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Data X:
0 22 31 139 39 6 2.1 2 2.1 12.2 0 12 67 224 52 5.5 2.7 2.3 2.1 12.6 0 20 38 119 30 5 2.2 2 1.5 10.6 0 19 69 176 78 5 2.7 2.3 1.9 12 0 20 54 163 66 5.5 2.5 2.2 1.8 11.9 0 21 42 137 42 3 2.3 2.1 2.2 9.6 0 23 112 148 25 6.5 3.5 2.2 1.6 13.8 0 23 20 150 61 4 1.9 2 1.9 9.9 0 16 22 153 41 5.5 1.9 2 2.1 11.5 0 16 23 94 46 2.5 1.9 1.9 1.9 8.3 0 21 55 97 31 4.5 2.5 1.8 1.5 10.3 0 22 71 166 49 2.5 2.8 2.2 1.8 9.3 0 18 23 59 34 6 1.9 1.9 2.4 12.3 0 19 61 90 42 1 2.6 2 2.2 7.9 0 28 34 164 73 3.5 2.1 2.1 1.5 9.3 0 23 48 162 17 6 2.4 2 2.1 12.5 0 19 43 202 64 9 2.3 2.2 2.4 15.9 0 20 28 66 25 3.5 2 1.8 1.8 9.1 0 19 34 104 35 6 2.1 2.1 1.9 12.2 0 25 80 177 56 5 2.9 2.2 2.1 12.3 0 23 59 99 59 7.5 2.6 2.1 2.4 14.6 0 14 32 139 50 6.5 2.1 2 1.9 12.6 0 24 38 108 27 6.5 2.2 2.1 1.8 12.6 0 27 52 194 68 8 4 3 2.1 17.1 0 23 72 159 143 7 4 3 2.1 16.1 0 18 15 67 9 8.5 2 0.75 2.1 13.35 0 25 23 114 19 7 3 2.25 2.25 14.5 0 21 4 32 25 2.5 1 3 2.1 8.6 0 23 23 126 43 9 4 2.25 2.4 17.65 0 23 36 149 51 8 4 2.25 2.1 16.35 0 15 40 120 36 5.5 3 3 2.1 13.6 0 16 31 109 44 7 3 2.25 2.1 14.35 0 24 38 172 34 9 4 3 2.25 18.25 0 24 37 156 39 9 4 3 2.25 18.25 0 28 43 167 80 10 4 2.25 2.7 18.95 0 21 4 87 42 8.5 2 3 2.4 15.9 0 20 32 118 54 6 3 2.25 2.1 13.35 0 20 28 146 51 7 4 2.25 2.1 15.35 0 30 32 73 43 8.5 2 2.25 2.1 14.85 0 22 15 65 28 8 2 1.5 2.1 13.6 0 23 28 152 27 7.5 4 1.5 2.25 15.25 0 18 12 77 27 7 2 2.25 1.95 13.2 0 29 44 112 51 8 3 2.25 2.4 15.65 0 16 11 131 12 6.5 4 3 2.1 15.6 0 22 24 56 24 8.5 1 3 2.7 15.2 0 23 13 121 81 10 3 3 2.4 18.4 0 19 20 149 42 9.5 4 3 2.55 19.05 0 4 52 168 22 9 4 3 2.55 18.55 0 15 34 85 28 5 2 3 2.4 12.4 0 23 29 114 51 8 3 1.5 2.1 14.6 0 20 51 119 24 5.5 3 3 2.55 14.05 0 24 40 142 14 3.5 4 2.25 2.1 11.85 0 22 27 64 24 3 2 0.75 2.1 7.85 0 20 25 105 51 8 3 1.5 2.7 15.2 1 21 18 149 68 7.5 1.8 2.1 1.5 12.9 1 22 39 148 32 6.5 2.2 2 2.1 12.8 1 18 46 158 62 1 2.3 2.1 1.9 7.4 1 23 31 128 33 1 2.1 2 1.6 6.7 1 20 35 159 62 8.5 2.1 2.1 2.1 14.8 1 22 52 105 77 6.5 2.4 2.1 2.2 13.3 1 21 77 159 76 4.5 2.9 2.2 1.5 11.1 1 19 37 167 41 2 2.2 2.1 1.9 8.2 1 22 32 165 48 5 2.1 2.1 2.2 11.4 1 15 36 159 63 0.5 2.2 2.1 1.6 6.4 1 15 26 91 31 5 2 2 2.2 11.3 1 15 23 121 45 4 1.9 2 2.1 10 1 16 34 153 21 0.5 2.1 1.8 1.9 6.4 1 21 35 221 44 4.5 2.1 2.2 1.9 10.8 1 18 47 188 69 7.5 2.3 1.7 2.2 13.8 1 25 47 149 54 5.5 2.3 2.1 1.8 11.7 1 20 24 92 42 7 1.9 1.9 2.5 13.4 1 19 32 156 39 5.5 2.1 2 2.1 11.7 1 25 30 132 34 3.5 2 2 1.5 9 1 18 92 161 51 2.5 3.2 2.1 1.9 9.7 1 23 43 105 42 4.5 2.3 2 2.1 10.8 1 14 49 131 