testSpatialAutocorrelation | R Documentation |

This function performs a Moran's I test for distance-based (spatial, phylogenetic or similar) autocorrelation on the calculated quantile residuals

testSpatialAutocorrelation(simulationOutput, x = NULL, y = NULL, distMat = NULL, alternative = c("two.sided", "greater", "less"), plot = T)

`simulationOutput` |
an object of class DHARMa, either created via |

`x` |
the x coordinate, in the same order as the data points. Must be specified unless distMat is provided. |

`y` |
the y coordinate, in the same order as the data points. Must be specified unless distMat is provided. |

`distMat` |
optional distance matrix. If not provided, euclidean distances based on x and y will be calculated. See details for explanation |

`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 |

The function performs Moran.I test from the package ape on the DHARMa residuals. If a distance matrix (distMat) is provided, calculations will be based on this distance matrix, and x,y coordinates will only used for the plotting (if provided). If distMat is not provided, the function will calculate the euclidean distances between x,y coordinates, and test Moran.I based on these distances.

Testing for spatial autocorrelation requires unique x,y values - if you have several observations per location, either use the recalculateResiduals function to aggregate residuals per location, or extract the residuals from the fitted object, and plot / test each of them independently for spatially repeated subgroups (a typical scenario would repeated spatial observation, in which case one could plot / test each time step separately for temporal autocorrelation). Note that the latter must be done by hand, outside testSpatialAutocorrelation.

Standard DHARMa simulations from models with (temporal / spatial / phylogenetic) conditional autoregressive terms will still have the respective temporal / spatial / phylogenetic correlation in the DHARMa residuals, unless the package you are using is modelling the autoregressive terms as explicit REs and is able to simulate conditional on the fitted REs. This has two consequences

If you check the residuals for such a model, they will still show significant autocorrelation, even if the model fully accounts for this structure.

Because the DHARMa residuals for such a model are not statistically independent any more, other tests (e.g. dispersion, uniformity) may have inflated type I error, i.e. you will have a higher likelihood of spurious residual problems.

There are three (non-exclusive) routes to address these issues when working with spatial / temporal / other autoregressive models:

Simulate conditional on the fitted CAR structures (see conditional simulations in the help of simulateResiduals)

Rotate simulations prior to residual calculations (see parameter rotation in simulateResiduals)

Use custom tests / plots that explicitly compare the correlation structure in the simulated data to the correlation structure in the observed data.

Florian Hartig

`testResiduals`

, `testUniformity`

, `testOutliers`

, `testDispersion`

, `testZeroInflation`

, `testGeneric`

, `testTemporalAutocorrelation`

, `testSpatialAutocorrelation`

, `testQuantiles`

, `testCategorical`

testData = createData(sampleSize = 40, family = gaussian()) fittedModel <- lm(observedResponse ~ Environment1, data = testData) res = simulateResiduals(fittedModel) # Standard use testSpatialAutocorrelation(res, x = testData$x, y = testData$y) # Alternatively, one can provide a distance matrix dM = as.matrix(dist(cbind(testData$x, testData$y))) testSpatialAutocorrelation(res, distMat = dM) # You could add a spatial variogram via # library(gstat) # dat = data.frame(res = residuals(res), x = testData$x, y = testData$y) # coordinates(dat) = ~x+y # vario = variogram(res~1, data = dat, alpha=c(0,45,90,135)) # plot(vario, ylim = c(-1,1)) # if there are multiple observations with the same x values, # create first ar group with unique values for each location # then aggregate the residuals per location, and calculate # spatial autocorrelation on the new group # modifying x, y, so that we have the same location per group # just for completeness testData$x = as.numeric(testData$group) testData$y = as.numeric(testData$group) # calculating x, y positions per group groupLocations = aggregate(testData[, 6:7], list(testData$group), mean) # calculating residuals per group res2 = recalculateResiduals(res, group = testData$group) # running the spatial test on grouped residuals testSpatialAutocorrelation(res2, groupLocations$x, groupLocations$y) # careful when using REs to account for spatially clustered (but not grouped) # data. this originates from https://github.com/florianhartig/DHARMa/issues/81 # Assume our data is divided into clusters, where observations are close together # but not at the same point, and we suspect that observations in clusters are # autocorrelated clusters = 100 subsamples = 10 size = clusters * subsamples testData = createData(sampleSize = size, family = gaussian(), numGroups = clusters ) testData$x = rnorm(clusters)[testData$group] + rnorm(size, sd = 0.01) testData$y = rnorm(clusters)[testData$group] + rnorm(size, sd = 0.01) # It's a good idea to use a RE to take out the cluster effects. This accounts # for the autocorrelation within clusters library(lme4) fittedModel <- lmer(observedResponse ~ Environment1 + (1|group), data = testData) # DHARMa default is to re-simulted REs - this means spatial pattern remains # because residuals are still clustered res = simulateResiduals(fittedModel) testSpatialAutocorrelation(res, x = testData$x, y = testData$y) # However, it should disappear if you just calculate an aggregate residuals per cluster # Because at least how the data are simulated, cluster are spatially independent res2 = recalculateResiduals(res, group = testData$group) testSpatialAutocorrelation(res2, x = aggregate(testData$x, list(testData$group), mean)$x, y = aggregate(testData$y, list(testData$group), mean)$x) # For lme4, it's also possible to simulated residuals conditional on fitted # REs (re.form). Conditional on the fitted REs (i.e. accounting for the clusters) # the residuals should now be indepdendent. The remaining RSA we see here is # probably due to the RE shrinkage res = simulateResiduals(fittedModel, re.form = NULL) testSpatialAutocorrelation(res, x = testData$x, y = testData$y)

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