Hi, as pointed out previously, the problem is in using the canned routine (lm) without including an intercept term. Here is a working, generic example with commented code.
#Simulate data x=rnorm(100) e=rnorm(100) y=x+e #Create X matrix with intercept X=cbind(1,x) #Projection matrix P=X%*%solve(t(X)%*%X)%*%t(X) #Fitted values fv=P%*%y #Run canned regression reg=lm(y~x) #Canned and hand computed fitted values cbind(fitted(reg),fv) #Are they all equal? all.equal(as.vector(as.numeric(fitted(reg))),as.vector(as.numeric(fv))) #This already implies that the R-squared is equal #Compute R-squared by hand R.sq=1-sum((y-fv)^2)/sum((y-mean(y))^2) #Is this equal to the R-squared from the canned routine? summary(reg)$r.squared==R.sq Daniel -- View this message in context: http://r.789695.n4.nabble.com/Calculation-of-r-squared-from-a-linear-regression-tp2251438p2255537.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
