Description Usage Arguments Details Note Author(s) See Also Examples

This function compares the observed number of zeros with the zeros expected from simulations.

1 | ```
testZeroInflation(simulationOutput, ...)
``` |

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

`...` |
further arguments to |

The plot shows the expected distribution of zeros against the observed values, the ratioObsSim shows observed vs. simulated zeros. A value < 1 means that the observed data has less zeros than expected, a value > 1 means that it has more zeros than expected (aka zero-inflation). Per default, the function tests both sides.

Some notes about common problems / questions:

Zero-inflation tests after fitting the model are crucial to see if you have zero-inflation. Just because there are a lot of zeros doesn't mean you have zero-inflation, see Warton, D. I. (2005). Many zeros does not mean zero inflation: comparing the goodness-of-fit of parametric models to multivariate abundance data. Environmetrics 16(3), 275-289.

That being said, zero-inflation tests are often not a reliable guide to decide wheter to add a zi term or not. In general, model structures should be decided on ideally a priori, if that is not possible via model selection techniques (AIC, BIC, WAIC, Bayes Factor). A zero-inflation test should only be run after that decision, and to validate the decision that was taken.

This function is a wrapper for `testGeneric`

, where the summary argument is set to function(x) sum(x == 0)

Florian Hartig

`testResiduals`

, `testUniformity`

, `testOutliers`

, `testDispersion`

, `testZeroInflation`

, `testGeneric`

, `testTemporalAutocorrelation`

, `testSpatialAutocorrelation`

, `testQuantiles`

, `testCategorical`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | ```
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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