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rwasp_pairs.wasp

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Kendall tau Correlation Matrix

Date of computation

Sun, 08 Dec 2013 17:39:35 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/08/t1386542463ygy2tq4w9azvsj0.htm/, Retrieved Wed, 24 Apr 2024 14:53:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231536, Retrieved Wed, 24 Apr 2024 14:53:48 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Kendall tau Correlation Matrix] [WS10: Kendall Tau...] [2013-12-08 22:39:35] [cb725e3dac64282bf746e0e2ee8aee47] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231536&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]8 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=231536&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231536&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 Output	view raw output of R engine
Computing time	8 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Correlations for all pairs of data series (method=kendall)
	Happiness	Software	Learning	Separate	Connected	Gender	Pop
Happiness	1	0.118	0.175	0.074	0.106	0.18	0.236
Software	0.118	1	0.485	0.158	0.133	0.173	0.277
Learning	0.175	0.485	1	0.179	0.164	0.172	0.317
Separate	0.074	0.158	0.179	1	0.279	0.134	0.124
Connected	0.106	0.133	0.164	0.279	1	0.046	0.126
Gender	0.18	0.173	0.172	0.134	0.046	1	0.197
Pop	0.236	0.277	0.317	0.124	0.126	0.197	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & Happiness & Software & Learning & Separate & Connected & Gender & Pop \tabularnewline
Happiness & 1 & 0.118 & 0.175 & 0.074 & 0.106 & 0.18 & 0.236 \tabularnewline
Software & 0.118 & 1 & 0.485 & 0.158 & 0.133 & 0.173 & 0.277 \tabularnewline
Learning & 0.175 & 0.485 & 1 & 0.179 & 0.164 & 0.172 & 0.317 \tabularnewline
Separate & 0.074 & 0.158 & 0.179 & 1 & 0.279 & 0.134 & 0.124 \tabularnewline
Connected & 0.106 & 0.133 & 0.164 & 0.279 & 1 & 0.046 & 0.126 \tabularnewline
Gender & 0.18 & 0.173 & 0.172 & 0.134 & 0.046 & 1 & 0.197 \tabularnewline
Pop & 0.236 & 0.277 & 0.317 & 0.124 & 0.126 & 0.197 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231536&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]Happiness[/C][C]Software[/C][C]Learning[/C][C]Separate[/C][C]Connected[/C][C]Gender[/C][C]Pop[/C][/ROW]
[ROW][C]Happiness[/C][C]1[/C][C]0.118[/C][C]0.175[/C][C]0.074[/C][C]0.106[/C][C]0.18[/C][C]0.236[/C][/ROW]
[ROW][C]Software[/C][C]0.118[/C][C]1[/C][C]0.485[/C][C]0.158[/C][C]0.133[/C][C]0.173[/C][C]0.277[/C][/ROW]
[ROW][C]Learning[/C][C]0.175[/C][C]0.485[/C][C]1[/C][C]0.179[/C][C]0.164[/C][C]0.172[/C][C]0.317[/C][/ROW]
[ROW][C]Separate[/C][C]0.074[/C][C]0.158[/C][C]0.179[/C][C]1[/C][C]0.279[/C][C]0.134[/C][C]0.124[/C][/ROW]
[ROW][C]Connected[/C][C]0.106[/C][C]0.133[/C][C]0.164[/C][C]0.279[/C][C]1[/C][C]0.046[/C][C]0.126[/C][/ROW]
[ROW][C]Gender[/C][C]0.18[/C][C]0.173[/C][C]0.172[/C][C]0.134[/C][C]0.046[/C][C]1[/C][C]0.197[/C][/ROW]
[ROW][C]Pop[/C][C]0.236[/C][C]0.277[/C][C]0.317[/C][C]0.124[/C][C]0.126[/C][C]0.197[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231536&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231536&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)
	Happiness	Software	Learning	Separate	Connected	Gender	Pop
Happiness	1	0.118	0.175	0.074	0.106	0.18	0.236
Software	0.118	1	0.485	0.158	0.133	0.173	0.277
Learning	0.175	0.485	1	0.179	0.164	0.172	0.317
Separate	0.074	0.158	0.179	1	0.279	0.134	0.124
Connected	0.106	0.133	0.164	0.279	1	0.046	0.126
Gender	0.18	0.173	0.172	0.134	0.046	1	0.197
Pop	0.236	0.277	0.317	0.124	0.126	0.197	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
Happiness;Software	0.1614	0.1585	0.1179
p-value	(0.006)	(0.007)	(0.0078)
Happiness;Learning	0.2183	0.2333	0.1749
p-value	(2e-04)	(1e-04)	(1e-04)
Happiness;Separate	0.0854	0.1038	0.0737
p-value	(0.1482)	(0.0786)	(0.0879)
Happiness;Connected	0.1144	0.1435	0.1058
p-value	(0.0525)	(0.0148)	(0.0142)
Happiness;Gender	0.2141	0.2084	0.1797
