tango.stat: Compute Tango's Statistic for General Clustering

In DCluster: Functions for the Detection of Spatial Clusters of Diseases

Description

Compute Tango's statistic for general clustering. See tango manual page for details.

tango.stat computes the test statistic and the test using a hi-square distribution whilst tango.test performs a bootstrap test.

Usage

tango.stat(data, listw, zero.policy)
tango.test(formula, data, model, R, ...)

Arguments

formula	Formula that specifies the underlying model. The observed cases are the response and the expected number of cases must be specified as an offset in the log scale (see example below). Note that now it is not necessary to use Observed and Expected and that any other names can be used to specify the observed and expected cases.
model	Parametric model to be used in the bootstrap test. One of "param", "multinom", "poisson" or "negbin". See the DCluster manpage for details.
...	The remaining arguments in 'tango.stat' not included in 'tango.test'. This is done so because tango.test calls tango.stat in order to perform the test.
R	Number of replicates used in the test to compute the significance of the observed value of the test statistic.
data	A dataframe containing the data, as specified in the DCluster manpage.
listw	Neighbours list with spatial weights created, for example, by 'nb2listw' (package spdep).
zero.policy	See nb2listw in package spdep.

References

Tango, Toshiro (1995). A Class of Tests for Detecting 'General' and 'Focused' Clustering of Rare Diseases. Statistics in Medicine 14, 2323-2334.

See Also

DCluster, tango, tango.boot, tango.pboot

Examples

library(spdep)
data(nc.sids)

sids<-data.frame(Observed=nc.sids$SID74)
sids<-cbind(sids, Expected=nc.sids$BIR74*sum(nc.sids$SID74)/sum(nc.sids$BIR74) )
sids<-cbind(sids, x=nc.sids$x, y=nc.sids$y)

#Calculate neighbours based on distance
coords<-as.matrix(sids[,c("x", "y")])

dlist<-dnearneigh(coords, 0, Inf)
dlist<-include.self(dlist)
dlist.d<-nbdists(dlist, coords)

#Calculate weights. They are globally standardised but it doesn't
#change significance.
col.W.tango<-nb2listw(dlist, glist=lapply(dlist.d, function(x) {exp(-x)}),
        style="C")

#use exp(-D) as closeness matrix
tango.stat(sids, col.W.tango, zero.policy=TRUE)

tango.test(Observed~offset(log(Expected)), sids, model="poisson", R=99, 
   list=col.W.tango, zero.policy=TRUE)

Example output

Loading required package: boot
Loading required package: spdep
Loading required package: sp
Loading required package: spData
To access larger datasets in this package, install the spDataLarge
package with: `install.packages('spDataLarge',
repos='https://nowosad.github.io/drat/', type='source')`
Loading required package: sf
Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
Loading required package: MASS
            [,1]
[1,] 0.003844675
Tango's test of global clustering 

	Type of boots.: parametric 
	Model used when sampling: Poisson 
	Number of simulations: 99 
	Statistic:  0.003844675 
	p-value :  0.01 

DCluster documentation built on May 2, 2019, 6:10 p.m.