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Data X:
358.59 122.36 362.96 123.33 362.42 123.04 364.97 124.53 364.04 125.13 361.06 125.85 358.48 126.50 352.96 126.53 359.59 127.07 360.39 124.55 357.40 124.90 362.93 124.32 364.55 122.84 365.73 123.31 364.70 123.31 364.65 124.87 359.43 124.64 362.14 124.73 356.97 124.90 354.82 124.04 353.17 123.28 357.06 123.86 356.18 122.29 355.01 124.09 355.65 124.54 357.31 125.65 357.07 125.70 357.91 125.53 358.48 125.61 358.97 125.55 351.77 125.41 352.16 127.60 359.08 124.68 360.35 124.41 359.53 126.43 359.30 126.38 358.41 125.78 359.68 124.70 355.31 125.07 357.08 125.25 349.71 126.58 354.13 127.13 345.49 125.82 341.69 123.70 344.25 124.39 340.17 123.70 342.47 124.42 344.43 121.05 333.23 121.02 339.72 123.23 342.61 121.32 346.36 120.91 339.09 120.72 339.73 123.31 341.12 119.58 335.94 119.53 333.46 120.59 335.66 118.63 341.12 118.47 342.21 111.81 342.62 114.71 346.06 117.34 344.43 115.77 346.65 118.38 343.74 117.84 335.67 118.83 342.75 120.02 341.77 116.21 345.84 117.08 346.52 120.20 350.79 119.83 345.44 118.92 345.87 118.03 338.48 117.71 337.21 119.55 340.81 116.13 339.86 115.97 342.86 115.99 343.33 114.96 341.73 116.46 351.38 116.55 351.13 113.05 345.99 117.44 347.55 118.84 346.02 117.06 345.29 117.54 347.03 119.31 348.01 118.72 345.48 121.55 349.40 122.61 351.05 121.53 349.70 123.31 350.86 124.07 354.45 123.59 355.30 122.97 357.48 123.22 355.24 123.04 351.79 122.96 355.22 122.81 351.02 122.81 350.28 122.62 350.17 120.82 348.16 119.41 340.30 121.56 343.75 121.59 344.71 118.50 344.13 118.77 342.14 118.86 345.04 117.60 346.02 119.90 346.43 121.83 347.07 121.84 339.33 122.12 339.10 122.12 337.19 121.36 339.58 119.66 327.85 119.32 326.81 120.36 321.73 117.06 320.45 117.48 327.69 115.60 323.95 113.86 320.47 116.92 322.13 117.75 316.34 117.75 314.78 115.31 308.90 116.28 308.62 115.22 314.41 115.65 306.88 115.11 310.60 118.67 321.60 118.04 321.50 116.50 325.68 119.78 324.35 119.95 320.01 120.37 326.88 119.79 332.39 119.43 331.48 121.06 332.62 121.74 324.79 121.09 327.12 122.97 328.91 120.50 328.37 117.18 324.83 115.03 325.90 113.36 326.18 112.59 328.94 111.65 333.78 111.98 328.06 114.87 325.87 114.67 325.41 114.09 318.86 114.77 319.13 117.05 310.16 117.22 311.73 113.18 306.54 110.95 311.16 112.14 311.98 112.72 306.72 110.01 308.05 110.29 300.76 110.74 301.90 110.32 293.09 105.89 292.76 108.97 294.58 109.34 289.90 106.57 296.69 99.49 297.21 101.81 293.31 104.29 296.25 109.73 298.60 105.06 296.87 107.97 301.02 108.13 304.73 109.86 301.92 108.95 295.72 111.20 293.18 110.69 298.35 106.10 297.99 105.68 299.85 104.12 299.85 104.71 304.45 104.30 299.45 103.52 298.14 107.76 298.78 107.80 297.02 107.30 301.33 108.64 294.96 105.03 296.69 108.30 300.73 107.21 301.96 109.27 297.38 109.50 293.87 111.68 285.96 111.80 285.41 111.75 283.70 106.68 284.76 106.37 277.11 105.76 274.73 109.01 274.73 109.01 274.73 109.01 274.73 109.01 274.69 107.69 275.42 105.19 264.15 105.48 276.24 102.22 268.88 100.54 277.97 105.00 280.49 105.44 281.09 107.89 276.16 108.64 272.58 106.70 270.94 109.10 284.31 105.23 283.94 108.41 284.18 108.80 282.83 110.39 283.84 110.22 282.71 110.86 279.29 108.58 280.70 107.70 274.47 106.62 273.44 109.84 275.49 107.16 279.46 107.26 280.19 108.70 288.21 109.85 284.80 109.41 281.41 112.36 