popest_clstr

Codes to run the hierarchical clustering model to estimate number of groups and group-size. The code uses two dataset, the spatial location of the capture and the maximum number of individuals captured at the location.

I used the WeightedCluster (http://mephisto.unige.ch/weightedcluster/), factoextra (https://rpkgs.datanovia.com/factoextra/index.html) and Nbclust (https://cran.r-project.org/web/packages/NbClust/index.html) package in R for the analysis.

library(WeightedCluster)
library(factoextra)
library(NbClust)

The dataset requird need to have the following format,

1. Geographic coordinate of the capture event
2. Number of individuals in the capture event

I have merged all the individuals captured within 5 minutes but that would depend on species and users should understand the independence between the captured individuals before merging them into one single capture event.

Latitude Longtitude Number of indiviudals 
                        photo-captured
20.27764  79.38614  4
20.29808  79.37403  3
20.252    79.38497  1
20.32906  79.29086  2
20.33533  79.34514  2
20.36006  79.31292  3

Running hierarchial clustering with the data

x<- cbind(Longitude, Latitude)
dst <-dist(x)
clstr <- hclust(dst, method = "complete", members = Number of individuals)

There are dfferent algorithms to understand the clustering efficacy. Some of the methods are outlined here below,

aggMvad <- wcAggregateCases(wddat[, 9:10])
uniqueMvad <- wddat[aggMvad$aggIndex, 9:10]
mvad.seq <- seqdef(uniqueMvad, weights = wddat$indv)

Plotting the clutsers on the dendrogram also reveals if the clustering algorithms are segregating the clusters consistently or not.

averageTree <- as.seqtree(clstr, seqdata = mvad.seq, diss = dst,ncluster = 6)
seqtreedisplay(averageTree, type = "d", border = NA)
avgClustQual <- as.clustrange(clstr,  dst, weights = wddat$indv, ncluster = 10)
plot(avgClustQual)
#write.csv(avgClustQual$stats, "2014_clustering_stat.csv")