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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 computationMon, 23 Jan 2017 09:16:22 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/23/t1485159436689qs3klp0b9vk8.htm/, Retrieved Thu, 31 Oct 2024 23:41:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=303691, Retrieved Thu, 31 Oct 2024 23:41:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [vraag 2] [2017-01-23 08:16:22] [bf81c5bba8521cd7c521397a61beff5a] [Current]
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Dataseries X:
1 0 0 0 3.2 6
0 1 0 1 3.3 7
0 1 1 1 3 2
0 1 0 1 3.5 11
0 1 0 0 3.7 13
1 0 0 0 2.7 3
0 1 1 1 3.6 17
0 1 0 1 3.5 10
1 0 0 0 3.8 4
0 1 0 0 3.4 12
0 0 0 1 3.7 7
0 1 0 0 3.5 11
0 0 1 0 2.8 3
1 0 1 0 3.8 5
0 1 0 0 4.3 1
0 0 0 1 3.3 12
0 0 0 0 3.6 18
1 0 1 0 3.6 8
1 1 0 0 3.3 6
0 0 0 0 2.8 1




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303691&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=303691&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303691&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 Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Correlations for all pairs of data series (method=pearson)
X1X2X3X4X5Score
X11-0.4360.126-0.48-0.034-0.337
X2-0.4361-0.1150.3140.2370.235
X30.126-0.11510.061-0.091-0.1
X4-0.480.3140.0611-0.0110.237
X5-0.0340.237-0.091-0.01110.273
Score-0.3370.235-0.10.2370.2731

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & X1 & X2 & X3 & X4 & X5 & Score \tabularnewline
X1 & 1 & -0.436 & 0.126 & -0.48 & -0.034 & -0.337 \tabularnewline
X2 & -0.436 & 1 & -0.115 & 0.314 & 0.237 & 0.235 \tabularnewline
X3 & 0.126 & -0.115 & 1 & 0.061 & -0.091 & -0.1 \tabularnewline
X4 & -0.48 & 0.314 & 0.061 & 1 & -0.011 & 0.237 \tabularnewline
X5 & -0.034 & 0.237 & -0.091 & -0.011 & 1 & 0.273 \tabularnewline
Score & -0.337 & 0.235 & -0.1 & 0.237 & 0.273 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303691&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]X1[/C][C]X2[/C][C]X3[/C][C]X4[/C][C]X5[/C][C]Score[/C][/ROW]
[ROW][C]X1[/C][C]1[/C][C]-0.436[/C][C]0.126[/C][C]-0.48[/C][C]-0.034[/C][C]-0.337[/C][/ROW]
[ROW][C]X2[/C][C]-0.436[/C][C]1[/C][C]-0.115[/C][C]0.314[/C][C]0.237[/C][C]0.235[/C][/ROW]
[ROW][C]X3[/C][C]0.126[/C][C]-0.115[/C][C]1[/C][C]0.061[/C][C]-0.091[/C][C]-0.1[/C][/ROW]
[ROW][C]X4[/C][C]-0.48[/C][C]0.314[/C][C]0.061[/C][C]1[/C][C]-0.011[/C][C]0.237[/C][/ROW]
[ROW][C]X5[/C][C]-0.034[/C][C]0.237[/C][C]-0.091[/C][C]-0.011[/C][C]1[/C][C]0.273[/C][/ROW]
[ROW][C]Score[/C][C]-0.337[/C][C]0.235[/C][C]-0.1[/C][C]0.237[/C][C]0.273[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303691&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303691&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)
X1X2X3X4X5Score
X11-0.4360.126-0.48-0.034-0.337
X2-0.4361-0.1150.3140.2370.235
X30.126-0.11510.061-0.091-0.1
X4-0.480.3140.0611-0.0110.237
X5-0.0340.237-0.091-0.01110.273
Score-0.3370.235-0.10.2370.2731







