R^2 in linear regression
Terias wrote:
Hello, I was doing a linear regression with the following formula: lm(y~x+0), so it passes through the origin. But when I called the summary of the regression i saw that R squared is abnormally high (it's a lot lower in other programs such as SigmaPlot and MS Excel).The manual explained the cause of the difference (because of the different computing method), but what should I do to get the same R^2 in excel and R?
If you insist, I think you can get what Excel does like this: > x <- 1:10 > y <- rnorm(10) ## Residual sums of squares > ss1 <- anova(lm(y~1))[1,2] > ss2 <- anova(lm(y~x+0))[2,2] ## Relative reduction in sum of squares > (ss1-ss2)/ss1 [1] -0.08576713 Now if you dislike the fact that R^2 can come out negative, that's your problem....
WITHOUT PASSING THROUGH THE ORIGIN: R^2: Multiple R-squared: 0.9711, Adjusted R-squared: 0.9654 In MS Excel: 0,9711 So it's OK. WITH PASSING THROUGH THE ORIGIN: Multiple R-squared: 0.9848, Adjusted R-squared: 0.9822 In MS Excel: 0,8907 So almost 10% difference. Thank you for your help. Csanad Bertok, Hungary
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