edmst.test | R Documentation |

`edmst.test`

implements the early stopping dynamic
Minimum Spanning Tree 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. This
procedure is repeated until adding a connected region
does not increase the test statistic (or the population
or distance upper bounds 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).

```
edmst.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:
`gedist(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 <- edmst.test(
coords = coords, cases = floor(nydf$cases),
pop = nydf$pop, w = nyw,
alpha = 0.12, longlat = TRUE,
nsim = 5, ubpop = 0.1, ubd = 0.2
)
# better plotting
if (require("sf", quietly = TRUE)) {
data(nysf)
plot(st_geometry(nysf), col = color.clusters(out))
}
```

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