```
# NOT RUN {
testData = createData(sampleSize = 200, family = poisson(),
randomEffectVariance = 1, numGroups = 10)
fittedModel <- glm(observedResponse ~ Environment1,
family = "poisson", data = testData)
simulationOutput <- simulateResiduals(fittedModel = fittedModel)
######### main plotting function #############
# for all functions, quantreg = T will be more
# informative, but slower
plot(simulationOutput, quantreg = FALSE)
############# Distribution ######################
plotQQunif(simulationOutput = simulationOutput,
testDispersion = FALSE,
testUniformity = FALSE,
testOutliers = FALSE)
hist(simulationOutput )
############# residual plots ###############
# rank transformation, using a simulationOutput
plotResiduals(simulationOutput, rank = TRUE, quantreg = FALSE)
# smooth scatter plot - usually used for large datasets, default for n > 10000
plotResiduals(simulationOutput, rank = TRUE, quantreg = FALSE, smoothScatter = TRUE)
# residual vs predictors, using explicit values for pred, residual
plotResiduals(simulationOutput, form = testData$Environment1,
quantreg = FALSE)
# if pred is a factor, or if asFactor = T, will produce a boxplot
plotResiduals(simulationOutput, form = testData$group)
# All these options can also be provided to the main plotting function
# If you want to plot summaries per group, use
simulationOutput = recalculateResiduals(simulationOutput, group = testData$group)
plot(simulationOutput, quantreg = FALSE)
# we see one residual point per RE
# }
```

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