testQuantiles: Test for quantiles

In DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models

Test for quantiles

Description

This function fits quantile regressions on the residuals, and compares their location to the expected location.

Usage

testQuantiles(simulationOutput, predictor = NULL, rank = TRUE,
  quantiles = c(0.25, 0.5, 0.75), plot = TRUE)

Arguments

simulationOutput	an object of class DHARMa, either created via simulateResiduals for supported models or by createDHARMa for simulations created outside DHARMa, or a supported model. Providing a supported model directly is discouraged, because simulation settings cannot be changed in this case.
predictor	an optional predictor variable to be used, instead of the predicted response (default). See details.
rank	if TRUE, the values provided in predictor will be rank transformed. This will usually make patterns easier to spot visually, especially if the distribution of the predictor is skewed.
quantiles	the quantiles to be tested.
plot	if TRUE, the function will create an additional plot.

Details

The function fits quantile regressions (via package qgam) on the residuals, and compares their location to the expected location (because of the uniform distribution, the expected location is 0.5 for the 0.5 quantile).

A significant p-value for the splines means the fitted spline deviates from a flat line at the expected location.

The p-values of the intercept and splines are combined into a total p-value via Benjamini & Hochberg adjustment to control the FDR.

Predictor can be a formula (e.g. predictor = ~predictor), in which case NAs are handled automatically (recommended). Predictor can also be a variable in your environment (e.g. predictor = data$predictor), but then you need to remove rows that were excluded by the model due to NAs by hand. A formula must contain exactly one variable of the data used to fit the model, e.g. ~x or ~log(x); transformations such as log() are applied, and other objects in the formula (e.g. ~log(x + k)) are taken from the environment of the formula (plotResiduals currently ignores transformations and uses the untransformed variable). To plot residuals against several predictors or by group, see the argument form in plotResiduals. Predictor must be numeric or an ordered factor (used in the order of its levels); for categorical predictors, use testCategorical.

When plotting (plot = TRUE), the shaded gray areas indicate 95% confidence intervals of the quantile estimates (1.96 * standard error).

Author(s)

Florian Hartig

See Also

testResiduals, testUniformity, testOutliers, testDispersion, testZeroInflation, testGeneric, testTemporalAutocorrelation, testSpatialAutocorrelation, testQuantiles, testCategorical

Examples

testData = createData(sampleSize = 200, overdispersion = 0.0, randomEffectVariance = 0)
fittedModel <- glm(observedResponse ~ Environment1, family = "poisson", data = testData)
simulationOutput <- simulateResiduals(fittedModel = fittedModel)

# run the quantile test
x = testQuantiles(simulationOutput)
x # the test shows a combined p-value, corrected for multiple testing

## Not run: 
# accessing results of the test
x$pvals # pvalues for the individual quantiles
x$qgamFits # access the fitted quantile regression
summary(x$qgamFits[[1]]) # summary of the first fitted quantile

# possible to test user-defined quantiles
testQuantiles(simulationOutput, quantiles = c(0.7))

#  example with missing environmental predictor
fittedModel <- glm(observedResponse ~ 1 , family = "poisson", data = testData)
simulationOutput <- simulateResiduals(fittedModel = fittedModel)

# specified as formula (recommended)
testQuantiles(simulationOutput, predictor = ~Environment1)

# or as variable in your environment
testQuantiles(simulationOutput, predictor = testData$Environment1)

plot(simulationOutput)
plotResiduals(simulationOutput)

## End(Not run)

DHARMa documentation built on Nov. 7, 2026, 5:08 p.m.