The function produces a uniform quantile-quantile plot from a DHARMa output. Optionally, tests for uniformity, outliers and dispersion can be added.
Usage
plotQQunif(simulationOutput, testUniformity = TRUE, testOutliers = TRUE,
testDispersion = TRUE, ...)Arguments
- simulationOutput
a DHARMa simulation output (class DHARMa).
- testUniformity
if T, the function testUniformity will be called and the result will be added to the plot.
- testOutliers
if T, the function testOutliers will be called and the result will be added to the plot.
- testDispersion
if T, the function testDispersion will be called and the result will be added to the plot.
- ...
arguments to be passed on to gap::qqunif.
Details
The function calls qqunif() from the R package gap to create a quantile-quantile plot for a uniform distribution, and overlays tests for particular distributional problems as specified.
When tests are displayed, significant p-values are highlighted in the color red by default. This can be changed by setting options(DHARMaSignalColor = "red") to a different color. See getOption("DHARMaSignalColor") for the current setting.
Examples
testData = createData(sampleSize = 200, family = poisson(),
fixedEffects = c(1,1),
randomEffectVariance = 1, numGroups = 10)
testData$Environment2[1] = NA
fittedModel <- glm(observedResponse ~ Environment1 + Environment2,
family = "poisson", data = testData)
simulationOutput <- simulateResiduals(fittedModel = fittedModel)
######### main plotting function #############
plot(simulationOutput)
# for all functions, quantreg = T (default) will be more informative
# but slower. Alternative:
plot(simulationOutput, quantreg = FALSE)
############# Distribution ######################
plotQQunif(simulationOutput = simulationOutput,
testDispersion = FALSE,
testUniformity = FALSE,
testOutliers = FALSE)
hist(simulationOutput)
