This function tests if there are probles in a res ~ group structure. It performs two tests: test for within-group uniformity, and test for between-group homogeneity of variances
testCategorical(simulationOutput, catPred, quantiles = c(0.25, 0.5, 0.75), plot = T)
an object of class DHARMa, either created via
a categorical predictor with the same dimensions as the residuals in simulationOutput
whether to draw the quantile lines.
if T, the function will create an additional plot
The function tests for two common problems: are residuals within each group distributed according to model assumptions, and is the variance between group heterogeneous.
The test for within-group uniformity is performed via multipe KS-tests, with adjustment of p-values for multiple testing. If the plot is drawn, problematic groups are highlighted in red, and a corresponding message is displayed in the plot.
The test for homogeneity of variances is done with a Levene test. A significant p-value means that group variances are not constant. In this case, you should consider modelling variances, e.g. via ~dispformula in glmmTMB.
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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