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Data X:
6.3 2.0 4.5 1000.00 6600.00 42.0 3.00 1.00 3.00 2.1 1.8 69.0 2547000.00 4603000.00 624.0 3.00 5.00 4.00 9.1 0.7 27.0 10550.00 179500.00 180.0 4.00 4.00 4.00 15.8 3.9 19.0 0.023 0.300 35.0 1.00 1.00 1.00 5.2 1.0 30.4 160000.00 169000.00 392.0 4.00 5.00 4.00 10.9 3.6 28.0 3300.00 25600.00 63.0 1.00 2.00 1.00 8.3 1.4 50.0 52160.00 440000.00 230.0 1.00 1.00 1.00 11.0 1.5 7.0 0.425 6400.00 112.0 5.00 4.00 4.00 3.2 0.7 30.0 465000.00 423000.00 281.0 5.00 5.00 5.00 6.3 2.1 3.5 0.075 1200.00 42.0 1.00 1.00 1.00 6.6 4.1 6.0 0.785 3500.00 42.0 2.00 2.00 2.00 9.5 1.2 10.4 0.200 5000.00 120.0 2.00 2.00 2.00 3.3 0.5 20.0 27660.00 115000.00 148.0 5.00 5.00 5.00 11.0 3.4 3.9 0.120 1000.00 16.0 3.00 1.00 2.00 4.7 1.5 41.0 85000.00 325000.00 310.0 1.00 3.00 1.00 10.4 3.4 9.0 0.101 4000.00 28.0 5.00 1.00 3.00 7.4 0.8 7.6 1040.00 5500.00 68.0 5.00 3.00 4.00 2.1 0.8 46.0 521000.00 655000.00 336.0 5.00 5.00 5.00 17.9 2.0 24.0 0.010 0.250 50.0 1.00 1.00 1.00 6.1 1.9 100.0 62000.00 1320000.00 267.0 1.00 1.00 1.00 11.9 1.3 3.2 .023 0.400 19.0 4.00 1.00 3.00 13.8 5.6 5.0 1700.00 6300.00 12.0 2.00 1.00 1.00 14.3 3.1 6.5 3500.00 10800.00 120.0 2.00 1.00 1.00 15.2 1.8 12.0 0.480 15500.00 140.0 2.00 2.00 2.00 10.0 0.9 20.2 10000.00 115000.00 170.0 4.00 4.00 4.00 11.9 1.8 13.0 1620.00 11400.00 17.0 2.00 1.00 2.00 6.5 1.9 27.0 192000.00 180000.00 115.0 4.00 4.00 4.00 7.5 0.9 18.0 2500.00 12100.00 31.0 5.00 5.00 5.00 10.6 2.6 4.7 0.280 1900.00 21.0 3.00 1.00 3.00 7.4 2.4 9.8 4235.00 50400.00 52.0 1.00 1.00 1.00 8.4 1.2 29.0 6800.00 179000.00 164.0 2.00 3.00 2.00 5.7 0.9 7.0 0.750 12300.00 225.0 2.00 2.00 2.00 4.9 0.5 6.0 3600.00 21000.00 225.0 3.00 2.00 3.00 3.2 0.6 20.0 55500.00 175000.00 151.0 5.00 5.00 5.00 11.0 2.3 4.5 0.900 2600.00 60.0 2.00 1.00 2.00 4.9 0.5 7.5 2000.00 12300.00 200.0 3.00 1.00 3.00 13.2 2.6 2.3 0.104 2500.00 46.0 3.00 2.00 2.00 9.7 0.6 24.0 4190.00 58000.00 210.0 4.00 3.00 4.00 12.8 6.6 3.0 3500.00 3900.00 14.0 2.00 1.00 1.00
Names of X columns:
SWS PS L Wb Wbr Tg P S D
Type of Correlation
pearson
pearson
spearman
kendall
Chart options
Title:
R Code
panel.tau <- function(x, y, digits=2, prefix='', cex.cor) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) rr <- cor.test(x, y, method=par1) r <- round(rr$p.value,2) txt <- format(c(r, 0.123456789), digits=digits)[1] txt <- paste(prefix, txt, sep='') if(missing(cex.cor)) cex <- 0.5/strwidth(txt) text(0.5, 0.5, txt, cex = cex) } panel.hist <- function(x, ...) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h$breaks; nB <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$estimate,3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'pair',1,TRUE) a<-table.element(a,'Pearson r',1,TRUE) a<-table.element(a,'Spearman rho',1,TRUE) a<-table.element(a,'Kendall tau',1,TRUE) a<-table.row.end(a) cor.test(y[1,],y[2,],method=par1) for (i in 1:(n-1)) { for (j in (i+1):n) { a<-table.row.start(a) dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='') a<-table.element(a,dum,header=TRUE) rp <- cor.test(y[i,],y[j,],method='pearson') a<-table.element(a,round(rp$estimate,4)) rs <- cor.test(y[i,],y[j,],method='spearman') a<-table.element(a,round(rs$estimate,4)) rk <- cor.test(y[i,],y[j,],method='kendall') a<-table.element(a,round(rk$estimate,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=T) a<-table.element(a,paste('(',round(rp$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rs$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rk$p.value,4),')',sep='')) a<-table.row.end(a) } } a<-table.end(a) table.save(a,file='mytable1.tab')
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