Description Usage Arguments Details Value Examples

View source: R/analysis_functions.R

Bayesian inference for a spatio-temporal LGCP model with or without covariates.

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`data` |
A data frame consisting of columns |

`data.t` |
A data frame containing any temporal covariates with a column |

`sp.covs` |
A vector with the names of spatially-varying covariate to use in the model
(can be |

`t.covs` |
A vector with the names of temporally-varying covariates (can be |

`pop.var` |
The name of the population density variable to be used for the population
offset (can be |

`boundary` |
A |

`covariates` |
A |

`cellwidth` |
The width of cells of the computational grid. |

`laglength` |
The number of time periods to include. The maximum value of |

`dirname` |
The directory root name to save model output. A directory is created for each
MCMC chain as |

`prevRun` |
Used to set prior distributions. Either output from a previous call to |

`mala.pars` |
Parameters for the MCMC sampler. A vector of three numbers: the total number of iterations, the number of warmup iterations, and the number to thin. |

`nchains` |
The number of MCMC chains, default is |

`lib` |
Library location if not the default, otherwise NULL |

The `lgcp`

function provides a wrapper to several functions from the `lgcp`

package.
It simplifies the workflow described in the vignette for that package, providing a single
function to generate the appropriate grid, covariate matrices and lists, and perform inference
with the function `lgcp::lgcpPredictSpatioTemporalPlusPars`

. See the vignette for this
package for a description of the model. The implementation here allows for spatially and/or
temporally varying covariates but not spatio-temporally varying covariates, as in the time-scales
relevant to real-time surveillance applications these are not generally available. For users
requiring additional functionality, please refer to the `lgcp`

package documentation.

An object of class lgcpReal

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