| *The author of this computation has been verified* | ||||||||||||
| R Software Module: Patrick.Wessa/rwasp_pairs.wasp (opens new window with default values) | ||||||||||||
| Title produced by software: Kendall tau Correlation Matrix | ||||||||||||
| Date of computation: Mon, 20 Dec 2010 10:55:57 +0000 | ||||||||||||
| Cite this page as follows: | ||||||||||||
| Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/20/t1292842497rr6ln102s6183sl.htm/, Retrieved Mon, 20 Dec 2010 11:54:57 +0100 | ||||||||||||
| BibTeX entries for LaTeX users: | ||||||||||||
@Manual{KEY,
author = {{YOUR NAME}},
publisher = {Office for Research Development and Education},
title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/20/t1292842497rr6ln102s6183sl.htm/},
year = {2010},
}
@Manual{R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2010},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org},
}
| ||||||||||||
| Original text written by user: | ||||||||||||
| IsPrivate? | ||||||||||||
| No (this computation is public) | ||||||||||||
| User-defined keywords: | ||||||||||||
| Dataseries X: | ||||||||||||
| » Textbox « » Textfile « » CSV « | ||||||||||||
| 22.037 17.759 14.116 104.708 158.620 21.732 17.530 13.504 101.817 154.583 21.172 17.139 13.168 97.898 149.377 21.388 16.916 13.064 95.559 146.927 22.053 16.543 12.828 92.822 144.246 22.687 16.317 12.541 90.848 142.393 24.793 18.161 13.547 101.141 157.642 26.113 19.144 13.710 105.841 164.808 23.708 16.947 12.535 93.647 146.837 23.554 16.491 12.386 90.923 143.354 23.222 16.428 12.253 89.130 141.033 23.363 16.639 12.484 90.212 142.698 24.023 16.821 12.966 93.196 147.006 23.355 16.765 12.770 91.861 144.751 23.276 16.533 12.660 90.593 143.062 23.085 16.554 12.514 89.895 142.048 23.173 16.494 12.430 88.819 140.916 23.487 16.612 12.372 87.924 140.395 25.576 17.933 13.085 96.906 153.500 26.311 19.070 13.454 101.217 160.052 27.109 18.179 13.361 98.709 157.358 27.060 17.830 13.713 98.139 156.742 26.490 17.349 13.601 95.529 152.969 27.157 17.919 14.090 98.577 157.743 26.973 18.269 14.452 100.772 160.466 27.589 18.385 14.108 100.180 160.262 27.246 18.260 14.036 99.200 158.742 26.845 17.905 13.3 etc... | ||||||||||||
| Output produced by software: | ||||||||||||
Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!
| ||||||||||||
| Charts produced by software: | ||||||||||||
| Parameters (Session): | ||||||||||||
| par1 = pearson ; | ||||||||||||
| Parameters (R input): | ||||||||||||
| par1 = pearson ; | ||||||||||||
| R code (references can be found in the software module): | ||||||||||||
| 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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$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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