[R-sig-ME] DHARMa package diagnostic plots give different results

[Alessandra Bielli]

Dear list.

I wonder if anyone has experience in using the DHARMa package for model
diagnostics?

I am fitting a poisson GLMM of the type

glmer(captured ~ treatment + offset(log(Effort)) + (1|Trip_Code),
      data=x, family="poisson", glmerControl(optimizer = "bobyqa"))

where captured is a count and treatment is a categorical variable with two
levels (control and experiment). The dependent variable contains mainly
zeros (Fig 1).

I am running some diagnostic tests using the DHARMa package and I did not
detect any singularity, overdispersion or zero inflation.

When I check the quantile residuals plots though, using two different
methods a) and b) I obtain two different results.

a) simulationOutput <- simulateResiduals(fittedModel = m1)
plot(simulationOutput)

b) testQuantiles(simulationOutput)

With method a) no significant quantile deviation is detected (Fig 2, right
plot), while it is detected with method b) (Fig 3). However, I was
expecting a) and b) to give the same results since the R documentation
for testQuantiles {DHARMa} explains:
"the quantile test is automatically performed in
## Not run:
plot(simulationOutput)
plotResiduals(simulationOutput)"

I understand that method a) plots rank transformed model predictions, while
b) doesn't but I wouldn't think this affects the p-value, am I correct?
I also understand that method a) automatically adds some noise to the
residuals to maintain a uniform response (see:  integerResponse parameter).
Does method b) do the same? If it doesn't, could that be the reason for the
different results?

Many thanks,

Alessandra

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