr p adj\nBA 0.9777414 0.1979466 1.757536 0.0100545\nCA 2.5454024 1.7656076 3.325197 0.0000000\nCB 1.5676610 0.7878662 2.347456 0.0000199\n<\/strong><\/pre>\n
 P de\u011feri, her program aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark olup olmad\u0131\u011f\u0131n\u0131 g\u00f6sterir. Sonu\u00e7lar, her program\u0131n ortalama kilo kayb\u0131 aras\u0131nda 0,05 anlaml\u0131l\u0131k d\u00fczeyinde istatistiksel olarak anlaml\u0131 bir fark oldu\u011funu g\u00f6stermektedir.<\/span><\/p>\n
 \u00d6zellikle:<\/span><\/p>\n