问题
I have a set of GLMMs fitted with a binary response variable and a set of continuous variables, and I would like to get confidence intervals for each model. I've been using confint() function, at 95% and with the profile method, and it works without any problems if it is applied to a model with no interactions.
However, when I apply confint() to a model with interactions (continuous*continuous), I've been getting this error:
m1CI <- confint(m1, level=0.95, method="profile")
Error in zeta(shiftpar, start = opt[seqpar1][-w]) : profiling detected new, lower deviance
The model runs without any problem (although I applied an optimizer because some of the models were having problems with convergence), and here is the final form of one of them:
m1 <- glmer(Use~RSr2*W+RSr3*W+RShw*W+RScon*W+
RSmix*W+(1|Pack/Year),
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=100000)),
data = data0516RS, family=binomial(link="logit"))
Does anyone know why this is happening, and how can I solve it?
I am using R version 3.4.3 and lme4 1.1-17
回答1:
The problem was solved by following these instructions:
The error message indicates that during profiling, the optimizer found a fitted value that was significantly better (as characterized by the 'devtol' parameter) than the supposed minimum-deviance solution returned in the first place. You can boost the 'devtol' parameter (which is currently set at a conservative 1e-9 ...) if you want to ignore this -- however, the non-monotonic profiles are also warning you that something may be wonky with the profile.
From https://stat.ethz.ch/pipermail/r-sig-mixed-models/2014q3/022394.html
I used the confint.merModfrom the lme4 package, and boosted the 'devtol' parameter, first to 1e-8, which didn't work for my models, and then to 1e-7. With this value, it worked
来源：https://stackoverflow.com/questions/53120614/error-when-estimating-ci-for-glmm-using-confint