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smerc (version 1.8.3)

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

773

Version

1.8.3

License

GPL (>= 2)

Maintainer

Joshua French

Last Published

October 10th, 2023

Functions in smerc (1.8.3)

dmst.test

Dynamic Minimum Spanning Tree spatial scan test
elliptic.zones

Determine zones for elliptic.test
edmst.test

Early Stopping Dynamic Minimum Spanning Tree spatial scan test
fast.sim

Perform fast.test on simulated data
elliptic.nn

Nearest neighbors for elliptic scan
elbow_point

Compute Elbow Point
elliptic.test

Elliptical Spatial Scan Test
flex_zones

Determine zones for flexibly shaped spatial scan test
flex.test

Flexibly-shaped Spatial Scan Test
flex.sim

Perform flex.test on simualated data
elliptic.sim.adj

Perform elliptic.test on simulated data
gedist

Compute distance for geographic coordinates
flex.zones

Determine zones for flexibly shaped spatial scan test
fast.zones

Determine sequence of fast subset scan zones
edmst.zones

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

Flexibly-shaped Spatial Scan Test
fast.test

Fast Subset Scan Test
edmst.sim

Perform edmst.test on simulated data
lget

Apply getElement over a list
elliptic.sim

Perform elliptic.test on simulated data
knn

K nearest neighbors
elliptic.penalty

Compute elliptic penalty
mlink.sim

Perform mlink.test on simulated data
mlf.zones

Determine zones for the maxima likelihood first algorithm.
mlf.test

Maxima Likelihood First Scan Test
logical2zones

Convert logical vector to zone
nndup

Determine duplicates in nearest neighbor list
mc.pvalue

Compute Monte Carlo p-value
nclusters

Number of clusters
mlink.test

Maximum Linkage spatial scan test
morancr.sim

Constant-risk Moran's I statistic
mlink.zones

Determine zones for the Maximum Linkage scan test
mst.all

Minimum spanning tree for all regions
nydf

Leukemia data for 281 regions in New York.
noc_enn

Returned ordered non-overlapping clusters
nn2zones

Convert nearest neighbors list to zones
morancr.stat

Constant-risk Moran's I statistic
neast

Breast cancer mortality in the Northeastern United States
mst.seq

Minimum spanning tree sequence
nypoly

SpatialPolygons object for New York leukemia data.
nysf

sf object for New York leukemia data.
nysp

SpatialPolygonsDataFrame for New York leukemia data.
print.smerc_cluster

Print object of class smerc_cluster.
print.smerc_optimal_ubpop

Print object of class smerc_optimal_ubpop.
tango.stat

Tango's statistic
scan.stat

Spatial scan statistic
scan.test

Spatial Scan Test
tango.test

Tango's clustering detection test
noz

Determine non-overlapping zones
noc_nn

Returned ordered non-overlapping clusters
nndist

Determine nearest neighbors based on maximum distance
plot.smerc_cluster

Plot object of class smerc_cluster.
rflex.zones

Determine zones for flexibly shaped spatial scan test
rflex.test

Restricted Flexibly-shaped Spatial Scan Test
morancr.test

Constant-risk Moran's I-based test
nn.cumsum

Cumulative sum over nearest neighbors
neastw

Binary adjacency matrix for neast
plot.smerc_optimal_ubpop

Plot object of class smerc_optimal_ubpop.
precog.test

PreCoG Scan Test
nyw

Adjacency matrix for New York leukemia data.
rflex.midp

Compute middle p-value
scan.zones

Determine zones for the spatial scan test
prep.mst

Return nicely formatted results from mst.all
scan_stat

Spatial scan statistic
optimal_ubpop

Optimal Population Upper Bound Statistics
sig_noc

Return most significant, non-overlapping zones
plot.tango

Plots an object of class tango.
sig_prune

Prune significant, non-overlapping zones
rflex.sim

Perform rflex.test on simualated data
seq_scan_sim

Perform scan test on simulated data sequentially
precog.sim

Perform precog.test on simulated data.
seq_scan_test

Sequential Scan Test
scan.sim

Perform scan.test on simulated data
scan.sim.adj

Perform scan.test on simulated data
rflex_zones

Determine zones for flexibly shaped spatial scan test
uls.sim

Perform uls.test on simulated data
tango.weights

Distance-based weights for tango.test
print.smerc_similarity_test

Print object of class smerc_similarity_test.
print.tango

Print object of class tango.
smerc

smerc
nnpop

Determine nearest neighbors with population constraint
zones.sum

Sum over zones
smerc_cluster

Prepare smerc_cluster
w2segments

Returns segments connecting neighbors
stat.poisson.adj

Compute Poisson test statistic
summary.smerc_cluster

Summary of smerc_cluster object
uls.test

Upper Level Set Spatial Scan Test
uls.zones

Determine sequence of ULS zones.
bn.test

Besag-Newell Test
bn.zones

Determine case windows (circles)
arg_check_dist_ellipse

Check argments if dist.ellipse
all_shape_dists

Return all shapes and distances for each zone
cepp.sim

Perform cepp.test on simulated data
dmst.zones

Determine zones for the Dynamic Minimum Spanning Tree scan test
cepp.test

Cluster Evalation Permutation Procedure Test
dc.zones

Determine zones for the Double Connected scan test
dc.test

Double Connection spatial scan test
dmst.sim

Perform dmst.test on simulated data
combine.zones

Combine distinct zones
csg2

Construct connected subgraphs
dc.sim

Perform dc.test on simulated data
color.clusters

Color clusters
cepp.weights

Compute region weights for cepp.test
dist.ellipse

Compute minor axis distance of ellipse
clusters

Extract clusters
distinct

Distinct elements of a list