testTemporalAutocorrelation {DHARMa}R Documentation

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.

Note

Important to note for all autocorrelation tests (spatial / temporal): the autocorrelation tests are valid to check for residual autocorrelation in models that don't assume such a correlation (in this case, you can use conditional or unconditional simulations), or if there is remaining residual autocorrelation after accounting for it in a spatial/temporal model (in that case, you have to use conditional simulations), but if checking unconditional simulations from a model with an autocorrelation structure on data that corresponds to this model, they will be significant, even if the model fully accounts for this structure.

This behavior is not really a bug, but rather originates from the definition of the quantile residuals: quantile residuals are calculated independently per data point, i.e. without consideratin of any correlation structure between data points that may exist in the simulations. As a result, the simulated distributions from a unconditional simulaton will typically not reflect the correlation structure that is present in each single simulation, and the same is true for the subsequently calculated quantile residuals.

The bottomline here is that spatial / temporal / other autoregressive models should either be tested based on conditional simulations, or (ideally) custom tests should be used that are not based on quantile residuals, but rather compare the correlation structure in the simulated data with the correlation structure in the observed data.

Author(s)

Florian Hartig

See Also

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

Examples

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))


[Package DHARMa version 0.4.3 Index]