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Data X:
45 73 7 31 32 15 54 67 20 50 59 16 45 63 10 53 70 14 54 70 8 34 68 14 55 73 13 60 74 15 51 59 NA 76 80 16 37 78 7 55 59 NA 43 50 NA 70 65 16 50 73 14 73 52 16 65 71 19 53 76 17 67 76 8 54 65 20 63 70 16 52 60 16 25 62 11 47 64 12 55 71 18 59 63 16 59 74 14 60 66 16 61 72 18 59 58 14 38 55 10 58 68 14 50 84 17 43 57 19 67 71 16 68 75 11 57 72 10 62 63 19 54 73 12 56 62 11 52 65 13 40 66 16 58 72 16 55 78 8 58 78 15 67 42 17 58 73 17 61 69 15 43 60 16 58 61 14 37 64 15 51 64 14 66 76 16 63 69 15 61 66 7 52 64 15 50 72 13 33 61 16 67 61 16 29 59 15 43 58 14 36 51 14 43 66 16 66 66 15 67 71 16 52 67 12 47 70 16 47 63 16 44 70 16 50 73 16 57 64 16 60 65 16 26 50 13 84 84 20 56 70 15 67 73 19 64 74 10 54 71 20 58 69 17 66 61 13 66 64 16 57 62 8 49 66 14 49 72 16 63 78 10 16 56 16 63 50 15 43 69 15 56 74 17 43 54 14 64 67 15 39 52 14 48 59 12 68 67 8 56 75 14 66 56 9 59 63 15 67 76 17 46 62 17 55 68 16 61 59 18 59 70 18 62 74 19 54 60 NA 57 78 14 50 68 16 35 76 16 53 71 16 42 64 8 72 71 16 56 75 13 67 80 17 71 69 16 51 77 NA 47 63 13 66 62 16 51 68 15 48 72 14 47 75 20 47 76 10 46 62 14 59 66 15 58 69 16 57 70 14 50 55 14 54 74 11 66 67 16 61 64 15 80 75 20 48 63 16 51 65 14 52 57 12 55 70 15 64 66 14 26 68 10 55 68 18 65 69 19 42 55 16 56 66 NA 46 66 13 53 65 16 43 69 16 60 66 12 39 58 9 43 61 16 48 60 13 51 67 15 63 68 15 51 69 14 66 59 16 51 79 11 58 71 17 53 62 10 58 69 16 37 54 14 63 68 14 56 70 11 55 71 15 57 71 18 62 71 15 62 70 18 44 55 16 38 59 12 56 60 16 56 60 16 56 73 15 56 69 16 54 67 15 64 57 16 73 61 18 53 59 16 74 81 14 60 79 14 50 74 7 53 72 16 51 70 16 59 71 13 69 68 19 53 74 15 52 62 14 56 61 13 49 66 15 59 71 14 52 51 16 52 76 16 50 57 14 58 61 16 54 66 17 59 53 13 46 63 16 52 73 14 56 69 12 67 75 16 51 63 16 64 66 15 49 59 16 29 70 14 42 67 15 30 65 16 44 47 10 37 60 15 60 68 15 37 64 16 41 48 14 41 75 15 61 58 14 66 81 15 58 64 16 50 67 16 56 68 12 46 69 16 58 72 15 51 63 17 62 69 13 45 66 NA 47 59 11 57 68 14 69 73 15 48 72 12 50 63 13 66 73 14 61 51 17 52 73 15 52 78 12 47 55 16 52 62 12 46 67 16 56 73 9 57 71 12 63 69 16 51 71 16 60 67 16 56 58 15 56 60 15 58 65 15 48 62 16 52 65 13 56 64 17 53 72 10 44 62 12 46 69 7 57 68 12 50 58 11 63 66 12 63 68 15 51 59 17 60 65 13 51 68 NA 51 48 9 55 59 10 40 59 16 49 53 13 63 74 13 53 60 12 51 61 16 51 62 14 62 64 16 56 60 11 51 57 14 58 62 15 62 72 16 34 65 12 43 52 13 61 68 16 48 60 16 54 66 13 64 78 19 54 60 16 38 48 13 41 60 13 32 65 14 52 49 15 35 62 14 41 75 9 45 65 8 50 47 16 44 53 16 47 58 9 41 65 12 55 56 15 47 63 11 50 66 15 22 60 17 33 56 6 47 19 16
Names of X columns:
AMS.I AMS.E CONFSTATTOT
Type of Correlation
Default
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') 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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Big Analytics Cloud Computing Center
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