<\/p>\n
R’de kar\u015f\u0131la\u015fabilece\u011finiz bir hata:<\/span><\/p>\n Bu hata genellikle R’ye bir regresyon modeli veya ANOVA modeli s\u0131\u011fd\u0131rmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ve de\u011fi\u015fkenlerden biri i\u00e7in vekt\u00f6r<\/strong> yerine bir liste<\/strong> kulland\u0131\u011f\u0131n\u0131zda ortaya \u00e7\u0131kar.<\/span><\/p>\n Bu e\u011fitimde bu hatan\u0131n pratikte nas\u0131l d\u00fczeltilece\u011fi a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n Diyelim ki R’ye basit bir do\u011frusal regresyon modeli<\/a> s\u0131\u011fd\u0131rmaya \u00e7al\u0131\u015f\u0131yorum:<\/span><\/p>\n lm()<\/strong> i\u015flevi girdi olarak yaln\u0131zca vekt\u00f6rleri alabildi\u011finden ve x de\u011fi\u015fkeni \u015fu anda bir liste oldu\u011fundan hata al\u0131yorum.<\/span><\/p>\n Bu hatay\u0131 \u00f6nlemenin en kolay yolu, liste de\u011fi\u015fkenini bir vekt\u00f6re d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in unlist()<\/a> i\u015flevini kullanmakt\u0131r:<\/span><\/span><\/p>\n x de\u011fi\u015fkenini bir vekt\u00f6re d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in unlist()<\/strong> y\u00f6ntemini kulland\u0131\u011f\u0131m\u0131z i\u00e7in basit do\u011frusal regresyon modelini bu sefer hatas\u0131z bir \u015fekilde s\u0131\u011fd\u0131rabildi\u011fimizi unutmay\u0131n.<\/span><\/p>\n \u00c7oklu do\u011frusal regresyon modeli<\/a> uyguluyorsan\u0131z ve halihaz\u0131rda liste nesneleri olan birden fazla tahmin de\u011fi\u015fkeniniz varsa, regresyon modelini yerle\u015ftirmeden \u00f6nce bunlar\u0131n her birini vekt\u00f6rlere d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in unlist() i\u015flevini<\/strong> kullanabilece\u011finizi unutmay\u0131n:<\/span><\/p>\n Yine listedeki nesnelerin her birini vekt\u00f6rlere d\u00f6n\u00fc\u015ft\u00fcrd\u00fc\u011f\u00fcm\u00fcz i\u00e7in herhangi bir hata alm\u0131yoruz.<\/span><\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde R’de di\u011fer yayg\u0131n i\u015flemlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n R’de glm \u00e7\u0131kt\u0131s\u0131 nas\u0131l yorumlan\u0131r<\/a> R’de kar\u015f\u0131la\u015fabilece\u011finiz bir hata: Error in model.frame.default(formula = y ~ x, drop.unused.levels = TRUE): invalid type (list) for variable ‘x’ Bu hata genellikle R’ye bir regresyon modeli veya ANOVA modeli s\u0131\u011fd\u0131rmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ve de\u011fi\u015fkenlerden biri i\u00e7in vekt\u00f6r yerine bir liste kulland\u0131\u011f\u0131n\u0131zda ortaya \u00e7\u0131kar. Bu e\u011fitimde bu hatan\u0131n pratikte nas\u0131l d\u00fczeltilece\u011fi a\u00e7\u0131klanmaktad\u0131r. Hata nas\u0131l yeniden olu\u015fturulur? […]<\/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-3219","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n Error in model.frame.default(formula = y ~ x, drop.unused.levels = TRUE): \n invalid type (list) for variable 'x' \n<\/strong><\/pre>\n
Hata nas\u0131l yeniden olu\u015fturulur?<\/strong><\/h2>\n
#define variables\nx <- list(1, 4, 4, 5, 7, 8, 9, 10, 13, 14)\ny <- c(10, 13, 13, 14, 18, 20, 22, 24, 29, 31)<\/span>\n\n#attempt to fit regression model\nmodel <- lm(y ~ x)\n\nError in model.frame.default(formula = y ~ x, drop.unused.levels = TRUE): \n invalid type (list) for variable 'x'\n<\/span><\/span><\/strong><\/span><\/pre>\n Hata nas\u0131l \u00f6nlenir<\/strong><\/span><\/h2>\n
#define variables\nx <- list(1, 4, 4, 5, 7, 8, 9, 10, 13, 14)\ny <- c(10, 13, 13, 14, 18, 20, 22, 24, 29, 31)\n\n#attempt to fit regression model\n<\/span>model <- lm(y ~ unlist(x))\n\n#view the model output<\/span>\nsummary(model)\n\nCall:\nlm(formula = y ~ unlist(x))\n\nResiduals:\n Min 1Q Median 3Q Max \n-1.1282 -0.4194 -0.1087 0.2966 1.7068 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 6.58447 0.55413 11.88 2.31e-06 ***\nunlist(x) 1.70874 0.06544 26.11 4.97e-09 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.8134 on 8 degrees of freedom\nMultiple R-squared: 0.9884, Adjusted R-squared: 0.987 \nF-statistic: 681.8 on 1 and 8 DF, p-value: 4.97e-09\n<\/span><\/span><\/strong><\/span><\/pre>\n #define variables\nx1 <- list(1, 4, 4, 5, 7, 8, 9, 10, 13, 14)\nx2 <- list(20, 16, 16, 15, 16, 12, 10, 8, 8, 4)\ny <- c(10, 13, 13, 14, 18, 20, 22, 24, 29, 31)\n\n#fit multiple linear regression model\n<\/span>model <- lm(y ~ unlist(x1) + unlist(x2))\n\n#view the model output\n<\/span>summary(model)\n\nCall:\nlm(formula = y ~ unlist(x1) + unlist(x2))\n\nResiduals:\n Min 1Q Median 3Q Max \n-1.1579 -0.4211 -0.1386 0.3108 1.7130 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 8.34282 4.44971 1.875 0.102932 \nunlist(x1) 1.61339 0.24899 6.480 0.000341 ***\nunlist(x2) -0.08346 0.20937 -0.399 0.702044 \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.8599 on 7 degrees of freedom\nMultiple R-squared: 0.9887, Adjusted R-squared: 0.9854 \nF-statistic: 305.1 on 2 and 7 DF, p-value: 1.553e-07<\/span><\/span><\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de ANOVA sonu\u00e7lar\u0131 nas\u0131l yorumlan\u0131r?<\/a>
R Uyar\u0131s\u0131 nas\u0131l ele al\u0131n\u0131r: glm.fit: algoritma yak\u0131nsamad\u0131<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"