Mixed models with a dichotomous outcome, random slopes, and accounting for autocorrelation
This seems to have slipped through the cracks, sorry about that.
lme4 doesn't do correlation structures, but glmmTMB does. The closest
analogue would be something like
## this should be observation number within group
dat$times <- numFactor(dat$times)
model2 <- glmmTMB(continuous_outcome ~ 1 + Predictor_X + Day +
(1 + Day | Team / Year) + ar1(times + 0 | Team:Year)
(your random effect has two | in it; was the second meant to be a / ?)
See https://glmmtmb.github.io/glmmTMB/articles/covstruct.html
On 2023-01-25 5:13 p.m., Adam Roebuck wrote:
Hello, I am attempting to set up a multilevel model with two grouping variables (team and year), random slopes for day of the year, and accounting for autocorrelation. I have a number of different outcome variables I can use. When the outcome variable is continuous, I can set up a simple growth model using the nlme package that looks something like the following: model1 <- lme(continuous_outcome ~ 1 + Predictor_X + Day, random = ~1 + Day | Team | Year, data = dat, method = "REML", control=list(opt="optim"), correlation = corAR1() However, I now want to build a model with a dichotomous outcome variable. As far as I know, nlme cannot account for all of the above and a dichotomous outcome. I have, however, seen a few references to glmer (in the lme4 package) and glmmTMB being capable of such. I've gone back through the archives, though, and am not seeing any clear explanations on how to set up such a model. Using the parameters above and the variables I specified, could someone please help specify what that model might look like in R or at least point me in the right direction? Thanks, Adam
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