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
X1;X2-0.4364-0.4364-0.4364
p-value(0.0544)(0.0544)(0.0571)
X1;X30.1260.1260.126
p-value(0.5966)(0.5966)(0.5829)
X1;X4-0.4804-0.4804-0.4804
p-value(0.032)(0.032)(0.0363)
X1;X5-0.03440.0190.0164
p-value(0.8854)(0.9365)(0.9339)
X1;Score-0.3368-0.3129-0.2654
p-value(0.1465)(0.1792)(0.1726)
X2;X3-0.1155-0.1155-0.1155
p-value(0.6278)(0.6278)(0.6147)
X2;X40.31450.31450.3145
p-value(0.1769)(0.1769)(0.1704)
X2;X50.23670.09590.0824
p-value(0.3151)(0.6875)(0.6759)
X2;Score0.23510.24330.2064
p-value(0.3184)(0.3012)(0.2888)
X3;X40.06050.06050.0605
p-value(0.7999)(0.7999)(0.7919)
X3;X5-0.09110.01010.0087
p-value(0.7025)(0.9664)(0.965)
X3;Score-0.1003-0.1405-0.1192
p-value(0.6739)(0.5547)(0.5403)
X4;X5-0.011-0.0548-0.0471
p-value(0.9632)(0.8183)(0.811)
X4;Score0.23680.2460.2087
p-value(0.3148)(0.2958)(0.2836)
X5;Score0.27310.2770.2044
p-value(0.244)(0.2371)(0.2251)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
X1;X2 & -0.4364 & -0.4364 & -0.4364 \tabularnewline
p-value & (0.0544) & (0.0544) & (0.0571) \tabularnewline
X1;X3 & 0.126 & 0.126 & 0.126 \tabularnewline
p-value & (0.5966) & (0.5966) & (0.5829) \tabularnewline
X1;X4 & -0.4804 & -0.4804 & -0.4804 \tabularnewline
p-value & (0.032) & (0.032) & (0.0363) \tabularnewline
X1;X5 & -0.0344 & 0.019 & 0.0164 \tabularnewline
p-value & (0.8854) & (0.9365) & (0.9339) \tabularnewline
X1;Score & -0.3368 & -0.3129 & -0.2654 \tabularnewline
p-value & (0.1465) & (0.1792) & (0.1726) \tabularnewline
X2;X3 & -0.1155 & -0.1155 & -0.1155 \tabularnewline
p-value & (0.6278) & (0.6278) & (0.6147) \tabularnewline
X2;X4 & 0.3145 & 0.3145 & 0.3145 \tabularnewline
p-value & (0.1769) & (0.1769) & (0.1704) \tabularnewline
X2;X5 & 0.2367 & 0.0959 & 0.0824 \tabularnewline
p-value & (0.3151) & (0.6875) & (0.6759) \tabularnewline
X2;Score & 0.2351 & 0.2433 & 0.2064 \tabularnewline
p-value & (0.3184) & (0.3012) & (0.2888) \tabularnewline
X3;X4 & 0.0605 & 0.0605 & 0.0605 \tabularnewline
p-value & (0.7999) & (0.7999) & (0.7919) \tabularnewline
X3;X5 & -0.0911 & 0.0101 & 0.0087 \tabularnewline
p-value & (0.7025) & (0.9664) & (0.965) \tabularnewline
X3;Score & -0.1003 & -0.1405 & -0.1192 \tabularnewline
p-value & (0.6739) & (0.5547) & (0.5403) \tabularnewline
X4;X5 & -0.011 & -0.0548 & -0.0471 \tabularnewline
p-value & (0.9632) & (0.8183) & (0.811) \tabularnewline
X4;Score & 0.2368 & 0.246 & 0.2087 \tabularnewline
p-value & (0.3148) & (0.2958) & (0.2836) \tabularnewline
X5;Score & 0.2731 & 0.277 & 0.2044 \tabularnewline
p-value & (0.244) & (0.2371) & (0.2251) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303691&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]X1;X2[/C][C]-0.4364[/C][C]-0.4364[/C][C]-0.4364[/C][/ROW]
[ROW][C]p-value[/C][C](0.0544)[/C][C](0.0544)[/C][C](0.0571)[/C][/ROW]
[ROW][C]X1;X3[/C][C]0.126[/C][C]0.126[/C][C]0.126[/C][/ROW]
[ROW][C]p-value[/C][C](0.5966)[/C][C](0.5966)[/C][C](0.5829)[/C][/ROW]
