Comments (1)
Hi Sina,
Apologies for delayed response. Currently the package doesn't officially support glmmTMB models. The list of supported models is provided in the README. Supporting new model types is based on user demand and my time, but in some cases might not be too difficult. I don't currently have much time to do that though, so maybe you could use an alternative supported model type (e.g. glmer
) in the meantime? Or you are always welcome to contribute code that provides support.
On the other question, yes you can use mixed and non-mixed models in the same SEM, if it makes sense to do so. The bootstrapping will however treat all models as mixed, as it will be assumed that the data is hieararchical and will resample based on the (highest-level) group (random effect). So it will resample groups instead of rows (that's if you use the default sampling type of 'nonparametric'). Is there a reason why you are not using mixed models for the lm()
models? Usually you should do so if the data is hiearchical, unless the random effect variance is close to zero.
Cheers,
Mark
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Related Issues (17)
- Considerations for GLMM? HOT 1
- Get the CI as a data.frame after bootstrap HOT 1
- In (function (m, w) : 101 model fit(s) or parameter estimation(s) failed. NAs reported/generated. HOT 12
- semEff documentation not clear for using psem objects HOT 1
- Documentation on standardization HOT 3
- Can't list correlated erros with cor.err HOT 1
- Transfer `digits` argument from `semEff()` to `print.semEff()`/`summary.semEff()`?
- lapply bootEff HOT 1
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- Error message "estimated adjustment 'a' is NA" HOT 1
- semEff() provides much lower estimates than piecewiseSEM::coefs() for psem objects with lme4 (poisson errors) HOT 1
- how to cite semEff? HOT 1
- getData does not work HOT 1
- summary(semEff(sem.Eff.boot)) output to matrix/dataframe HOT 2
- confidence intervals for getEff() functions HOT 3
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