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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 07:20:39 -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/t1225895077kd2pcxst8ewy59v.htm/, Retrieved Sun, 19 May 2024 12:16:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21757, Retrieved Sun, 19 May 2024 12:16:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146

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] [part2 Q1] [2008-11-05 14:20:39] [bca67c46bcf35400430db40e8c261de9] [Current]
Feedback Forum
2008-11-12 10:42:06 [Erik Geysen] [reply] 
De student heeft de Kendall tau correlation plot gebruikt, wat hier ook de juiste is. Er is wel weinig of geen uitleg gegeven. Enkel dat de cashflow de beste correlatie hebben. Hier volgt een verklaring waarom dit ook effectief zo is. De 0,01 die bij de cashflow hoort, is niet de correlatiecoëfficiënt maar de P-value. Als de P-value kleiner of gelijk is aan 0,05 kan het verband (of de correlatie) tussen de 2 variabelen niet te wijden zijn aan toeval. De gegevens liggen bijna op een diagonaal en de p-value voor cashflow is 0,01 wat wil zeggen dat het niet toevallig is dat de cashflow de beste predictor is. Er bestaat een groot verband.
2008-11-12 11:17:41 [Nicolaj Wuyts] [reply] 
RNR en RCF hebben de beste correlatie en de kans dat deze te wijten is aan toeval is ook slechts 1%

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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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21757&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21757&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21757&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






Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( RNVM , RNR )0.7142857142857140.0301587301587301
tau( RNVM , RCF )0.5238095238095240.136111111111111
tau( RNVM , RLEZ )0.2646280620124820.427262856745706
tau( RNVM , REV )0.3333333333333330.381349206349206
tau( RNR , RCF )0.809523809523810.0107142857142857
tau( RNR , RLEZ )-0.05292561240249630.873844698517373
tau( RNR , REV )0.04761904761904761
tau( RCF , RLEZ )-0.2646280620124820.427262856745706
tau( RCF , REV )-0.1428571428571430.772619047619048
tau( RLEZ , 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 , RCF ) & 0.523809523809524 & 0.136111111111111 \tabularnewline
tau( RNVM , RLEZ ) & 0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RNVM , 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=21757&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 , RCF )[/C][C]0.523809523809524[/C][C]0.136111111111111[/C][/ROW]
[ROW][C]tau( RNVM , RLEZ )[/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 , 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=21757&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21757&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 , RCF )0.5238095238095240.136111111111111
tau( RNVM , RLEZ )0.2646280620124820.427262856745706
tau( RNVM , REV )0.3333333333333330.381349206349206
tau( RNR , RCF )0.809523809523810.0107142857142857
tau( RNR , RLEZ )-0.05292561240249630.873844698517373
tau( RNR , REV )0.04761904761904761
tau( RCF , RLEZ )-0.2646280620124820.427262856745706
tau( RCF , REV )-0.1428571428571430.772619047619048
tau( RLEZ , 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')