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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, 17 Dec 2013 05:13:41 -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/2013/Dec/17/t1387275335wk29o4z3q9bx46v.htm/, Retrieved Fri, 29 Mar 2024 08:11:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232402, Retrieved Fri, 29 Mar 2024 08:11:08 +0000
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-       [Kendall tau Correlation Matrix] [] [2013-12-17 10:13:41] [9e6a405f514733ea23d87e4507d39d29] [Current]
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Dataseries X:
22 0 15 4 4
2 1 0 3 3
0 0 3 2 3
4 0 2 4 5
14 5 3 3 4
2 0 12 5 3
0 4 3 3 5
4 4 0 5 5
6 0 12 3 3
25 0 15 4 4
0 0 0 5 5
25 5 10 3 3
0 0 12 3 4
2 2 20 4 4
30 3 20 4 4
1 0 2 4 4
0 0 3 3 5
0 0 16 5 3
8 0 4 4 5
0 4 2 3 3
0 0 4 3 4
0 8 16 3 4
6 0 0 3 4
0 0 0 4 3
6 0 15 5 5
12 3 9 4 4
1 0 1 5 5
20 24 15 3 2
5 15 5 4 5
0 0 4 2 4
21 12 15 5 5
3 0 4 2 4
5 0 12 3 5
8 0 2 3 3
10 4 4 3 3
5 1 2 3 4
8 0 4 4 4
6 16 8 3 5
15 9 30 5 5
9 0 6 2 3
14 8 6 3 5
9 10 7 3 5
5 0 4 4 4
9 6 17 5 5
10 0 5 3 3
12 0 0 3 4
9 15 3 3 5
7 0 4 3 3
15 0 15 3 5
14 0 0 3 4
16 0 8 3 3
6 0 10 4 5
6 0 4 3 4
2 0 0 2 5
8 10 6 3 3
0 7 11 3 5
6 2 10 3 4
4 0 0 4 5
15 2 0 2 4
0 0 0 5 5
12 3 0 4 5
0 12 0 2 5
13 0 0 2 4
18 3 0 3 5
4 0 7 4 4
9 0 4 5 5
12 0 12 3 4
14 8 6 3 3
0 0 12 4 4
4 7 10 3 3
12 0 9 4 5
15 18 6 3 4
0 0 0 3 4
30 13 16 4 4
0 0 2 2 4
0 0 0 2 4
3 0 0 4 3
2 0 1 2 4
15 0 10 4 3
3 2 10 3 4
4 0 14 5 5
12 9 12 3 4
8 16 12 3 3
12 10 12 4 4
18 0 5 3 4
15 7 0 3 4
3 8 4 4 5
0 0 3 3 4
0 0 0 3 5
21 0 14 3 4
10 0 4 2 2
5 1 3 4 5
0 0 0 3 5
1 0 12 5 5
0 0 12 3 4
6 0 15 5 5
12 0 0 4 5
10 20 8 3 4
0 9 6 3 4
25 0 14 5 5
3 0 5 4 4
15 0 10 3 5
10 0 16 5 5
15 4 4 4 4
4 0 0 2 4
10 2 8 5 5
2 0 12 4 4
12 0 6 2 4
9 0 4 2 2
1 28 20 5 3
4 0 0 3 4
2 0 13 3 4
0 0 0 4 2
1 0 0 2 5
0 0 0 4 4
0 0 0 3 4
0 0 10 3 4
18 10 6 3 4
3 0 16 2 5
6 0 6 4 3
0 16 0 3 1
2 1 0 3 5
4 10 4 3 3
15 0 9 4 5
6 0 17 3 3
30 15 12 4 4
3 10 3 4 4
18 0 6 3 4
10 0 8 3 4
0 0 3 4 4
7 2 7 4 5
0 3 0 3 4
22 4 10 5 5
7 1 3 3 3
4 4 0 4 4
15 0 8 4 4
5 0 0 2 4
14 8 4 4 4
11 0 13 2 4
24 0 12 4 5
24 6 16 4 5
0 0 20 4 5
20 2 20 3 5
12 0 21 5 5
7 0 10 4 4
0 0 14 3 4
28 0 12 3 4
12 0 15 4 4
15 27 9 5 5
0 0 4 3 4
7 4 8 3 5
8 0 0 2 4
30 0 13 3 5
14 0 0 5 5
3 0 21 3 5
3 1 0 3 4
0 0 1 4 4
15 4 16 2 4
0 0 12 4 5
11 0 2 3 4
1 0 0 3 3
30 9 4 5 5
4 0 6 5 5
0 0 10 3 5
3 0 3 4 4
0 0 0 5 5
0 17 16 2 4
0 3 4 4 5
0 0 0 2 3
12 0 0 4 3
26 0 0 3 4
0 0 0 4 4
6 12 3 4 5
0 5 4 4 2
4 17 15 5 5
19 0 25 5 5
16 0 12 4 4
8 0 4 3 4
10 2 0 4 4
6 0 9 5 5
2 0 5 5 5
0 4 15 4 4
30 2 0 5 5
8 1 10 4 5
0 0 3 2 5
10 18 12 2 4
15 0 0 5 5
21 0 12 3 4
1 3 5 3 4
5 9 15 3 5
0 2 1 3 5
4 12 2 4 4
1 0 4 4 4
4 0 3 3 3
24 4 8 5 5
11 0 0 3 5
0 0 0 4 4
0 0 0 3 5
0 0 2 4 4
1 0 22 5 5
0 0 0 3 4
30 0 26 5 5
6 0 21 3 5
0 0 0 4 3
9 21 0 5 5
5 3 4 4 5
2 0 4 4 4
8 0 0 3 5
16 0 18 5 5
0 20 1 4 4
12 0 18 4 4
0 0 6 3 3
0 18 6 5 5
9 0 0 3 4
10 1 8 4 4
5 0 3 4 4
2 0 7 3 5
6 5 15 3 4
