Nothing
set.seed(123)
testData = createData(sampleSize = 200, overdispersion = 1, randomEffectVariance = 0)
fittedModel <- glm(observedResponse ~ Environment1 , family = "poisson", data = testData)
simulationOutput <- simulateResiduals(fittedModel = fittedModel)
# default outlier test (with plot)
testOutliers(simulationOutput)
# default test uses "bootstrap" for nObs <= 500, or else binomial
# binomial is faster, but not exact for integer-valued distributions, see help
testOutliers(simulationOutput, type = "binomial")
testOutliers(simulationOutput, type = "bootstrap")
# note that default is to test outliers at BOTH margins for both an excess AND a lack
# of outliers. In the case above, the test reported an excess of outliers (you)
# can see this because expected frequency < observed. Moreover, if we plot the residuals
plotResiduals(simulationOutput, quantreg = FALSE)
# we see that we mostly have an excess of outliers at the upper margin.
# Let's see what would have happened if we would just have checked the lower margin
# (lower margin means residuals with value 0, i.e. lower tail of the simualtion
# envelope)
testOutliers(simulationOutput, margin = "lower", plot = FALSE)
# OK, now the frequency of outliers is 0, so we have too few, but this is n.s. against
# the expectation
# just for completeness, what would have happened if we would have checked both
# margins, but just for a lack of outliers (sign of underdispersion)
testOutliers(simulationOutput, alternative = "less", plot = FALSE)
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