I have a linear mixed-effect model in R with two continuous fixed-effects and one random effect, like this:
To graphically display the independent effect of
y, while controlling the effects of
x2 (fixed effect) and
r (random effect), is it appropriate to do a partial regression plot using the same logic used for multiple linear regression models? I.e.:
#removing the effect of x2 and r on y res.y<-residuals(lmer(y~x2+(1|r),data)) #removing the effect of x2 and r on x1 res.x1<-residuals(lmer(x1~x2+(1|r),data)) #partial regression plot to display the pure effect of x1 on y plot(res.x1,res.y)
Also, I used the "plotLMER.fnc function" from the "LMERConvenienceFunctions" R package to plot the partial effect size of each fixed effect as follows:
However, I am not sure what this package means by "effect size". Is it β1 and β2?
I will be very grateful for any help in this issue.