testQuantiles: Test for quantiles

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

Test for quantiles

Description

This function tests

Usage

testQuantiles(simulationOutput, predictor = NULL, 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)
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 distributionm, 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 (p-values of intercept and spline are combined via Benjamini & Hochberg adjustment to control the FDR)

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

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)
testQuantiles(simulationOutput, predictor = testData$Environment1)

plot(simulationOutput)
plotResiduals(simulationOutput)

## End(Not run)

DHARMa documentation built on Oct. 18, 2024, 5:09 p.m.