Description Usage Arguments Details Value References See Also

Monte Carlo Inference of Temporal Changes in Spatial Segregation An approximate Monte Carlo test of temporal changes in a multivariate spatial-temporal point process.

1 | ```
mcpat.test(pts, marks, t, h, ntest = 100, proc = TRUE)
``` |

`pts` |
matrix containing the |

`marks` |
numeric/character vector of the marked type labels of the data points. |

`t` |
numeric vector of the associated time-periods. |

`h` |
numeric vector of the bandwidths at which to calculate the cross-validated log-likelihood function pooled over times. |

`ntest` |
integer with default 100, number of simulations for the Monte Carlo test |

`proc` |
logical, default |

The spatial-temporal data are denoted as *(x_i, m_i, t_i)*,
where *x_i* are the spatial locations, *m_i* are the categorical
mark sequence numbers, and *t_i* are the associated time-periods.

The null hypothesis is that the type-specific probability surfaces are
constant over time-periods, *i.e.*, *p_k(x, t)=p_k(x)*, for any
*t*, where *p_k(x, t)* are the type-specific probabilities for
*k*th category within time-period *t*.

Each Monte Carlo simulation is sampled from an approximate *true*
type-specific probability surfaces — the estimated one from the data.
Approximately, the simulated data and the original data are samples from
the same probability distribution under the null hypothesis. See Diggle,
P.J. *et al* (2005) for more details.

A list with components

- pvalue
*p*-value of the approximate Monte Carlo test.- ...
copy of

`pts, marks, t, h, ntest`

.

Diggle, P. J. and Zheng, P. and Durr, P. A. (2005) Nonparametric estimation of spatial segregation in a multivariate point process: bovine tuberculosis in Cornwall, UK.

*J. R. Stat. Soc. C*,**54**, 3, 645–658.

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