Calculates the delta R-squares, also known as squared semi-partial correlation coefficients.
The change in the R-square when a variable is removed from a regression is called delta R-square. It is sometimes suggested as a way to determine whether a variable has a substantial effect on an outcome. This is also known as the squared semi-partial correlation coefficient.
getDeltaRsquare(model)
model |
a fitted regression model |
a vector of estimates of the delta R-squares
Paul E. Johnson <pauljohn@ku.edu>
dat1 <- genCorrelatedData(N=250, means=c(100,100), sds=c(30,20), rho=0.0, stde = 7, beta=c(1.1, 2.4, 4.1, 0)) m1 <- lm(y ~ x1 + x2, data=dat1) getDeltaRsquare(m1) ## more problematic in presence of collinearity dat2 <- genCorrelatedData(N=250, means=c(100,100), sds=c(30,20), rho=0.6, stde = 7, beta=c(1.1, 2.4, 4.1, 0)) m2 <- lm(y ~ x1 + x2, data=dat2) getDeltaRsquare(m2)
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