DHARMa (version 0.4.7)

plotQQunif: Quantile-quantile plot for a uniform distribution

Description

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.

See Also

plotSimulatedResiduals, plotResiduals

Examples

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 = TRUE, will produce a boxplot
plotResiduals(simulationOutput, form = testData$group)

# to diagnose overdispersion and heteroskedasticity it can be useful to 
# display residuals as absolute deviation from the expected mean 0.5
plotResiduals(simulationOutput, absoluteDeviation = TRUE, quantreg = FALSE)

# 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