Interpretation of DHARMa output - GLMER (binomial) about dharma HOT 2 CLOSED

Hello,

sorry for the late reply. About your question: I'm citing from the DHARMa vignette:

Once an residual effect is statistically significant, look the magnitude to decide if there is a problem: finally, it is crucial to note that significance is NOT a measures of the strength of the residual pattern, it is a measure of the signal/noise ratio, i.e. whether you are sure there is a pattern at all. Significance in hypothesis tests depends on at least 2 ingredients: strength of the signal, and the number of data points. If you have a lot of data points, residual diagnostics will nearly inevitably become significant, because having a perfectly fitting model is very unlikely. That, however, doesn’t necessarily mean that you need to change your model. The p-values confirm that there is a deviation from your null hypothesis. It is, however, in your discretion to decide whether this deviation is worth worrying about. For example, if you see a dispersion parameter of 1.01, I would not worry, even if the dispersion test is significant. A significant value of 5, however, is clearly a reason to move to a model that accounts for overdispersion.

So, regarding your plots: the tests are significant, but I don't see a strong deviation, so I would just ignore it.

from dharma.

OK, I will consider this closed for the moment, in case you have further questions feel free to re-open the issue!

from dharma.