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Data X:
696688.604651165 19676 352705.439528025 164151.080000001 319574.638709679 78510 477423.742857145 84907 367801.886792454 24611 274864.902439025 20398 268557.456896553 58634 278133.333333334 14542 295714.876033059 55694 248817.238095239 10445 269479.037037038 13210 339098.412698414 94347 282146.621621623 37707 276913.437500001 47598.4900000002 299348.974576272 115364.3 286877.061728396 39132 294213.424657535 34479 274111.111111112 18190 273200 16482 254486.301369864 33246.0400000001 241181.44329897 48014 242297.647058824 24736 231933.287234043 47013 304091.452991454 57152 227886.363636364 10967 206437.500000001 11802 203580.645161291 15196.1400000001 189857.142857144 7007 186250.62295082 28530 202128.648648649 17942 159677.419354839 15495 205985.294117648 42019.9000000002 138800 7441 184500 4814 233863.636363637 5094 247600 4574 NA NA NA NA 180545 5311 195693.367816093 40622 186400 4811 195004.925925927 12787 173002.344827587 13570 768799.666666669 35135.9100000001 396408.351398603 443489.880000002 333012.196473553 310566.580000001 450103.638211384 188593.950000001 402611.020833335 35336 329363.597826088 70963 307309.390243904 156678 307136.375000001 41468 337954.417475729 76512 298323.513513515 28003 321571.42857143 16194 379975.988212182 398136 342990.655405407 117811.82 305157.674329503 203185 367618.938271606 313847.400000001 312401.407079647 88039 314522.227272728 120103.06 295640.170731708 63356 300461.333333334 59671 294039.740458016 101447 260929.465909092 282523.480000001 236330.319148937 75290 237333.042968751 201325 348898.986595175 295136.400000001 268294.642857144 42415 233048.492063493 49525 246930.500000001 73591 211025.742857144 28634 219798.310810812 115997 209255.319148937 73974 199053.40909091 71526 240813.197674419 206413 191339.344262296 49047 191263.861111112 29782 296692.307692309 20739 180113.636363637 17764 219879.62962963 21451 200283.333333334 25451 183236.437500001 39354 230024.657534247 115424 199255.400000001 19590 201685.129870131 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108406.230769231 41624.2500000001 97578.9523809527 2595 124246.785714286 4537 97294.016393443 7911 93838.7096774197 4832 100456.521739131 4393 85212.3243243246 10175 88542.172727273 14635.8900000001 419029.581901491 308607.200000001 245263.78997462 310650.640000001 249489.736547086 173031.360000001 247720.680939638 623488.140000002 222739.553339981 188220.210000001 198559.529850747 79740 254770.846946868 230311.070000001 192198.51458886 142512.53 182138.747076024 132402.22 171772.493273543 43558.1300000002 178771.545876888 164147.790000001 162862.712230216 53898.5300000002 250599.05612245 147971.670000001 190272.042553192 16709.6300000001 178501.398843931 36245 200223.269662922 96626.6800000004 167838 10433 172913.36673774 93344.7300000003 157965.400000001 28995 163252.977777778 206480.010000001 193686.886543536 76125 173217.90909091 24269 154679.29842932 39864 234845.122015916 77180.4700000003 169130.586387435 77718.8200000003 123144.103448276 17283 106043.209876544 15176 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206617.708463951 131375.55 176421.022556392 56876 192922.89608637 305876.670000001 190188.429752067 217064 193396.518867925 96678.8400000003 177340.702054795 129007 258227.678004536 189602.110000001 175371.081012659 174831 149076.923076924 45125 133719.117647059 29974 213398.41509434 44572 167209.048192772 35463 157016.666666667 29672 146969.277227723 42554 135344.090909091 27975 161780.606666667 68072 170496.106250001 133737.1 155032.68627451 66724.0300000002 160577.255659122 308579 146648.419161677 71773 146194 108171.54 154064.405844156 125846.76 149563.513157895 60816 121971.904761905 117141 128148.687747036 210732.010000001 127362.026722926 296019 675784.354838712 22423 293262.911051214 283624 248916.903890161 334207.050000001 312587.818991099 251501 259544.269961978 195370.390000001 226855.351851853 123149.37 268128.913357402 210292.470000001 257471.288732395 107313.41 242785.803921569 113374.3 222969.387500001 60712.4100000002 219032.377483444 226389.300000001 208784.750000001 109010.9 275275 101175 244401.111111112 21563 217052.848101267 61358 231508.301369864 54333 203027.346938776 35699 228718.824175825 68368 197185.51724138 44715 208909.875576038 162229.740000001 199909.575609757 321890 199154.32900433 179832 190190.61904762 178888 276288.987603307 185127 188839.647928995 260040.930000001 148092.787878788 75980 146304.347826087 34920 247008.461538462 39489 185958.238805971 52651 163739.090909091 43886 167317.307692308 40856 148669.250000001 51466 183875.339130435 89115.3800000003 207199.875000001 157300.600000001 170844.444444445 97828 199296.824615385 247829.820000001 150692.298245615 85947 152465.506329114 121709 172008 93780.7000000003 177301.525000001 29522 149796.656000001 97071 150577.433179724 162420.580000001 149494.747081713 189442 427927.700000002 36628 353345.77922078 441334 289408.575757577 587588 405286.972602741 290465.490000001 348284.020618558 410936.980000001 283750.11594203 288496 331440.867256638 336406.250000001 332993.261904763 150146.750000001 338918.91891892 124228.53 287036.842105264 138233.600000001 299266.058139536 227795.910000001 281497.594339624 415181 336326.606557378 255234 457237.333333335 73980 317777 228926 300930 62680.1400000002 268893.090909092 117445 277078.214285715 103012.95 276275.652173914 94824.0200000003 288352 160431 235477.428571429 432534 229799.285714287 244729 246401.105263159 144777 374510.177966103 376934.350000001 254836.180904524 626373 207863.160000001 223756 171177.419354839 100455 285483.333333334 63398 225083.265306123 151573 201556.744680852 124176 224169.047619048 63122 183288.884615385 208465 241385.576923078 122965.78 265201.839622642 303384.330000001 214480.183908047 221010 269347.556701032 233802 225812.44871795 340514 230992.475609757 235337 248674.016393443 170739 180400 21667 193260.066666667 253405 188882.025316456 263951.820000001 206357.160000001 160047
Names of X columns:
averageprice totalsurface
Type of Correlation
pearson
spearman
kendall
Chart options
Title:
R Code
par1 <- 'pearson' 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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } x <- na.omit(x) y <- t(na.omit(t(y))) bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) print(n) a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$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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Raw Input
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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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