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This function tests the overall uniformity of the simulated residuals in a DHARMa object
testUniformity(simulationOutput, alternative = c("two.sided", "less",
"greater"), plot = T)
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 character string specifying whether the test should test if observations are "greater", "less" or "two.sided" compared to the simulated null hypothesis. See ks.test
for details
if T, plots calls plotQQunif
as well
The function applies a ks.test
for uniformity on the simulated residuals.
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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