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