Timeline for Is an overdispersion parameter of 5.17 for GLMM with Beta family too high to yield reliable results?
Current License: CC BY-SA 4.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 22, 2021 at 7:51 | history | edited | Florian Hartig | CC BY-SA 4.0 |
text clarifications
|
| May 31, 2020 at 17:14 | comment | added | Florian Hartig | @tiantianchen - the help of glmmTMB states that "Beta distribution: parameterization of Ferrari and Cribari-Neto (2004) and the betareg package (Cribari-Neto and Zeileis 2010); V=mu*(1-mu)*phi" | |
| May 26, 2020 at 11:14 | comment | added | tiantianchen | Isnt the overdispersion parameter a precision parameter: $\phi=\alpha + \beta$ and $var(y)=\mu(1-\mu)/(1+\phi)$, which means a relative smaller spread of your data around mean? | |
| Feb 26, 2020 at 15:59 | comment | added | Florian Hartig | p.s. - sometimes, even with zero-inflation, dispersion for the beta doesn't fit. I have seen this before. I have normally interpreted this as some kind of problem in the distribution. | |
| Feb 26, 2020 at 15:55 | comment | added | Florian Hartig | One possible reason for this is that you have 0 or 1 inflation. Have you tried testing your model against a model with zero-inflation switched on? If you have mostly ones, you can transform your response to 1-y to get zeros. If that doesn't help, and you want to provide an example, ideally post it here github.com/florianhartig/DHARMa/issues | |
| Feb 26, 2020 at 15:29 | comment | added | Peter |
Thanks, I understand. For a similar other beta model I get a summary dispersion value of 27.4, and a DHARMa::testDispersio p-value of < 2.2e-16. Does this mean that this model is doomed? Can I fix the overdispersion in any way? I could provide some data if you think it's useful to take a look.
|
|
| Feb 26, 2020 at 15:00 | comment | added | Florian Hartig | a n.s. p-value indicates no overdispersion. The warning probably occurs because you have several 0 or 1 in the data. That can happen naturally in the beta, but if you want to be sure, you could test for 0 / 1 inflation (which frequently happens with the beta) | |
| Feb 26, 2020 at 14:47 | comment | added | Peter |
Thanks for the elaborate answer. Idk why DHARMa::testOverdispersion didn't work before, must have done something wrong. When I run res <- simulateResiduals(myglmm) it returns Model family was recognized or set as continuous, but duplicate values were detected in the response. Consider if you are fitting an appropriate model. Should I be worried? When I then run testDispersion(res) it returns me data: simulationOutput ratioObsSim = 1.1511, p-value = 0.248 alternative hypothesis: two.sided, does this insignificant p value indicate that there is no overdispersion?
|
|
| Feb 26, 2020 at 14:42 | vote | accept | Peter | ||
| Feb 26, 2020 at 13:16 | history | answered | Florian Hartig | CC BY-SA 4.0 |