Description Usage Arguments Details Value References See Also Examples
Run hierarchical clustering on a set of crimes using the log Bayes Factor as the similarity metric.
1 2  crimeClust_hier(crimedata, varlist, estimateBF, linkage = c("average",
"single", "complete"), ...)

crimedata 
data.frame of crime incidents. Must contain a column named

varlist 
a list of the variable names (columns of 
estimateBF 
function to estimate the log bayes factor from evidence variables 
linkage 
the type of linkage for hierarchical clustering

... 
other arguments passed to 
This function first compares all crime pairs using compareCrimes
,
then uses estimateBF
to estimate the log Bayes factor for every pair.
Next, it passes this information into hclust
to carry out the
agglomerative hierarchical clustering. Because hclust
requires
a dissimilarity, this uses the negative log Bayes factor.
The input varlist
is a list with elements named: crimeID, spatial,
temporal, categorical, and numerical. Each element should be a vector of
the column names of crimedata
corresponding to that feature. See
compareCrimes
for more details.
An object of class hclust
(from hclust
).
Porter, M. D. (2014). A Statistical Approach to Crime Linkage. arXiv preprint arXiv:1410.2285.. http://arxiv.org/abs/1410.2285
1 2 3 4 5 6 7 8 9 10  data(crimes)
# cluster the first 10 crime incidents
crimedata = crimes[1:10,]
varlist = list(spatial = c("X", "Y"), temporal = c("DT.FROM","DT.TO"),
categorical = c("MO1", "MO2", "MO3"))
estimateBF < function(X) rnorm(NROW(X)) # random estimation of log Bayes Factor
HC = crimeClust_hier(crimedata,varlist,estimateBF)
plot_hcc(HC,yticks=2:2)
# See vignette: "Crime Series Identification and Clustering" for more examples.

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