createDHARMa {DHARMa} | R Documentation |

Create a DHARMa object from hand-coded simulations or Bayesian posterior predictive simulations

createDHARMa(simulatedResponse, observedResponse, fittedPredictedResponse = NULL, integerResponse = F, seed = 123, method = c("PIT", "traditional"))

`simulatedResponse` |
matrix of observations simulated from the fitted model - row index for observations and colum index for simulations |

`observedResponse` |
true observations |

`fittedPredictedResponse` |
optional fitted predicted response. For Bayesian posterior predictive simulations, using the median posterior prediction as fittedPredictedResponse is recommended. If not provided, the mean simulatedResponse will be used. |

`integerResponse` |
if T, noise will be added at to the residuals to maintain a uniform expectations for integer responses (such as Poisson or Binomial). Unlike in |

`seed` |
the random seed to be used within DHARMa. The default setting, recommended for most users, is keep the random seed on a fixed value 123. This means that you will always get the same randomization and thus teh same result when running the same code. NULL = no new seed is set, but previous random state will be restored after simulation. FALSE = no seed is set, and random state will not be restored. The latter two options are only recommended for simulation experiments. See vignette for details. |

`method` |
the quantile randomization method used. The two options implemented at the moment are probability integral transform (PIT-) residuals (current default), and the "traditional" randomization procedure, that was used in DHARMa until version 0.3.0. For details, see |

The use of this function is to convert simulated residuals (e.g. from a point estimate, or Bayesian p-values) to a DHARMa object, to make use of the plotting / test functions in DHARMa

Either scaled residuals or (simulatedResponse AND observed response) have to be provided

## READING IN HAND-CODED SIMULATIONS testData = createData(sampleSize = 50, randomEffectVariance = 0) fittedModel <- glm(observedResponse ~ Environment1, data = testData, family = "poisson") # in DHARMA, using the simulate.glm function of glm sims = simulateResiduals(fittedModel) plot(sims, quantreg = FALSE) # Doing the same with a handcoded simulate function. # of course this code will only work with a 1-par glm model simulateMyfit <- function(n=10, fittedModel){ int = coef(fittedModel)[1] slo = coef(fittedModel)[2] pred = exp(int + slo * testData$Environment1) predSim = replicate(n, rpois(length(pred), pred)) return(predSim) } sims = simulateMyfit(250, fittedModel) dharmaRes <- createDHARMa(simulatedResponse = sims, observedResponse = testData$observedResponse, fittedPredictedResponse = predict(fittedModel, type = "response"), integer = TRUE) plot(dharmaRes, quantreg = FALSE)

[Package *DHARMa* version 0.4.3 Index]