jamovi

it's coming out in a few days

Done, robust standard error for poisson and other generalized models are now implemented. Check version 3.6.4 on monday

great

[this is equivalent to gamlj results in R file<-"local/data/mer_jasp.csv" data<-read.csv(file) data$Framing<-factor(data$Framing) contrasts(data$Framing)<--contr.sum(2)/2 data$BothDisliked<<-factor(data$BothDisliked) contrasts(data$BothDisliked)<--contr.sum(2)/2 data$Participant<-factor(da...

you should also be sure that the random component is the same in jasè and jamovi. If I am not mistaken, in gamlj you did not include the random intercept.

jasp uses deviation constrasts. So, in gamlj set constrats to "deviation" coding. However, your model has singular fit, so results are not exactly the same because gamlj tries different optimizers (i do not know how jasp handles these cases). Nonetheless, results are pretty similar, roundi...

Please update the module and it will work

consider testing the results you obtain with HC SE against the results of gamlj3 Poisson (overdispersion), which employs a quasi-poisson distribution that corrects for overdispersion, pretty much what the HC SE do.

Well, everything can be done if can be useful. The question is: what robust standard error do you actually use? To be precise, do you mean the standard error STATA produces with vce(robust). If so, they are the HC estimators, aka heteroskedasticity-consistent covariance matrix estimators. If that's ...

At present, they are not implemented. Nevertheless, what would be the theoretical rationale for employing robust standard errors in generalized linear models? In standard GLMs, robust estimators are typically used to correct for heteroscedasticity or related violations of model assumptions; however,...