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R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Fri, 11 Dec 2009 04:14:13 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/11/t1260530107bh8n1ruk35vrcnn.htm/, Retrieved Mon, 29 Apr 2024 07:52:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65995, Retrieved Mon, 29 Apr 2024 07:52:59 +0000

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User-defined keywords

Estimated Impact

147

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Kendall tau Correlation Matrix] [3/11/2009] [2009-11-02 21:25:00] [b98453cac15ba1066b407e146608df68]
-    D  [Kendall tau Correlation Matrix] [WS 6] [2009-11-05 11:06:22] [eaf42bcf5162b5692bb3c7f9d4636222]
-    D    [Kendall tau Correlation Matrix] [Workshop 6: Kenda...] [2009-11-06 09:20:07] [1433a524809eda02c3198b3ae6eebb69]
-   PD        [Kendall tau Correlation Matrix] [Kendall Tau Rank ...] [2009-12-11 11:14:13] [a5c6be3c0aa55fdb2a703a08e16947ef] [Current]
-    D          [Kendall tau Correlation Matrix] [Kendall Tau Rank ...] [2009-12-11 11:58:45] [1433a524809eda02c3198b3ae6eebb69] 

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Dataseries X:

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0.7461	0.5857	12.413	8.9	2.8600	0.5270
0.7775	0.5858	12.758	8.6	2.5500	0.4720
0.7790	0.5717	12.729	8.3	2.2700	0.0000
0.7744	0.5945	12.695	8.3	2.2600	0.0520
0.7905	0.5961	12.858	8.3	2.5700	0.3130
0.7719	0.5973	12.741	8.4	3.0700	0.3640
0.7811	0.6036	12.823	8.5	2.7600	0.3630
0.7557	0.6096	12.588	8.4	2.5100	-0.1550
0.7637	0.6315	12.856	8.6	2.8700	0.0520
0.7595	0.6262	12.619	8.5	3.1400	0.5680
0.7471	0.6121	12.590	8.5	3.1100	0.6680
0.7615	0.6326	12.865	8.5	3.1600	1.3780
0.7487	0.6214	12.667	8.5	2.4700	0.2520
0.7389	0.6274	12.505	8.5	2.5700	-0.4020
0.7337	0.6175	12.205	8.5	2.8900	-0.0500
0.7510	0.6208	12.220	8.5	2.6300	0.5550
0.7382	0.6225	11.990	8.5	2.3800	0.0500
0.7159	0.5889	11.583	8.5	1.6900	0.1500
0.7542	0.6020	11.931	8.5	1.9600	0.4500
0.7636	0.5932	12.028	8.5	2.1900	0.2990
0.7433	0.5841	11.802	8.6	1.8700	0.1990
0.7658	0.6000	12.084	8.4	1.6000	0.4960
0.7627	0.5947	11.996	8.1	1.6300	0.4440
0.7480	0.5891	11.870	8.00	1.2200	-0.3930
0.7692	0.6051	12.013	8.00	1.2100	-0.4440
0.7850	0.5960	12.120	8.00	1.4900	0.1980
0.7913	0.6012	12.133	8.00	1.6400	0.4940
0.7720	0.5957	11.866	7.9	1.6600	0.1330
0.7880	0.5959	12.067	7.8	1.7700	0.3880
0.8070	0.6049	12.240	7.8	1.8200	0.4840
0.8268	0.6064	12.566	7.9	1.7800	0.2780
0.8244	0.6137	12.608	8.1	1.2800	0.3690
0.8487	0.6311	13.005	8.00	1.2900	0.1650
0.8572	0.6258	12.955	7.6	1.3700	0.1550
0.8214	0.6010	12.500	7.3	1.1200	0.0870
0.8827	0.6232	13.158	7.00	1.5100	0.4140
0.9216	0.6384	13.358	6.8	2.2400	0.3600
0.8865	0.6014	12.817	7.00	2.9400	0.9750
0.8816	0.5980	12.707	7.1	3.0900	0.2700
0.8884	0.5987	12.595	7.2	3.4600	0.3590
0.9466	0.6237	13.182	7.1	3.6400	0.1690
0.9180	0.5813	12.665	6.9	4.3900	0.3810
0.9337	0.5991	12.715	6.7	4.1500	0.1540
0.9559	0.6160	13.041	6.7	5.2100	0.4860
0.9626	0.6096	13.106	6.6	5.8000	0.9250
0.9434	0.6051	12.911	6.9	5.9100	0.7280
0.8639	0.5857	12.233	7.3	5.3900	-0.0140
0.7996	0.5565	11.438	7.5	5.4600	0.0460
0.6680	0.5223	0.9895	7.3	4.7200	-0.8190
0.6572	0.5091	19.903	7.1	3.1400	-1.6740
0.6928	0.4919	0.9967	6.9	2.6300	-0.7880
0.6438	0.4995	0.9708	7.1	2.3200	0.2790
0.6454	0.5069	0.9946	7.5	1.9300	0.3960
0.6873	0.5190	10.441	7.7	0.6200	-0.1410
0.7265	0.5460	10.757	7.8	0.6000	-0.0190
0.7912	0.5648	11.461	7.8	-0.3700	0.0990
0.8114	0.5751	11.749	7.7	-1.1000	0.7420
0.8281	0.5862	11.926	7.7	-1.6800	0.0050
0.8393	0.5877	12.109	7.8	-0.7800	0.4480