0 0 0 5 5
0 0 0 4 3
0 3 2 4 5
24 0 27 3 5
0 0 3 5 5
18 11 8 2 3
1 0 4 3 5
4 0 0 3 4
0 0 0 4 4
2 2 0 2 2
12 15 8 4 5
0 4 4 2 4
10 0 3 4 4
0 0 8 4 4
0 0 0 5 5
0 0 6 5 4
2 0 4 2 3
0 1 5 3 5
20 0 0 4 4
0 0 6 4 4
8 4 8 4 5
1 0 5 3 5
2 6 6 3 4
10 0 12 3 4
10 0 0 3 4
12 0 8 2 3
17 18 13 3 3
6 0 6 4 5
4 2 11 3 4
21 3 12 5 5
0 0 0 2 4
15 0 4 2 4
1 13 5 4 5
14 25 0 4 4
7 0 7 4 4
19 0 0 4 4
6 5 0 5 5
14 0 4 5 5
26 0 8 5 5
0 0 0 3 3
0 0 0 3 2
0 0 2 4 5
0 0 9 4 4
0 15 4 4 4
2 0 0 4 5
8 0 0 3 3
20 4 12 5 5
7 2 0 2 4
1 0 1 4 4
0 0 3 5 5
13 10 11 2 3
0 0 0 2 3
19 0 12 3 3
0 30 1 5 5
0 2 0 4 4
6 1 6 4 5
0 0 0 5 5
1 0 3 3 4
6 2 9 2 3
30 0 12 3 4
0 0 5 5 5
5 0 0 3 2
15 6 0 4 5
0 0 15 3 4
6 9 5 4 4
0 2 0 5 4
14 0 0 4 4
21 0 0 4 5
0 30 6 4 4
16 0 10 4 4
30 8 18 3 3
15 25 8 3 5
30 0 10 4 4
9 2 4 5 5
0 10 1 3 4
0 10 13 4 4
8 0 8 3 3
29 10 0 3 4
21 0 0 3 4
6 0 4 3 2
0 0 12 2 3
6 21 4 3 5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232402&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]5 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=232402&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232402&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 time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Correlations for all pairs of data series (method=kendall)
walking.dayscycling.dayssports.daysfruitsvegetables
walking.days10.1070.2280.0360.044
cycling.days0.10710.10.0240.047
sports.days0.2280.110.0840.063
fruits0.0360.0240.08410.379
vegetables0.0440.0470.0630.3791

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & walking.days & cycling.days & sports.days & fruits & vegetables \tabularnewline
walking.days & 1 & 0.107 & 0.228 & 0.036 & 0.044 \tabularnewline
cycling.days & 0.107 & 1 & 0.1 & 0.024 & 0.047 \tabularnewline
sports.days & 0.228 & 0.1 & 1 & 0.084 & 0.063 \tabularnewline
fruits & 0.036 & 0.024 & 0.084 & 1 & 0.379 \tabularnewline
vegetables & 0.044 & 0.047 & 0.063 & 0.379 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232402&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]walking.days[/C][C]cycling.days[/C][C]sports.days[/C][C]fruits[/C][C]vegetables[/C][/ROW]
[ROW][C]walking.days[/C][C]1[/C][C]0.107[/C][C]0.228[/C][C]0.036[/C][C]0.044[/C][/ROW]
[ROW][C]cycling.days[/C][C]0.107[/C][C]1[/C][C]0.1[/C][C]0.024[/C][C]0.047[/C][/ROW]
[ROW][C]sports.days[/C][C]0.228[/C][C]0.1[/C][C]1[/C][C]0.084[/C][C]0.063[/C][/ROW]
[ROW][C]fruits[/C][C]0.036[/C][C]0.024[/C][C]0.084[/C][C]1[/C][C]0.379[/C][/ROW]
[ROW][C]vegetables[/C][C]0.044[/C][C]0.047[/C][C]0.063[/C][C]0.379[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232402&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232402&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)
walking.dayscycling.dayssports.daysfruitsvegetables
walking.days10.1070.2280.0360.044
cycling.days0.10710.10.0240.047
sports.days0.2280.110.0840.063
fruits0.0360.0240.08410.379
vegetables0.0440.0470.0630.3791







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
