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Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationTue, 28 Dec 2010 19:05:34 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/28/t12935630649f16hppjz7rruyr.htm/, Retrieved Sat, 04 May 2024 23:56:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116496, Retrieved Sat, 04 May 2024 23:56:00 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180
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]
- R  D  [Kendall tau Correlation Matrix] [Kendall tau corre...] [2009-11-12 15:08:30] [54d83950395cfb8ca1091bdb7440f70a]
- R PD      [Kendall tau Correlation Matrix] [] [2010-12-28 19:05:34] [4afc4ea409ad669ec2851bc39795365d] [Current]
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Dataseries X:
6.8	3.0	597141	25
16.5	3.0	593408	24
23.4	2.3	590072	21
32.7	2.7	579799	22
31.3	2.9	574205	20
32.4	2.7	572775	24
21.2	2.6	572942	24
20.7	2.7	619567	24
11.8	2.8	625809	24
14.2	2.9	619916	28
16.1	2.9	587625	27
16.6	2.9	565742	18
16.5	3.0	557274	25
13.7	3.1	560576	27
11.9	2.9	548854	25
11	2.9	531673	28
10.1	2.3	525919	28
11.3	2.3	511038	27
17.9	2.4	498662	25
23	2.2	555362	24
28.6	2.3	564591	24
29.9	2.8	541657	25
32	3.2	527070	18
33.9	3.6	509846	22
39.1	3.8	514258	20
29.8	4.1	516922	23
30	4.9	507561	23
22.8	4.7	492622	19
29.8	5.6	490243	17
26.6	6.1	469357	15
29.2	6.2	477580	13
21.2	5.9	528379	15
18.9	6.1	533590	17
17.3	5.8	517945	9
14.2	4.9	506174	4
9.1	4.5	501866	1
5.5	4.3	516141	6
0.9	3.8	528222	2
1	2.6	532638	2
3.3	2.5	536322	4
3.8	1.7	536535	7
3.9	0.9	523597	8
1.1	0.3	536214	9
4.5	0.8	586570	15
9.6	0.4	596594	15
14.8	0.4	580523	14
16.8	0.7	564478	16
23.5	0.7	557560	11
31.4	0.9	575093	11
34.8	1.1	580112	11
30.5	1.9	574761	13
33.1	2.1	563250	18
37.8	2.6	551531	13
45.1	3.0	537034	17
46.9	3.2	544686	19
44.4	3.1	600991	22
42.4	3.6	604378	22
30.5	3.5	586111	24
28.9	3.5	563668	26
27.1	3.6	548604	24




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=116496&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=116496&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116496&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'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=pearson)
persinflwerklooscv
pers10.2020.0540.315
infl0.2021-0.5240.002
werkloos0.054-0.52410.337
cv0.3150.0020.3371

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & pers & infl & werkloos & cv \tabularnewline
pers & 1 & 0.202 & 0.054 & 0.315 \tabularnewline
infl & 0.202 & 1 & -0.524 & 0.002 \tabularnewline
werkloos & 0.054 & -0.524 & 1 & 0.337 \tabularnewline
cv & 0.315 & 0.002 & 0.337 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116496&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]pers[/C][C]infl[/C][C]werkloos[/C][C]cv[/C][/ROW]
[ROW][C]pers[/C][C]1[/C][C]0.202[/C][C]0.054[/C][C]0.315[/C][/ROW]
[ROW][C]infl[/C][C]0.202[/C][C]1[/C][C]-0.524[/C][C]0.002[/C][/ROW]
[ROW][C]werkloos[/C][C]0.054[/C][C]-0.524[/C][C]1[/C][C]0.337[/C][/ROW]
[ROW][C]cv[/C][C]0.315[/C][C]0.002[/C][C]0.337[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116496&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116496&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Correlations for all pairs of data series (method=pearson)
persinflwerklooscv
pers10.2020.0540.315
infl0.2021-0.5240.002
werkloos0.054-0.52410.337
cv0.3150.0020.3371







