Comments (3)
drizopoulos
commented on June 2, 2024
1
Thanks! This should now work in GLMMadaptive_0.4-9999999.
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florianhartig
commented on June 2, 2024
yes, thanks! Side comment: I find it a bit confusing that the S3 methods in GLMMadaptive deviate so strongly from most other packages, e.g. that the usual type = "response" etc. can't be used in this way. One can find out from the help of course, but it isn't very intuitive for a new user.
Not sure if it's worth switching now though.
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drizopoulos
commented on June 2, 2024
I'm aware of that, but to maintain consistency with the use of the type argument in other functions in the package, I decided to do it like this. I can understand that this may cause issues, but I hope that it is well-documented.
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Related Issues (20)
- resids_plot for beta family HOT 1
- how to use the predict function with Gaussian mixed model HOT 1
- Discussion: Model Selection with GLMMadaptive HOT 3
- Fitting Zero-inflated models for rare events in repeated measures data HOT 1
- Implement a refit function HOT 3
- Behaviour of weights in GLMMadaptive HOT 2
- emmeans functions in two-part models: only gives output for "main" model HOT 2
- Possibility to evaluate marginal log-likelihood HOT 2
- Error in chol.default(X[[i]], ...) HOT 1
- Random intercept only for zero-inflated portion, not count portion HOT 1
- Documentation contains dead links HOT 1
- May I know how to get the effect size of a two-part mixed effects model? HOT 4
- emmeans produces strange (wrong) estimates from beta regression after mixed_model HOT 1
- Adding "sandwich" argument to mixed_model HOT 1
- Error: No glance method for objects of class MixMod HOT 6
- Issue with data prep for backward formulation HOT 4
- A problem with the censored.normal() family? HOT 2
- emmeans support doesn't return the link HOT 4
- Predict function for hurdle.lognormal response
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