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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 computationSun, 02 Nov 2008 03:51:56 -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/02/t1225623187ddvltr9noqgoqxr.htm/, Retrieved Sun, 19 May 2024 06:26:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20488, Retrieved Sun, 19 May 2024 06:26:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact213

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] [] [2008-11-02 10:51:56] [19ef54504342c1b076371d395a2ab19f] [Current]
F    D    [Kendall tau Correlation Matrix] [] [2008-11-02 11:37:16] [a4ee3bef49b119f4bd2e925060c84f5e]
F           [Kendall tau Correlation Matrix] [Hypothesis testin...] [2008-11-02 15:19:36] [ffbe22449df335faef31f462015daa42]
F RMPD      [Notched Boxplots] [Hypothesis testin...] [2008-11-02 15:41:51] [ffbe22449df335faef31f462015daa42]
F           [Kendall tau Correlation Matrix] [Q2] [2008-11-03 19:39:45] [2b46c8b774ad566be9a33a8da3812a44]
-           [Kendall tau Correlation Matrix] [] [2008-11-03 20:27:15] [888addc516c3b812dd7be4bd54caa358]
F             [Kendall tau Correlation Matrix] [] [2008-11-03 21:02:55] [2a0ad3a9bcadca2da0acb91636601c6c]
F RMPD    [Notched Boxplots] [] [2008-11-02 11:49:32] [a4ee3bef49b119f4bd2e925060c84f5e]
F           [Notched Boxplots] [Q3] [2008-11-03 19:42:07] [2b46c8b774ad566be9a33a8da3812a44]
-           [Notched Boxplots] [] [2008-11-03 20:30:55] [888addc516c3b812dd7be4bd54caa358]
F             [Notched Boxplots] [] [2008-11-03 21:04:10] [2a0ad3a9bcadca2da0acb91636601c6c]
F         [Kendall tau Correlation Matrix] [Q1] [2008-11-03 19:37:31] [2b46c8b774ad566be9a33a8da3812a44]
-         [Kendall tau Correlation Matrix] [] [2008-11-03 20:24:58] [888addc516c3b812dd7be4bd54caa358]
F           [Kendall tau Correlation Matrix] [] [2008-11-03 21:01:37] [2a0ad3a9bcadca2da0acb91636601c6c]
Feedback Forum
2008-11-06 14:45:42 [Dana Molenberghs] [reply] 
De laagste waarde = het minst kans dat het verband tot stand kwam op basis van toeval.

We merken op dat 0,01 de laagste waarde is (RNR en RCF) slechts 1% bedraagt -> er is dus 1% kans dat de correlatie te wijten is aan toeval.
2008-11-10 12:15:41 [Kevin Neelen] [reply] 
Er is hier gebruik gemaakt van de juiste methode om deze vraag op te lossen, namelijk het Kendall Tau Correlation Plot. Als we kijken naar de Tau correlatie (2e kolom) is deze het hoogst bij RNR/RCF, namelijk 0,80. Als we kijken naar de P-value (de kans dat het verband tot stand kwam door toeval)zien we dat deze waarde het kleinst is bij RNR/RCF.
De voordelen van dez methode zijn ondermeer dat we een overzicht krijgen van de scatterplots van de verschillende combinaties. De gebruikte Tau correlatie is een rangordecorrelatie (iedere waarde krijgt een rangorde, dus ook de outliers) waardoor deze methode veel robuuster is voor outliers.
2008-11-10 22:20:54 [Chi-Kwong Man] [reply] 
RCF is de beste predictor als we kijken naar de Kendal Tau methode. RCF en RNR beschikt over de laagste waarde, namelijk 0.01. Hoe kleiner het getal hoe beter.
2008-11-10 23:20:13 [Ilknur Günes] [reply] 
Juiste techniek, maar verkeerde interpretatie/conclusie
De cijfers geven de graad van toeval weer: hoe groter de cijfer, hoe meer toeval. Maw we moeten naar de kleinste cijfer kijken. In dit geval kan het 0,01 en 0,03 zijn: RCF en RNVM. Want onder 0,05 kunnen we van correlatie spreken. 0,01 is kleiner, dus RCF is de beste predictor voor RNR
2008-11-11 11:03:21 [Dries Van Gheluwe] [reply] 
Zoals eerder vermeld is de conclusie inderdaad verkeerd. Je kan gemakkelijk je conclusie trekken door de tabel boven het plot te bekijken. Het plot geeft echter wel een resultaat in detail weer.

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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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20488&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20488&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20488&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






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=20488&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=20488&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20488&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')