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	'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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65995&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65995&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65995&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	'Gwilym Jenkins' @ 72.249.127.135

Kendall tau rank correlations for all pairs of data series
pair	tau	p-value
tau( US_DOLLAR , EURO )	0.231646769490374	0.00960308512272845
tau( US_DOLLAR , SINGAPORE_DOLLAR )	0.436586791350088	1.03430459930109e-06
tau( US_DOLLAR , Werkloosheidsgraad )	-0.458698961995371	6.13056772784723e-07
tau( US_DOLLAR , Inflatie_Belgie )	0.071365923933903	0.424946734316361
tau( US_DOLLAR , Inflatie_Amerika )	0.231511263910197	0.00960617513666961
tau( EURO , SINGAPORE_DOLLAR )	0.480842536669412	7.62419760658162e-08
tau( EURO , Werkloosheidsgraad )	0.167771965471964	0.0683704086657477
tau( EURO , Inflatie_Belgie )	0.0942622950819672	0.292344464675358
tau( EURO , Inflatie_Amerika )	0.199531924152611	0.0257371010898011
tau( SINGAPORE_DOLLAR , Werkloosheidsgraad )	-0.050832074944078	0.580499330469954
tau( SINGAPORE_DOLLAR , Inflatie_Belgie )	0.297163027528055	0.000892874099977625
tau( SINGAPORE_DOLLAR , Inflatie_Amerika )	0.206957038950024	0.0206114013459127
tau( Werkloosheidsgraad , Inflatie_Belgie )	-0.0490596722138235	0.594068135064721
tau( Werkloosheidsgraad , Inflatie_Amerika )	0.0320819953027586	0.727309781534422
tau( Inflatie_Belgie , Inflatie_Amerika )	0.101228806095019	0.257874984151108

