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Data X:
12.9 7.5 1.8 1.5 21 86 149 12.8 6.5 2.2 2.1 22 71 148 7.4 1 2.3 1.9 18 108 158 6.7 1 2.1 1.6 23 64 128 14.8 8.5 2.1 2.1 20 97 159 13.3 6.5 2.4 2.2 22 129 105 11.1 4.5 2.9 1.5 21 153 159 8.2 2 2.2 1.9 19 78 167 11.4 5 2.1 2.2 22 80 165 6.4 0.5 2.2 1.6 15 99 159 11.3 5 2 2.2 15 57 91 10 4 1.9 2.1 15 68 121 6.4 0.5 2.1 1.9 16 55 153 10.8 4.5 2.1 1.9 21 79 221 13.8 7.5 2.3 2.2 18 116 188 11.7 5.5 2.3 1.8 25 101 149 13.4 7 1.9 2.5 20 66 92 11.7 5.5 2.1 2.1 19 71 156 9 3.5 2 1.5 25 64 132 9.7 2.5 3.2 1.9 18 143 161 10.8 4.5 2.3 2.1 23 85 105 12.7 6 2.4 2.1 14 69 131 11.8 5 2.3 2.4 26 96 157 5.9 0 2 2.1 23 60 111 11.4 5 2.5 1.9 23 95 145 13 6.5 2.3 2.1 24 100 162 11.3 4.5 2.6 2.1 23 105 187 6.7 1 1.8 2.2 17 41 42 12.1 6.5 1.9 1.5 21 50 155 13.3 7 2.4 1.9 18 93 125 5.7 0 2 1.8 21 54 128 13.3 7.5 1.9 1.8 29 69 96 7.6 1.5 1.8 2.4 21 58 99 11.1 4 2.8 2.1 18 136 183 13 6.5 2 2.2 19 126 214 9.9 3.5 2.2 2.4 12 64 74 11.1 5.5 1.8 1.9 19 36 99 4.35 0.5 1 2.1 23 35 48 12.7 7.5 1 2.7 22 61 50 18.1 9 4 2.1 21 70 150 12.6 7 2 2.1 17 24 68 19.1 10 4 2.1 23 147 158 18.4 9 4 2.4 18 84 147 14.7 9.5 1 1.95 23 30 39 10.6 4 3 2.1 19 77 100 12.6 6 3 2.1 15 46 111 16.2 8 4 1.95 20 61 138 18.9 9.5 4 2.4 24 159 131 14.1 7.5 3 2.1 25 57 101 16.15 7.5 4 2.4 19 163 165 14.75 8 3 2.25 19 76 114 14.8 7 3 2.55 16 94 111 12.45 7 2 1.95 19 45 75 12.65 6 2 2.4 19 78 82 17.35 10 3 2.1 23 47 121 18.4 9 4 2.4 22 97 150 11.6 6 2 2.1 20 33 71 17.75 8.5 4 2.25 20 51 165 15.25 6 4 2.25 3 118 154 17.65 9 4 2.4 20 89 145 14.75 5.5 4 2.25 7 56 132 9.9 2 4 2.4 17 60 169 16 8.5 3 2.25 24 109 114 13.85 7.5 2 2.1 20 58 89 17.1 8 4 2.1 19 92 173 14.6 7 4 2.1 29 95 141 15.4 7.5 4 1.65 25 50 165 17.6 9.5 3 2.1 20 80 110 13.9 7 3 2.4 18 68 121 16.25 8 3 2.25 21 79 110 15.65 8 3 2.4 20 57 117 14.6 9 2 2.1 22 69 63 11.2 7.5 1 1.95 25 49 42 16.35 8 4 2.1 24 100 154 15.85 8.5 3 2.1 18 78 96 7.65 3.5 1 2.4 15 38 49 12.35 6.5 3 2.1 29 42 110 15.6 10 2 2.1 23 90 86 13.1 7.5 2 2.1 24 52 88 12.85 4.5 4 2.1 20 64 168 9.5 4.5 2 2.25 4 31 94 11.85 6.5 1 2.1 22 27 48 13.6 4.5 4 2.1 16 105 145 17.6 8.5 4 2.1 17 71 164 16.1 7 4 2.1 22 63 126 13.35 5 4 2.1 19 47 132 15.15 8.5 2 2.4 15 78 81 12.2 6 2.1 2.1 22 70 139 12.6 5.5 2.7 2.1 12 119 224 10.6 5 2.2 1.5 20 68 119 12 5 2.7 1.9 19 147 176 11.9 5.5 2.5 1.8 20 120 163 9.6 3 2.3 2.2 21 84 137 13.8 6.5 3.5 1.6 23 137 148 9.9 4 1.9 1.9 23 81 150 11.5 5.5 1.9 2.1 16 63 153 8.3 2.5 1.9 1.9 16 69 94 10.3 4.5 2.5 1.5 21 86 97 9.3 2.5 2.8 1.8 22 120 166 12.3 6 1.9 2.4 18 57 59 7.9 1 2.6 2.2 19 103 90 9.3 3.5 2.1 1.5 28 107 164 12.5 6 2.4 2.1 23 65 162 15.9 9 2.3 2.4 19 107 202 9.1 3.5 2 1.8 20 53 66 12.2 6 2.1 1.9 19 69 104 12.3 5 2.9 2.1 25 136 177 14.6 7.5 2.6 2.4 23 118 99 12.6 6.5 2.1 1.9 14 82 139 12.6 6.5 2.2 1.8 24 65 108 17.1 8 4 2.1 27 120 194 16.1 7 4 2.1 23 215 159 13.35 8.5 2 2.1 18 24 67 14.5 7 3 2.25 25 42 114 8.6 2.5 1 2.1 21 29 32 17.65 9 4 2.4 23 66 126 16.35 8 4 2.1 23 87 149 13.6 5.5 3 2.1 15 76 120 14.35 7 3 2.1 16 75 109 18.25 9 4 2.25 24 72 172 18.25 9 4 2.25 24 76 156 18.95 10 4 2.7 28 123 167 15.9 8.5 2 2.4 21 46 87 13.35 6 3 2.1 20 86 118 15.35 7 4 2.1 20 79 146 14.85 8.5 2 2.1 30 75 73 13.6 8 2 2.1 22 43 65 15.25 7.5 4 2.25 23 55 152 13.2 7 2 1.95 18 39 77 15.65 8 3 2.4 29 95 112 15.6 6.5 4 2.1 16 23 131 15.2 8.5 1 2.7 22 48 56 18.4 10 3 2.4 23 94 121 19.05 9.5 4 2.55 19 62 149 18.55 9 4 2.55 4 74 168 12.4 5 2 2.4 15 62 85 14.6 8 3 2.1 23 80 114 14.05 5.5 3 2.55 20 75 119 11.85 3.5 4 2.1 24 54 142 7.85 3 2 2.1 22 51 64 15.2 8 3 2.7 20 76 105
Names of X columns:
Totaal EX PR PA NUM H LFM
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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } x <- na.omit(x) y <- t(na.omit(t(y))) bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$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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