tango.stat | R Documentation |
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.
tango.stat(data, listw, zero.policy)
tango.test(formula, data, model, R, ...)
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. |
Tango, Toshiro (1995). A Class of Tests for Detecting 'General' and 'Focused' Clustering of Rare Diseases. Statistics in Medicine 14, 2323-2334.
DCluster, tango, tango.boot, tango.pboot
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)
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