p-value	(3e-04)	(4e-04)	(4e-04)
Happiness;Pop	0.2759	0.2735	0.2358
p-value	(0)	(0)	(0)
Software;Learning	0.6547	0.5986	0.4848
p-value	(0)	(0)	(0)
Software;Separate	0.2304	0.2125	0.1578
p-value	(1e-04)	(3e-04)	(3e-04)
Software;Connected	0.2006	0.1796	0.1331
p-value	(6e-04)	(0.0022)	(0.0023)
Software;Gender	0.1681	0.199	0.1732
p-value	(0.0042)	(7e-04)	(7e-04)
Software;Pop	0.3257	0.3187	0.2774
p-value	(0)	(0)	(0)
Learning;Separate	0.2966	0.2392	0.1791
p-value	(0)	(0)	(0)
Learning;Connected	0.2981	0.2166	0.1644
p-value	(0)	(2e-04)	(2e-04)
Learning;Gender	0.1583	0.1977	0.1721
p-value	(0.0071)	(7e-04)	(8e-04)
Learning;Pop	0.3627	0.3646	0.3174
p-value	(0)	(0)	(0)
Separate;Connected	0.5244	0.3741	0.2786
p-value	(0)	(0)	(0)
Separate;Gender	0.1395	0.1582	0.1342
p-value	(0.0179)	(0.0072)	(0.0074)
Separate;Pop	0.1578	0.146	0.1238
p-value	(0.0073)	(0.0132)	(0.0134)
Connected;Gender	0.0603	0.0548	0.0465
p-value	(0.3081)	(0.3537)	(0.3528)
Connected;Pop	0.1971	0.1493	0.1265
p-value	(8e-04)	(0.0112)	(0.0115)
Gender;Pop	0.1966	0.1966	0.1966
p-value	(8e-04)	(8e-04)	(9e-04)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Happiness;Software & 0.1614 & 0.1585 & 0.1179 \tabularnewline
p-value & (0.006) & (0.007) & (0.0078) \tabularnewline
Happiness;Learning & 0.2183 & 0.2333 & 0.1749 \tabularnewline
p-value & (2e-04) & (1e-04) & (1e-04) \tabularnewline
Happiness;Separate & 0.0854 & 0.1038 & 0.0737 \tabularnewline
p-value & (0.1482) & (0.0786) & (0.0879) \tabularnewline
Happiness;Connected & 0.1144 & 0.1435 & 0.1058 \tabularnewline
p-value & (0.0525) & (0.0148) & (0.0142) \tabularnewline
Happiness;Gender & 0.2141 & 0.2084 & 0.1797 \tabularnewline
p-value & (3e-04) & (4e-04) & (4e-04) \tabularnewline
Happiness;Pop & 0.2759 & 0.2735 & 0.2358 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Software;Learning & 0.6547 & 0.5986 & 0.4848 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Software;Separate & 0.2304 & 0.2125 & 0.1578 \tabularnewline
p-value & (1e-04) & (3e-04) & (3e-04) \tabularnewline
Software;Connected & 0.2006 & 0.1796 & 0.1331 \tabularnewline
p-value & (6e-04) & (0.0022) & (0.0023) \tabularnewline
Software;Gender & 0.1681 & 0.199 & 0.1732 \tabularnewline
p-value & (0.0042) & (7e-04) & (7e-04) \tabularnewline
Software;Pop & 0.3257 & 0.3187 & 0.2774 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Learning;Separate & 0.2966 & 0.2392 & 0.1791 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Learning;Connected & 0.2981 & 0.2166 & 0.1644 \tabularnewline
p-value & (0) & (2e-04) & (2e-04) \tabularnewline
Learning;Gender & 0.1583 & 0.1977 & 0.1721 \tabularnewline
p-value & (0.0071) & (7e-04) & (8e-04) \tabularnewline
Learning;Pop & 0.3627 & 0.3646 & 0.3174 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Separate;Connected & 0.5244 & 0.3741 & 0.2786 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Separate;Gender & 0.1395 & 0.1582 & 0.1342 \tabularnewline
p-value & (0.0179) & (0.0072) & (0.0074) \tabularnewline
Separate;Pop & 0.1578 & 0.146 & 0.1238 \tabularnewline
p-value & (0.0073) & (0.0132) & (0.0134) \tabularnewline
Connected;Gender & 0.0603 & 0.0548 & 0.0465 \tabularnewline
p-value & (0.3081) & (0.3537) & (0.3528) \tabularnewline
Connected;Pop & 0.1971 & 0.1493 & 0.1265 \tabularnewline
p-value & (8e-04) & (0.0112) & (0.0115) \tabularnewline
Gender;Pop & 0.1966 & 0.1966 & 0.1966 \tabularnewline
p-value & (8e-04) & (8e-04) & (9e-04) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231536&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]Happiness;Software[/C][C]0.1614[/C][C]0.1585[/C][C]0.1179[/C][/ROW]
[ROW][C]p-value[/C][C](0.006)[/C][C](0.007)[/C][C](0.0078)[/C][/ROW]
[ROW][C]Happiness;Learning[/C][C]0.2183[/C][C]0.2333[/C][C]0.1749[/C][/ROW]
[ROW][C]p-value[/C][C](2e-04)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]Happiness;Separate[/C][C]0.0854[/C][C]0.1038[/C][C]0.0737[/C][/ROW]
[ROW][C]p-value[/C][C](0.1482)[/C][C](0.0786)[/C][C](0.0879)[/C][/ROW]
