Confidence interval for sum of coefficients
Hello, I suspect this to be simple, but I can't figure it out.
library(lme4) data(Machines) fm1 <- lmer(score ~ Machine + (Machine | Worker), data = Machines) summary(fm1)
Fixed effects:
Estimate Std. Error t value
(Intercept) 52.356 1.681 31.151
MachineB 7.967 2.421 3.291
MachineC 13.917 1.540 9.036
confint(fm1)
2.5 % 97.5 % [...] (Intercept) 48.7964047 55.9147119 MachineB 2.8401623 13.0931789 MachineC 10.6552809 17.1780575 [and 14 warnings, but it's just an example: In optwrap(optimizer, par = start, fn = function(x) dd(mkpar(npar1, ... : convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceeded ... In profile.merMod(object, signames = oldNames, ...) : non-monotonic profile] I'd like to have confidence intervals for the overall score of MachineA, MachineB, and MachineB. MachineA is easy (CI of the intercept), but how do I combine the CI of the intercept with the CI of the MachineB parameter, and likewise the CI of the intercept with the parameter of MachineC? Can I simply add the lower and upper bounds of the two intervals or is this naive? Thank you for your time, Michael