Description Usage Arguments Details Value Author(s) References See Also Examples

This function generates realizations (point patterns) from a given Marked IPPP via conditioning of the joint intensity surface on its marked component. See details for the choice of models for the mark distribution. For each mark value we obtain a ground process. There processes are standard IPPP (unmarked) with mixture intensity surfaces. The mark distribution is responsible for the number of events in the point pattern.

For examples see

http://faculty.missouri.edu/~micheasa/sppmix/sppmix_all_examples.html#rMIPPP_cond_mark

1 2 3 4 |

`lambda` |
Average number of mark values observed over the window. This is the total number of points observed (on the average). |

`params` |
Parameters for the mark
distribution. The value depends on the |

`mark_distr_choice` |
A number indicating which
mark distribution to use. In the
discrete mark case, the mark distribution
is discrete over the marks |

`truncate` |
Logical variable indicating whether or not we
normalize the densities of the mixture components
to have all their mass within the window defined
in the window |

`discrete_mark` |
Logical flag indicating whether the mark is discrete or not. Default is TRUE. For continuous marks set this to FALSE. |

`win` |
Object of type |

`bigwin` |
Object of type |

`open_new_window` |
Open a new window for a plot. |

`grayscale` |
Logical to request plots in grayscale. |

`show_plots` |
Logical variable requesting to produce exploratory plots of the Marked IPPP intensity surface and generated point pattern for each mark. |

For discrete marks, we assume that the joint intensity function of a
marked point pattern `N=[s,m]`

with `n`

events is of the form:

`intensity(s,m)=lambda*M(m|theta1)*g(s(m)|theta2(m))`

where `m`

denotes a mark and `s=s(m)`

a location with mark `m`

, lambda a parameter
with the interpretation as the average number of events
over the window of observation, and `M`

the mark distribution and
`g`

the ground intensity are proper densities.

In order to simulate from this Marked IPPP
we first simulate the number of events
and their marks from an IPPP with
intensity `lambda*M(m|theta1)`

, and then generate
the ground intensities for each mark. Marks are assumed to be
independnet of each other and the mixture parameters
describing each ground process are also assumed to be independent
of each other.

The continuous mark case will be implemented in future releases.

See Micheas (2014) for more details on Marked IPPP models via conditioning arguments.

A list containing the following components:

`groundsurfs` |
A list of |

`groundPPs` |
A list of |

`genMPP` |
The generated point pattern as an object of class |

`mark_distr_choice` |
The choice of mark distribution. Same as the supplied parameter. |

`params` |
The default or supplied parameter |

Sakis Micheas

Hierarchical Bayesian Modeling of Marked Non-Homogeneous Poisson Processes with finite mixtures and inclusion of covariate information. Micheas, A.C. (2014). Journal of Applied Statistics, 41, 12, 2596-2615, DOI: 10.1080/02664763.2014.922167.

1 2 3 4 5 6 7 8 9 10 | ```
# Create a marked point pattern; use randomization and 2 discrete uniform
# marks (default values)
newMPP=rMIPPP_cond_mark(bigwin = spatstat::owin(c(-10,10),c(-10,10)))
newMPP$params
plot(newMPP$genMPP, showmarks=TRUE)+add_title("Marked Poisson point pattern",
n=newMPP$genMPP$n, nmarks=2)
plotmix_2d(newMPP$groundsurfs[[1]], newMPP$groundPPs[[1]])+ add_title(
"Poisson point pattern for mark 1", n=newMPP$genMPP$n, m=newMPP$groundsurfs[[1]]$m)
plotmix_2d(newMPP$groundsurfs[[2]], newMPP$groundPPs[[2]])+ add_title(
"Poisson point pattern for mark 2", n=newMPP$genMPP$n, m=newMPP$groundsurfs[[2]]$m)
``` |

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