DHARMa (version 0.2.4)

plotResiduals: Generic residual plot with either spline or quantile regression

Description

The function creates a generic residual plot with either spline or quantile regression

Usage

plotResiduals(pred, residuals = NULL, quantreg = NULL, rank = F,
  asFactor = NULL, ...)

Arguments

pred

either the predictor variable against which the residuals should be plotted, or a DHARMa object

residuals

residuals values. Leave empty if pred is a DHARMa object

quantreg

whether to perform a quantile regression on 0.25, 0.5, 0.75 on the residuals. If F, a spline will be created instead. Default NULL chooses T for nObs < 2000, and F otherwise.

rank

if T, the values of pred will be rank transformed. This will usually make patterns easier to spot visually, especially if the distribution of the predictor is skewed. If pred is a factor, this has no effect.

asFactor

should the predictor variable be treated as a factor. Default is to choose this for <10 unique predictions, as long as enough predictions are available to draw a boxplot.

...

additional arguments to plot / boxplot.

Details

Plots residuals against a predictor. Outliers are highlighted in red (for more on oultiers, see testOutliers). For a correctly specified model, we would expect uniformity in y direction when plotting against any predictor.

To provide a visual aid in detecting deviations from uniformity in y-direction, the plot of the residuals against the predited values also performs an (optional) quantile regression, which provides 0.25, 0.5 and 0.75 quantile lines across the plots. These lines should be straight, horizontal, and at y-values of 0.25, 0.5 and 0.75. Note, however, that some deviations from this are to be expected by chance, even for a perfect model, especially if the sample size is small.

The quantile regression can take some time to calculate, especially for larger datasets. For that reason, quantreg = F can be set to produce a smooth spline instead.

See Also

plotSimulatedResiduals, plotQQunif

Examples

# NOT RUN {
testData = createData(sampleSize = 200, family = poisson(), 
                      randomEffectVariance = 1, numGroups = 5)
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)

hist(simulationOutput )

#############  residual plots  ###############

# rank transformation, using a simulationOutput
plotResiduals(simulationOutput, rank = TRUE, quantreg = FALSE)

# residual vs predictors, using explicit values for pred, residual 
plotResiduals(pred = testData$Environment1, 
              residuals = simulationOutput$scaledResiduals, quantreg = FALSE)

# if pred is a factor, or asFactor = T, will produce a boxplot
plotResiduals(pred = testData$group, residuals = simulationOutput$scaledResiduals, 
              quantreg = FALSE, asFactor = TRUE)

# All these options can also be provided to the main plotting function
plot(simulationOutput, quantreg = FALSE, rank = FALSE)

# If you want to plot summaries per group, use
simulationOutput = recalculateResiduals(simulationOutput, group = testData$group)
plot(simulationOutput, asFactor = TRUE) # we see one residual point per RE


# }