[ROW][C]Happiness;Connected[/C][C]0.1144[/C][C]0.1435[/C][C]0.1058[/C][/ROW]
[ROW][C]p-value[/C][C](0.0525)[/C][C](0.0148)[/C][C](0.0142)[/C][/ROW]
[ROW][C]Happiness;Gender[/C][C]0.2141[/C][C]0.2084[/C][C]0.1797[/C][/ROW]
[ROW][C]p-value[/C][C](3e-04)[/C][C](4e-04)[/C][C](4e-04)[/C][/ROW]
[ROW][C]Happiness;Pop[/C][C]0.2759[/C][C]0.2735[/C][C]0.2358[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Software;Learning[/C][C]0.6547[/C][C]0.5986[/C][C]0.4848[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Software;Separate[/C][C]0.2304[/C][C]0.2125[/C][C]0.1578[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](3e-04)[/C][C](3e-04)[/C][/ROW]
[ROW][C]Software;Connected[/C][C]0.2006[/C][C]0.1796[/C][C]0.1331[/C][/ROW]
[ROW][C]p-value[/C][C](6e-04)[/C][C](0.0022)[/C][C](0.0023)[/C][/ROW]
[ROW][C]Software;Gender[/C][C]0.1681[/C][C]0.199[/C][C]0.1732[/C][/ROW]
[ROW][C]p-value[/C][C](0.0042)[/C][C](7e-04)[/C][C](7e-04)[/C][/ROW]
[ROW][C]Software;Pop[/C][C]0.3257[/C][C]0.3187[/C][C]0.2774[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Learning;Separate[/C][C]0.2966[/C][C]0.2392[/C][C]0.1791[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Learning;Connected[/C][C]0.2981[/C][C]0.2166[/C][C]0.1644[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](2e-04)[/C][C](2e-04)[/C][/ROW]
[ROW][C]Learning;Gender[/C][C]0.1583[/C][C]0.1977[/C][C]0.1721[/C][/ROW]
[ROW][C]p-value[/C][C](0.0071)[/C][C](7e-04)[/C][C](8e-04)[/C][/ROW]
[ROW][C]Learning;Pop[/C][C]0.3627[/C][C]0.3646[/C][C]0.3174[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Separate;Connected[/C][C]0.5244[/C][C]0.3741[/C][C]0.2786[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Separate;Gender[/C][C]0.1395[/C][C]0.1582[/C][C]0.1342[/C][/ROW]
[ROW][C]p-value[/C][C](0.0179)[/C][C](0.0072)[/C][C](0.0074)[/C][/ROW]
[ROW][C]Separate;Pop[/C][C]0.1578[/C][C]0.146[/C][C]0.1238[/C][/ROW]
[ROW][C]p-value[/C][C](0.0073)[/C][C](0.0132)[/C][C](0.0134)[/C][/ROW]
[ROW][C]Connected;Gender[/C][C]0.0603[/C][C]0.0548[/C][C]0.0465[/C][/ROW]
[ROW][C]p-value[/C][C](0.3081)[/C][C](0.3537)[/C][C](0.3528)[/C][/ROW]
[ROW][C]Connected;Pop[/C][C]0.1971[/C][C]0.1493[/C][C]0.1265[/C][/ROW]
[ROW][C]p-value[/C][C](8e-04)[/C][C](0.0112)[/C][C](0.0115)[/C][/ROW]
[ROW][C]Gender;Pop[/C][C]0.1966[/C][C]0.1966[/C][C]0.1966[/C][/ROW]
[ROW][C]p-value[/C][C](8e-04)[/C][C](8e-04)[/C][C](9e-04)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231536&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231536&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
pair	Pearson r	Spearman rho	Kendall tau
Happiness;Software	0.1614	0.1585	0.1179
p-value	(0.006)	(0.007)	(0.0078)
Happiness;Learning	0.2183	0.2333	0.1749
p-value	(2e-04)	(1e-04)	(1e-04)
Happiness;Separate	0.0854	0.1038	0.0737
p-value	(0.1482)	(0.0786)	(0.0879)
Happiness;Connected	0.1144	0.1435	0.1058
p-value	(0.0525)	(0.0148)	(0.0142)
Happiness;Gender	0.2141	0.2084	0.1797
p-value	(3e-04)	(4e-04)	(4e-04)
Happiness;Pop	0.2759	0.2735	0.2358
p-value	(0)	(0)	(0)
Software;Learning	0.6547	0.5986	0.4848
p-value	(0)	(0)	(0)
Software;Separate	0.2304	0.2125	0.1578
p-value	(1e-04)	(3e-04)	(3e-04)
Software;Connected	0.2006	0.1796	0.1331
p-value	(6e-04)	(0.0022)	(0.0023)
Software;Gender	0.1681	0.199	0.1732
p-value	(0.0042)	(7e-04)	(7e-04)
Software;Pop	0.3257	0.3187	0.2774
p-value	(0)	(0)	(0)
Learning;Separate	0.2966	0.2392	0.1791
p-value	(0)	(0)	(0)
Learning;Connected	0.2981	0.2166	0.1644
p-value	(0)	(2e-04)	(2e-04)
Learning;Gender	0.1583	0.1977	0.1721
p-value	(0.0071)	(7e-04)	(8e-04)
Learning;Pop	0.3627	0.3646	0.3174
p-value	(0)	(0)	(0)
Separate;Connected	0.5244	0.3741	0.2786
p-value	(0)	(0)	(0)
Separate;Gender	0.1395	0.1582	0.1342
p-value	(0.0179)	(0.0072)	(0.0074)
Separate;Pop	0.1578	0.146	0.1238
p-value	(0.0073)	(0.0132)	(0.0134)
Connected;Gender	0.0603	0.0548	0.0465
p-value	(0.3081)	(0.3537)	(0.3528)
Connected;Pop	0.1971	0.1493	0.1265
p-value	(8e-04)	(0.0112)	(0.0115)
Gender;Pop	0.1966	0.1966	0.1966
p-value	(8e-04)	(8e-04)	(9e-04)

