Calculates the partial correlation of determination for a variable of interest in a multiple regression.
partial.R2(nested.lm, ref.lm)
A linear model without the variable of interest.
A linear model with the variable of interest.
The partial \(R^2\) is returned.
Coefficients of partial determination measure the proportional reduction in sums of squares after a variable of interest, X, is introduced into a model. We can see how this would be of interest in a multiple regression.
Kutner, M. H., Nachtsheim, C. J., Neter, J., and W. Li. (2005) Applied Linear Statistical Models, 5th edition. McGraw-Hill, Boston.
# NOT RUN { Soil.C<-c(13,20,10,11,2,25,30,25,23) Soil.N<-c(1.2,2,1.5,1,0.3,2,3,2.7,2.5) Slope<-c(15,14,16,12,10,18,25,24,20) Aspect<-c(45,120,100,56,5,20,5,15,15) Y<-as.vector(c(20,30,10,15,5,45,60,55,45)) lm.with<-lm(Y~Soil.C+Soil.N+Slope+Aspect) lm.without<-update(lm.with, ~. - Soil.N) partial.R2(lm.without,lm.with) # }
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