
This function tests if a user-defined summary differs when applied to simulated / observed data.
testGeneric(simulationOutput, summary, alternative = c("two.sided",
"greater", "less"), plot = T,
methodName = "DHARMa generic simulation test")
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.
a function that can be applied to simulated / observed data. See examples below
a character string specifying whether the test should test if observations are "greater", "less" or "two.sided" compared to the simulated null hypothesis
whether to plot the simulated summary
name of the test (will be used in plot)
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
testResiduals
, testUniformity
, testOutliers
, testDispersion
, testZeroInflation
, testGeneric
, testTemporalAutocorrelation
, testSpatialAutocorrelation
, testQuantiles
, testCategorical
# 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)
# 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)
# }
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