DHARMa (version 0.4.1)

testTemporalAutocorrelation: Test for temporal autocorrelation

Description

This function performs a standard test for temporal autocorrelation on the simulated residuals

Usage

testTemporalAutocorrelation(simulationOutput, time,
  alternative = c("two.sided", "greater", "less"), 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.

time

the time, in the same order as the data points.

alternative

a character string specifying whether the test should test if observations are "greater", "less" or "two.sided" compared to the simulated null hypothesis

plot

whether to plot output

Details

The function performs a Durbin-Watson test on the uniformly scaled residuals, and plots the residuals against time. The DB test was originally be designed for normal residuals. In simulations, I didn't see a problem with this setting though. The alternative is to transform the uniform residuals to normal residuals and perform the DB test on those.

Testing for temporal autocorrelation requires unique time values - if you have several observations per time value, either use the recalculateResiduals function to aggregate residuals per time step, or extract the residuals from the fitted object, and plot / test each of them independently for temporally repeated subgroups (typical choices would be location / subject etc.). Note that the latter must be done by hand, outside testTemporalAutocorrelation.

See Also

testResiduals, testUniformity, testOutliers, testDispersion, testZeroInflation, testGeneric, testTemporalAutocorrelation, testSpatialAutocorrelation, testQuantiles, testCategorical

Examples

# NOT RUN {
testData = createData(sampleSize = 40, family = gaussian(), 
                      randomEffectVariance = 0)
fittedModel <- lm(observedResponse ~ Environment1, data = testData)
res = simulateResiduals(fittedModel)

# Standard use
testTemporalAutocorrelation(res, time =  testData$time)

# If you have several observations per time step, e.g. 
# because you have several locations, you will have to 
# aggregate

timeSeries1 = createData(sampleSize = 40, family = gaussian(), 
                         randomEffectVariance = 0)
timeSeries1$location = 1
timeSeries2 = createData(sampleSize = 40, family = gaussian(), 
                         randomEffectVariance = 0)
timeSeries2$location = 2
testData = rbind(timeSeries1, timeSeries2)

fittedModel <- lm(observedResponse ~ Environment1, data = testData)
res = simulateResiduals(fittedModel)

# Will not work because several residuals per time
# testTemporalAutocorrelation(res, time = testData$time)

# aggregating residuals by time
res = recalculateResiduals(res, group = testData$time)
testTemporalAutocorrelation(res, time = unique(testData$time))

# testing only subgroup location 1, could do same with loc 2
res = recalculateResiduals(res, sel = testData$location == 1)
testTemporalAutocorrelation(res, time = unique(testData$time))

# }