getDiag | R Documentation |

Extract posterior summaries of random effects

getDiag( inla_mod, field = c("space", "time", "spacetime")[1], CI = 0.95, draws = NULL, nsim = 1000, ... )

`inla_mod` |
output from |

`field` |
which random effects to plot. It can be one of the following: space, time, and spacetime. |

`CI` |
Desired level of credible intervals |

`draws` |
Posterior samples drawn from the fitted model. This argument allows the previously sampled draws (by setting save.draws to be TRUE) be used in new aggregation tasks. |

`nsim` |
number of simulations, only applicable for the cluster-level model space-time interaction terms when random slopes are included. |

`...` |
Unused arguments, for users with fitted object from the package before v1.0.0, arguments including Amat, year_label, and year_range can still be specified manually. |

List of diagnostic plots

Zehang Richard Li

## Not run: data(DemoMap) years <- levels(DemoData[[1]]$time) # obtain direct estimates data <- getDirectList(births = DemoData, years = years, regionVar = "region", timeVar = "time", clusterVar = "~clustid+id", ageVar = "age", weightsVar = "weights", geo.recode = NULL) # obtain direct estimates data_multi <- getDirectList(births = DemoData, years = years, regionVar = "region", timeVar = "time", clusterVar = "~clustid+id", ageVar = "age", weightsVar = "weights", geo.recode = NULL) data <- aggregateSurvey(data_multi) # national model years.all <- c(years, "15-19") fit1 <- smoothDirect(data = data, geo = DemoMap$geo, Amat = DemoMap$Amat, year_label = years.all, year_range = c(1985, 2019), rw = 2, is.yearly=FALSE, m = 5) random.time <- getDiag(fit1, field = "time") random.space <- getDiag(fit1, field = "space") random.spacetime <- getDiag(fit1, field = "spacetime") ## End(Not run)

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