See:
http://en.wikipedia.org/wiki/Coefficient_of_determination#Adjusted_R2
and the implementation in summary.lm :
ans$adj.r.squared <- 1 - (1 - ans$r.squared) * ((n -
df.int)/rdf)
Brian Smith wrote:
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:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.13084 0.28845 -0.454 0.658
x 0.01923 0.25961 0.074 0.942
Residual standard error: 1.106 on 13 degrees of freedom
Multiple R-squared: 0.0004217, Adjusted R-squared: -0.07647
F-statistic: 0.005485 on 1 and 13 DF, p-value: 0.942
cor(x,y)
[1] 0.02053617
- What factors are included in the adjustment?
many thanks!
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