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Data X:
493 335 514 341.6 522 340.8 490 336.3 484 325.5 506 323.7 501 317.5 462 313.9 465 308.6 454 303.7 464 303.1 427 305.1 460 304 473 307.1 465 304.3 422 294.7 415 286.9 413 279 420 271.9 363 266.7 376 259.6 380 253.8 384 250.6 346 249.1 389 250.8 407 247.6 393 237.8 346 226.4 348 217.2 353 211.4 364 207.6 305 204.3 307 197.5 312 193.6 312 192.3 286 192 324 196.1 336 191.9 327 185.6 302 179.4 299 173.9 311 169.2 315 166.8 264 165.2 278 161.4 278 160.8 287 163.7 279 170.8 324 182.7 354 190.9 354 197.8 360 205.1 363 210.7 385 220.2 412 229.7 370 237.1 389 241.6 395 250.4 417 258.6 404 269.9
Names of X columns:
Werkloze Huizenmarkt
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='kendall') 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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Kendall tau rank correlations for all pairs of data series',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'pair',1,TRUE) a<-table.element(a,'tau',1,TRUE) a<-table.element(a,'p-value',1,TRUE) a<-table.row.end(a) n <- length(y[,1]) n cor.test(y[1,],y[2,],method='kendall') for (i in 1:(n-1)) { for (j in (i+1):n) { a<-table.row.start(a) dum <- paste('tau(',dimnames(t(x))[[2]][i]) dum <- paste(dum,',') dum <- paste(dum,dimnames(t(x))[[2]][j]) dum <- paste(dum,')') a<-table.element(a,dum,header=TRUE) r <- cor.test(y[i,],y[j,],method='kendall') a<-table.element(a,r$estimate) a<-table.element(a,r$p.value) a<-table.row.end(a) } } a<-table.end(a) table.save(a,file='mytable.tab')
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