Contents
How are residual plots used in generalized linear mixed models?
One of the difficult things about working with generalized linear models (GLM) and generalized linear mixed models (GLMM) is figuring out how to interpret residual plots. We don’t really expect residual plots from a GLMM to look like one from a linear model, sure, but how do we tell when something looks “bad”?
What does the binned residuals plot look like?
The QQ-Plot and the binned residuals as suggested by Gelman and Hill (2007) look fine. The normal residuals plot, however, looks a bit weird to me: it is somewhat turned by 45 degrees and the straight “line” looks off.
How are residuals used in stats IQ regression?
(Stats iQ presents residuals as standardized residuals, which means every residual plot you look at with any model is on the same standardized y-axis.) In the plot on the right, each point is one day, where the prediction made by the model is on the x-axis and the accuracy of the prediction is on the y-axis.
How is the accuracy of a residual plot determined?
In the plot on the right, each point is one day, where the prediction made by the model is on the x-axis and the accuracy of the prediction is on the y-axis. The distance from the line at 0 is how bad the prediction was for that value.
Can you use dharma for GLMM residual checks?
This left me unable to recommend this as a general approach to folks I consult with. Since then, the author of the DHARMa package has come up with a clever way to use a simulation-based approach for residuals checks of GLMM’s. If you are interested in trying the package out, it has a very nice vignette to get you started.
Why does Dharma fail to calculate scaled residuals?
If you try to calculate the scaled residuals via DHARMa functions for an unsupported model, you will get a warning and then an error. DHARMa attempts to make predictions from the model to simulate with, but it will then fail. This is an indication that you’d to use createDHARMa () to make the residuals instead.
Can you use dharma to check unsupported models?
Below I fit zero-inflated negative binomial model with glmmTMB (). If you try to calculate the scaled residuals via DHARMa functions for an unsupported model, you will get a warning and then an error. DHARMa attempts to make predictions from the model to simulate with, but it will then fail.