<\/p>\n
R’de kar\u015f\u0131la\u015fabilece\u011finiz bir hata:<\/span><\/p>\n Bu hata, yeni bir veri \u00e7er\u00e7evesinin yan\u0131t de\u011ferlerini tahmin etmek i\u00e7in R’de bir regresyon modeli kullanmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ortaya \u00e7\u0131kar, ancak yeni veri \u00e7er\u00e7evesindeki s\u00fctun adlar\u0131, eskiden kulland\u0131\u011f\u0131n\u0131z veri \u00e7er\u00e7evesindeki s\u00fctun adlar\u0131yla e\u015fle\u015fmez. modele uygun. .<\/span><\/p>\n Bu e\u011fitimde bu hatan\u0131n nas\u0131l d\u00fczeltilece\u011fi tam olarak a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n R’ye basit bir do\u011frusal regresyon modeline uydu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n \u015eimdi yeni bir veri \u00e7er\u00e7evesi i\u00e7in yan\u0131t de\u011ferlerini tahmin etmek amac\u0131yla tahmin()<\/strong> i\u015flevini kullanmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Modeli uydururken kulland\u0131\u011f\u0131m\u0131z veri \u00e7er\u00e7evesinin x<\/strong> ad\u0131nda bir yorday\u0131c\u0131 de\u011fi\u015fkeni olmas\u0131 nedeniyle hata al\u0131yoruz, ancak yeni veri \u00e7er\u00e7evesinde yorday\u0131c\u0131 de\u011fi\u015fkeni x1<\/strong> olarak adland\u0131rd\u0131k.<\/span><\/span><\/p>\n Bu isimler e\u015fle\u015fmedi\u011fi i\u00e7in hata al\u0131yoruz.<\/span><\/p>\n Bu hatay\u0131 d\u00fczeltmenin yolu, yeni veri \u00e7er\u00e7evesindeki tahmin de\u011fi\u015fkeninin ayn\u0131 ada sahip olmas\u0131n\u0131 sa\u011flamakt\u0131r.<\/span><\/p>\n Bu nedenle yeni veri blo\u011funda tahmin de\u011fi\u015fkeni x’i<\/strong> adland\u0131rd\u0131\u011f\u0131m\u0131zdan emin olaca\u011f\u0131z:<\/span><\/p>\n Art\u0131k yeni veri \u00e7er\u00e7evesinin yan\u0131t de\u011ferlerini tahmin etmek i\u00e7in tahmin()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n S\u00fctun adlar\u0131 e\u015fle\u015fti\u011fi i\u00e7in yeni veri \u00e7er\u00e7evesi i\u00e7in y de\u011ferlerini hatas\u0131z bir \u015fekilde ba\u015far\u0131l\u0131 bir \u015fekilde tahmin edebiliyoruz.<\/span><\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde R’deki di\u011fer yayg\u0131n hatalar\u0131n nas\u0131l \u00e7\u00f6z\u00fclece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n R’de nas\u0131l d\u00fczeltilir: Adlar \u00f6nceki adlarla e\u015fle\u015fmiyor<\/a> R’de kar\u015f\u0131la\u015fabilece\u011finiz bir hata: Error in eval(predvars, data, env): object ‘x’ not found Bu hata, yeni bir veri \u00e7er\u00e7evesinin yan\u0131t de\u011ferlerini tahmin etmek i\u00e7in R’de bir regresyon modeli kullanmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ortaya \u00e7\u0131kar, ancak yeni veri \u00e7er\u00e7evesindeki s\u00fctun adlar\u0131, eskiden kulland\u0131\u011f\u0131n\u0131z veri \u00e7er\u00e7evesindeki s\u00fctun adlar\u0131yla e\u015fle\u015fmez. modele uygun. . Bu e\u011fitimde bu hatan\u0131n nas\u0131l d\u00fczeltilece\u011fi tam […]<\/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-3076","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n Error in eval(predvars, data, env): object 'x' not found \n<\/strong><\/pre>\n
Hata nas\u0131l yeniden olu\u015fturulur?<\/strong><\/h3>\n
#create data frame\ndata <- data. frame<\/span> (x=c(1, 2, 2, 3, 5, 6, 8, 9),\n y=c(7, 8, 8, 6, 9, 8, 12, 14))\n\n#fit linear regression model to data\n<\/span>model <- lm(y ~ x, data=data)\n\n#view summary of model\n<\/span>summary(model)\n\nCall:\nlm(formula = y ~ x, data = data)\n\nResiduals:\n Min 1Q Median 3Q Max \n-2.1613 -0.7500 0.5000 0.9355 1.5161 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 5.5161 0.9830 5.611 0.00137 **\nx 0.7742 0.1858 4.167 0.00590 **\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 1.463 on 6 degrees of freedom\nMultiple R-squared: 0.7432, Adjusted R-squared: 0.7004 \nF-statistic: 17.37 on 1 and 6 DF, p-value: 0.005896\n<\/span><\/span><\/strong><\/span><\/pre>\n #define new data frame\nnew_data <- data. frame<\/span> (x1=c(4, 5, 7, 8, 9))\n<\/span>\n#attempt to predict y values for new data frame\npredict(model, newdata=new_data)\n\nError in eval(predvars, data, env): object 'x' not found\n<\/span><\/span><\/strong><\/span><\/pre>\n Hata nas\u0131l d\u00fczeltilir?<\/strong><\/span><\/h3>\n
#define new data frame\nnew_data <- data. frame<\/span> (x=c(4, 5, 7, 8, 9)) \n<\/span><\/span><\/strong><\/span><\/pre>\n #predict y values for new data frame\n<\/span>predict(model, newdata=new_data)\n\n 1 2 3 4 5 \n 8.612903 9.387097 10.935484 11.709677 12.483871<\/span>\n<\/span><\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de nas\u0131l d\u00fczeltilir: Daha uzun bir nesnenin uzunlu\u011fu, daha k\u0131sa bir nesnenin uzunlu\u011funun kat\u0131 de\u011fildir<\/a>
R’de nas\u0131l d\u00fczeltilir: kontrastlar yaln\u0131zca 2 veya daha fazla seviyeye sahip fakt\u00f6rlere uygulanabilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"