walking.days;cycling.days0.08880.13480.1067
p-value(0.1249)(0.0195)(0.0179)
walking.days;sports.days0.32720.29630.2282
p-value(0)(0)(0)
walking.days;fruits0.08640.04670.0365
p-value(0.1352)(0.4198)(0.4235)
walking.days;vegetables0.0710.05410.0441
p-value(0.2204)(0.3502)(0.3442)
cycling.days;sports.days0.10130.12730.1
p-value(0.0797)(0.0275)(0.0272)
cycling.days;fruits0.06030.0290.0242
p-value(0.2979)(0.6172)(0.621)
cycling.days;vegetables0.00280.05440.0474
p-value(0.9619)(0.3478)(0.3419)
sports.days;fruits0.14440.10640.0841
p-value(0.0123)(0.0657)(0.0664)
sports.days;vegetables0.1160.07670.0625
p-value(0.0448)(0.185)(0.1814)
fruits;vegetables0.41290.42380.3788
p-value(0)(0)(0)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
walking.days;cycling.days & 0.0888 & 0.1348 & 0.1067 \tabularnewline
p-value & (0.1249) & (0.0195) & (0.0179) \tabularnewline
walking.days;sports.days & 0.3272 & 0.2963 & 0.2282 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
walking.days;fruits & 0.0864 & 0.0467 & 0.0365 \tabularnewline
p-value & (0.1352) & (0.4198) & (0.4235) \tabularnewline
walking.days;vegetables & 0.071 & 0.0541 & 0.0441 \tabularnewline
p-value & (0.2204) & (0.3502) & (0.3442) \tabularnewline
cycling.days;sports.days & 0.1013 & 0.1273 & 0.1 \tabularnewline
p-value & (0.0797) & (0.0275) & (0.0272) \tabularnewline
cycling.days;fruits & 0.0603 & 0.029 & 0.0242 \tabularnewline
p-value & (0.2979) & (0.6172) & (0.621) \tabularnewline
cycling.days;vegetables & 0.0028 & 0.0544 & 0.0474 \tabularnewline
p-value & (0.9619) & (0.3478) & (0.3419) \tabularnewline
sports.days;fruits & 0.1444 & 0.1064 & 0.0841 \tabularnewline
p-value & (0.0123) & (0.0657) & (0.0664) \tabularnewline
sports.days;vegetables & 0.116 & 0.0767 & 0.0625 \tabularnewline
p-value & (0.0448) & (0.185) & (0.1814) \tabularnewline
fruits;vegetables & 0.4129 & 0.4238 & 0.3788 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232402&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]walking.days;cycling.days[/C][C]0.0888[/C][C]0.1348[/C][C]0.1067[/C][/ROW]
[ROW][C]p-value[/C][C](0.1249)[/C][C](0.0195)[/C][C](0.0179)[/C][/ROW]
[ROW][C]walking.days;sports.days[/C][C]0.3272[/C][C]0.2963[/C][C]0.2282[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]walking.days;fruits[/C][C]0.0864[/C][C]0.0467[/C][C]0.0365[/C][/ROW]
[ROW][C]p-value[/C][C](0.1352)[/C][C](0.4198)[/C][C](0.4235)[/C][/ROW]
[ROW][C]walking.days;vegetables[/C][C]0.071[/C][C]0.0541[/C][C]0.0441[/C][/ROW]
[ROW][C]p-value[/C][C](0.2204)[/C][C](0.3502)[/C][C](0.3442)[/C][/ROW]
[ROW][C]cycling.days;sports.days[/C][C]0.1013[/C][C]0.1273[/C][C]0.1[/C][/ROW]
[ROW][C]p-value[/C][C](0.0797)[/C][C](0.0275)[/C][C](0.0272)[/C][/ROW]
[ROW][C]cycling.days;fruits[/C][C]0.0603[/C][C]0.029[/C][C]0.0242[/C][/ROW]
[ROW][C]p-value[/C][C](0.2979)[/C][C](0.6172)[/C][C](0.621)[/C][/ROW]
[ROW][C]cycling.days;vegetables[/C][C]0.0028[/C][C]0.0544[/C][C]0.0474[/C][/ROW]