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
pers;infl0.2020.20590.1296
p-value(0.1216)(0.1144)(0.1489)
pers;werkloos0.05420.08530.0515
p-value(0.6811)(0.5169)(0.5616)
pers;cv0.31530.1340.0562
p-value(0.0141)(0.3075)(0.5348)
infl;werkloos-0.5242-0.4625-0.3229
p-value(0)(2e-04)(3e-04)
infl;cv0.00240.03490.0035
p-value(0.9852)(0.7914)(0.9693)
werkloos;cv0.33710.28490.2036
p-value(0.0084)(0.0274)(0.0243)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
pers;infl & 0.202 & 0.2059 & 0.1296 \tabularnewline
p-value & (0.1216) & (0.1144) & (0.1489) \tabularnewline
pers;werkloos & 0.0542 & 0.0853 & 0.0515 \tabularnewline
p-value & (0.6811) & (0.5169) & (0.5616) \tabularnewline
pers;cv & 0.3153 & 0.134 & 0.0562 \tabularnewline
p-value & (0.0141) & (0.3075) & (0.5348) \tabularnewline
infl;werkloos & -0.5242 & -0.4625 & -0.3229 \tabularnewline
p-value & (0) & (2e-04) & (3e-04) \tabularnewline
infl;cv & 0.0024 & 0.0349 & 0.0035 \tabularnewline
p-value & (0.9852) & (0.7914) & (0.9693) \tabularnewline
werkloos;cv & 0.3371 & 0.2849 & 0.2036 \tabularnewline
p-value & (0.0084) & (0.0274) & (0.0243) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116496&T=2

[TABLE]
[ROW][C]Correlations for all pairs of data series with p-values[/C][/ROW]
[ROW][C]pair[/C][C]Pearson r[/C][C]Spearman rho[/C][C]Kendall tau[/C][/ROW]
[ROW][C]pers;infl[/C][C]0.202[/C][C]0.2059[/C][C]0.1296[/C][/ROW]
[ROW][C]p-value[/C][C](0.1216)[/C][C](0.1144)[/C][C](0.1489)[/C][/ROW]
[ROW][C]pers;werkloos[/C][C]0.0542[/C][C]0.0853[/C][C]0.0515[/C][/ROW]
[ROW][C]p-value[/C][C](0.6811)[/C][C](0.5169)[/C][C](0.5616)[/C][/ROW]
[ROW][C]pers;cv[/C][C]0.3153[/C][C]0.134[/C][C]0.0562[/C][/ROW]
[ROW][C]p-value[/C][C](0.0141)[/C][C](0.3075)[/C][C](0.5348)[/C][/ROW]
[ROW][C]infl;werkloos[/C][C]-0.5242[/C][C]-0.4625[/C][C]-0.3229[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](2e-04)[/C][C](3e-04)[/C][/ROW]
[ROW][C]infl;cv[/C][C]0.0024[/C][C]0.0349[/C][C]0.0035[/C][/ROW]
[ROW][C]p-value[/C][C](0.9852)[/C][C](0.7914)[/C][C](0.9693)[/C][/ROW]
[ROW][C]werkloos;cv[/C][C]0.3371[/C][C]0.2849[/C][C]0.2036[/C][/ROW]
[ROW][C]p-value[/C][C](0.0084)[/C][C](0.0274)[/C][C](0.0243)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116496&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116496&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
pers;infl0.2020.20590.1296
p-value(0.1216)(0.1144)(0.1489)
pers;werkloos0.05420.08530.0515
p-value(0.6811)(0.5169)(0.5616)
pers;cv0.31530.1340.0562
p-value(0.0141)(0.3075)(0.5348)
infl;werkloos-0.5242-0.4625-0.3229
p-value(0)(2e-04)(3e-04)
infl;cv0.00240.03490.0035
p-value(0.9852)(0.7914)(0.9693)
werkloos;cv0.33710.28490.2036
p-value(0.0084)(0.0274)(0.0243)



Parameters (Session):
par1 = pearson ;
Parameters (R input):
par1 = pearson ;
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=par1)
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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
cor.test(y[1,],y[2,],method=par1)
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
a<-table.element(a,dum,header=TRUE)
rp <- cor.test(y[i,],y[j,],method='pearson')
a<-table.element(a,round(rp$estimate,4))
rs <- cor.test(y[i,],y[j,],method='spearman')
a<-table.element(a,round(rs$estimate,4))
rk <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,round(rk$estimate,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=T)
a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')