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Maria@70
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1 source("MetadataPlots.R")
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Maria@70
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2
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m@77
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3 PlotCountryOutliers(df=read.csv("../data/results/global_outliers.csv",header=TRUE), output="../data/results/global_outliers.pdf")
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m@77
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4 PlotCountryOutliers(df=read.csv("../data/results/global_outliers_rhy.csv",header=TRUE), output="../data/results/global_outliers_rhy.pdf")
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m@77
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5 PlotCountryOutliers(df=read.csv("../data/results/global_outliers_mel.csv",header=TRUE), output="../data/results/global_outliers_mel.pdf")
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m@77
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6 PlotCountryOutliers(df=read.csv("../data/results/global_outliers_mfc.csv",header=TRUE), output="../data/results/global_outliers_mfc.pdf")
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m@77
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7 PlotCountryOutliers(df=read.csv("../data/results/global_outliers_chr.csv",header=TRUE), output="../data/results/global_outliers_chr.pdf")
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m@77
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8 PlotCountryOutliers(df=read.csv("../data/results/spatial_outliers.csv",header=TRUE), output="../data/results/spatial_outliers.pdf")
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Maria@70
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9
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m@77
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10 library(ape)
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m@77
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11 library(cluster)
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m@77
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12
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m@77
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13 df = read.csv("../data/results/cluster_freq.csv")
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m@77
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14 data = df[,2:dim(df)[2]]
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m@77
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15 rownames(data) <- df$labels
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m@77
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16 distMahal = as.dist(apply(data, 1, function(i) mahalanobis(data, i, cov = cov(data),tol=1e-18)))
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m@77
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17 hc=hclust(distMahal, method="average")
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m@77
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18 mypal = c("#000000", "#9B0000", "#9B0000", "#9B0000", "#9B0000")
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m@80
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19 clus5 = cutree(hc, 4)
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m@77
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20 pdf('../data/results/hierarchical_cluster.pdf')
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m@77
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21 par(mar=c(1,1,1,1))
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m@77
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22 plot(as.phylo(hc),type="fan",tip.color=mypal[clus5], cex=.5, label.offset=.5)
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m@77
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23 dev.off()
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