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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, 13 Dec 2011 03:41:30 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323765711pdiut6x32kcbskv.htm/, Retrieved Thu, 31 Oct 2024 23:12:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154280, Retrieved Thu, 31 Oct 2024 23:12:21 +0000
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Original text written by user:
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Estimated Impact131
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD    [Kendall tau Correlation Matrix] [Kendall's tau Cor...] [2011-12-13 08:41:30] [7357ea4f05edbe0d796a101c4acf63d9] [Current]
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Dataseries X:
1	1	3	3	1
1	1	NA	NA	NA
0	1	2	4	2
NA	1	NA	NA	NA
NA	1	NA	NA	NA
NA	1	NA	NA	NA
1	1	3	4	1
NA	1	NA	NA	NA
NA	0	NA	NA	NA
1	1	4	5	1
0	1	3	5	2
0	1	2	5	1
NA	1	NA	NA	NA
0	1	2	5	1
NA	1	NA	NA	NA
NA	1	NA	NA	NA
0	1	2	4	1
1	1	5	4	4
0	1	2	3	1
1	1	NA	NA	NA
NA	1	NA	NA	NA
1	1	1	3	1
0	1	1	5	1
1	1	NA	NA	NA
NA	1	NA	NA	NA
0	1	2	4	1
0	1	2	4	1
1	1	2	3	4
0	1	2	4	1
0	1	2	5	1
NA	1	NA	NA	NA
1	1	2	3	3
1	1	2	5	1
NA	1	NA	NA	NA
NA	1	NA	NA	NA
1	1	2	5	1
NA	0	NA	NA	NA
NA	1	NA	NA	NA
0	1	4	0	1
0	0	2	5	1
NA	1	NA	NA	NA
1	1	2	5	4
1	0	4	4	3
0	1	2	4	0
NA	1	NA	NA	NA
1	1	2	3	1
0	0	3	3	1
1	1	3	5	1
0	1	1	3	1
NA	1	NA	NA	NA
0	1	1	5	3
NA	1	NA	NA	NA
1	1	3	4	3
NA	1	NA	NA	NA
NA	1	NA	NA	NA
0	1	NA	NA	NA
0	1	NA	NA	NA
0	1	2	4	3
NA	1	NA	NA	NA
NA	1	NA	NA	NA
0	1	4	5	3
0	1	3	4	1
0	0	3	3	2
1	0	2	5	1
NA	1	NA	NA	NA
0	1	2	4	2
NA	1	NA	NA	NA
NA	1	NA	NA	NA
1	1	NA	NA	NA
NA	1	NA	NA	NA
NA	1	NA	NA	NA
0	1	2	0	1
0	1	NA	NA	NA
1	1	3	4	1
0	1	NA	NA	NA
1	1	2	2	2
NA	1	NA	NA	NA
NA	1	NA	NA	NA
0	1	2	5	1
NA	1	NA	NA	NA
1	1	3	5	1
1	1	3	4	1
NA	1	NA	NA	NA
1	1	4	4	2
0	1	NA	NA	NA
NA	1	NA	NA	NA
0	1	2	5	1
NA	1	NA	NA	NA
0	1	3	3	1
NA	1	NA	NA	NA
0	1	2	4	1
NA	1	NA	NA	NA
0	1	2	5	1
1	1	3	5	1
0	0	3	4	1
0	1	2	4	2
NA	1	NA	NA	NA
0	1	2	5	2
NA	1	NA	NA	NA
1	1	3	5	1
0	1	3	5	1
0	1	2	5	1
0	1	2	4	1
0	1	3	5	1
NA	1	NA	NA	NA
0	1	2	5	1
1	1	5	4	3
0	0	3	4	2
1	1	NA	NA	NA
1	1	3	4	2
0	1	4	4	3
1	1	NA	NA	NA
NA	1	NA	NA	NA
NA	1	NA	NA	NA
0	1	4	5	2
NA	1	NA	NA	NA
NA	1	NA	NA	NA
0	0	4	4	1
1	1	2	4	2
0	0	2	4	1
0	0	2	5	1
0	0	4	4	2
NA	0	NA	NA	NA
0	1	5	3	1
NA	1	NA	NA	NA
NA	0	NA	NA	NA
1	0	2	4	1
0	0	3	4	1
0	1	2	4	1
0	1	5	3	2
NA	0	NA	NA	NA
0	1	1	4	1
NA	1	NA	NA	NA
1	0	4	2	1
0	1	4	4	3
1	1	3	3	1
0	1	NA	NA	NA
0	1	NA	NA	NA
1	0	4	4	1
1	1	NA	NA	NA
0	0	3	5	2
0	0	4	4	1
NA	0	NA	NA	NA
0	1	2	4	2
NA	1	NA	NA	NA
NA	0	NA	NA	NA
1	1	1	3	1
1	1	2	5	4
NA	1	NA	NA	NA
NA	1	NA	NA	NA
0	1	2	4	1
NA	1	2	3	1
0	0	1	5	2
1	1	3	4	3
NA	1	NA	NA	NA
0	1	4	4	2
1	1	NA	NA	NA
NA	0	NA	NA	NA
NA	1	NA	NA	NA