283.39 111.03 287.97 110.67 290.77 109.21 290.60 113.58 289.67 113.88 289.84 114.08 298.55 112.33 296.07 113.92 297.14 114.41 295.34 114.57 296.25 115.35 294.30 113.13 296.15 113.29 296.49 112.56 298.05 113.06 301.03 113.46 300.52 115.39 301.50 116.62 296.93 117.04 289.84 117.42 291.44 115.62 286.88 115.16 286.74 115.69 288.93 112.85 292.19 114.05 295.39 112.00 295.86 113.74 293.36 116.26 292.86 118.63 292.73 116.49 296.73 118.23 285.02 116.83 285.24 118.82 288.62 114.36 283.36 112.02 285.84 113.24 291.48 109.75 291.41 110.33 287.77 112.86 284.97 113.04 286.05 113.80 278.19 110.90 281.21 109.96 277.92 108.69 280.08 108.84 269.24 108.47 268.48 108.07 268.83 107.94 269.54 108.11 262.37 108.11 265.12 106.81 265.34 105.58 263.32 105.61 267.18 106.52 260.75 103.86 261.78 104.60 257.27 104.73 255.63 105.12 251.39 104.76 259.49 103.85 261.18 103.83 261.65 103.22 262.01 101.64 265.23 102.13 268.10 104.33 262.27 104.92 263.59 107.78 257.85 104.49 265.69 102.80 271.15 102.86 266.69 104.51 265.77 104.73 262.32 102.58 270.48 99.93 273.03 101.41 269.13 101.05 280.65 99.86 282.75 101.11 281.44 100.89 281.99 101.09 282.86 98.31 287.21 98.08 283.11 99.55 280.66 99.62 282.39 97.37 280.83 98.16 284.71 97.98 279.99 98.15 283.50 97.10 284.88 97.24 288.60 96.70 284.80 96.64 287.20 100.65 286.22 96.75 286.54 97.74 279.58 97.92 283.08 98.34 288.88 93.84 280.18 97.80 284.16 96.20 290.57 95.99 286.82 95.18 273.00 95.95 278.69 92.23 264.54 91.78 271.92 92.97 283.60 89.76 269.25 92.88 263.58 96.23 264.16 95.79 268.85 93.97 269.67 93.90 249.41 93.60 268.99 93.96 268.65 88.69 260.16 88.57 256.55 85.62 251.47 86.25 234.93 85.33 232.96 83.33 215.49 77.78 213.68 78.70 236.07 72.05 235.41 80.75 214.77 81.41 225.85 82.65 224.64 75.85 238.26 75.70 232.44 78.25 222.50 77.41 225.28 76.84 220.49 74.25 216.86 74.95 234.70 68.78 230.06 73.21 238.27 73.26 238.56 78.67 242.70 75.63 249.14 74.99 234.89 83.87 227.78 79.62 234.04 80.13 230.70 79.76 230.17 78.20 218.23 78.05 232.20 79.05 220.76 73.32 215.60 75.17 217.69 73.26 204.35 73.72 191.44 73.57 203.84 70.60 211.86 71.25 210.57 74.22 219.57 73.32 219.98 73.01 226.01 74.21 207.04 75.32 212.52 71.73 217.92 71.94 210.45 72.94 218.53 72.47 223.32 71.94 218.76 74.30 217.63 74.30
Names of X columns:
Arabia Asia
Type of Correlation
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='kendall') 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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Kendall tau rank correlations for all pairs of data series',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'pair',1,TRUE) a<-table.element(a,'tau',1,TRUE) a<-table.element(a,'p-value',1,TRUE) a<-table.row.end(a) n <- length(y[,1]) n cor.test(y[1,],y[2,],method='kendall') for (i in 1:(n-1)) { for (j in (i+1):n) { a<-table.row.start(a) dum <- paste('tau(',dimnames(t(x))[[2]][i]) dum <- paste(dum,',') dum <- paste(dum,dimnames(t(x))[[2]][j]) dum <- paste(dum,')') a<-table.element(a,dum,header=TRUE) r <- cor.test(y[i,],y[j,],method='kendall') a<-table.element(a,r$estimate) a<-table.element(a,r$p.value) a<-table.row.end(a) } } a<-table.end(a) table.save(a,file='mytable.tab')
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R Server
Big Analytics Cloud Computing Center
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