<\/p>\n
R’ye do\u011frusal bir regresyon modeli s\u0131\u011fd\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanan lm()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n Daha sonra modeli tek bir de\u011feri tahmin etmek amac\u0131yla kullanmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, R’deki uygun regresyon modellerini kullanarak tek bir de\u011ferin nas\u0131l tahmin edilece\u011fini g\u00f6sterir.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, R’ye basit bir do\u011frusal regresyon modelinin nas\u0131l s\u0131\u011fd\u0131r\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Yeni bir g\u00f6zlemin yan\u0131t de\u011ferini tahmin etmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n Model, bu yeni g\u00f6zlemin 25,36364<\/strong> yan\u0131t de\u011ferine sahip olaca\u011f\u0131n\u0131 \u00f6ng\u00f6r\u00fcyor.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, R’ye \u00e7oklu do\u011frusal regresyon modelinin nas\u0131l s\u0131\u011fd\u0131r\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Yeni bir g\u00f6zlemin yan\u0131t de\u011ferini tahmin etmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n Model, bu yeni g\u00f6zlemin 26,17073<\/strong> yan\u0131t de\u011ferine sahip olaca\u011f\u0131n\u0131 \u00f6ng\u00f6r\u00fcyor.<\/span><\/p>\n Yeni bir de\u011feri tahmin etmeye \u00e7al\u0131\u015f\u0131rken kar\u015f\u0131la\u015fabilece\u011finiz en yayg\u0131n hata , regresyon modelini s\u0131\u011fd\u0131rmak i\u00e7in kulland\u0131\u011f\u0131n\u0131z veri k\u00fcmesinin, tahmin etmeye \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131z yeni g\u00f6zlemle ayn\u0131 s\u00fctun adlar\u0131na sahip olmamas\u0131d\u0131r<\/strong> .<\/span><\/p>\n \u00d6rne\u011fin, R’de a\u015fa\u011f\u0131daki \u00e7oklu do\u011frusal regresyon modelini uygulad\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Daha sonra, bu yeni g\u00f6zlemin yan\u0131t de\u011ferini tahmin etmek i\u00e7in modeli kullanmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Yeni g\u00f6zlemin s\u00fctun adlar\u0131 (x_1, x_2), regresyon modeline s\u0131\u011fd\u0131rmak i\u00e7in kulland\u0131\u011f\u0131m\u0131z orijinal veri \u00e7er\u00e7evesinin (x1, x2) s\u00fctun adlar\u0131yla e\u015fle\u015fmedi\u011finden bir hata ald\u0131k.<\/span><\/p>\n R’de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> R’ye do\u011frusal bir regresyon modeli s\u0131\u011fd\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanan lm() i\u015flevini kullanabiliriz: model <- lm(y ~ x1 + x2, data=df) Daha sonra modeli tek bir de\u011feri tahmin etmek amac\u0131yla kullanmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz: predict(model, newdata = new) A\u015fa\u011f\u0131daki \u00f6rnekler, R’deki uygun regresyon modellerini kullanarak tek bir de\u011ferin nas\u0131l tahmin edilece\u011fini g\u00f6sterir. \u00d6rnek 1: […]<\/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-1587","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n model <- lm(y ~ x1 + x2, data=df)\n<\/strong><\/pre>\n
predict(model, newdata = new)<\/strong><\/pre>\n
\u00d6rnek 1: Basit bir do\u011frusal regresyon modeli kullanarak tahminde bulunun<\/strong><\/span><\/h3>\n
#create data\n<\/span>df <- data. frame<\/span> (x=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12),\n y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36))\n\n#fit simple linear regression model\n<\/span>model <- lm(y ~ x, data=df)<\/strong><\/pre>\n #define new observation\n<\/span>new <- data. frame<\/span> (x=c(5))\n\n#use the fitted model to predict the value for the new observation\n<\/span>predict(model, newdata = new)\n\n 1 \n25.36364 \n<\/strong><\/pre>\n \u00d6rnek 2: \u00c7oklu do\u011frusal regresyon modeli kullanarak tahminde bulunun<\/strong><\/span><\/h3>\n
#create data\n<\/span>df <- data. frame<\/span> (x1=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12),\n x2=c(6, 6, 7, 7, 8, 9, 11, 13, 14, 14),\n y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36))\n\n#fit multiple linear regression model\n<\/span>model <- lm(y ~ x1 + x2, data=df)<\/strong><\/pre>\n #define new observation\n<\/span>new <- data.frame(x1=c(5),\n x2=c(10))\n\n#use the fitted model to predict the value for the new observation\n<\/span>predict(model, newdata = new)\n\n 1 \n26.17073 \n<\/strong><\/pre>\n Yeni de\u011ferleri tahmin ederken olas\u0131 hatalar<\/strong><\/span><\/h3>\n
#createdata\n<\/span>df <- data. frame<\/span> (x1=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12),\n x2=c(6, 6, 7, 7, 8, 9, 11, 13, 14, 14),\n y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36))\n\n#fit multiple linear regression model\n<\/span>model <- lm(y ~ x1 + x2, data=df)<\/strong><\/pre>\n #define new observation\n<\/span>new <- data.frame(x_1=c(5),\n x_2=c(10))\n\n#use the fitted model to predict the value for the new observation\n<\/span>predict(model, newdata = new)\n\nError in eval(predvars, data, env): object 'x1' not found\n<\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\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":"