0	1	1	5	2
NA	1	NA	NA	NA
0	1	NA	NA	NA
1	0	3	4	1
1	1	3	5	1
1	1	2	5	1
1	1	5	4	2
NA	1	NA	NA	NA
NA	1	NA	NA	NA
1	0	3	4	1
0	0	1	3	1
0	1	NA	NA	NA
1	1	3	5	2
0	1	0	5	1
1	1	2	4	3
NA	1	NA	NA	NA
NA	0	NA	NA	NA
NA	1	NA	NA	NA
0	0	3	5	1
1	1	3	4	1
1	0	2	5	1
0	0	2	3	1
1	0	4	5	4
NA	1	NA	NA	NA
0	1	1	5	1
0	1	3	4	1
NA	1	NA	NA	NA
NA	1	NA	NA	NA
NA	1	NA	NA	NA
NA	0	NA	NA	NA
0	1	2	4	2
NA	1	NA	NA	NA
1	0	2	4	1
NA	0	NA	NA	NA
1	0	3	5	1
0	0	3	4	1
1	0	2	5	1
0	0	4	5	2
0	0	1	4	1
NA	0	NA	NA	NA
0	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
1	0	2	5	3
1	0	2	4	2
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
0	0	3	4	1
0	0	1	5	1
NA	0	NA	NA	NA
NA	0	NA	NA	NA
1	0	4	3	3
NA	0	NA	NA	NA
1	0	2	5	1
1	0	1	4	1
1	0	3	4	1
NA	0	NA	NA	NA
0	0	1	5	1
0	0	NA	NA	NA
NA	0	NA	NA	NA
0	0	3	5	1
0	0	3	4	2
1	0	3	4	1
NA	0	NA	NA	NA
0	0	NA	NA	NA
1	0	NA	NA	NA
NA	0	NA	NA	NA
0	0	2	5	1
NA	0	NA	NA	NA
0	0	1	5	1
1	0	2	4	1
0	0	1	4	1
NA	0	NA	NA	NA
NA	0	NA	NA	NA
0	0	2	0	1
0	0	3	4	1
0	0	3	4	1
NA	0	NA	NA	NA
0	0	2	4	1
NA	0	NA	NA	NA
0	0	NA	NA	NA
0	0	NA	NA	NA
NA	0	NA	NA	NA
1	0	5	4	2
NA	0	NA	NA	NA
NA	0	NA	NA	NA
1	0	2	4	1
0	0	3	5	1
NA	0	NA	NA	NA
NA	0	NA	NA	NA
0	0	3	4	1
0	0	5	4	1
NA	0	NA	NA	NA
1	0	3	5	1
1	0	1	5	3
1	0	4	4	1
0	0	2	5	1
0	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	2	5	1
NA	0	NA	NA	NA
NA	0	NA	NA	NA
1	0	4	4	2
1	0	3	4	2
1	0	3	4	1
1	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
0	0	NA	NA	NA
NA	0	NA	NA	NA
1	0	4	5	2
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
0	0	3	4	2
NA	0	NA	NA	NA
NA	0	NA	NA	NA
NA	0	NA	NA	NA
0	0	NA	NA	NA




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154280&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154280&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154280&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'Herman Ole Andreas Wold' @ wold.wessa.net







Correlations for all pairs of data series (method=kendall)
GeslachtpopulatiestressbeloningCESD
Geslacht10.0090.173-0.030.163
populatie0.0091-0.094-0.0350.118
stress0.173-0.0941-0.1310.203
beloning-0.03-0.035-0.1311-0.012
CESD0.1630.1180.203-0.0121

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & Geslacht & populatie & stress & beloning & CESD \tabularnewline
Geslacht & 1 & 0.009 & 0.173 & -0.03 & 0.163 \tabularnewline
populatie & 0.009 & 1 & -0.094 & -0.035 & 0.118 \tabularnewline
stress & 0.173 & -0.094 & 1 & -0.131 & 0.203 \tabularnewline
beloning & -0.03 & -0.035 & -0.131 & 1 & -0.012 \tabularnewline
CESD & 0.163 & 0.118 & 0.203 & -0.012 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154280&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]Geslacht[/C][C]populatie[/C][C]stress[/C][C]beloning[/C][C]CESD[/C][/ROW]
