<\/p>\n
R’de bir regresyon modelini s\u0131\u011fd\u0131rmak i\u00e7in lm()<\/a> i\u015flevini kullan\u0131rken, bir veri \u00e7er\u00e7evesinin yaln\u0131zca bir alt k\u00fcmesini kullanmak i\u00e7in alt k\u00fcme ba\u011f\u0131ms\u0131z<\/strong> de\u011fi\u015fkenini kullanabilirsiniz:<\/span><\/p>\n Bu \u00f6zel \u00f6rnek, yan\u0131t de\u011fi\u015fkeni olarak noktalar\u0131<\/strong> ve tahmin de\u011fi\u015fkenleri olarak kalabal\u0131klar\u0131<\/strong> ve dakikalar\u0131<\/strong> kullanan bir regresyon modeline kar\u015f\u0131l\u0131k gelir.<\/span><\/p>\n Alt k\u00fcme<\/strong> ba\u011f\u0131ms\u0131z de\u011fi\u015fkeni, regresyon modeli uyarlan\u0131rken yaln\u0131zca dakika<\/strong> de\u011fi\u015fkeninin 10’dan b\u00fcy\u00fck oldu\u011fu veri \u00e7er\u00e7evesindeki sat\u0131rlar\u0131n kullan\u0131lmas\u0131 gerekti\u011fini belirtir.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n R’de oynanan dakikalar, toplam fauller ve 10 basketbolcunun att\u0131\u011f\u0131 toplam puanlar hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesine sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00e7oklu do\u011frusal regresyon modeline uymak istedi\u011fimizi varsayal\u0131m:<\/span><\/p>\n puan = \u03b2 0<\/sub> + \u03b2 1<\/sub> (dakika) + \u03b2 2<\/sub> (faul)<\/strong><\/span><\/p>\n Ancak veri \u00e7er\u00e7evesinde yaln\u0131zca dakika<\/strong> de\u011fi\u015fkeninin 10’dan b\u00fcy\u00fck oldu\u011fu sat\u0131rlar\u0131 kullanmak istedi\u011fimizi varsayal\u0131m.<\/span><\/p>\n Bu regresyon modeline uymak i\u00e7in lm()<\/strong> i\u015flevini alt k\u00fcme<\/strong> arg\u00fcman\u0131yla birlikte kullanabiliriz:<\/span><\/p>\n Regresyon modeline uymak i\u00e7in veri \u00e7er\u00e7evesinden ka\u00e7 g\u00f6zlemin ger\u00e7ekten kullan\u0131ld\u0131\u011f\u0131n\u0131 g\u00f6rmek i\u00e7in nobs()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n Modele uyum sa\u011flamak i\u00e7in veri \u00e7er\u00e7evesinin 8<\/strong> sat\u0131r\u0131n\u0131n kullan\u0131ld\u0131\u011f\u0131n\u0131 g\u00f6rebiliriz.<\/span><\/p>\n Orijinal veri \u00e7er\u00e7evesine bakarsak, dakika<\/strong> de\u011fi\u015fkeni i\u00e7in tam olarak 8<\/strong> sat\u0131r\u0131n 10’dan b\u00fcy\u00fck bir de\u011fere sahip oldu\u011funu g\u00f6rebiliriz, bu da regresyon modeli uydurulurken yaln\u0131zca bu sat\u0131rlar\u0131n kullan\u0131ld\u0131\u011f\u0131 anlam\u0131na gelir.<\/span><\/p>\n Veri \u00e7er\u00e7evesini birden fazla ko\u015fula g\u00f6re alt k\u00fcmelemek i\u00e7in alt k\u00fcme ba\u011f\u0131ms\u0131z<\/strong> de\u011fi\u015fkeninde &<\/strong> operat\u00f6r\u00fcn\u00fc de kullanabiliriz.