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R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Wed, 05 Nov 2008 05:16:26 -0700

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/Nov/05/t1225887539ojr6hmli0qldmw3.htm/, Retrieved Sun, 19 May 2024 06:42:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21734, Retrieved Sun, 19 May 2024 06:42:31 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

160

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

F       [Kendall tau Correlation Matrix] [deel 2 vraag 1] [2008-11-05 12:16:26] [f7fbcd402030df685d3fe4ce577d7846] [Current]

Feedback Forum

2008-11-12 09:55:37 [Evelyn Gabriel] [reply] 
Er wordt niet veel uitleg gegeven bij de conclusie. De RCF is inderdaad de beste voorspeller omdat het verband niet aan het toeval kan worden toegeschreven. Er is een grote betrouwbaarheid aangezien de correlatie onder 0,05 ligt.
2008-11-12 11:46:59 [Bénédicte Soens] [reply] 
Uw antwoord klopt inderdaad wel, maar de uitleg is veel te beknopt. Eerst zou er moeten uitgelegd worden wat deze kendall tau correlation weergeeft en hoe het wordt gebruikt. Het uiteindelijke antwoord dat RCF de beste voorspeller zou zijn is wel juist maar waarom? Er wordt een p-waarde van 0,01 gevonden, dus er is maar 1% kans op toevalligheid. Dit is zeer weinig dus RCF is beste predictor.  
Hoe kleiner de waarde, hoe minder toevalligheid, hoe beter.

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Dataseries X:

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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
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 & 2 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=21734&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]
[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=21734&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21734&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
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
pair	tau	p-value
tau( rnvm , rnr )	0.714285714285714	0.0301587301587301
tau( rnvm , rcfr )	0.523809523809524	0.136111111111111
tau( rnvm , lez )	0.264628062012482	0.427262856745706
tau( rnvm , rev )	0.333333333333333	0.381349206349206
tau( rnr , rcfr )	0.80952380952381	0.0107142857142857
tau( rnr , lez )	-0.0529256124024963	0.873844698517373
tau( rnr , rev )	0.0476190476190476	1
tau( rcfr , lez )	-0.264628062012482	0.427262856745706
tau( rcfr , rev )	-0.142857142857143	0.772619047619048
tau( lez , 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( 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=21734&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=21734&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21734&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( rnvm , rnr )	0.714285714285714	0.0301587301587301
tau( rnvm , rcfr )	0.523809523809524	0.136111111111111
tau( rnvm , lez )	0.264628062012482	0.427262856745706
tau( rnvm , rev )	0.333333333333333	0.381349206349206
tau( rnr , rcfr )	0.80952380952381	0.0107142857142857
tau( rnr , lez )	-0.0529256124024963	0.873844698517373
tau( rnr , rev )	0.0476190476190476	1
tau( rcfr , lez )	-0.264628062012482	0.427262856745706
tau( rcfr , rev )	-0.142857142857143	0.772619047619048
tau( lez , 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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Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code