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Data X:
6.9 4.8 2.28 1145.11 6.8 4.8 2.26 1176.86 6.7 4.7 2.71 1206.41 6.6 4.7 2.77 1192.72 6.5 4.7 2.77 1214.82 6.5 4.6 2.64 1199.07 7.0 5.0 2.56 1157.47 7.5 5.4 2.07 1100.10 7.6 5.5 2.32 1095.63 7.6 5.6 2.16 1105.63 7.6 5.6 2.23 1137.79 7.8 5.8 2.40 1124.72 8.0 6.0 2.84 1152.60 8.0 6.1 2.77 1211.85 8.0 6.1 2.93 1239.62 7.9 6.0 2.91 1244.13 7.9 6.0 2.69 1198.42 8.0 6.1 2.38 1227.99 8.5 6.5 2.58 1304.92 9.2 7.1 3.19 1340.26 9.4 7.4 2.82 1307.32 9.5 7.4 2.72 1356.51 9.5 7.5 2.53 1383.29 9.6 7.6 2.70 1437.87 9.7 7.8 2.42 1494.56 9.7 7.8 2.50 1521.42 9.6 7.7 2.31 1498.76 9.5 7.6 2.41 1488.75 9.4 7.5 2.56 1524.62 9.3 7.3 2.76 1439.27 9.6 7.6 2.71 1423.11 10.2 8.0 2.44 1466.85 10.2 8.8 2.46 1425.83 10.1 7.9 2.12 1363.45 9.9 7.8 1.99 1389.18 9.8 7.7 1.86 1395.89 9.8 7.8 1.88 1368.43 9.7 7.7 1.82 1349.03 9.5 7.5 1.74 1299.88 9.3 7.3 1.71 1365.41 9.1 7.1 1.38 1451.04 9.0 7.0 1.27 1433.75 9.5 7.3 1.19 1464.65 10.0 7.8 1.28 1475.57 10.2 7.9 1.19 1571.16 10.1 7.9 1.22 1429.12 10.0 7.8 1.47 1452.46 9.9 7.8 1.46 1538.09 10.0 7.9 1.96 1631.59 9.9 7.8 1.88 1665.50 9.7 7.6 2.03 1690.60 9.5 7.4 2.04 1711.74 9.2 7.2 1.90 1734.10 9.0 6.9 1.80 1748.09 9.3 7.1 1.92 1703.45 9.8 7.5 1.92 1745.74 9.8 7.6 1.97 1751.01 9.6 7.4 2.46 1795.65 9.4 7.3 2.36 1852.13 9.3 7.2 2.53 1877.10 9.2 7.3 2.31 1989.31 9.2 7.2 1.98 2097.76 9.0 7.1 1.46 2154.87 8.8 7.0 1.26 2152.18 8.7 6.9 1.58 2250.27 8.7 6.8 1.74 2346.90 9.1 7.2 1.89 2525.56 9.7 7.6 1.85 2409.36 9.8 7.7 1.62 2394.36 9.6 7.6 1.30 2401.33 9.4 7.5 1.42 2354.32 9.4 7.5 1.15 2450.41 9.5 7.6 0.42 2504.67 9.4 7.6 0.74 2661.39 9.3 7.6 1.02 2880.40 9.2 7.5 1.51 3064.42 9.0 7.3 1.86 3141.12 8.9 7.2 1.59 3327.70 9.2 7.4 1.03 3564.95 9.8 8.0 0.44 3403.13 9.9 8.2 0.82 3149.90 9.6 8.0 0.86 3006.84 9.2 7.7 0.58 3230.66 9.1 7.7 0.59 3361.13 9.1 7.8 0.95 3484.74 9.0 7.8 0.98 3411.13 8.9 7.7 1.23 3288.18 8.7 7.5 1.17 3280.37 8.5 7.3 0.84 3173.95 8.3 7.1 0.74 3165.26 8.5 7.1 0.65 3092.71 8.7 7.2 0.91 3053.05 