DHARMa (version 0.3.3.0)

testDispersion: DHARMa dispersion tests

Description

This function performs a simulation-based test for over/underdispersion

Usage

testDispersion(simulationOutput, alternative = c("two.sided", "greater",
  "less"), 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

alternative

a character string specifying whether the test should test if observations are "greater", "less" or "two.sided" compared to the simulated null hypothesis. Greater corresponds to overdispersion.

plot

whether to plot output

...

arguments to pass on to testGeneric

Details

The function implements two tests, depending on whether it is applied on a simulation with refit = F, or refit = T.

If refit = F, the function tests the sd of the data against the sd of the simulated data.

If refit = T, the function compares the approximate deviance (via squared pearson residuals) with the same quantity from the models refitted with simulated data. Applying this is much slower than the previous alternative. Given the computational cost, I would suggest that most users will be satisfied with the standard dispersion test.

See Also

testResiduals, testUniformity, testOutliers, testZeroInflation, 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) 




# }