testCategorical: Test for categorical dependencies

Description

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

Usage

testCategorical(simulationOutput, catPred, quantiles = c(0.25, 0.5, 0.75),
  plot = T)

Arguments

simulationOutput: 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.
catPred: a categorical predictor with the same dimensions as the residuals in simulationOutput
quantiles: whether to draw the quantile lines.
plot: if T, the function will create an additional plot

Author

Florian Hartig

Details

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.

Examples

Run this code

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)