jamovi

Hi,

I have two questions regarding mixed models and generalized mixed models analyses within GAMLj:

1. The fixed effects estimates are unstandardized or standardized betas? 
2. When the model converges and we did not get singular fit warnings, should we still simplify the model (or try other remedies) when r-squared's cannot be computed?

Thanks!
Best,
Tomás

1. The estimates are unstandardized. In generalized models, there are no betas. If you wish a sort of standardization, you can standardize the independent variables in "Covariate Scaling". Obviously, the dependent variable cannot be standardized.

2. No, for some generalized models the pseudo r-squared cannot be computed, but the model may be fine nonetheless. What model are you using?

As regards the optimization (referring to the other message), gamlj tries "bobyqa" "Nelder_Mead" and "nloptwrap" options before yielding that the model did not converge

Thanks!
It's a GLMM prediction recognition performance (yes/no) as a function of target race, target typicality, and confidence (continuously measured and mean-centered), with participants and stimuli as random factors (cluster variables). Here the model info:

Model Info

Info Value Comment
Model Type Logistic Model for binary y
Call glm Recognition ~ 1 + Race + Tipicality + Predictive_Conf + Race:Predictive_Conf + Tipicality:Predictive_Conf + Race:Tipicality + Race:Tipicality:Predictive_Conf + (1 | Face_id) + (Race:Predictive_Conf + Tipicality:Predictive_Conf | Subject_id)
Link function Logit Log of the odd of y=1 over y=0
Direction P(y=1)/P(y=0) P( Recognition = 1 ) / P( Recognition = 0 )
Distribution Binomial Dichotomous event distribution of y
LogLikel. -2030.185 Unconditional Log-Likelihood
-2*LogLikel. 4060.369 Unconditional absolute deviance
Deviance 3688.095 Conditional relative deviance
R-squared NaN Marginal
R-squared NaN Conditional
AIC 4098.37 Less is better
BIC 4214.644 Less is better
Residual DF 3341
Chi-squared/DF 0.888 Overdispersion indicator
Converged yes
Optimizer bobyqa
Note. R-squared cannot be computed.

I have another question, sorry.

In the covariate scaling menu, the "centered" option means centering around the grand mean, and the option "centered clusterwise" means around participant's own mean?

correct

mcfanda@gmail.com wrote: ↑Wed May 19, 2021 6:07 am
2. No, for some generalized models the pseudo r-squared cannot be computed, but the model may be fine nonetheless. What model are you using?

Hi mcfanda! I found the same problem as TPalma and your answer has helped me a lot but I keep wondering why for some generalized models the pseudo r-squared cannot be computed? and why may be fine nonotheless?

Thanks in advanceª
Best,

Sorry, I got lost. Is the question about the mixed model, the generalized mixed or generalized linear modes?