glmmTMB: dispformula for mixed beta regression
Dear Mollie, Thank you for your clarification. So, I can conclude that X2 has a logarithmically inverse association with residuals' variance. But then for interpretation purposes, should I say that: 0.147987 for X2 in the dispersion model is the slope of that association when X2 is 0? OR should I say that: for each unit of increase in X2, residuals' variance decreases by exp(0.147987) = 1.16? Thank you, Tim M
On Mon, Jan 30, 2023 at 6:09 AM Mollie Brooks <mollieebrooks at gmail.com> wrote:
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 at 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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