DHARMa (version 0.3.3.0)

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

The plot shows the expected distribution of zeros against the observed values, the ratioObsSim shows observed vs. simulated zeros. A value < 1 means that the observed data has less zeros than expected, a value > 1 means that it has more zeros than expected (aka zero-inflation). Per default, the function tests both sides.

Some notes about common problems / questions:

* Zero-inflation tests after fitting the model are crucial to see if you have zero-inflation. Just because there are a lot of zeros doesn't mean you have zero-inflation, see Warton, D. I. (2005). Many zeros does not mean zero inflation: comparing the goodness-of-fit of parametric models to multivariate abundance data. Environmetrics 16(3), 275-289.

* That being said, zero-inflation tests are often not a reliable guide to decide wheter to add a zi term or not. In general, model structures should be decided on ideally a priori, if that is not possible via model selection techniques (AIC, BIC, WAIC, Bayes Factor). A zero-inflation test should only be run after that decision, and to validate the decision that was taken.

See Also

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

Examples

# NOT RUN {
testData = createData(sampleSize = 100, overdispersion = 0.5, randomEffectVariance = 0)
fittedModel <- glm(observedResponse ~ Environment1 , family = "poisson", data = testData)
simulationOutput <- simulateResiduals(fittedModel = fittedModel)

# the plot function runs 4 tests
# i) KS test i) Dispersion test iii) Outlier test iv) quantile test
plot(simulationOutput, quantreg = TRUE)

# testResiduals tests distribution, dispersion and outliers
# testResiduals(simulationOutput)

####### Individual tests #######

# KS test for correct distribution of residuals
testUniformity(simulationOutput)

# Dispersion test
testDispersion(simulationOutput) # tests under and overdispersion
testDispersion(simulationOutput, alternative = "less") # only underdispersion

# if model is refitted, a different test will be called
simulationOutput2 <- simulateResiduals(fittedModel = fittedModel, refit = TRUE, seed = 12)
testDispersion(simulationOutput2)

# often useful to test dispersion per group (e.g. binomial data, see vignette)
simulationOutput3 = recalculateResiduals(simulationOutput, group = testData$group)
testDispersion(simulationOutput3)

# Outlier test (number of observations outside simulation envelope)
# Should set type = "boostrap" for exact values
testOutliers(simulationOutput, type = "binomial") 

# 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) 




# }