plotQQunif {DHARMa} | R Documentation |
Quantile-quantile plot for a uniform distribution
Description
The function produces a uniform quantile-quantile plot from a DHARMa output
Usage
plotQQunif(simulationOutput, testUniformity = T, testOutliers = T,
testDispersion = T, ...)
Arguments
simulationOutput |
a DHARMa simulation output (class DHARMa) |
testUniformity |
if T, the function |
testOutliers |
if T, the function |
testDispersion |
if T, the function |
... |
arguments to be passed on to |
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.
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$Environment1,
quantreg = FALSE)
# if pred is a factor, or if asFactor = T, will produce a boxplot
plotResiduals(simulationOutput, form = testData$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
[Package DHARMa version 0.4.6 Index]