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Data X:
0.017412729 1 0 0.041917293 0 1.917293233 0 0.419172932 0.010282153 2.542567603 0 4.191729323 0.120676692 2.312030075 0.164661654 12.7443609 0.477443609 13.64661654 0.004863755 0.258150829 0.008011489 0.074134358 0.004204897 0.014340967 0.00089631 0.213179752 0.018278432 0.489104359 0.037325002 0.731137284 0 14.83082707 0.022377834 0.400574454 0.018278432 0.350208531 0.117138411 2.706233144 0.103398426 13.73840223 1.590225564 0.460526316 0.001103383 5.639097744 0.000265038 4.586466165 0.001018797 6.879699248 0.001193609 0.937969925 0.001291353 0.477443609 0.00106391 1.191729323
Data Y:
7.28 0.44 4.36 1.29 4.52 0 11.89 0.65 3.85 0 10.21 0 2.69 0.43 3.78 0 0.45 0 4.42 0.37 10.9 1.32 7.81 1.89 5.11 1.22 0.83 2.16 5.32 0 6.47 0 2.98 0.33 2.69 0.88 5.38 0 4.55 0.28 0.67 1.64 6.59 0.56 7.44 0.21 8.55 0.44 3.29 1.95 1.95 0.57 4.88 0.74
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R Code
k <- cor.test(x,y,method='spearman') bitmap(file='test1.png') plot(x,y,main='Scatterplot',xlab=xlab,ylab=ylab) grid() dev.off() bitmap(file='test2.png') plot(rank(x),rank(y),main='Scatterplot of Ranks',xlab=xlab,ylab=ylab) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Spearman Rank Correlation',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'rho',header=TRUE) a<-table.element(a,k$estimate) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'2-sided p-value',header=TRUE) a<-table.element(a,k$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'S',header=TRUE) a<-table.element(a,k$statistic) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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