DHARMa (version 0.2.2)

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

a DHARMa object with simulated residuals created with simulateResiduals

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

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)


# }