[ROW][C]Geslacht[/C][C]1[/C][C]0.009[/C][C]0.173[/C][C]-0.03[/C][C]0.163[/C][/ROW]
[ROW][C]populatie[/C][C]0.009[/C][C]1[/C][C]-0.094[/C][C]-0.035[/C][C]0.118[/C][/ROW]
[ROW][C]stress[/C][C]0.173[/C][C]-0.094[/C][C]1[/C][C]-0.131[/C][C]0.203[/C][/ROW]
[ROW][C]beloning[/C][C]-0.03[/C][C]-0.035[/C][C]-0.131[/C][C]1[/C][C]-0.012[/C][/ROW]
[ROW][C]CESD[/C][C]0.163[/C][C]0.118[/C][C]0.203[/C][C]-0.012[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154280&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154280&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=kendall)
GeslachtpopulatiestressbeloningCESD
Geslacht10.0090.173-0.030.163
populatie0.0091-0.094-0.0350.118
stress0.173-0.0941-0.1310.203
beloning-0.03-0.035-0.1311-0.012
CESD0.1630.1180.203-0.0121







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Geslacht;populatie0.00940.00940.0094
p-value(0.9026)(0.9026)(0.9022)
Geslacht;stress0.1850.18870.1728
p-value(0.0259)(0.023)(0.0235)
Geslacht;beloning0.0154-0.032-0.0304
p-value(0.8542)(0.7024)(0.701)
Geslacht;CESD0.22610.17010.1626
p-value(0.0062)(0.0408)(0.0413)
populatie;stress-0.0785-0.1031-0.0945
p-value(0.3448)(0.2139)(0.2128)
populatie;beloning-0.0528-0.0369-0.035
p-value(0.5257)(0.6575)(0.6559)
populatie;CESD0.14040.12360.1182
p-value(0.0899)(0.136)(0.1354)
stress;beloning-0.1237-0.1484-0.1307
p-value(0.1355)(0.0728)(0.0692)
stress;CESD0.230.22830.2031
p-value(0.0051)(0.0054)(0.0051)
beloning;CESD0.0427-0.0129-0.0119
p-value(0.6076)(0.8765)(0.8736)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Geslacht;populatie & 0.0094 & 0.0094 & 0.0094 \tabularnewline
p-value & (0.9026) & (0.9026) & (0.9022) \tabularnewline
Geslacht;stress & 0.185 & 0.1887 & 0.1728 \tabularnewline
p-value & (0.0259) & (0.023) & (0.0235) \tabularnewline
Geslacht;beloning & 0.0154 & -0.032 & -0.0304 \tabularnewline
p-value & (0.8542) & (0.7024) & (0.701) \tabularnewline
Geslacht;CESD & 0.2261 & 0.1701 & 0.1626 \tabularnewline
p-value & (0.0062) & (0.0408) & (0.0413) \tabularnewline
populatie;stress & -0.0785 & -0.1031 & -0.0945 \tabularnewline
p-value & (0.3448) & (0.2139) & (0.2128) \tabularnewline
populatie;beloning & -0.0528 & -0.0369 & -0.035 \tabularnewline
p-value & (0.5257) & (0.6575) & (0.6559) \tabularnewline
populatie;CESD & 0.1404 & 0.1236 & 0.1182 \tabularnewline
p-value & (0.0899) & (0.136) & (0.1354) \tabularnewline
stress;beloning & -0.1237 & -0.1484 & -0.1307 \tabularnewline
p-value & (0.1355) & (0.0728) & (0.0692) \tabularnewline
stress;CESD & 0.23 & 0.2283 & 0.2031 \tabularnewline
p-value & (0.0051) & (0.0054) & (0.0051) \tabularnewline
