partial.resid.plot: Partial residual plots for interpretation of multiple...

In asbio: A Collection of Statistical Tools for Biologists

Partial residual plots for interpretation of multiple regression.

Description

The function creates partial residual plots which help a user graphically determine the effect of a single predictor with respect to all other predictors in a multiple regression model.

Usage

partial.resid.plot(x, smooth.span = 0.8, lf.col = 2, sm.col = 4,...)

Arguments

x	A output object of class lm or class glm
smooth.span	Degree of smoothing for smoothing line.
lf.col	Color for linear fit.
sm.col	Color for smoother fit.
...	Additional arguments from plot.

Details

Creates partial residual plots (see Kutner et al. 2002). Smoother lines from lowess and linear fits from lm are imposed over plots to help an investigator determine the effect of a particular X variable on Y with all other variables in the model. The function automatically inserts explanatory variable names on axes.

Value

Returns p partial residual plots, where p = the number of explanatory variables.

Author(s)

Ken Aho

References

Kutner, M. H., Nachtsheim, C. J., Neter, J., and W. Li. (2005) Applied Linear Statistical Models, 5th edition. McGraw-Hill, Boston.

See Also

partial.R2

Examples

if(interactive()){
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<-c(20,30,10,15,5,45,60,55,45)
x <- lm(Y ~ Soil.N + Soil.C + Slope + Aspect)
op <- par(mfrow=c(2,2),mar=c(5,4,1,1.5))
partial.resid.plot(x)
par(op)
}

asbio documentation built on May 29, 2024, 5:57 a.m.