[ROW][C]X1;X4[/C][C]-0.4804[/C][C]-0.4804[/C][C]-0.4804[/C][/ROW]
[ROW][C]p-value[/C][C](0.032)[/C][C](0.032)[/C][C](0.0363)[/C][/ROW]
[ROW][C]X1;X5[/C][C]-0.0344[/C][C]0.019[/C][C]0.0164[/C][/ROW]
[ROW][C]p-value[/C][C](0.8854)[/C][C](0.9365)[/C][C](0.9339)[/C][/ROW]
[ROW][C]X1;Score[/C][C]-0.3368[/C][C]-0.3129[/C][C]-0.2654[/C][/ROW]
[ROW][C]p-value[/C][C](0.1465)[/C][C](0.1792)[/C][C](0.1726)[/C][/ROW]
[ROW][C]X2;X3[/C][C]-0.1155[/C][C]-0.1155[/C][C]-0.1155[/C][/ROW]
[ROW][C]p-value[/C][C](0.6278)[/C][C](0.6278)[/C][C](0.6147)[/C][/ROW]
[ROW][C]X2;X4[/C][C]0.3145[/C][C]0.3145[/C][C]0.3145[/C][/ROW]
[ROW][C]p-value[/C][C](0.1769)[/C][C](0.1769)[/C][C](0.1704)[/C][/ROW]
[ROW][C]X2;X5[/C][C]0.2367[/C][C]0.0959[/C][C]0.0824[/C][/ROW]
[ROW][C]p-value[/C][C](0.3151)[/C][C](0.6875)[/C][C](0.6759)[/C][/ROW]
[ROW][C]X2;Score[/C][C]0.2351[/C][C]0.2433[/C][C]0.2064[/C][/ROW]
[ROW][C]p-value[/C][C](0.3184)[/C][C](0.3012)[/C][C](0.2888)[/C][/ROW]
[ROW][C]X3;X4[/C][C]0.0605[/C][C]0.0605[/C][C]0.0605[/C][/ROW]
[ROW][C]p-value[/C][C](0.7999)[/C][C](0.7999)[/C][C](0.7919)[/C][/ROW]
[ROW][C]X3;X5[/C][C]-0.0911[/C][C]0.0101[/C][C]0.0087[/C][/ROW]
[ROW][C]p-value[/C][C](0.7025)[/C][C](0.9664)[/C][C](0.965)[/C][/ROW]
[ROW][C]X3;Score[/C][C]-0.1003[/C][C]-0.1405[/C][C]-0.1192[/C][/ROW]
[ROW][C]p-value[/C][C](0.6739)[/C][C](0.5547)[/C][C](0.5403)[/C][/ROW]
[ROW][C]X4;X5[/C][C]-0.011[/C][C]-0.0548[/C][C]-0.0471[/C][/ROW]
[ROW][C]p-value[/C][C](0.9632)[/C][C](0.8183)[/C][C](0.811)[/C][/ROW]
[ROW][C]X4;Score[/C][C]0.2368[/C][C]0.246[/C][C]0.2087[/C][/ROW]
[ROW][C]p-value[/C][C](0.3148)[/C][C](0.2958)[/C][C](0.2836)[/C][/ROW]
[ROW][C]X5;Score[/C][C]0.2731[/C][C]0.277[/C][C]0.2044[/C][/ROW]
[ROW][C]p-value[/C][C](0.244)[/C][C](0.2371)[/C][C](0.2251)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303691&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303691&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
X1;X2-0.4364-0.4364-0.4364
p-value(0.0544)(0.0544)(0.0571)
X1;X30.1260.1260.126
p-value(0.5966)(0.5966)(0.5829)
X1;X4-0.4804-0.4804-0.4804
p-value(0.032)(0.032)(0.0363)
X1;X5-0.03440.0190.0164
p-value(0.8854)(0.9365)(0.9339)
X1;Score-0.3368-0.3129-0.2654
p-value(0.1465)(0.1792)(0.1726)
X2;X3-0.1155-0.1155-0.1155
p-value(0.6278)(0.6278)(0.6147)
X2;X40.31450.31450.3145
p-value(0.1769)(0.1769)(0.1704)
X2;X50.23670.09590.0824
p-value(0.3151)(0.6875)(0.6759)
X2;Score0.23510.24330.2064
p-value(0.3184)(0.3012)(0.2888)
X3;X40.06050.06050.0605
p-value(0.7999)(0.7999)(0.7919)
X3;X5-0.09110.01010.0087
p-value(0.7025)(0.9664)(0.965)
X3;Score-0.1003-0.1405-0.1192
p-value(0.6739)(0.5547)(0.5403)
X4;X5-0.011-0.0548-0.0471
p-value(0.9632)(0.8183)(0.811)
X4;Score0.23680.2460.2087
p-value(0.3148)(0.2958)(0.2836)
X5;Score0.27310.2770.2044
p-value(0.244)(0.2371)(0.2251)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.01000
0.02000
0.03000
0.040.070.070.07
0.050.070.070.07
0.060.130.130.13
0.070.130.130.13
0.080.130.130.13
0.090.130.130.13
0.10.130.130.13