beloning;CESD & 0.0427 & -0.0129 & -0.0119 \tabularnewline
p-value & (0.6076) & (0.8765) & (0.8736) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154280&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]Geslacht;populatie[/C][C]0.0094[/C][C]0.0094[/C][C]0.0094[/C][/ROW]
[ROW][C]p-value[/C][C](0.9026)[/C][C](0.9026)[/C][C](0.9022)[/C][/ROW]
[ROW][C]Geslacht;stress[/C][C]0.185[/C][C]0.1887[/C][C]0.1728[/C][/ROW]
[ROW][C]p-value[/C][C](0.0259)[/C][C](0.023)[/C][C](0.0235)[/C][/ROW]
[ROW][C]Geslacht;beloning[/C][C]0.0154[/C][C]-0.032[/C][C]-0.0304[/C][/ROW]
[ROW][C]p-value[/C][C](0.8542)[/C][C](0.7024)[/C][C](0.701)[/C][/ROW]
[ROW][C]Geslacht;CESD[/C][C]0.2261[/C][C]0.1701[/C][C]0.1626[/C][/ROW]
[ROW][C]p-value[/C][C](0.0062)[/C][C](0.0408)[/C][C](0.0413)[/C][/ROW]
[ROW][C]populatie;stress[/C][C]-0.0785[/C][C]-0.1031[/C][C]-0.0945[/C][/ROW]
[ROW][C]p-value[/C][C](0.3448)[/C][C](0.2139)[/C][C](0.2128)[/C][/ROW]
[ROW][C]populatie;beloning[/C][C]-0.0528[/C][C]-0.0369[/C][C]-0.035[/C][/ROW]
[ROW][C]p-value[/C][C](0.5257)[/C][C](0.6575)[/C][C](0.6559)[/C][/ROW]
[ROW][C]populatie;CESD[/C][C]0.1404[/C][C]0.1236[/C][C]0.1182[/C][/ROW]
[ROW][C]p-value[/C][C](0.0899)[/C][C](0.136)[/C][C](0.1354)[/C][/ROW]
[ROW][C]stress;beloning[/C][C]-0.1237[/C][C]-0.1484[/C][C]-0.1307[/C][/ROW]
[ROW][C]p-value[/C][C](0.1355)[/C][C](0.0728)[/C][C](0.0692)[/C][/ROW]
[ROW][C]stress;CESD[/C][C]0.23[/C][C]0.2283[/C][C]0.2031[/C][/ROW]
[ROW][C]p-value[/C][C](0.0051)[/C][C](0.0054)[/C][C](0.0051)[/C][/ROW]
[ROW][C]beloning;CESD[/C][C]0.0427[/C][C]-0.0129[/C][C]-0.0119[/C][/ROW]
[ROW][C]p-value[/C][C](0.6076)[/C][C](0.8765)[/C][C](0.8736)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154280&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154280&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
Geslacht;populatie0.00940.00940.0094
p-value(0.9026)(0.9026)(0.9022)
Geslacht;stress0.1850.18870.1728
p-value(0.0259)(0.023)(0.0235)
Geslacht;beloning0.0154-0.032-0.0304
p-value(0.8542)(0.7024)(0.701)
Geslacht;CESD0.22610.17010.1626
p-value(0.0062)(0.0408)(0.0413)
populatie;stress-0.0785-0.1031-0.0945
p-value(0.3448)(0.2139)(0.2128)
populatie;beloning-0.0528-0.0369-0.035
p-value(0.5257)(0.6575)(0.6559)
populatie;CESD0.14040.12360.1182
p-value(0.0899)(0.136)(0.1354)
stress;beloning-0.1237-0.1484-0.1307
p-value(0.1355)(0.0728)(0.0692)
stress;CESD0.230.22830.2031
p-value(0.0051)(0.0054)(0.0051)
beloning;CESD0.0427-0.0129-0.0119
p-value(0.6076)(0.8765)(0.8736)



Parameters (Session):
par1 = kendall ;
Parameters (R input):
par1 = kendall ;
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')