DHARMa (version 0.3.0)

testGeneric: Generic simulation test of a summary statistic

Description

This function tests if a user-defined summary differs when applied to simulated / observed data.

Usage

testGeneric(simulationOutput, summary, alternative = c("two.sided", "greater",
  "less"), plot = T, methodName = "DHARMa generic simulation test")

Arguments

simulationOutput

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

summary

a function that can be applied to simulated / observed data. See examples below

alternative

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

plot

whether to plot the simulated summary

methodName

name of the test (will be used in plot)

Details

This function tests if a user-defined summary differs when applied to simulated / observed data. the function can easily be remodeled to apply summaries on the residuals, by simply defining f = function(x) summary (x - predictions), as done in testDispersion

See Also

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

Examples

# NOT RUN {
testData = createData(sampleSize = 200, 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
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)
testOutliers(simulationOutput) 

# 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) 




# }