8.4 6.8 1.19 3181.96 8.1 6.6 1.30 2999.93 7.8 6.4 1.53 3249.57 7.7 6.4 1.94 3210.52 7.5 6.5 1.79 3030.29 7.2 6.3 1.95 2803.47 6.8 5.9 2.26 2767.63 6.7 5.5 2.04 2882.60 6.4 5.2 2.16 2863.36 6.3 4.9 2.75 2897.06 6.8 5.4 2.79 3012.61 7.3 5.8 2.88 3142.95 7.1 5.7 3.36 3032.93 7.0 5.6 2.97 3045.78 6.8 5.5 3.10 3110.52 6.6 5.4 2.49 3013.24 6.3 5.4 2.20 2987.10 6.1 5.4 2.25 2995.55 6.1 5.5 2.09 2833.18 6.3 5.8 2.79 2848.96 6.3 5.7 3.14 2794.83 6.0 5.4 2.93 2845.26 6.2 5.6 2.65 2915.02 6.4 5.8 2.67 2892.63 6.8 6.2 2.26 2604.42 7.5 6.8 2.35 2641.65 7.5 6.7 2.13 2659.81 7.6 6.7 2.18 2638.53 7.6 6.4 2.90 2720.25 7.4 6.3 2.63 2745.88 7.3 6.3 2.67 2735.70 7.1 6.4 1.81 2811.70 6.9 6.3 1.33 2799.43 6.8 6.0 0.88 2555.28 7.5 6.3 1.28 2304.98 7.6 6.3 1.26 2214.95 7.8 6.6 1.26 2065.81 8.0 7.5 1.29 1940.49 8.1 7.8 1.10 2042.00 8.2 7.9 1.37 1995.37 8.3 7.8 1.21 1946.81 8.2 7.6 1.74 1765.90 8.0 7.5 1.76 1635.25 7.9 7.6 1.48 1833.42 7.6 7.5 1.04 1910.43 7.6 7.3 1.62 1959.67 8.3 7.6 1.49 1969.60 8.4 7.5 1.79 2061.41 8.4 7.6 1.80 2093.48 8.4 7.9 1.58 2120.88 8.4 7.9 1.86 2174.56 8.6 8.1 1.74 2196.72 8.9 8.2 1.59 2350.44 8.8 8.0 1.26 2440.25 8.3 7.5 1.13 2408.64 7.5 6.8 1.92 2472.81 7.2 6.5 2.61 2407.60 7.4 6.6 2.26 2454.62 8.8 7.6 2.41 2448.05 9.3 8.0 2.26 2497.84 9.3 8.1 2.03 2645.64 8.7 7.7 2.86 2756.76 8.2 7.5 2.55 2849.27 8.3 7.6 2.27 2921.44 8.5 7.8 2.26 2981.85 8.6 7.8 2.57 3080.58 8.5 7.8 3.07 3106.22 8.2 7.5 2.76 3119.31 8.1 7.5 2.51 3061.26 7.9 7.1 2.87 3097.31 8.6 7.5 3.14 3161.69 8.7 7.5 3.11 3257.16 8.7 7.6 3.16 3277.01 8.5 7.7 2.47 3295.32 8.4 7.7 2.57 3363.99 8.5 7.9 2.89 3494.17 8.7 8.1 2.63 3667.03 8.7 8.2 2.38 3813.06 8.6 8.2 1.69 3917.96 8.5 8.2 1.96 3895.51 8.3 7.9 2.19 3801.06 8.0 7.3 1.87 3570.12 8.2 6.9 1.6 3701.61 8.1 6.6 1.63 3862.27 8.1 6.7 1.22 3970.10 8.0 6.9 1.21 4138.52 7.9 7.0 1.49 4199.75 7.9 7.1 1.64 4290.89
Names of X columns:
totwerkl werklman infl bel20
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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