DHARMa (version 0.2.7)

testZeroInflation: Tests for zero-inflation

Description

This function compares the observed number of zeros with the zeros expected from simulations.

Usage

testZeroInflation(simulationOutput, ...)

Arguments

simulationOutput

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

...

further arguments to testGeneric

Details

shows the expected distribution of zeros against the observed

See Also

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

Examples

# NOT RUN {
# creating test data

testData = createData(sampleSize = 200, overdispersion = 0.5, randomEffectVariance = 0)
fittedModel <- glm(observedResponse ~ Environment1 , family = "poisson", data = testData)
simulationOutput <- simulateResiduals(fittedModel = fittedModel)

plot(simulationOutput, quantreg = FALSE)

###### Distribution tests #####

testUniformity(simulationOutput)

###### Dispersion tests #######

testDispersion(simulationOutput, alternative = "less") # underdispersion

###### Both together###########

testResiduals(simulationOutput)

###### Special tests ##########

# testing zero inflation
testZeroInflation(simulationOutput)

# testing generic summaries
countOnes <- function(x) sum(x == 1)  # testing for number of 1s
testGeneric(simulationOutput, summary = countOnes) # 1-inflation
testGeneric(simulationOutput, summary = countOnes, alternative = "less") # 1-deficit

means <- function(x) mean(x) # testing if mean prediction fits
testGeneric(simulationOutput, summary = means) 

spread <- function(x) sd(x) # testing if mean sd fits
testGeneric(simulationOutput, summary = spread) 

###### Refited ##############

# if model is refitted, a different test will be called

simulationOutput <- simulateResiduals(fittedModel = fittedModel, refit = TRUE, seed = 12)
testDispersion(simulationOutput)

###### Test per group ##############

simulationOutput = recalculateResiduals(simulationOutput, group = testData$group)
testDispersion(simulationOutput)


# }