Schiphol: Pearson correlatie

*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: Fri, 10 Dec 2010 22:30:24 +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/10/t1292020120bo4qqrbxdxrh6t3.htm/, Retrieved Fri, 10 Dec 2010 23:28:42 +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/10/t1292020120bo4qqrbxdxrh6t3.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 «

0 1979900 7 0 1737275 1742117 1522522 1276674 1086979 1149822 0 2061036 8 0 1979900 1737275 1742117 1522522 1276674 1086979 0 1867943 9 0 2061036 1979900 1737275 1742117 1522522 1276674 0 1707752 10 0 1867943 2061036 1979900 1737275 1742117 1522522 0 1298756 11 0 1707752 1867943 2061036 1979900 1737275 1742117 0 1281814 12 0 1298756 1707752 1867943 2061036 1979900 1737275 0 1281151 13 0 1281814 1298756 1707752 1867943 2061036 1979900 0 1164976 14 0 1281151 1281814 1298756 1707752 1867943 2061036 0 1454329 15 0 1164976 1281151 1281814 1298756 1707752 1867943 0 1645288 16 0 1454329 1164976 1281151 1281814 1298756 1707752 0 1817743 17 0 1645288 1454329 1164976 1281151 1281814 1298756 0 1895785 18 0 1817743 1645288 1454329 1164976 1281151 1281814 0 2236311 19 0 1895785 1817743 1645288 1454329 1164976 1281151 0 2295951 20 0 2236311 1895785 1817743 1645288 1454329 1164976 0 2087315 21 0 2295951 2236311 1895785 1817743 1645288 1454329 0 1980891 22 0 2087315 2295951 2236311 1895785 1817743 1645288 0 1 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! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Correlations for all pairs of data series (method=pearson) 9/11 Yt t T_9/11 Yt-1 Yt-2 Yt-3 Yt-4 Yt-5 Yt-6 9/11 1 0.657 0.866 0.967 0.667 0.678 0.684 0.69 0.693 0.694 Yt 0.657 1 0.795 0.679 0.929 0.837 0.698 0.555 0.475 0.428 t 0.866 0.795 1 0.927 0.796 0.796 0.796 0.796 0.798 0.799 T_9/11 0.967 0.679 0.927 1 0.686 0.693 0.696 0.698 0.7 0.701 Yt-1 0.667 0.929 0.796 0.686 1 0.929 0.837 0.698 0.558 0.478 Yt-2 0.678 0.837 0.796 0.693 0.929 1 0.929 0.837 0.7 0.56 Yt-3 0.684 0.698 0.796 0.696 0.837 0.929 1 0.929 0.839 0.702 Yt-4 0.69 0.555 0.796 0.698 0.698 0.837 0.929 1 0.931 0.841 Yt-5 0.693 0.475 0.798 0.7 0.558 0.7 0.839 0.931 1 0.931 Yt-6 0.694 0.428 0.799 0.701 0.478 0.56 0.702 0.841 0.931 1 Correlations for all pairs of data series with p-values pair Pearson r Spearman rho Kendall tau 9/11;Yt 0.6567 0.6642 0.5435 p-value (0) (0) (0) 9/11;t 0.866 0.866 0.7087 p-value (0) (0) (0) 9/11;T_9/11 0.9674 0.9258 0.8177 p-value (0) (0) (0) 9/11;Yt-1 0.6669 0.6748 0.5522 p-value (0) (0) (0) 9/11;Yt-2 0.6777 0.6861 0.5615 p-value (0) (0) (0) 9/11;Yt-3 0.6843 0.6933 0.5673 p-value (0) (0) (0) 9/11;Yt-4 0.6903 0.7 0.5728 p-value (0) (0) (0) 9/11;Yt-5 0.693 0.7044 0.5765 p-value (0) (0) (0) 9/11;Yt-6 0.6941 0.7063 0.578 p-value (0) (0) (0) Yt;t 0.7954 0.795 0.605 p-value (0) (0) (0) Yt;T_9/11 0.6791 0.6786 0.5149 p-value (0) (0) (0) Yt;Yt-1 0.9287 0.9284 0.7788 p-value (0) (0) (0) Yt;Yt-2 0.8365 0.8341 0.6479 p-value (0) (0) (0) Yt;Yt-3 0.6979 0.694 0.5076 p-value (0) (0) (0) Yt;Yt-4 0.555 0.5533 0.3926 p-value (0) (0) (0) Yt;Yt-5 0.4747 0.4761 0.3117 p-value (0) (0) (0) Yt;Yt-6 0.4276 0.4296 0.2672 p-value (0) (0) (0) t;T_9/11 0.9273 0.9354 0.8667 p-value (0) (0) (0) t;Yt-1 0.7957 0.7953 0.605 p-value (0) (0) (0) t;Yt-2 0.7961 0.7957 0.6052 p-value (0) (0) (0) t;Yt-3 0.7959 0.7957 0.6049 p-value (0) (0) (0) t;Yt-4 0.796 0.7959 0.6051 p-value (0) (0) (0) t;Yt-5 0.7976 0.7972 0.6074 p-value (0) (0) (0) t;Yt-6 0.7992 0.7985 0.6096 p-value (0) (0) (0) T_9/11;Yt-1 0.6856 0.6826 0.5165 p-value (0) (0) (0) T_9/11;Yt-2 0.6926 0.6871 0.5185 p-value (0) (0) (0) T_9/11;Yt-3 0.6958 0.6891 0.5187 p-value (0) (0) (0) T_9/11;Yt-4 0.6981 0.691 0.5186 p-value (0) (0) (0) T_9/11;Yt-5 0.6999 0.693 0.52 p-value (0) (0) (0) T_9/11;Yt-6 0.7008 0.694 0.5211 p-value (0) (0) (0) Yt-1;Yt-2 0.9288 0.9288 0.7796 p-value (0) (0) (0) Yt-1;Yt-3 0.8368 0.8344 0.6484 p-value (0) (0) (0) Yt-1;Yt-4 0.6983 0.6944 0.5077 p-value (0) (0) (0) Yt-1;Yt-5 0.5576 0.556 0.3945 p-value (0) (0) (0) Yt-1;Yt-6 0.4777 0.4795 0.3137 p-value (0) (0) (0) Yt-2;Yt-3 0.9291 0.929 0.78 p-value (0) (0) (0) Yt-2;Yt-4 0.8372 0.8346 0.6484 p-value (0) (0) (0) Yt-2;Yt-5 0.6998 0.6962 0.5088 p-value (0) (0) (0) Yt-2;Yt-6 0.56 0.5588 0.3963 p-value (0) (0) (0) Yt-3;Yt-4 0.929 0.929 0.7797 p-value (0) (0) (0) Yt-3;Yt-5 0.8386 0.8356 0.6495 p-value (0) (0) (0) Yt-3;Yt-6 0.7019 0.6979 0.5104 p-value (0) (0) (0) Yt-4;Yt-5 0.9306 0.9297 0.7816 p-value (0) (0) (0) Yt-4;Yt-6 0.8407 0.8367 0.6513 p-value (0) (0) (0) Yt-5;Yt-6 0.9315 0.9302 0.7827 p-value (0) (0) (0)

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t1292020120bo4qqrbxdxrh6t3/1y2uq1292020215.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1292020120bo4qqrbxdxrh6t3/1y2uq1292020215.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.3 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = pearson ; par2 = 0.3 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

R code (references can be found in the software module):

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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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