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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationWed, 05 Nov 2008 05:07:35 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/05/t1225887039uenzjpqnuownml3.htm/, Retrieved Sun, 19 May 2024 08:56:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21721, Retrieved Sun, 19 May 2024 08:56:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact173

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Kendall tau Correlation Matrix] [Kendell Tau] [2008-11-05 12:07:35] [0458bd763b171003ec052ce63099d477] [Current]
Feedback Forum
2008-11-11 10:25:37 [94a54c888ac7f7d6874c3108eb0e1808] [reply] 
De 0.01 is het betrouwbaarheidsinterval en heeft niets te maken met de correlatie. Voor de correlatie is het best dat men de tabel ook mee overneemt in het word document.
2008-11-11 14:24:18 [Sandra Hofmans] [reply] 
De student heeft er goed aan gedaan om de Kendall Tau Correlation te gebruiken: deze geeft namelijk een overzichtelijk beeld van de verschillende predictors.
De uitkomst die de student geeft is correct: de beste predictor voor RNR is RCF. Je kan dit allereerst reeds zien op de tekening die de correlatie weergeeft. Maar ook is de p-value zeer belangrijk, zoals de student reeds vermeldde, deze geeft de probaliteit weer; in hoeverre de correlatie is toegeschreven aan het toeval, deze moet best onder 0,05 liggen om te spreken over een goede correlatie.
2008-11-12 09:40:20 [Erik Geysen] [reply] 
De Kendall Tau Correlation is duidelijk de juiste manier om de beste predictor te vinden. De correlatiecoëfficiënt is het kleinst bij de cashflow. Dit zien we ook bij de bijhorende grafiek. De gegevens liggen hier het meest op een diagonaal. De P-value geeft de probaliteit weer. Deze moet kleiner of gelijk zijn aan 0,05 om van een goed verband te kunnen spreken. Dat is hier het geval, dus kan het zeker niet te wijden zijn aan het toeval.
2008-11-12 10:10:29 [Erik Geysen] [reply] 
Graag wil ik mezelf even verbeteren. Het is inderdaad de P-value (en niet de correlatiecoëfficiënt!) die bepaald of het vervand tussen 2 variabelen aan het toeval te wijden kan zijn. Er bestaat dus een groot verband of correlatie. De correlatiecoëfficiënt is hier dan ook het grootst!

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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21721&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21721&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21721&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( RNVM , RNR )0.7142857142857140.0301587301587301
tau( RNVM , RCFR )0.5238095238095240.136111111111111
tau( RNVM , LEZ )0.2646280620124820.427262856745706
tau( RNVM , REV )0.3333333333333330.381349206349206
tau( RNR , RCFR )0.809523809523810.0107142857142857
tau( RNR , LEZ )-0.05292561240249630.873844698517373
tau( RNR , REV )0.04761904761904761
tau( RCFR , LEZ )-0.2646280620124820.427262856745706
tau( RCFR , REV )-0.1428571428571430.772619047619048
tau( LEZ , REV )0.3704792868174740.266379923342483

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( RNVM , RNR ) & 0.714285714285714 & 0.0301587301587301 \tabularnewline
tau( RNVM , RCFR ) & 0.523809523809524 & 0.136111111111111 \tabularnewline
tau( RNVM , LEZ ) & 0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RNVM , REV
 ) & 0.333333333333333 & 0.381349206349206 \tabularnewline
tau( RNR , RCFR ) & 0.80952380952381 & 0.0107142857142857 \tabularnewline
tau( RNR , LEZ ) & -0.0529256124024963 & 0.873844698517373 \tabularnewline
tau( RNR , REV
 ) & 0.0476190476190476 & 1 \tabularnewline
tau( RCFR , LEZ ) & -0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RCFR , REV
 ) & -0.142857142857143 & 0.772619047619048 \tabularnewline
tau( LEZ , REV
 ) & 0.370479286817474 & 0.266379923342483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21721&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( RNVM , RNR )[/C][C]0.714285714285714[/C][C]0.0301587301587301[/C][/ROW]
[ROW][C]tau( RNVM , RCFR )[/C][C]0.523809523809524[/C][C]0.136111111111111[/C][/ROW]
[ROW][C]tau( RNVM , LEZ )[/C][C]0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( RNVM , REV
 )[/C][C]0.333333333333333[/C][C]0.381349206349206[/C][/ROW]
[ROW][C]tau( RNR , RCFR )[/C][C]0.80952380952381[/C][C]0.0107142857142857[/C][/ROW]
[ROW][C]tau( RNR , LEZ )[/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( RCFR , LEZ )[/C][C]-0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( RCFR , REV
 )[/C][C]-0.142857142857143[/C][C]0.772619047619048[/C][/ROW]
[ROW][C]tau( LEZ , REV
 )[/C][C]0.370479286817474[/C][C]0.266379923342483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21721&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21721&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( RNVM , RNR )0.7142857142857140.0301587301587301
tau( RNVM , RCFR )0.5238095238095240.136111111111111
tau( RNVM , LEZ )0.2646280620124820.427262856745706
tau( RNVM , REV )0.3333333333333330.381349206349206
tau( RNR , RCFR )0.809523809523810.0107142857142857
tau( RNR , LEZ )-0.05292561240249630.873844698517373
tau( RNR , REV )0.04761904761904761
tau( RCFR , LEZ )-0.2646280620124820.427262856745706
tau( RCFR , REV )-0.1428571428571430.772619047619048
tau( LEZ , REV )0.3704792868174740.266379923342483


Figures (Output of Computation)

Input Parameters & R Code

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')