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Data X:
182 159 348 104 198 110 159 327 76 153 162 161 421 51 150 155 259 258 73 148 141 176 206 78 147 165 131 269 72 138 101 148 145 25 137 152 183 244 65 136 127 177 273 56 136 128 188 240 68 135 121 105 206 77 130 112 153 176 74 129 142 214 448 100 127 164 114 218 100 126 156 157 244 74 126 161 197 236 72 122 176 224 213 52 121 152 163 149 66 121 155 162 306 119 121 85 166 185 49 120 164 134 246 78 118 155 135 328 57 118 88 188 257 69 116 145 203 200 77 116 122 150 189 70 115 161 166 217 75 112 150 179 265 67 112 132 145 307 77 112 171 244 255 74 111 118 164 145 73 111 96 157 217 52 110 114 158 115 62 108 140 156 249 69 107 139 202 204 64 107 184 124 264 80 107 121 187 203 46 106 139 186 318 69 106 140 85 244 69 104 146 187 217 76 104 148 125 320 45 104 112 149 165 54 103 146 162 245 59 103 126 139 149 52 103 169 147 146 49 102 75 162 256 54 102 83 168 228 66 101 163 153 212 80 101 180 151 228 69 100 181 159 150 63 99 168 157 257 53 99 94 188 274 28 97 114 159 291 62 97 107 185 132 52 96 152 145 125 39 95 186 159 208 61 95 118 158 184 55 94 133 153 202 61 94 90 152 179 40 91 127 97 84 31 91 87 191 165 52 90 121 147 302 67 90 103 165 167 48 90 50 186 148 37 89 134 212 154 54 89 89 116 120 55 89 84 109 129 47 87 163 99 130 54 87 50 164 146 58 87 98 161 202 47 87 96 149 202 68 86 123 173 180 54 86 104 108 141 51 86 122 163 131 45 86 124 109 182 55 86 128 139 214 50 85 123 105 343 42 85 76 177 210 35 84 85 110 161 51 84 121 186 395 49 84 88 95 155 51 83 116 165 169 48 82 137 161 279 42 82 66 150 196 61 82 136 213 164 44 80 159 142 213 45 79 102 174 184 47 79 110 124 243 48 79 104 116 97 43 79 107 221 272 44 79 158 167 161 41 78 126 45 97 38 77 83 127 76 30 76 48 60 210 39 75 97 138 160 41 74 63 80 145 28 74 131 156 183 39 73 93 64 115 37 72 97 124 124 35 72 105 94 164 46 72 88 148 188 32 72 89 146 313 63 71 95 163 123 48 71 70 139 193 39 71 81 77 103 36 70 107 171 264 49 69 84 123 171 45 69 129 166 143 42 69 77 119 68 30 68 134 154 197 39 67 84 96 153 53 66 58 162 194 17 66 69 128 112 33 65 93 153 359 41 65 166 163 242 35 65 40 105 253 50 65 102 132 111 34 64 97 83 109 43 63 75 152 301 55 63 67 126 99 29 63 81 112 101 41 61 146 116 118 35 60 103 243 197 33 60 48 111 108 31 60 63 99 209 40 58 147 249 226 31 58 70 153 216 21 56 64 113 119 43 56 77 151 189 39 55 146 105 240 43 55 103 106 203 37 54 63 160 275 24 51 126 148 282 30 50 41 78 37 3 50 65 120 109 28 42 30 99 39 19 41 35 54 39 19 40 56 169 232 9 38 37 101 53 4 36 30 41 32 13 28 20 66 33 16 25 49 27 57 15 22 8 89 76 13 20 22 67 23 7 20 21 61 33 7 18 23 30 25 7 17 12 13 6 6 13 13 64 44 5 12 16 21 9 4 11 18 9 14 4 9 1 22 23 2 9 12 0 1 2 7 8 7 6 4 7 4 0 6 2 6 4 0 1 2 6 0 4 4 2 5 7 0 2 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Names of X columns:
B LFM KCS CH H
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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$estimate,3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') 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) } } a<-table.end(a) table.save(a,file='mytable1.tab')
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