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I error	Pearson r	Spearman rho	Kendall tau
0.01	0.81	0.76	0.76
0.02	0.86	0.9	0.9
0.03	0.86	0.9	0.9
0.04	0.86	0.9	0.9
0.05	0.86	0.9	0.9
0.06	0.9	0.9	0.9
0.07	0.9	0.9	0.9
0.08	0.9	0.95	0.9
0.09	0.9	0.95	0.95
0.1	0.9	0.95	0.95

\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 & 0.81 & 0.76 & 0.76 \tabularnewline
0.02 & 0.86 & 0.9 & 0.9 \tabularnewline
0.03 & 0.86 & 0.9 & 0.9 \tabularnewline
0.04 & 0.86 & 0.9 & 0.9 \tabularnewline
0.05 & 0.86 & 0.9 & 0.9 \tabularnewline
0.06 & 0.9 & 0.9 & 0.9 \tabularnewline
0.07 & 0.9 & 0.9 & 0.9 \tabularnewline
0.08 & 0.9 & 0.95 & 0.9 \tabularnewline
0.09 & 0.9 & 0.95 & 0.95 \tabularnewline
0.1 & 0.9 & 0.95 & 0.95 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231536&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]0.81[/C][C]0.76[/C][C]0.76[/C][/ROW]
[ROW][C]0.02[/C][C]0.86[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.03[/C][C]0.86[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.04[/C][C]0.86[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.05[/C][C]0.86[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.06[/C][C]0.9[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.07[/C][C]0.9[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.08[/C][C]0.9[/C][C]0.95[/C][C]0.9[/C][/ROW]
[ROW][C]0.09[/C][C]0.9[/C][C]0.95[/C][C]0.95[/C][/ROW]
[ROW][C]0.1[/C][C]0.9[/C][C]0.95[/C][C]0.95[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231536&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231536&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 error	Pearson r	Spearman rho	Kendall tau
0.01	0.81	0.76	0.76
0.02	0.86	0.9	0.9
0.03	0.86	0.9	0.9
0.04	0.86	0.9	0.9
0.05	0.86	0.9	0.9
0.06	0.9	0.9	0.9
0.07	0.9	0.9	0.9
0.08	0.9	0.95	0.9
0.09	0.9	0.95	0.95
0.1	0.9	0.95	0.95

Figure 1

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code