It is likely that x is not the predictions but is a predictor that goes into a linear regression. Perform the regression, fm
, in which case the predicted values are fitted(fm)
and then get the R squared from summary
or get it directly as shown in the alternatives.
fm <- lm(yield ~ x, mydat)
summary(fm)$r.squared
# [1] 0.02508245
# same
cor(mydat$yield, fitted(fm))^2
# [1] 0.02508245
# same
with(mydat, cor(yield, x)^2)
# [1] 0.02508245
# same
tss <- with(mydat, sum((yield - mean(yield))^2))
rss <- deviance(fm)
1 - rss/tss
# [1] 0.02508245
# same
tss <- with(mydat, sum((yield - mean(yield))^2))
rss <- sum(resid(fm)^2)
1 - rss/tss
# [1] 0.02508245
plot(yield ~ x, mydat)
abline(fm)
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