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.

Usage

getDeltaRsquare(model)

Arguments

model

a fitted regression model

Value

a vector of estimates of the delta R-squares

Author

Paul E. Johnson pauljohn@ku.edu

Examples

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)
#> The deltaR-square values: the change in the R-square
#>       observed when a single term is removed.
#> Same as the square of the 'semi-partial correlation coefficient'
#>    deltaRsquare
#> x1    0.3817684
#> x2    0.5348138
## 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)
#> The deltaR-square values: the change in the R-square
#>       observed when a single term is removed.
#> Same as the square of the 'semi-partial correlation coefficient'
#>    deltaRsquare
#> x1    0.1889698
#> x2    0.2708700