mlf.test | R Documentation |

`mlf.test`

implements the Maxima Likelihood First
scan test of Yao et al. (2011), which is actually a
special case of the Dynamic Minimum Spanning Tree of
Assuncao et al. (2006). Find the single region that
maximizes the likelihood ratio test statistic. Starting
with this single region as a current zone, new candidate
zones are constructed by combining the current zone with
the connected region that maximizes the likelihood ratio
test statisic. This procedure is repeated until the
population and/or distance upper bound is reached.

mlf.test( coords, cases, pop, w, ex = sum(cases)/sum(pop) * pop, nsim = 499, alpha = 0.1, ubpop = 0.5, ubd = 0.5, 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 |

Only a single candidate zone is ever returned because the algorithm only constructs a single sequence of starting zones, and overlapping zones are not returned. Only the zone that maximizes the likelihood ratio test statistic is returned.

Returns a list of length two of class scan. The first element (clusters) is a list containing the significant, non-ovlappering clusters, and has the the following components:

`locids` |
The location ids of regions in a significant cluster. |

`pop` |
The total population in the cluser window. |

`cases` |
The observed number of cases in the cluster window. |

`expected` |
The expected number of cases in the cluster window. |

`smr` |
Standarized mortaility ratio (observed/expected) in the cluster window. |

`rr` |
Relative risk in the cluster window. |

`loglikrat` |
The loglikelihood ratio for the cluster window (i.e., the log of the test statistic). |

`pvalue` |
The pvalue of the test statistic associated with the cluster window. |

`w` |
The adjacency matrix of the cluster. |

`r` |
The maximum radius of the cluster (in terms of intercentroid distance from the starting region). |

The second element of the list is the centroid coordinates. This is needed for plotting purposes.

Joshua French

Yao, Z., Tang, J., & Zhan, F. B. (2011). Detection of arbitrarily-shaped clusters using a neighbor-expanding approach: A case study on murine typhus in South Texas. International journal of health geographics, 10(1), 1.

Assuncao, R.M., Costa, M.A., Tavares, A. and Neto, S.J.F. (2006). Fast detection of arbitrarily shaped disease clusters, Statistics in Medicine, 25, 723-742.

`print.smerc_cluster`

,
`summary.smerc_cluster`

,
`plot.smerc_cluster`

,
`scan.stat`

, `scan.test`

data(nydf) data(nyw) coords <- with(nydf, cbind(longitude, latitude)) out <- mlf.test( coords = coords, cases = floor(nydf$cases), pop = nydf$pop, w = nyw, alpha = 0.12, longlat = TRUE, nsim = 10, ubpop = 0.1, ubd = 0.5 ) data(nypoly) library(sp) plot(nypoly, col = color.clusters(out))

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