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Data X:
7.5 96 2.5 75 6.0 70 6.5 88 1.0 114 1.0 69 5.5 176 8.5 114 6.5 121 4.5 110 2.0 158 5.0 116 0.5 181 5.0 77 5.0 141 2.5 35 5.0 80 5.5 152 3.5 97 3.0 99 4.0 84 0.5 68 6.5 101 4.5 107 7.5 88 5.5 112 4.0 171 7.5 137 7.0 77 4.0 66 5.5 93 2.5 105 5.5 131 0.5 89 3.5 102 2.5 161 4.5 120 4.5 127 4.5 77 6.0 108 2.5 85 5.0 168 0.0 48 5.0 152 6.5 75 5.0 107 6.0 62 4.5 121 5.5 124 1.0 72 7.5 40 6.0 58 5.0 97 1.0 88 5.0 126 6.5 104 7.0 148 4.5 146 0.0 80 8.5 97 3.5 25 7.5 99 3.5 118 6.0 58 1.5 63 9.0 139 3.5 50 3.5 60 4.0 152 6.5 142 7.5 94 6.0 66 5.0 127 5.5 67 3.5 90 7.5 75 1.0 96 6.5 128 NA 41 6.5 146 6.5 69 7.0 186 3.5 81 1.5 85 4.0 54 7.5 46 4.5 106 0.0 34 3.5 60 5.5 95 5.0 57 4.5 62 2.5 36 7.5 56 7.0 54 0.0 64 4.5 76 3.0 98 1.5 88 3.5 35 2.5 102 5.5 61 8.0 80 1.0 49 5.0 78 4.5 90 3.0 45 3.0 55 8.0 96 2.5 43 7.0 52 0.0 60 1.0 54 3.5 51 5.5 51 5.5 38 0.5 41 7.5 146 9 182 9.5 192 8.5 263 7 35 8 439 10 214 7 341 8.5 58 9 292 9.5 85 4 200 6 158 8 199 5.5 297 9.5 227 7.5 108 7 86 7.5 302 8 148 7 178 7 120 6 207 10 157 2.5 128 9 296 8 323 6 79 8.5 70 6 146 9 246 8 145 8 196 9 199 5.5 127 5 91 7 153 5.5 299 9 228 2 190 8.5 180 9 212 8.5 269 9 130 7.5 179 10 243 9 190 7.5 299 6 121 10.5 137 8.5 305 8 157 10 96 10.5 183 6.5 52 9.5 238 8.5 40 7.5 226 5 190 8 214 10 145 7 119 7.5 222 7.5 222 9.5 159 6 165 10 249 7 125 3 122 6 186 7 148 10 274 7 172 3.5 84 8 168 10 102 5.5 106 6 2 6.5 139 6.5 95 8.5 130 4 72 9.5 141 8 113 8.5 206 5.5 268 7 175 9 77 8 125 10 255 8 111 6 132 8 211 5 92 9 76 4.5 171 8.5 83 7 119 9.5 266 8.5 186 7.5 50 7.5 117 5 219 7 246 8 279 5.5 148 8.5 137 7.5 130 9.5 181 7 98 8 226 8.5 234 3.5 138 6.5 85 6.5 66 10.5 236 8.5 106 8 135 10 122 10 218 9.5 199 9 112 10 278 7.5 94 4.5 113 4.5 84 0.5 86 6.5 62 4.5 222 5.5 167 5 82 6 207 4 184 8 83 10.5 183 8.5 85 6.5 89 8 225 8.5 237 5.5 102 7 221 5 128 3.5 91 5 198 9 204 8.5 158 5 138 9.5 226 3 44 1.5 196 6 83 0.5 79 6.5 52 7.5 105 4.5 116 8 83 9 196 7.5 153 8.5 157 7 75 9.5 106 6.5 58 9.5 75 6 74 8 185 9.5 265 8 131 8 139 9 196 5 78
Names of X columns:
Ex GebBer
Type of Correlation
14
pearson
spearman
kendall
Chart options
Title:
R Code
par1 <- 'kendall' 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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$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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