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Data X:
-999.0 2.0 -999.0 -999.0 1.8 .7 3.9 1.0 3.6 1.4 1.5 .7 2.7 -999.0 2.1 .0 4.1 1.2 1.3 6.1 .3 .5 3.4 -999.0 1.5 -999.0 3.4 .8 .8 -999.0 -999.0 1.4 2.0 1.9 2.4 2.8 1.3 2.0 5.6 3.1 1.0 1.8 .9 1.8 1.9 .9 -999.0 2.6 2.4 1.2 .9 .5 -999.0 .6 -999.0 2.2 2.3 .5 2.6 .6 6.6 -999.0
Data Y:
3 3 1 3 4 4 1 4 1 1 4 5 2 5 1 2 2 2 1 1 5 5 2 1 1 1 3 4 5 1 4 4 1 1 1 3 3 3 1 1 5 2 4 2 4 5 2 3 1 2 2 3 5 5 2 2 2 3 2 4 1 1
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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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Big Analytics Cloud Computing Center
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