testGeneric {DHARMa}

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, either created via simulateResiduals for supported models or by createDHARMa for simulations created outside DHARMa, or a supported model. Providing a supported model directly is discouraged, because simulation settings cannot be changed in this case.

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

Note

The function that you supply is applied on the data as it is represented in your fitted model, which may not always correspond to how you think. This is important in particular when you use k/n binomial data, and want to test for 1-inflation. As an example, if have k/20 observations, and you provide your data via cbind (y, y-20), you have to test for 20-inflation (because this is how the data is represented in the model). However, if you provide data via y/20, and weights = 20, you should test for 1-inflation. In doubt, check how the data is internally represented in model.frame(model), or via simulate(model)

Author(s)

Florian Hartig

See Also

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

Examples

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)

# KS test for correct distribution within and between groups
testCategorical(simulationOutput, testData$group)

# Dispersion test - for details see ?testDispersion
testDispersion(simulationOutput) # tests under and overdispersion

# Outlier test (number of observations outside simulation envelope)
# Use type = "boostrap" for exact values, see ?testOutliers
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)