20 6 2.4 2.2 2.1 12.7 1 26 43 157 53 5 2.3 2.1 2.4 11.8 1 23 29 111 31 0 2 1.8 2.1 5.9 1 23 56 145 39 5 2.5 1.9 1.9 11.4 1 24 46 162 54 6.5 2.3 2.1 2.1 13 1 23 59 187 46 4.5 2.6 2.2 2.1 11.3 1 17 16 42 25 1 1.8 1.7 2.2 6.7 1 21 22 155 28 6.5 1.9 2.2 1.5 12.1 1 18 48 125 45 7 2.4 2 1.9 13.3 1 21 26 128 28 0 2 2 1.8 5.7 1 29 24 96 45 7.5 1.9 2.1 1.8 13.3 1 21 18 99 40 1.5 1.8 1.9 2.4 7.6 1 18 71 183 65 4 2.8 2.3 2.1 11.1 1 19 26 214 100 6.5 2 2.3 2.2 13 1 12 36 74 28 3.5 2.2 1.8 2.4 9.9 1 19 19 99 17 5.5 1.8 1.8 1.9 11.1 1 23 23 48 12 0.5 1 0.75 2.1 4.35 1 22 16 50 45 7.5 1 1.5 2.7 12.7 1 21 33 150 37 9 4 3 2.1 18.1 1 17 14 68 10 7 2 1.5 2.1 12.6 1 23 75 158 72 10 4 3 2.1 19.1 1 18 29 147 55 9 4 3 2.4 18.4 1 23 13 39 17 9.5 1 2.25 1.95 14.7 1 19 40 100 37 4 3 1.5 2.1 10.6 1 15 19 111 27 6 3 1.5 2.1 12.6 1 20 24 138 37 8 4 2.25 1.95 16.2 1 24 93 131 66 9.5 4 3 2.4 18.9 1 25 36 101 21 7.5 3 1.5 2.1 14.1 1 19 85 165 78 7.5 4 2.25 2.4 16.15 1 19 41 114 35 8 3 1.5 2.25 14.75 1 16 46 111 48 7 3 2.25 2.55 14.8 1 19 18 75 27 7 2 1.5 1.95 12.45 1 19 35 82 43 6 2 2.25 2.4 12.65 1 23 17 121 30 10 3 2.25 2.1 17.35 1 22 28 150 69 9 4 3 2.4 18.4 1 20 10 71 23 6 2 1.5 2.1 11.6 1 20 38 165 13 8.5 4 3 2.25 17.75 1 3 57 154 61 6 4 3 2.25 15.25 1 20 22 145 67 9 4 2.25 2.4 17.65 1 7 11 132 45 5.5 4 3 2.25 14.75 1 17 24 169 36 2 4 1.5 2.4 9.9 1 24 37 114 72 8.5 3 2.25 2.25 16 1 20 2 89 56 7.5 2 2.25 2.1 13.85 1 19 31 173 61 8 4 3 2.1 17.1 1 29 42 141 53 7 4 1.5 2.1 14.6 1 25 21 165 29 7.5 4 2.25 1.65 15.4 1 20 25 110 55 9.5 3 3 2.1 17.6 1 18 29 121 39 7 3 1.5 2.4 13.9 1 21 35 110 44 8 3 3 2.25 16.25 1 20 18 117 39 8 3 2.25 2.4 15.65 1 22 46 63 23 9 2 1.5 2.1 14.6 1 25 12 42 37 7.5 1 0.75 1.95 11.2 1 24 31 154 69 8 4 2.25 2.1 16.35 1 18 34 96 44 8.5 3 2.25 2.1 15.85 1 15 7 49 31 3.5 1 0.75 2.4 7.65 1 29 29 110 13 6.5 3 0.75 2.1 12.35 1 23 60 86 30 10 2 1.5 2.1 15.6 1 24 25 88 27 7.5 2 1.5 2.1 13.1 1 20 39 168 25 4.5 4 2.25 2.1 12.85 1 4 9 94 22 4.5 2 0.75 2.25 9.5 1 22 13 48 14 6.5 1 2.25 2.1 11.85 1 16 60 145 45 4.5 4 3 2.1 13.6 1 17 -2 164 73 8.5 4 3 2.1 17.6 1 22 2 126 61 7 4 3 2.1 16.1 1 19 24 132 23 5 4 2.25 2.1 13.35 1 15 16 81 62 8.5 2 2.25 2.4 15.15
Names of X columns:
Geslacht NUMERACYTOT PRH LFM CH EX PR PE PA TOT
Type of Correlation
kendall
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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } x <- na.omit(x) y <- t(na.omit(t(y))) bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$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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