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Data X:
29.82 0.79 3.16 2.21 38.48 2.12 20.82 0.93 0.09 5.38 41.09 3.14 2.97 2.23 0.1 11.88 23.05 9.31 8.46 6.06 9.31 2.31 0.37 6.84 1.32 7.49 154.7 0.72 0.28 4.48 9.4 5.09 11.06 7.44 10.05 1.41 0.06 5.77 0.74 4.84 10.5 2.96 3.83 3.12 2 3.83 198.66 3.11 0.03 2.86 0.41 4.06 7.28 3.32 16.46 1.21 9.85 0.8 0.49 2.52 14.86 1.21 21.7 1.17 34.84 8.17 0.06 5.65 4.53 1.24 12.45 1.46 17.46 4.36 1408.04 3.38 47.7 1.87 0.72 1.03 4.34 1.29 65.7 0.82 4.8 2.84 19.84 1.27 4.31 3.92 11.27 1.95 1.13 4.21 10.66 5.19 5.6 5.51 0.86 2.19 0.07 2.57 10.28 1.53 15.49 2.17 80.72 2.15 6.3 2.07 0.74 3.97 6.13 0.42 1.29 6.86 91.73 1.02 0.88 2.9 5.41 5.87 63.98 5.14 0.24 2.34 0.27 4.73 1.63 2.02 1.79 1.03 4.36 1.58 82.8 5.3 25.37 1.97 11.12 4.38 0.1 2.98 0.46 3.23 15.08 1.89 11.45 1.41 1.66 1.53 0.8 3.07 10.17 0.61 7.94 1.68 9.98 2.92 1236.69 1.16 246.86 1.58 76.42 2.79 32.78 1.88 4.58 5.57 7.64 6.22 60.92 4.61 2.77 1.89 127.25 5.02 7.01 2.1 16.27 5.55 43.18 1.03 24.76 1.17 49 5.69 3.25 8.13 5.47 1.91 6.65 1.22 2.06 6.29 4.65 3.84 2.05 1.66 4.19 1.21 6.16 3.69 3.03 5.83 0.52 15.82 2.11 3.26 22.29 0.99 15.91 0.81 29.24 3.71 14.85 1.53 0.4 2.08 3.8 2.54 1.24 3.46 120.85 2.89 3.51 1.78 2.8 6.08 0.62 3.78 0 7.78 32.52 1.68 25.2 0.87 52.8 1.43 2.26 2.48 0.01 2.94 27.47 0.98 16.71 5.28 0.25 3.58 4.46 5.6 5.99 1.39 17.16 1.56 168.83 1.16 4.99 4.98 3.31 7.52 179.16 0.79 3.8 2.79 7.17 1.91 6.69 4.16 29.99 2.28 96.71 1.1 38.21 4.44 10.6 3.88 2.05 10.8 0.86 3.65 21.76 2.71 143.17 5.69 11.46 0.87 0.05 4.94 0.18 2.45 0.11 3.11 0.19 2.77 0.19 1.49 28.29 5.61 13.73 1.21 9.55 2.7 5.98 1.24 5.3 7.97 5.45 4.06 2.07 5.81 0.55 1.29 10.2 1.24 52.39 3.31 46.76 3.67 21.1 1.32 0.54 4.25 1.23 2.01 9.51 7.25 8 5.79 21.89 1.51 8.01 0.91 47.78 1.32 66.78 2.66 1.11 0.48 6.64 1.13 0.1 2.7 1.34 7.92 10.88 2.34 74 3.33 5.17 5.47 36.35 1.24 45.53 2.84 63.03 4.94 9.206 7.93 317.5 8.22 3.4 2.91 28.54 2.32 29.96 3.57 90.8 1.65 0.01 2.07 23.85 1.03 14.08 0.99 13.72 1.37
Names of X columns:
Population_(millions) Total_Ecological_Footprint
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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } x <- na.omit(x) y <- t(na.omit(t(y))) bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) print(n) a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$estimate,3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') ncorrs <- (n*n -n)/2 mycorrs <- array(0, dim=c(10,3)) 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) for (iii in 1:10) { iiid100 <- iii / 100 if (rp$p.value < iiid100) mycorrs[iii, 1] = mycorrs[iii, 1] + 1 if (rs$p.value < iiid100) mycorrs[iii, 2] = mycorrs[iii, 2] + 1 if (rk$p.value < iiid100) mycorrs[iii, 3] = mycorrs[iii, 3] + 1 } } } a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Correlation Tests',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of significant by total number of Correlations',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Type I error',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) for (iii in 1:10) { iiid100 <- iii / 100 a<-table.row.start(a) a<-table.element(a,round(iiid100,2),header=T) a<-table.element(a,round(mycorrs[iii,1]/ncorrs,2)) a<-table.element(a,round(mycorrs[iii,2]/ncorrs,2)) a<-table.element(a,round(mycorrs[iii,3]/ncorrs,2)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab')
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