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( US_DOLLAR , EURO ) & 0.231646769490374 & 0.00960308512272845 \tabularnewline
tau( US_DOLLAR , SINGAPORE_DOLLAR ) & 0.436586791350088 & 1.03430459930109e-06 \tabularnewline
tau( US_DOLLAR , Werkloosheidsgraad ) & -0.458698961995371 & 6.13056772784723e-07 \tabularnewline
tau( US_DOLLAR , Inflatie_Belgie ) & 0.071365923933903 & 0.424946734316361 \tabularnewline
tau( US_DOLLAR , Inflatie_Amerika ) & 0.231511263910197 & 0.00960617513666961 \tabularnewline
tau( EURO , SINGAPORE_DOLLAR ) & 0.480842536669412 & 7.62419760658162e-08 \tabularnewline
tau( EURO , Werkloosheidsgraad ) & 0.167771965471964 & 0.0683704086657477 \tabularnewline
tau( EURO , Inflatie_Belgie ) & 0.0942622950819672 & 0.292344464675358 \tabularnewline
tau( EURO , Inflatie_Amerika ) & 0.199531924152611 & 0.0257371010898011 \tabularnewline
tau( SINGAPORE_DOLLAR , Werkloosheidsgraad ) & -0.050832074944078 & 0.580499330469954 \tabularnewline
tau( SINGAPORE_DOLLAR , Inflatie_Belgie ) & 0.297163027528055 & 0.000892874099977625 \tabularnewline
tau( SINGAPORE_DOLLAR , Inflatie_Amerika ) & 0.206957038950024 & 0.0206114013459127 \tabularnewline
tau( Werkloosheidsgraad , Inflatie_Belgie ) & -0.0490596722138235 & 0.594068135064721 \tabularnewline
tau( Werkloosheidsgraad , Inflatie_Amerika ) & 0.0320819953027586 & 0.727309781534422 \tabularnewline
tau( Inflatie_Belgie , Inflatie_Amerika ) & 0.101228806095019 & 0.257874984151108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65995&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( US_DOLLAR , EURO )[/C][C]0.231646769490374[/C][C]0.00960308512272845[/C][/ROW]
[ROW][C]tau( US_DOLLAR , SINGAPORE_DOLLAR )[/C][C]0.436586791350088[/C][C]1.03430459930109e-06[/C][/ROW]
[ROW][C]tau( US_DOLLAR , Werkloosheidsgraad )[/C][C]-0.458698961995371[/C][C]6.13056772784723e-07[/C][/ROW]
[ROW][C]tau( US_DOLLAR , Inflatie_Belgie )[/C][C]0.071365923933903[/C][C]0.424946734316361[/C][/ROW]
[ROW][C]tau( US_DOLLAR , Inflatie_Amerika )[/C][C]0.231511263910197[/C][C]0.00960617513666961[/C][/ROW]
[ROW][C]tau( EURO , SINGAPORE_DOLLAR )[/C][C]0.480842536669412[/C][C]7.62419760658162e-08[/C][/ROW]
[ROW][C]tau( EURO , Werkloosheidsgraad )[/C][C]0.167771965471964[/C][C]0.0683704086657477[/C][/ROW]
[ROW][C]tau( EURO , Inflatie_Belgie )[/C][C]0.0942622950819672[/C][C]0.292344464675358[/C][/ROW]
[ROW][C]tau( EURO , Inflatie_Amerika )[/C][C]0.199531924152611[/C][C]0.0257371010898011[/C][/ROW]
[ROW][C]tau( SINGAPORE_DOLLAR , Werkloosheidsgraad )[/C][C]-0.050832074944078[/C][C]0.580499330469954[/C][/ROW]
[ROW][C]tau( SINGAPORE_DOLLAR , Inflatie_Belgie )[/C][C]0.297163027528055[/C][C]0.000892874099977625[/C][/ROW]
[ROW][C]tau( SINGAPORE_DOLLAR , Inflatie_Amerika )[/C][C]0.206957038950024[/C][C]0.0206114013459127[/C][/ROW]
[ROW][C]tau( Werkloosheidsgraad , Inflatie_Belgie )[/C][C]-0.0490596722138235[/C][C]0.594068135064721[/C][/ROW]
[ROW][C]tau( Werkloosheidsgraad , Inflatie_Amerika )[/C][C]0.0320819953027586[/C][C]0.727309781534422[/C][/ROW]
[ROW][C]tau( Inflatie_Belgie , Inflatie_Amerika )[/C][C]0.101228806095019[/C][C]0.257874984151108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65995&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65995&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( US_DOLLAR , EURO )	0.231646769490374	0.00960308512272845
tau( US_DOLLAR , SINGAPORE_DOLLAR )	0.436586791350088	1.03430459930109e-06
tau( US_DOLLAR , Werkloosheidsgraad )	-0.458698961995371	6.13056772784723e-07
tau( US_DOLLAR , Inflatie_Belgie )	0.071365923933903	0.424946734316361
tau( US_DOLLAR , Inflatie_Amerika )	0.231511263910197	0.00960617513666961
tau( EURO , SINGAPORE_DOLLAR )	0.480842536669412	7.62419760658162e-08
tau( EURO , Werkloosheidsgraad )	0.167771965471964	0.0683704086657477
tau( EURO , Inflatie_Belgie )	0.0942622950819672	0.292344464675358
tau( EURO , Inflatie_Amerika )	0.199531924152611	0.0257371010898011
tau( SINGAPORE_DOLLAR , Werkloosheidsgraad )	-0.050832074944078	0.580499330469954
tau( SINGAPORE_DOLLAR , Inflatie_Belgie )	0.297163027528055	0.000892874099977625
tau( SINGAPORE_DOLLAR , Inflatie_Amerika )	0.206957038950024	0.0206114013459127
tau( Werkloosheidsgraad , Inflatie_Belgie )	-0.0490596722138235	0.594068135064721
tau( Werkloosheidsgraad , Inflatie_Amerika )	0.0320819953027586	0.727309781534422
tau( Inflatie_Belgie , Inflatie_Amerika )	0.101228806095019	0.257874984151108

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = grey ; par2 = grey ; par3 = TRUE ; par4 = Inflatie Amerika ; par5 = US DOLLAR ;

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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