mlink.test | R Documentation |

`mlink.test`

implements the Maximum Linkage spatial
scan test of Costa et al. (2012). Starting with a single
region as a current zone, new candidate zones are
constructed by combining the current zone with the
connected region that maximizes the resulting likelihood
ratio test statistic, with the added constraint that the
region has the maximum connections (i.e., shares a border
with) with the regions in the current zone. This
procedure is repeated until the population or distance
upper bounds constraints are reached. The same procedure
is repeated for each region. The clusters returned are
non-overlapping, ordered from most significant to least
significant. The first cluster is the most likely to be a
cluster. If no significant clusters are found, then the
most likely cluster is returned (along with a warning).

mlink.test( coords, cases, pop, w, ex = sum(cases)/sum(pop) * pop, nsim = 499, alpha = 0.1, ubpop = 0.5, ubd = 1, longlat = FALSE, cl = NULL )

`coords` |
An |

`cases` |
The number of cases observed in each region. |

`pop` |
The population size associated with each region. |

`w` |
A binary spatial adjacency matrix for the regions. |

`ex` |
The expected number of cases for each region. The default is calculated under the constant risk hypothesis. |

`nsim` |
The number of simulations from which to compute the p-value. |

`alpha` |
The significance level to determine whether a cluster is signficant. Default is 0.10. |

`ubpop` |
The upperbound of the proportion of the total population to consider for a cluster. |

`ubd` |
A proportion in (0, 1]. The distance of
potential clusters must be no more than |

`longlat` |
The default is |

`cl` |
A cluster object created by |

The maximum intercentroid distance can be found by
executing the command:
`sp::spDists(as.matrix(coords), longlat = longlat)`

,
based on the specified values of `coords`

and
`longlat`

.

Returns a `smerc_cluster`

object.

Joshua French

Costa, M.A. and Assuncao, R.M. and Kulldorff, M. (2012) Constrained spanning tree algorithms for irregularly-shaped spatial clustering, Computational Statistics & Data Analysis, 56(6), 1771-1783. <doi:10.1016/j.csda.2011.11.001>

`print.smerc_cluster`

,
`summary.smerc_cluster`

,
`plot.smerc_cluster`

,
`scan.stat`

, `scan.test`

data(nydf) data(nyw) coords <- with(nydf, cbind(longitude, latitude)) out <- mlink.test( coords = coords, cases = floor(nydf$cases), pop = nydf$pop, w = nyw, alpha = 0.12, longlat = TRUE, nsim = 2, ubpop = 0.05, ubd = 0.1 ) data(nypoly) library(sp) plot(nypoly, col = color.clusters(out))

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