<\/span><\/p>\n \u00d6rne\u011fin, yaln\u0131zca veri \u00e7er\u00e7evesindeki dakikalar\u0131n<\/strong> 10’dan b\u00fcy\u00fck ve hatalar\u0131n<\/strong> 4’ten k\u00fc\u00e7\u00fck oldu\u011fu sat\u0131rlar\u0131 kullanarak bir regresyon modeline uyum sa\u011flamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Sonu\u00e7tan, bu \u00f6zel modele uyacak \u015fekilde veri \u00e7er\u00e7evesinin 7<\/strong> sat\u0131r\u0131n\u0131n kullan\u0131ld\u0131\u011f\u0131n\u0131 g\u00f6rebiliriz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde R’de di\u011fer ortak g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n R’de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> R’de bir regresyon modelini s\u0131\u011fd\u0131rmak i\u00e7in lm() i\u015flevini kullan\u0131rken, bir veri \u00e7er\u00e7evesinin yaln\u0131zca bir alt k\u00fcmesini kullanmak i\u00e7in alt k\u00fcme ba\u011f\u0131ms\u0131z de\u011fi\u015fkenini kullanabilirsiniz: fit <- lm(points ~ fouls + minutes, data=df, subset=(minutes> 10 )) Bu \u00f6zel \u00f6rnek, yan\u0131t de\u011fi\u015fkeni olarak noktalar\u0131 ve tahmin de\u011fi\u015fkenleri olarak kalabal\u0131klar\u0131 ve dakikalar\u0131 kullanan bir regresyon modeline kar\u015f\u0131l\u0131k gelir. Alt […]<\/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-4268","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n fit <- lm(points ~ fouls + minutes, data=df, subset=(minutes><\/span> 10<\/span> ))<\/span><\/strong><\/pre>\n \u00d6rnek: R’de lm() ile bir veri \u00e7er\u00e7evesi alt k\u00fcmesi nas\u0131l kullan\u0131l\u0131r?<\/strong><\/span><\/h2>\n
#create data frame\n<\/span>df <- data. frame<\/span> (minutes=c(5, 10, 13, 14, 20, 22, 26, 34, 38, 40),\n fouls=c(5, 5, 3, 4, 2, 1, 3, 2, 1, 1),\n points=c(6, 8, 8, 7, 14, 10, 22, 24, 28, 30))\n\n#view data frame\n<\/span>df\n\n minutes fouls points\n1 5 5 6\n2 10 5 8\n3 13 3 8\n4 14 4 7\n5 20 2 14\n6 22 1 10\n7 26 3 22\n8 34 2 24\n9 38 1 28\n10 40 1 30<\/strong><\/pre>\n #fit multiple linear regression model (only for rows where minutes>10)\n<\/span><\/strong>fit <- lm(points ~ fouls + minutes, data=df, subset=(minutes><\/span> 10<\/span> ))\n\n#view model summary<\/span>\nsummary(fit)\n\nCall:\nlm(formula = points ~ fouls + minutes, data = df, subset = (minutes > \n 10))\n\nResiduals:\n 3 4 5 6 7 8 9 10 \n 1.2824 -2.5882 2.2000 -1.9118 2.3588 -1.7176 0.1824 0.1941 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) -11.8353 4.9696 -2.382 0.063046 . \nfouls 1.8765 1.0791 1.739 0.142536 \nminutes 0.9941 0.1159 8.575 0.000356 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.255 on 5 degrees of freedom\nMultiple R-squared: 0.9574, Adjusted R-squared: 0.9404 \nF-statistic: 56.19 on 2 and 5 DF, p-value: 0.0003744\n<\/span><\/strong><\/pre>\n #view number of observations used to fit model\n<\/span>nobs(fit)\n\n[1] 8\n<\/span><\/span><\/strong><\/pre>\n #fit multiple linear regression model (only where minutes>10 & fouls<4)\n<\/span><\/strong>fit <- lm(points ~ fouls + minutes, data=df, subset=(minutes><\/span> 10 &<\/span> fouls< 4<\/span><\/span><\/span> ))\n\n#view number of observations used to fit model<\/span>\nnobs(fit)\n\n[1] 7\n<\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
R’de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de art\u0131k arsa nas\u0131l olu\u015fturulur<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"