Bilinen iki de\u011fer (x 1<\/sub> , y 1<\/sub> ) ve (x 2<\/sub> , y 2<\/sub> ) verildi\u011finde, a\u015fa\u011f\u0131daki form\u00fcl\u00fc kullanarak bir x noktas\u0131 i\u00e7in y de\u011ferini tahmin edebiliriz:<\/span><\/p>\n y = y 1<\/sub> + (xx 1<\/sub> )(y 2<\/sub> -y 1<\/sub> )\/(x 2<\/sub> -x 1<\/sub> )<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, R’de do\u011frusal enterpolasyonun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini g\u00f6sterir.<\/span><\/p>\n R’de x ve y de\u011ferlerine sahip a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesine sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Veri \u00e7er\u00e7evesindeki (x,y) de\u011ferlerini g\u00f6rselle\u015ftirmek amac\u0131yla da\u011f\u0131l\u0131m grafi\u011fi olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span> <\/p>\n \u015eimdi, 13’l\u00fck<\/strong> yeni bir x de\u011feriyle ili\u015fkili y de\u011ferini bulmak istedi\u011fimizi varsayal\u0131m.<\/span><\/p>\n Bunu yapmak i\u00e7in R’deki approx()<\/strong> fonksiyonunu kullanabiliriz:<\/span><\/p>\n Tahmin edilen y de\u011feri 33,5<\/strong> olarak \u00e7\u0131k\u0131yor.<\/span><\/p>\n E\u011fer (13, 33.5) noktas\u0131n\u0131 \u00e7izimimize eklersek, fonksiyonla olduk\u00e7a iyi e\u015fle\u015fti\u011fi g\u00f6r\u00fcl\u00fcyor:<\/span> <\/p>\n Herhangi bir yeni x de\u011feri i\u00e7in do\u011frusal enterpolasyon ger\u00e7ekle\u015ftirmek amac\u0131yla bu tam form\u00fcl\u00fc kullanabiliriz.<\/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 bir denklem sistemi nas\u0131l \u00e7\u00f6z\u00fcl\u00fcr?<\/a> Do\u011frusal enterpolasyon, bir fonksiyonun bilinen iki de\u011feri aras\u0131ndaki bilinmeyen bir de\u011ferini tahmin etme i\u015flemidir. Bilinen iki de\u011fer (x 1 , y 1 ) ve (x 2 , y 2 ) verildi\u011finde, a\u015fa\u011f\u0131daki form\u00fcl\u00fc kullanarak bir x noktas\u0131 i\u00e7in y de\u011ferini tahmin edebiliriz: y = y 1 + (xx 1 )(y 2 -y 1 )\/(x 2 […]<\/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-3152","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n \u00d6rnek: R’de do\u011frusal enterpolasyon<\/strong><\/span><\/h3>\n
#define data frame\n<\/span>df <- data. frame<\/span> (x=c(2, 4, 6, 8, 10, 12, 14, 16, 18, 20),\n y=c(4, 7, 11, 16, 22, 29, 38, 49, 63, 80))\n\n#view data frame\n<\/span>df\n\n xy\n1 2 4\n2 4 7\n3 6 11\n4 8 16\n5 10 22\n6 12 29\n7 14 38\n8 16 49\n9 18 63\n10 20 80<\/strong><\/pre>\n #create scatterplot<\/span>\nplot(df$x, df$y, col=' blue<\/span> ', pch= 19<\/span> )\n<\/span><\/span><\/strong><\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lineairer1.jpg\")
<\/p>\n #fit linear regression model using data frame<\/span>\nmodel <- lm(y ~ x, data = df)\n\n#interpolate y value based on x value of 13\n<\/span>y_new = approx(df$x, df$y, xout= 13<\/span> )\n\n#view interpolated y value\n<\/span>y_new\n\n$x\n[1] 13\n\n$y\n[1] 33.5\n<\/strong><\/span><\/pre>\n #create scatterplot\n<\/span>plot(df$x, df$y, col=' blue<\/span> ', pch= 19<\/span> )\n\n#add the predicted point to the scatterplot\n<\/span>points(13, y_new$y, col=' red<\/span> ', pch= 19<\/span> )\n<\/span><\/span><\/strong><\/pre>\n![\"R'de](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lineairer2.jpg\")
<\/p>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
\u00c7oklu regresyon modeli kullan\u0131larak R’deki de\u011ferler nas\u0131l tahmin edilir?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"