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R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Thu, 30 Oct 2008 05:43:50 -0600

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/30/t1225367254iorw7v3g13ocunr.htm/, Retrieved Sun, 19 May 2024 14:54:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19978, Retrieved Sun, 19 May 2024 14:54:57 +0000

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No (this computation is public)

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Estimated Impact

260

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F       [Kendall tau Correlation Matrix] [Hypothesis Testin...] [2008-10-30 11:43:50] [25d75405d700c34901b109463a9659f5] [Current]
F         [Kendall tau Correlation Matrix] [Part 2 q1] [2008-11-03 17:57:37] [4ad596f10399a71ad29b7d76e6ab90ac] 
F         [Kendall tau Correlation Matrix] [Part 2 g1] [2008-11-03 19:33:24] [ed2ba3b6182103c15c0ab511ae4e6284] 
F         [Kendall tau Correlation Matrix] [] [2008-11-03 20:34:26] [af90f76a5211a482a7c35f2c76d2fd61] 
-         [Kendall tau Correlation Matrix] [] [2008-11-03 21:18:19] [29747f79f5beb5b2516e1271770ecb47] 

Feedback Forum

2008-11-09 21:30:10 [Tamara Witters] [reply] 
Je hebt een juiste oplossing maar niet uitgebreid genoeg geargumenteerd. 
Verbetering: 
Boven de diagonaal zie ja allemaal scatter plots met rode correlatielijnen deze geven de correlatie tussen 2 variabelen. In dit geval zien we dat de correlatie tussen RNR en RCF het grootst is.  
 
Als we dan onder de diagonaal kijken vinden we getallen die de betrouwbaarheid weergeven (zijn geen correlatiecoëffiënten). Hoe kleiner het getal, hoe groter de betrouwbaarheid. Indien het getal onder de 0,05 zit kunnen we spreken van correlatie. RNR is het meest gecorreleerd met RCF, want hier is het getal het laagste, namelijk 0,01. 
2008-11-10 22:04:43 [Chi-Kwong Man] [reply] 
Correct. RCF (cashflow) is de beste predictor voor RNR. Boven de diagonaal ziet men de scatterplots en onder de diagonaal de getallen die duiden op betrouwbaarheid van de correlatiecoëffient. Hoe kleiner het getal hoe beter. In het geval voor RCF is dit 0.01 wat zeer betrouwbaar is want het is nog kleiner dan 0.05.

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Dataseries X:

Download CSV

Histogram

Boxplots

4.2	4.8	20.8	0.9	39.6
2.6	-4.2	17.1	0.85	36.1
3	1.6	22.3	0.83	34.4
3.8	5.2	25.1	0.84	33.4
4	9.2	27.7	0.85	34.8
3.5	4.6	24.9	0.83	33.7
4.1	10.6	29.5	0.83	36.3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19978&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19978&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19978&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Kendall tau rank correlations for all pairs of data series
pair	tau	p-value
tau( RLEZ , RNR )	0.714285714285714	0.0301587301587301
tau( RLEZ , RCF )	0.523809523809524	0.136111111111111
tau( RLEZ , RLEZ )	0.264628062012482	0.427262856745706
tau( RLEZ , REV )	0.333333333333333	0.381349206349206
tau( RNR , RCF )	0.80952380952381	0.0107142857142857
tau( RNR , RLEZ )	-0.0529256124024963	0.873844698517373
tau( RNR , REV )	0.0476190476190476	1
tau( RCF , RLEZ )	-0.264628062012482	0.427262856745706
tau( RCF , REV )	-0.142857142857143	0.772619047619048
tau( RLEZ , REV )	0.370479286817474	0.266379923342483

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( RLEZ , RNR ) & 0.714285714285714 & 0.0301587301587301 \tabularnewline
tau( RLEZ , RCF ) & 0.523809523809524 & 0.136111111111111 \tabularnewline
tau( RLEZ , RLEZ ) & 0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RLEZ , REV ) & 0.333333333333333 & 0.381349206349206 \tabularnewline
tau( RNR , RCF ) & 0.80952380952381 & 0.0107142857142857 \tabularnewline
tau( RNR , RLEZ ) & -0.0529256124024963 & 0.873844698517373 \tabularnewline
tau( RNR , REV ) & 0.0476190476190476 & 1 \tabularnewline
tau( RCF , RLEZ ) & -0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RCF , REV ) & -0.142857142857143 & 0.772619047619048 \tabularnewline
tau( RLEZ , REV ) & 0.370479286817474 & 0.266379923342483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19978&T=1

[TABLE]
[ROW][C]Kendall tau rank correlations for all pairs of data series[/C][/ROW]
[ROW][C]pair[/C][C]tau[/C][C]p-value[/C][/ROW]
[ROW][C]tau( RLEZ , RNR )[/C][C]0.714285714285714[/C][C]0.0301587301587301[/C][/ROW]
[ROW][C]tau( RLEZ , RCF )[/C][C]0.523809523809524[/C][C]0.136111111111111[/C][/ROW]
[ROW][C]tau( RLEZ , RLEZ )[/C][C]0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( RLEZ , REV )[/C][C]0.333333333333333[/C][C]0.381349206349206[/C][/ROW]
[ROW][C]tau( RNR , RCF )[/C][C]0.80952380952381[/C][C]0.0107142857142857[/C][/ROW]
[ROW][C]tau( RNR , RLEZ )[/C][C]-0.0529256124024963[/C][C]0.873844698517373[/C][/ROW]
[ROW][C]tau( RNR , REV )[/C][C]0.0476190476190476[/C][C]1[/C][/ROW]
[ROW][C]tau( RCF , RLEZ )[/C][C]-0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( RCF , REV )[/C][C]-0.142857142857143[/C][C]0.772619047619048[/C][/ROW]
[ROW][C]tau( RLEZ , REV )[/C][C]0.370479286817474[/C][C]0.266379923342483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19978&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19978&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Kendall tau rank correlations for all pairs of data series
pair	tau	p-value
tau( RLEZ , RNR )	0.714285714285714	0.0301587301587301
tau( RLEZ , RCF )	0.523809523809524	0.136111111111111
tau( RLEZ , RLEZ )	0.264628062012482	0.427262856745706
tau( RLEZ , REV )	0.333333333333333	0.381349206349206
tau( RNR , RCF )	0.80952380952381	0.0107142857142857
tau( RNR , RLEZ )	-0.0529256124024963	0.873844698517373
tau( RNR , REV )	0.0476190476190476	1
tau( RCF , RLEZ )	-0.264628062012482	0.427262856745706
tau( RCF , REV )	-0.142857142857143	0.772619047619048
tau( RLEZ , REV )	0.370479286817474	0.266379923342483

Figure 1

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Postscript link

PDF link

Parameters (Session):

Parameters (R input):

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='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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