DHARMa (version 0.3.3.0)

testResiduals: DHARMa general residual test

Description

Calls both uniformity and dispersion test

Usage

testResiduals(simulationOutput, 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

plot

if T, plots functions of the tests are called

Details

This function is a wrapper for the various test functions implemented in DHARMa. Currently, this function calls the testUniformity and the testDispersion functions. All other tests (see list below) have to be called by hand.

See Also

testUniformity, testOutliers, testDispersion, 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) 




# }