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*Unverified author*

R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Mon, 03 Nov 2008 13:34:26 -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/03/t12257444872du4con0bqomitt.htm/, Retrieved Sun, 19 May 2024 03:52:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21205, Retrieved Sun, 19 May 2024 03:52:09 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

221

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] [c5e27150943bc3d623392efb0d98f8d3]
F         [Kendall tau Correlation Matrix] [] [2008-11-03 20:34:26] [e0f4dc76ea1a8276fe566258b977cec4] [Current]

Feedback Forum

2008-11-10 16:57:42 [Kenny Simons] [reply] 
Deze vraag heb je goed opgelost. De beste predictor voor netto rendabiliteit op EV is inderdaad de variabele RCF. Dit kunnen we niet enkel mathematisch bewijzen (door in de tabel te gaan zoeken naar de grootste tau waarde en de kleinste p-waarde), maar dit kunnen we ook grafisch aflezen. We zien op de grafiek duidelijk een correlatie tussen deze 2 variabele. De betrouwbaarheidscoefficient is hier 0.03 dus deze correlatie is significant en niet toevallig. 
2008-11-10 18:21:39 [Stéphanie Claes] [reply] 
Om deze methode uit te voeren vertrekken we van de gegevens die we vinden in de Excel file, daar kunnen we zien dat er een probleem is met de data. De variabelen moeten vanboven komen en de data (observaties) op de y as => tabel exponeren. 
 
De student gebruikte de juiste calculator om deze vraag op te lossen, namelijk de Kendall tau Correlation, deze is veel robuuster voor outliers. Alle correlaties worden berekend, we krijgen een overzicht van de verschillende relaties. 
De getallen (= betrouwbaarheidscoefficient) zeggen iets over de correlatie maar zeggen niet wat het is, hoe hoger het getal, hoe meer toevallig je iets gevonden hebt. Het meest gecorreleerde is RCF, zoals de student reeds correct had besloten. Ook in de tabel kunnen we dit terugvinden.
2008-11-11 19:54:14 [Elias Van Deun] [reply] 
Deze vraag heb ik correct opgelost.
2008-11-12 11:47:35 [Nilay Erdogdu] [reply] 
Correcte antwoord.

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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	'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21205&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21205&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21205&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	'George Udny Yule' @ 72.249.76.132

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=21205&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=21205&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21205&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

PNG link

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