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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 computationThu, 30 Oct 2008 08:09:39 -0600
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/Oct/30/t12253759155j12ohqs6khyzlf.htm/, Retrieved Sun, 19 May 2024 15:41:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20051, Retrieved Sun, 19 May 2024 15:41:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact177

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Mean Plot] [workshop 3] [2007-10-26 12:14:28] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F RMPD  [Blocked Bootstrap Plot - Central Tendency] [vraag 4] [2008-10-29 19:47:27] [c45c87b96bbf32ffc2144fc37d767b2e]
F RMPD      [Kendall tau Correlation Matrix] [vraag 1] [2008-10-30 14:09:39] [3dc594a6c62226e1e98766c4d385bfaa] [Current]
F    D        [Kendall tau Correlation Matrix] [vraag 1] [2008-10-30 14:28:38] [c45c87b96bbf32ffc2144fc37d767b2e]
F RM D        [] [taak 2] [-0001-11-30 00:00:00] [c45c87b96bbf32ffc2144fc37d767b2e]
F    D        [Kendall tau Correlation Matrix] [Hypothesis Testin...] [2008-10-31 13:09:45] [063e4b67ad7d3a8a83eccec794cd5aa7]
F    D          [Kendall tau Correlation Matrix] [Hypothesis Testin...] [2008-10-31 13:20:14] [063e4b67ad7d3a8a83eccec794cd5aa7]
F               [Kendall tau Correlation Matrix] [Kendall tau corre...] [2008-11-06 06:12:21] [1b742211e88d1643c42c5773474321b2]
Feedback Forum
2008-11-05 19:12:51 [Michaël De Kuyer] [reply] 
Deze vraag heb ik juist gereproduceerd. Mijn interpretatie is echter foutief. De Kendall Tau Correlation geeft niet de correlatie weer, maar de betrouwbaarheid van de correlatie. Praktisch betekent dit dat als het getal klein is, hoe minder toeval er is en hoe betrouwbaarder het resultaat is.

De Kendall Tau Correlation zal ook leiden tot een robuuster resultaat aangezien het een rangordecorrelatie is. Aan elke waarde wordt er een rangorde gegeven en de mate van afwijking van de rangorde tot de mediaan is niet in verhouding met de afwijking van een spreiding tot de nominale mediaan. M.a.w. de outliers wegen niet zo zwaar door als bij de Pearson Correlation.

Bovendien was het niet zinvol om de woorden van 1985 en van 1993 buiten beschouwing te laten.

Aan de hand van deze informatie kan ik de vraag juist beantwoorden, namelijk dat de beste predictor voor RNR, RCF is.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.2	20.8	4.8	0.9	39.6
2.6	17.1	-4.2	0.85	36.1
3	22.3	1.6	0.83	34.4
3.8	25.1	5.2	0.84	33.4
4	27.7	9.2	0.85	34.8
3.5	24.9	4.6	0.83	33.7
4.1	29.5	10.6	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=20051&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=20051&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20051&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 , RCF )0.5238095238095240.136111111111111
tau( RNVM , RNR )0.7142857142857140.0301587301587301
tau( RNVM , RLEZ )0.2646280620124820.427262856745706
tau( RNVM , REV )0.3333333333333330.381349206349206
tau( RCF , RNR )0.809523809523810.0107142857142857
tau( RCF , RLEZ )-0.2646280620124820.427262856745706
tau( RCF , REV )-0.1428571428571430.772619047619048
tau( RNR , RLEZ )-0.05292561240249630.873844698517373
tau( RNR , REV )0.04761904761904761
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 , RCF ) & 0.523809523809524 & 0.136111111111111 \tabularnewline
tau( RNVM , RNR ) & 0.714285714285714 & 0.0301587301587301 \tabularnewline
tau( RNVM , RLEZ ) & 0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RNVM , REV ) & 0.333333333333333 & 0.381349206349206 \tabularnewline
tau( RCF , RNR ) & 0.80952380952381 & 0.0107142857142857 \tabularnewline
tau( RCF , RLEZ ) & -0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RCF , REV ) & -0.142857142857143 & 0.772619047619048 \tabularnewline
tau( RNR , RLEZ ) & -0.0529256124024963 & 0.873844698517373 \tabularnewline
tau( RNR , REV ) & 0.0476190476190476 & 1 \tabularnewline
tau( RLEZ , REV ) & 0.370479286817474 & 0.266379923342483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20051&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 , RCF )[/C][C]0.523809523809524[/C][C]0.136111111111111[/C][/ROW]
[ROW][C]tau( RNVM , RNR )[/C][C]0.714285714285714[/C][C]0.0301587301587301[/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( RCF , RNR )[/C][C]0.80952380952381[/C][C]0.0107142857142857[/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( 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( RLEZ , REV )[/C][C]0.370479286817474[/C][C]0.266379923342483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20051&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20051&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 , RCF )0.5238095238095240.136111111111111
tau( RNVM , RNR )0.7142857142857140.0301587301587301
tau( RNVM , RLEZ )0.2646280620124820.427262856745706
tau( RNVM , REV )0.3333333333333330.381349206349206
tau( RCF , RNR )0.809523809523810.0107142857142857
tau( RCF , RLEZ )-0.2646280620124820.427262856745706
tau( RCF , REV )-0.1428571428571430.772619047619048
tau( RNR , RLEZ )-0.05292561240249630.873844698517373
tau( RNR , REV )0.04761904761904761
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')