Hi all! I would like to estimate confidence intervals for a non lm model. For example, I use a mixed model of the form: md=lme(y~x1+I(x1^2)+x2 ...) Parameters x1+I(x1^2) are fixed effects and I would like to plot the predicted (partial) curve corresponding to these ones, along with 90% CI bands. Thus, I simulate (partial) predictions: Lx=c() Ux=c() Mx=c() z=c() for (j in 1:length(x1)){ x=x1[j] for(i in 1:2000){ s1 <- rnorm(1,.026,.027) # mean and sd estimated by lme for x1 s2 <- rnorm(1,-.01,.005) # mean and sd estimated by lme for I(x1^2) z[i] <- s1*x+s2*(x^2) } Lx[j]=quantile(z,.05) Ux[j]=quantile(z,.95) Mx[j]=mean(z) }
And then plot vectors Lx, Mx and Ux for lower, mean and upper curves, respectively. Is this approach correct? Any alternatives? Thank you!! ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.