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Data X:
60635408600 79717000 64463389370 82855667 66750859382 79346667 69032584255.95 76270333 72841303906.58 89532667 72838685607.53 83541333 75887977453.76 82456000 78936158549.66 85818333 81986612544.38 92106667 85038924238.38 96873333 87328862789.67 96580000 85794984680.72 87769000 88082838620.17 88396000 91885510657.4 90995667 96355573323.8 92785000 101321342608.8 98112667 105791395836.8 105779667 113241494103 103660333 117215849652.8 105435000 120940898880.6 105204000 125658810316.5 107466333 130873880200.8 118554333 139583536623.4 123614333 148226520849 125620000 153789461350 121524333 161871513310.4 130830333 171781295610.4 138871333 178996585758.2 135215667 176620960002 122096333 186604680395 130005333 187772917033.2 126566000 193109744878.6 134808667 197630528464 138761333 206483683756.4 133778333 203906590621.6 122598667 206783719069.34 116446000 206759082201.76 100745333 211878483671.4 104419333 214009149959 103477000 217203709135.4 102054333 222331811922.6 102446667 232825724880 99528000 241180274038.7 106549667 248494123822.1 107587333 253049202253.08 117091333 256922518700.16 114029667 254451243638.75 109721333 262662316800.94 114697000 268926171539.67 115063667 272037080259.13 121234667 281118338865.04 118393000 286389613939.39 121308000 296190694318.91 117579000 307294826961.69 117414000 309697986635.6 117740333 314369521231.48 109417000 317533640477.42 116442333 327005697520.69 113311000 332458473876 109391333 339794119726.27 107198667
Names of X columns:
GDP Co2
Type of Correlation
36
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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