Maria@70: source("MetadataPlots.R")
Maria@70: 
m@77: PlotCountryOutliers(df=read.csv("../data/results/global_outliers.csv",header=TRUE), output="../data/results/global_outliers.pdf")
m@77: PlotCountryOutliers(df=read.csv("../data/results/global_outliers_rhy.csv",header=TRUE), output="../data/results/global_outliers_rhy.pdf")
m@77: PlotCountryOutliers(df=read.csv("../data/results/global_outliers_mel.csv",header=TRUE), output="../data/results/global_outliers_mel.pdf")
m@77: PlotCountryOutliers(df=read.csv("../data/results/global_outliers_mfc.csv",header=TRUE), output="../data/results/global_outliers_mfc.pdf")
m@77: PlotCountryOutliers(df=read.csv("../data/results/global_outliers_chr.csv",header=TRUE), output="../data/results/global_outliers_chr.pdf")
m@77: PlotCountryOutliers(df=read.csv("../data/results/spatial_outliers.csv",header=TRUE), output="../data/results/spatial_outliers.pdf")
Maria@70: 
m@77: library(ape)
m@77: library(cluster) 
m@77: 
m@77: df = read.csv("../data/results/cluster_freq.csv")
m@77: data = df[,2:dim(df)[2]]
m@77: rownames(data) <- df$labels
m@77: distMahal = as.dist(apply(data, 1, function(i) mahalanobis(data, i, cov = cov(data),tol=1e-18)))
m@77: hc=hclust(distMahal, method="average")
m@77: mypal = c("#000000", "#9B0000", "#9B0000", "#9B0000", "#9B0000")
m@77: clus5 = cutree(hc, 5)
m@77: pdf('../data/results/hierarchical_cluster.pdf')
m@77: par(mar=c(1,1,1,1))
m@77: plot(as.phylo(hc),type="fan",tip.color=mypal[clus5], cex=.5, label.offset=.5)
m@77: dev.off()