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Correlation Tests \tabularnewline
Number of significant by total number of Correlations \tabularnewline
Type I error & Pearson r & Spearman rho & Kendall tau \tabularnewline
0.01 & 0 & 0 & 0 \tabularnewline
0.02 & 0 & 0 & 0 \tabularnewline
0.03 & 0 & 0 & 0 \tabularnewline
0.04 & 0.07 & 0.07 & 0.07 \tabularnewline
0.05 & 0.07 & 0.07 & 0.07 \tabularnewline
0.06 & 0.13 & 0.13 & 0.13 \tabularnewline
0.07 & 0.13 & 0.13 & 0.13 \tabularnewline
0.08 & 0.13 & 0.13 & 0.13 \tabularnewline
0.09 & 0.13 & 0.13 & 0.13 \tabularnewline
0.1 & 0.13 & 0.13 & 0.13 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303691&T=3

[TABLE]
[ROW][C]Meta Analysis of Correlation Tests[/C][/ROW]
[ROW][C]Number of significant by total number of Correlations[/C][/ROW]
[ROW][C]Type I error[/C][C]Pearson r[/C][C]Spearman rho[/C][C]Kendall tau[/C][/ROW]
[ROW][C]0.01[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.02[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.03[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.04[/C][C]0.07[/C][C]0.07[/C][C]0.07[/C][/ROW]
[ROW][C]0.05[/C][C]0.07[/C][C]0.07[/C][C]0.07[/C][/ROW]
[ROW][C]0.06[/C][C]0.13[/C][C]0.13[/C][C]0.13[/C][/ROW]
[ROW][C]0.07[/C][C]0.13[/C][C]0.13[/C][C]0.13[/C][/ROW]
[ROW][C]0.08[/C][C]0.13[/C][C]0.13[/C][C]0.13[/C][/ROW]
[ROW][C]0.09[/C][C]0.13[/C][C]0.13[/C][C]0.13[/C][/ROW]
[ROW][C]0.1[/C][C]0.13[/C][C]0.13[/C][C]0.13[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303691&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303691&T=3

As an alternative you can also use a QR Code:  

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

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.01000
0.02000
0.03000
0.040.070.070.07
0.050.070.070.07
0.060.130.130.13
0.070.130.130.13
0.080.130.130.13
0.090.130.130.13
0.10.130.130.13



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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
x <- na.omit(x)
y <- t(na.omit(t(y)))
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
print(n)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
ncorrs <- (n*n -n)/2
mycorrs <- array(0, dim=c(10,3))
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)
for (iii in 1:10) {
iiid100 <- iii / 100
if (rp$p.value < iiid100) mycorrs[iii, 1] = mycorrs[iii, 1] + 1
if (rs$p.value < iiid100) mycorrs[iii, 2] = mycorrs[iii, 2] + 1
if (rk$p.value < iiid100) mycorrs[iii, 3] = mycorrs[iii, 3] + 1
}
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Correlation Tests',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of significant by total number of Correlations',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Type I error',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)
for (iii in 1:10) {
iiid100 <- iii / 100
a<-table.row.start(a)
a<-table.element(a,round(iiid100,2),header=T)
a<-table.element(a,round(mycorrs[iii,1]/ncorrs,2))
a<-table.element(a,round(mycorrs[iii,2]/ncorrs,2))
a<-table.element(a,round(mycorrs[iii,3]/ncorrs,2))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')