testResiduals {DHARMa} | R Documentation |
Calls both uniformity and dispersion test
testResiduals(simulationOutput, plot = T)
simulationOutput |
an object of class DHARMa, either created via |
plot |
if T, plots functions of the tests are called |
This function is a wrapper for the various test functions implemented in DHARMa. Currently, this function calls the testUniformity
and the testDispersion
functions. All other tests (see list below) have to be called by hand.
Florian Hartig
testResiduals
, testUniformity
, testOutliers
, testDispersion
, testZeroInflation
, testGeneric
, testTemporalAutocorrelation
, testSpatialAutocorrelation
, testQuantiles
, testCategorical
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)