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smerc (version 1.2)

Statistical Methods for Regional Counts

Description

Implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering. The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) and Kulldorff (1997) .

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Version

Install

install.packages('smerc')

Monthly Downloads

1,190

Version

1.2

License

GPL (>= 2)

Maintainer

Joshua French

Last Published

May 26th, 2020

Functions in smerc (1.2)

Determine case windows (circles)

Perform cepp.test on simulated data

Cluster Evalation Permutation Procedure Test

all_shape_dists

Return all shapes and distances for each zone

Besag-Newell Test

arg_check_rflex_zones

Check arguments of rflex.zones

arg_check_dist_ellipse

Check argments if dist.ellipse

Perform edmst.test on simulated data

Perform dc.test on simulated data

Compute region weights for cepp.test

Perform dmst.test on simulated data

Construct connected subgraphs

Determine zones for the Dynamic Minimum Spanning Tree scan test

Compute minor axis distance of ellipse

Distinct elements of a list

Dynamic Minimum Spanning Tree spatial scan test

Combine distinct zones

Perform flex.test on simualated data

Determine sequence of fast subset scan zones

Elliptical Spatial Scan Test

Determine zones for elliptic.test

Perform elliptic.test on simulated data

Fast Subset Scan Test

Perform fast.test on simulated data

Nearest neighbors for elliptic scan

Determine zones for the Double Connected scan test

Double Connection spatial scan test

Early Stopping Dynamic Minimum Spanning Tree spatial scan test

Determine zones for the early stopping dynamic Minimum Spanning Tree scan test

Determine zones for the maxima likelihood first algorithm.

K nearest neighbors

Determine zones for the Maximum Linkage scan test

Maximum Linkage spatial scan test

Flexibly-shaped Spatial Scan Test

Determine zones for flexibly shaped spatial scan test

Maxima Likelihood First Scan Test

Compute Monte Carlo p-value

Apply getElement over a list

Perform mlink.test on simulated data

Minimum spanning tree for all regions

Adjacency matrix for New York leukemia data.

SpatialPolygonsDataFrame for New York leukemia data.

Minimum spanning tree sequence

Convert nearest neighbors list to zones

Cumulative sum over nearest neighbors

Determine non-overlapping zones

Leukemia data for 281 regions in New York.

Determine duplicates in nearest neighbor list

Print object of class tango.

Determine nearest neighbors based on maximum distance

Return nicely formatted results from mst.all

print.smerc_cluster

Print object of class smerc_cluster.

Compute middle p-value

plot.smerc_cluster

Plot object of class smerc_cluster.

Plots an object of class tango.

Perform rflex.test on simualated data

Determine zones for flexibly shaped spatial scan test

Restricted Flexibly-shaped Spatial Scan Test

Determine nearest neighbors with population constraint

Return most significant, non-overlapping zones

Perform scan.test on simulated data

Spatial Scan Test

summary.smerc_cluster

Summary of smerc_cluster object

Distance-based weights for tango.test

Tango's cluster detection test

Spatial scan statistic

Prepare smerc_cluster

Determine zones for the spatial scan test

Tango's statistic

Determine sequence of ULS zones.

Returns segments connecting neighbors

Upper Level Set Spatial Scan Test

Perform uls.test on simulated data