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Author*Unverified author*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationThu, 12 Nov 2009 07:20:07 -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/2009/Nov/12/t1258035666abwmw5tf4bp8xuv.htm/, Retrieved Fri, 03 May 2024 21:41:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=56014, Retrieved Fri, 03 May 2024 21:41:50 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151
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...] [2009-11-12 14:20:07] [8f072ead2c7c0b3cf3fdae49bab9dd9b] [Current]
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Dataseries X:
95.1	8.9	269	117.1
97	8.8	270	118.7
112.7	8.3	271	126.5
102.9	7.5	267	127.5
97.4	7.2	271	134.6
111.4	7.4	270	131.8
87.4	8.8	271	135.9
96.8	9.3	270	142.7
114.1	9.3	271	141.7
110.3	8.7	267	153.4
103.9	8.2	264	145
101.6	8.3	266	137.7
94.6	8.5	269	148.3
95.9	8.6	269	152.2
104.7	8.5	272	169.4
102.8	8.2	268	168.6
98.1	8.1	268	161.1
113.9	7.9	271	174.1
80.9	8.6	273	179
95.7	8.7	273	190.6
113.2	8.7	270	190
105.9	8.5	271	181.6
108.8	8.4	270	174.8
102.3	8.5	269	180.5
99	8.7	274	196.8
100.7	8.7	274	193.8
115.5	8.6	277	197
100.7	8.5	275	216.3
109.9	8.3	271	221.4
114.6	8	271	217.9
85.4	8.2	271	229.7
100.5	8.1	274	227.4
114.8	8.1	274	204.2
116.5	8	272	196.6
112.9	7.9	273	198.8
102	7.9	271	207.5
106	8	274	190.7
105.3	8	275	201.6
118.8	7.9	281	210.5
106.1	8	278	223.5
109.3	7.7	280	223.8
117.2	7.2	283	231.2
92.5	7.5	280	244
104.2	7.3	282	234.7
112.5	7	290	250.2
122.4	7	284	265.7
113.3	7	288	287.6
100	7.2	285	283.3
110.7	7.3	288	295.4
112.8	7.1	290	312.3
109.8	6.8	289	333.8
117.3	6.4	288	347.7
109.1	6.1	290	383.2
115.9	6.5	290	407.1
96	7.7	288	413.6
99.8	7.9	289	362.7
116.8	7.5	295	321.9
115.7	6.9	305	239.4
99.4	6.6	309	191
94.3	6.9	310	159.7
91	7.7	322	163.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56014&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56014&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56014&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( ind.prod.index , bruto.werkzoekenden )-0.2523413355185560.00473533639624063
tau( ind.prod.index , Brutoschuld.Schatkist )0.1654589550326980.0653686264597082
tau( ind.prod.index , prijsindexcijfer.van.grondstoffen )0.2519814250978680.00412007193349506
tau( bruto.werkzoekenden , Brutoschuld.Schatkist )-0.5182978997183541.37723664008314e-08
tau( bruto.werkzoekenden , prijsindexcijfer.van.grondstoffen )-0.4599933470010342.6033505625123e-07
tau( Brutoschuld.Schatkist , prijsindexcijfer.van.grondstoffen )0.5999752657447582.33029151530673e-11

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( ind.prod.index , bruto.werkzoekenden ) & -0.252341335518556 & 0.00473533639624063 \tabularnewline
tau( ind.prod.index , Brutoschuld.Schatkist ) & 0.165458955032698 & 0.0653686264597082 \tabularnewline
tau( ind.prod.index , prijsindexcijfer.van.grondstoffen ) & 0.251981425097868 & 0.00412007193349506 \tabularnewline
tau( bruto.werkzoekenden , Brutoschuld.Schatkist ) & -0.518297899718354 & 1.37723664008314e-08 \tabularnewline
tau( bruto.werkzoekenden , prijsindexcijfer.van.grondstoffen ) & -0.459993347001034 & 2.6033505625123e-07 \tabularnewline
tau( Brutoschuld.Schatkist , prijsindexcijfer.van.grondstoffen ) & 0.599975265744758 & 2.33029151530673e-11 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56014&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( ind.prod.index , bruto.werkzoekenden )[/C][C]-0.252341335518556[/C][C]0.00473533639624063[/C][/ROW]
[ROW][C]tau( ind.prod.index , Brutoschuld.Schatkist )[/C][C]0.165458955032698[/C][C]0.0653686264597082[/C][/ROW]
[ROW][C]tau( ind.prod.index , prijsindexcijfer.van.grondstoffen )[/C][C]0.251981425097868[/C][C]0.00412007193349506[/C][/ROW]
[ROW][C]tau( bruto.werkzoekenden , Brutoschuld.Schatkist )[/C][C]-0.518297899718354[/C][C]1.37723664008314e-08[/C][/ROW]
[ROW][C]tau( bruto.werkzoekenden , prijsindexcijfer.van.grondstoffen )[/C][C]-0.459993347001034[/C][C]2.6033505625123e-07[/C][/ROW]
[ROW][C]tau( Brutoschuld.Schatkist , prijsindexcijfer.van.grondstoffen )[/C][C]0.599975265744758[/C][C]2.33029151530673e-11[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56014&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56014&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
pairtaup-value
tau( ind.prod.index , bruto.werkzoekenden )-0.2523413355185560.00473533639624063
tau( ind.prod.index , Brutoschuld.Schatkist )0.1654589550326980.0653686264597082
tau( ind.prod.index , prijsindexcijfer.van.grondstoffen )0.2519814250978680.00412007193349506
tau( bruto.werkzoekenden , Brutoschuld.Schatkist )-0.5182978997183541.37723664008314e-08
tau( bruto.werkzoekenden , prijsindexcijfer.van.grondstoffen )-0.4599933470010342.6033505625123e-07
tau( Brutoschuld.Schatkist , prijsindexcijfer.van.grondstoffen )0.5999752657447582.33029151530673e-11



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')