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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 computationMon, 03 Nov 2008 17:26:14 -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/04/t12257584408mqv03i4gmija5s.htm/, Retrieved Wed, 15 May 2024 12:26:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21417, Retrieved Wed, 15 May 2024 12:26:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsopdracht 5 eda part 2 q1
Estimated Impact200

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Kendall Tau Corre...] [2007-10-26 13:10:27] [aa4de776bce95665a02a61eda10fcf15]
F   PD    [Kendall tau Correlation Matrix] [Kendall Tau Corre...] [2008-11-04 00:26:14] [3efbb18563b4564408d69b3c9a8e9a6e] [Current]
-   P       [Kendall tau Correlation Matrix] [] [2008-11-14 13:45:58] [c29178f7f550574a75dc881e636e0923]
Feedback Forum
2008-11-08 15:18:11 [Jeroen Aerts] [reply] 
Je berekening is juist, goed dat je de assen hebt verwisseld.


Ook ik kom uit dat tau ( rnr,rcf) de beste predictor is omdat we gezien hebben dat de p-value onder de 0.05 betrouwbaar is. Dus komen we correlatie uit van 0.80952380952381.
2008-11-09 16:01:25 [2df1bcd103d52957f4a39bd4617794c8] [reply] 
De student past de invoer van de gegevens correct aan waardoor de assen worden verwisseld.

Vervolgens wordt ook een correcte conclusie getrokken.

Grafisch geeft de diagonaal de verdeling van de verschillende variabelen weer. De getallen hebben betrekking op de betrouwbaarheid van uw correlatie en zeggen dus niet wat de correlatie zelf is. We nemen ook nog de correlatielijnen waar op de scatterplots.
2008-11-10 13:48:28 [339a57d8a4d5d113e4804fc423e4a59e] [reply] 
De student heeft voor de berekening de Kendall Tau-software gebruikt. Bij de Kendall Tau krijgt de data een rangorde, outliers worden hierdoor opgenomen in de rangorde, waardoor men een veel robuuster resultaat krijgt. De getallen die men krijgt zijn geen correlatiecoëfficienten, maar betrouwbaarheidspercentages. Men kan uit de tabel afleiden dat er een mooie correlatie is tussen RNR & RCF (0,01) en ook tussen RNVM & RCF (0,03).
2008-11-11 15:09:32 [Bernard Femont] [reply] 
De berekening is juist, goed dat je de assen hebt verwisseld. Ook ik kom uit dat tau ( rnr,rcf) de beste predictor is omdat we gezien hebben dat de p-value onder de 0.05 betrouwbaar is. Dus komen we correlatie uit van 0.80952380952381.

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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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21417&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21417&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21417&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( X1 , X2 )0.7142857142857140.0301587301587301
tau( X1 , X3 )0.5238095238095240.136111111111111
tau( X1 , X4 )0.2646280620124820.427262856745706
tau( X1 , X5 )0.3333333333333330.381349206349206
tau( X2 , X3 )0.809523809523810.0107142857142857
tau( X2 , X4 )-0.05292561240249630.873844698517373
tau( X2 , X5 )0.04761904761904761
tau( X3 , X4 )-0.2646280620124820.427262856745706
tau( X3 , X5 )-0.1428571428571430.772619047619048
tau( X4 , X5 )0.3704792868174740.266379923342483

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( X1 , X2 ) & 0.714285714285714 & 0.0301587301587301 \tabularnewline
tau( X1 , X3 ) & 0.523809523809524 & 0.136111111111111 \tabularnewline
tau( X1 , X4 ) & 0.264628062012482 & 0.427262856745706 \tabularnewline
tau( X1 , X5 ) & 0.333333333333333 & 0.381349206349206 \tabularnewline
tau( X2 , X3 ) & 0.80952380952381 & 0.0107142857142857 \tabularnewline
tau( X2 , X4 ) & -0.0529256124024963 & 0.873844698517373 \tabularnewline
tau( X2 , X5 ) & 0.0476190476190476 & 1 \tabularnewline
tau( X3 , X4 ) & -0.264628062012482 & 0.427262856745706 \tabularnewline
tau( X3 , X5 ) & -0.142857142857143 & 0.772619047619048 \tabularnewline
tau( X4 , X5 ) & 0.370479286817474 & 0.266379923342483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21417&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( X1 , X2 )[/C][C]0.714285714285714[/C][C]0.0301587301587301[/C][/ROW]
[ROW][C]tau( X1 , X3 )[/C][C]0.523809523809524[/C][C]0.136111111111111[/C][/ROW]
[ROW][C]tau( X1 , X4 )[/C][C]0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( X1 , X5 )[/C][C]0.333333333333333[/C][C]0.381349206349206[/C][/ROW]
[ROW][C]tau( X2 , X3 )[/C][C]0.80952380952381[/C][C]0.0107142857142857[/C][/ROW]
[ROW][C]tau( X2 , X4 )[/C][C]-0.0529256124024963[/C][C]0.873844698517373[/C][/ROW]
[ROW][C]tau( X2 , X5 )[/C][C]0.0476190476190476[/C][C]1[/C][/ROW]
[ROW][C]tau( X3 , X4 )[/C][C]-0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( X3 , X5 )[/C][C]-0.142857142857143[/C][C]0.772619047619048[/C][/ROW]
[ROW][C]tau( X4 , X5 )[/C][C]0.370479286817474[/C][C]0.266379923342483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21417&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21417&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( X1 , X2 )0.7142857142857140.0301587301587301
tau( X1 , X3 )0.5238095238095240.136111111111111
tau( X1 , X4 )0.2646280620124820.427262856745706
tau( X1 , X5 )0.3333333333333330.381349206349206
tau( X2 , X3 )0.809523809523810.0107142857142857
tau( X2 , X4 )-0.05292561240249630.873844698517373
tau( X2 , X5 )0.04761904761904761
tau( X3 , X4 )-0.2646280620124820.427262856745706
tau( X3 , X5 )-0.1428571428571430.772619047619048
tau( X4 , X5 )0.3704792868174740.266379923342483


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
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')