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Data X:
6.3 0.301029995663981 0.653212513775344 0.000000000000000 0.819543935541869 1.623249290397900 3 1 3 2.1 0.255272505103306 1.838849090737260 3.406028944963610 3.663040974893970 2.795184589682420 3 5 4 9.1 -0.154901959985743 1.431363764158990 1.023252459633710 2.254064452914340 2.255272505103310 4 4 4 15.8 0.591064607026499 1.278753600952830 -1.638272163982410 -0.522878745280338 1.544068044350280 1 1 1 5.2 0.000000000000000 1.482873583608750 2.204119982655920 2.227886704613670 2.593286067020460 4 5 4 10.9 0.556302500767287 1.447158031342220 0.518513939877887 1.408239965311850 1.799340549453580 1 2 1 8.3 0.146128035678238 1.698970004336020 1.717337582723860 2.643452676486190 2.361727836017590 1 1 1 11 0.176091259055681 0.845098040014257 -0.371611069949688 0.806179973983887 2.049218022670180 5 4 4 3.2 -0.154901959985743 1.477121254719660 2.667452952889950 2.626340367375040 2.448706319905080 5 5 5 6.3 0.322219294733919 0.544068044350276 -1.124938736608300 0.079181246047625 1.623249290397900 1 1 1 6.6 0.612783856719735 0.778151250383644 -0.105130343254747 0.544068044350276 1.623249290397900 2 2 2 9.5 0.079181246047625 1.017033339298780 -0.698970004336019 0.698970004336019 2.079181246047620 2 2 2 3.3 -0.301029995663981 1.301029995663980 1.441852175773290 2.060697840353610 2.170261715394960 5 5 5 11 0.531478917042255 0.591064607026499 -0.920818753952375 0.000000000000000 1.204119982655920 3 1 2 4.7 0.176091259055681 1.612783856719740 1.929418925714290 2.511883360978870 2.491361693834270 1 3 1 10.4 0.531478917042255 0.954242509439325 -0.995678626217357 0.602059991327962 1.447158031342220 5 1 3 7.4 -0.096910013008056 0.880813592280791 0.017033339298780 0.740362689494244 1.832508912706240 5 3 4 2.1 -0.096910013008056 1.662757831681570 2.716837723299520 2.816241299991780 2.526339277389840 5 5 5 17.9 0.301029995663981 1.380211241711610 -2.000000000000000 -0.602059991327962 1.698970004336020 1 1 1 6.1 0.278753600952829 2.000000000000000 1.792391689498250 3.120573931205850 2.426511261364580 1 1 1 11.9 0.113943352306837 0.505149978319906 -1.638272163982410 -0.397940008672038 1.278753600952830 4 1 3 13.8 0.748188027006200 0.698970004336019 0.230448921378274 0.799340549453582 1.079181246047620 2 1 1 14.3 0.491361693834273 0.812913356642856 0.544068044350276 1.033423755486950 2.079181246047620 2 1 1 15.2 0.255272505103306 1.079181246047620 -0.318758762624413 1.190331698170290 2.146128035678240 2 2 2 10 -0.045757490560675 1.305351369446620 1.000000000000000 2.060697840353610 2.230448921378270 4 4 4 11.9 0.255272505103306 1.113943352306840 0.209515014542631 1.056904851336470 1.230448921378270 2 1 2 6.5 0.278753600952829 1.431363764158990 2.283301228703550 2.255272505103310 2.060697840353610 4 4 4 7.5 -0.045757490560675 1.255272505103310 0.397940008672038 1.082785370316450 1.491361693834270 5 5 5 10.6 0.414973347970818 0.672097857935718 -0.552841968657781 0.278753600952829 1.322219294733920 3 1 3 7.4 0.380211241711606 0.991226075692495 0.626853414666726 1.702430536445530 1.716003343634800 1 1 1 8.4 0.079181246047625 1.462397997898960 0.832508912706236 2.252853030979890 2.214843848047700 2 3 2 5.7 -0.045757490560675 0.845098040014257 -0.124938736608300 1.089905111439400 2.352182518111360 2 2 2 4.9 -0.301029995663981 0.778151250383644 0.556302500767287 1.322219294733920 2.352182518111360 3 2 3 3.2 -0.221848749616356 1.301029995663980 1.744292983122680 2.243038048686290 2.178976947293170 5 5 5 11 0.361727836017593 0.653212513775344 -0.045757490560675 0.414973347970818 1.778151250383640 2 1 2 4.9 -0.301029995663981 0.875061263391700 0.301029995663981 1.089905111439400 2.301029995663980 3 1 3 13.2 0.414973347970818 0.361727836017593 -0.982966660701220 0.397940008672038 1.662757831681570 3 2 2 9.7 -0.221848749616356 1.380211241711610 0.622214022966295 1.763427993562940 2.322219294733920 4 3 4 12.8 0.819543935541869 0.477121254719662 0.544068044350276 0.591064607026499 1.146128035678240 2 1 1
Names of X columns:
SWS logPS logL LogWb logWbr logGT PI SEI ODI
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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Big Analytics Cloud Computing Center
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