DHARMa general residual test
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
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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