DHARMa (version 0.3.3.0)

testQuantiles: Test for quantiles

Description

This function tests

Usage

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

Arguments

simulationOutput

an object of class DHARMa with simulated quantile residuals, either created via simulateResiduals or by createDHARMa for simulations created outside DHARMa

predictor

an optional predictor variable to be used, instead of the predicted response (default)

quantiles

the quantiles to be tested

plot

if T, 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.

See Also

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

Examples

# NOT RUN {
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
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)

# the quantile test is automatically performed in 
# }
# NOT RUN {
plot(simulationOutput)
plotResiduals(simulationOutput)
# }
# NOT RUN {
# }