[ROW][C]p-value[/C][C](0.9619)[/C][C](0.3478)[/C][C](0.3419)[/C][/ROW]
[ROW][C]sports.days;fruits[/C][C]0.1444[/C][C]0.1064[/C][C]0.0841[/C][/ROW]
[ROW][C]p-value[/C][C](0.0123)[/C][C](0.0657)[/C][C](0.0664)[/C][/ROW]
[ROW][C]sports.days;vegetables[/C][C]0.116[/C][C]0.0767[/C][C]0.0625[/C][/ROW]
[ROW][C]p-value[/C][C](0.0448)[/C][C](0.185)[/C][C](0.1814)[/C][/ROW]
[ROW][C]fruits;vegetables[/C][C]0.4129[/C][C]0.4238[/C][C]0.3788[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232402&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232402&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
walking.days;cycling.days0.08880.13480.1067
p-value(0.1249)(0.0195)(0.0179)
walking.days;sports.days0.32720.29630.2282
p-value(0)(0)(0)
walking.days;fruits0.08640.04670.0365
p-value(0.1352)(0.4198)(0.4235)
walking.days;vegetables0.0710.05410.0441
p-value(0.2204)(0.3502)(0.3442)
cycling.days;sports.days0.10130.12730.1
p-value(0.0797)(0.0275)(0.0272)
cycling.days;fruits0.06030.0290.0242
p-value(0.2979)(0.6172)(0.621)
cycling.days;vegetables0.00280.05440.0474
p-value(0.9619)(0.3478)(0.3419)
sports.days;fruits0.14440.10640.0841
p-value(0.0123)(0.0657)(0.0664)
sports.days;vegetables0.1160.07670.0625
p-value(0.0448)(0.185)(0.1814)
fruits;vegetables0.41290.42380.3788
p-value(0)(0)(0)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.20.20.2
0.020.30.30.3
0.030.30.40.4
0.040.30.40.4
0.050.40.40.4
0.060.40.40.4
0.070.40.50.5
0.080.50.50.5
0.090.50.50.5
0.10.50.50.5

\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 & 0.2 & 0.2 & 0.2 \tabularnewline
0.02 & 0.3 & 0.3 & 0.3 \tabularnewline
0.03 & 0.3 & 0.4 & 0.4 \tabularnewline
0.04 & 0.3 & 0.4 & 0.4 \tabularnewline
0.05 & 0.4 & 0.4 & 0.4 \tabularnewline
0.06 & 0.4 & 0.4 & 0.4 \tabularnewline
0.07 & 0.4 & 0.5 & 0.5 \tabularnewline
0.08 & 0.5 & 0.5 & 0.5 \tabularnewline
0.09 & 0.5 & 0.5 & 0.5 \tabularnewline
0.1 & 0.5 & 0.5 & 0.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232402&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]0.2[/C][C]0.2[/C][C]0.2[/C][/ROW]
[ROW][C]0.02[/C][C]0.3[/C][C]0.3[/C][C]0.3[/C][/ROW]
[ROW][C]0.03[/C][C]0.3[/C][C]0.4[/C][C]0.4[/C][/ROW]
[ROW][C]0.04[/C][C]0.3[/C][C]0.4[/C][C]0.4[/C][/ROW]
[ROW][C]0.05[/C][C]0.4[/C][C]0.4[/C][C]0.4[/C][/ROW]
[ROW][C]0.06[/C][C]0.4[/C][C]0.4[/C][C]0.4[/C][/ROW]
[ROW][C]0.07[/C][C]0.4[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]0.08[/C][C]0.5[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]0.09[/C][C]0.5[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]0.1[/C][C]0.5[/C][C]0.5[/C][C]0.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232402&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232402&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
0.010.20.20.2
0.020.30.30.3
0.030.30.40.4
0.040.30.40.4
0.050.40.40.4
0.060.40.40.4
0.070.40.50.5
0.080.50.50.5
0.090.50.50.5
0.10.50.50.5



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')
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')