createData {DHARMa} | R Documentation |

This function creates synthetic dataset with various problems such as overdispersion, zero-inflation, etc.

createData(sampleSize = 100, intercept = 0, fixedEffects = 1, quadraticFixedEffects = NULL, numGroups = 10, randomEffectVariance = 1, overdispersion = 0, family = poisson(), scale = 1, cor = 0, roundPoissonVariance = NULL, pZeroInflation = 0, binomialTrials = 1, temporalAutocorrelation = 0, spatialAutocorrelation = 0, factorResponse = F, replicates = 1, hasNA = F)

`sampleSize` |
sample size of the dataset |

`intercept` |
intercept (linear scale) |

`fixedEffects` |
vector of fixed effects (linear scale) |

`quadraticFixedEffects` |
vector of quadratic fixed effects (linear scale) |

`numGroups` |
number of groups for the random effect |

`randomEffectVariance` |
variance of the random effect (intercept) |

`overdispersion` |
if this is a numeric value, it will be used as the sd of a random normal variate that is added to the linear predictor. Alternatively, a random function can be provided that takes as input the linear predictor. |

`family` |
family |

`scale` |
scale if the distribution has a scale (e.g. sd for the Gaussian) |

`cor` |
correlation between predictors |

`roundPoissonVariance` |
if set, this creates a uniform noise on the possion response. The aim of this is to create heteroscedasticity |

`pZeroInflation` |
probability to set any data point to zero |

`binomialTrials` |
Number of trials for the binomial. Only active if family == binomial |

`temporalAutocorrelation` |
strength of temporalAutocorrelation |

`spatialAutocorrelation` |
strength of spatial Autocorrelation |

`factorResponse` |
should the response be transformed to a factor (inteded to be used for 0/1 data) |

`replicates` |
number of datasets to create |

`hasNA` |
should an NA be added to the environmental predictor (for test purposes) |

testData = createData(sampleSize = 500, intercept = 2, fixedEffects = c(1), overdispersion = 0, family = poisson(), quadraticFixedEffects = c(-3), randomEffectVariance = 0) par(mfrow = c(1,2)) plot(testData$Environment1, testData$observedResponse) hist(testData$observedResponse) # with zero-inflation testData = createData(sampleSize = 500, intercept = 2, fixedEffects = c(1), overdispersion = 0, family = poisson(), quadraticFixedEffects = c(-3), randomEffectVariance = 0, pZeroInflation = 0.6) par(mfrow = c(1,2)) plot(testData$Environment1, testData$observedResponse) hist(testData$observedResponse) # binomial with multiple trials testData = createData(sampleSize = 40, intercept = 2, fixedEffects = c(1), overdispersion = 0, family = binomial(), quadraticFixedEffects = c(-3), randomEffectVariance = 0, binomialTrials = 20) plot(observedResponse1 / observedResponse0 ~ Environment1, data = testData, ylab = "Proportion 1") # spatial / temporal correlation testData = createData(sampleSize = 100, family = poisson(), spatialAutocorrelation = 3, temporalAutocorrelation = 3) plot(log(observedResponse) ~ time, data = testData) plot(log(observedResponse) ~ x, data = testData)

[Package *DHARMa* version 0.4.3 Index]