[R-sig-ME] glmmTMB: dispformula for mixed beta regression

[Mollie Brooks]

Dear Tim,

For the beta family, the conditional variance is mu*(1-mu)/(1+phi) (i.e., increasing phi decreases the variance.) This is in the helpfile ?sigma.glmmTMB. The dispersion model is for log(phi). So higher X2 values give lower variance.  Does that agree with your data?

Cheers,
Mollie

> On 28 Jan 2023, at 06.22, Timothy MacKenzie <fswfswt using gmail.com> wrote:
> 
> Dear All,
> 
> I have a mixed beta-regression model whose residuals don't spread
> evenly across its fitted values unless I add X1 and X2 (two numeric
> predictors) to the "dispformula" argument (see below).
> 
> Based on the Dispersion model results below, can I say the higher the
> value of X2 (0.147987), the more spread-out the residuals but not so
> much so for X1 (0.003548)?
> 
> In other words, can I think of the variables in dispformula as
> "variance covariates"?
> 
> glmmTMB(y ~ X1 + X2 + (1 | id), family = beta_family("logit"),
> dispformula = ~ X1 + X2)
> 
> Dispersion model:
>   (Intercept)           X1                   X2
>      0.752923        0.003548        0.147987
> 
> Thanks,
> Tim M
> 
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