search for: coefficient_of_determination

...seem to exist several formula's to calculate Adjusted R-squared. Wherry’s formula [1-(1-R2)·(n-1)/(n-v)] McNemar’s formula [1-(1-R2)·(n-1)/(n-v-1)] Lord’s formula [1-(1-R2)(n+v-1)/(n-v-1)] Stein 1-(n-1/n-k-1)(n-2)/n-k-2) (n+1/n) Theil's formula (found here: http://en.wikipedia.org/wiki/Coefficient_of_determination) According to the textbook Field, Discovering Statistics Using R (2012, p. 273) R uses Wherry's equation which "tells us how much variance in Y would be accounted for if the model had been derived from th. population from which the sample was taken". He does not give the formula for...

...(and therefore contradicting the i.i.d. assumption of the regression model). I then used gls() [library nlme] to fit a linear regression model with AR(1)-residuals. The residuals look perfect (residual plot, ACF, PACF, QQPlot, Ljung- Box test). As mentioned on http://en.wikipedia.org/wiki/ Coefficient_of_determination (citation [2008-04-24]: "For cases other than fitting by ordinary least squares, the R^2 statistic can be calculated as above" and later: "Values for R^2 can be calculated for any type of predictive model"), I tried to calculate the standard R^2 for the model with AR(1)...

Hi, Sorry for the naive question, but what exactly does the 'Adjusted R-squared' coefficient in the summary of linear model adjust for? Sample code: > x <- rnorm(15) > y <- rnorm(15) > lmr <- lm(y~x) > summary(lmr) Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max -1.7828 -0.7379 -0.4485 0.7563 2.1570 Coefficients: