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Data X:
0 0 264530 165119 0 0 135248 107269 0 0 207253 93497 0 0 202898 100269 0 0 145249 91627 0 0 65295 47552 0 0 439387 233933 0 0 33186 6853 0 0 183696 104380 0 0 190673 98431 0 0 287239 156949 0 0 205260 81817 0 0 141987 59238 0 0 322679 101138 0 0 199717 107158 0 0 349227 155499 0 0 276709 156274 0 0 273576 121777 0 0 157448 105037 0 0 242782 118661 0 0 256814 131187 0 0 405874 145026 0 0 161189 107016 0 0 156189 87242 0 0 200181 91699 0 0 192645 110087 0 0 249893 145447 0 0 241171 143307 0 0 143182 61678 0 0 285266 210080 0 0 243048 165005 0 0 176062 97806 0 0 305210 184471 0 0 87995 27786 0 0 343613 184458 0 0 264159 98765 0 0 394976 178441 0 0 192718 100619 0 0 114673 58391 0 0 310108 151672 0 0 292891 124437 0 0 157518 79929 0 0 180362 123064 0 0 146175 50466 0 0 140319 100991 0 0 405267 79367 0 0 78800 56968 0 0 201970 106257 0 0 305322 178412 0 0 164733 98520 0 1 199186 153670 0 1 24188 15049 0 1 346142 174478 0 1 65029 25109 0 1 101097 45824 0 1 255082 116772 0 1 287314 189150 1 1 308944 194404 1 1 280943 185881 1 1 225816 67508 1 1 348943 188597 1 1 283283 203618 1 1 199642 87232 1 1 232791 110875 1 1 212262 144756 1 1 201345 129825 1 1 180424 92189 1 1 204450 121158 1 1 197813 96219 1 1 138731 84128 1 1 216153 97960 1 1 73566 23824 1 1 219392 103515 1 1 181728 91313 1 1 150006 85407 1 1 325723 95871 1 1 265348 143846 1 1 202410 155387 1 1 173420 74429 1 1 162366 74004 1 1 136341 71987 1 1 390163 150629 1 1 145905 68580 1 1 238921 119855 1 1 80953 55792 1 1 133301 25157 1 1 138630 90895 1 1 334082 117510 1 1 277542 144774 1 1 170849 77529 1 1 236398 103123 1 1 207178 104669 1 1 157125 82414 1 1 242395 82390 1 1 273632 128446 1 1 178489 111542 1 1 207720 136048 1 1 268066 197257 1 1 349934 162079 1 1 368833 206286 1 1 247804 109858 1 1 265849 182125 1 1 174311 74168 1 1 43287 19630 1 1 176724 88634 1 1 189021 128321 1 1 237531 118936 1 1 279589 127044 1 1 106655 178377 1 1 135798 69581 1 1 290495 168019 1 1 266805 113598 1 1 23623 5841 1 1 174970 93116 1 1 61857 24610 1 1 147760 60611 1 1 358662 226620 1 1 21054 6622 1 1 230091 121996 1 1 31414 13155 1 1 284519 154158 1 1 209481 78489 1 1 161691 22007 1 1 137093 72530 1 1 38214 13983 1 1 166059 73397 1 1 319346 143878 1 1 186273 119956 1 1 374212 181558 1 1 275578 208236 1 1 368863 237085 1 1 179928 110297 1 1 94381 61394 1 1 251253 81420 1 1 382564 191154 1 1 118033 11798 1 1 370878 135724 1 1 147989 68614 1 1 236370 139926 1 1 193220 105203 1 1 189020 80338 1 1 341992 121376 1 1 224936 124922 1 1 173260 10901 1 1 286161 135471 1 1 130908 66395 1 1 209639 134041 1 1 262412 153554 1 1 1 0 1 1 14688 7953 1 1 98 0 1 1 455 0 1 1 0 0 1 1 0 0 1 1 195822 98922 1 1 347930 165395 1 1 0 0 1 1 203 0 1 1 7199 4245 1 1 46660 21509 1 1 17547 7670 1 1 107465 15167 1 1 969 0 1 1 179994 63891
Names of X columns:
Pop Gender Time_RFC_sec Compendium_writing_time_sec
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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$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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Big Analytics Cloud Computing Center
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