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*The author of this computation has been verified*

R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Wed, 17 Dec 2014 14:00:49 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/17/t1418824903pqi0rrt22tel41m.htm/, Retrieved Thu, 16 May 2024 17:14:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270280, Retrieved Thu, 16 May 2024 17:14:40 +0000

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-       [Kendall tau Correlation Matrix] [] [2014-12-17 14:00:49] [ef562ec391a3ad5a7cbe41e167f467b9] [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	1 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270280&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270280&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270280&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	1 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Correlations for all pairs of data series (method=pearson)
	AMS.I1	AMS.I2	AMS.I3	AMS.E1	AMS.E2	AMS.E3
AMS.I1	1	0.579	0.593	0.446	0.227	0.083
AMS.I2	0.579	1	0.521	0.228	0.503	0.068
AMS.I3	0.593	0.521	1	0.248	0.189	-0.032
AMS.E1	0.446	0.228	0.248	1	0.222	0.426
AMS.E2	0.227	0.503	0.189	0.222	1	0.362
AMS.E3	0.083	0.068	-0.032	0.426	0.362	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & AMS.I1 & AMS.I2 & AMS.I3 & AMS.E1 & AMS.E2 & AMS.E3 \tabularnewline
AMS.I1 & 1 & 0.579 & 0.593 & 0.446 & 0.227 & 0.083 \tabularnewline
AMS.I2 & 0.579 & 1 & 0.521 & 0.228 & 0.503 & 0.068 \tabularnewline
AMS.I3 & 0.593 & 0.521 & 1 & 0.248 & 0.189 & -0.032 \tabularnewline
AMS.E1 & 0.446 & 0.228 & 0.248 & 1 & 0.222 & 0.426 \tabularnewline
AMS.E2 & 0.227 & 0.503 & 0.189 & 0.222 & 1 & 0.362 \tabularnewline
AMS.E3 & 0.083 & 0.068 & -0.032 & 0.426 & 0.362 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270280&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]AMS.I1[/C][C]AMS.I2[/C][C]AMS.I3[/C][C]AMS.E1[/C][C]AMS.E2[/C][C]AMS.E3[/C][/ROW]
[ROW][C]AMS.I1[/C][C]1[/C][C]0.579[/C][C]0.593[/C][C]0.446[/C][C]0.227[/C][C]0.083[/C][/ROW]
[ROW][C]AMS.I2[/C][C]0.579[/C][C]1[/C][C]0.521[/C][C]0.228[/C][C]0.503[/C][C]0.068[/C][/ROW]
[ROW][C]AMS.I3[/C][C]0.593[/C][C]0.521[/C][C]1[/C][C]0.248[/C][C]0.189[/C][C]-0.032[/C][/ROW]
[ROW][C]AMS.E1[/C][C]0.446[/C][C]0.228[/C][C]0.248[/C][C]1[/C][C]0.222[/C][C]0.426[/C][/ROW]
[ROW][C]AMS.E2[/C][C]0.227[/C][C]0.503[/C][C]0.189[/C][C]0.222[/C][C]1[/C][C]0.362[/C][/ROW]
[ROW][C]AMS.E3[/C][C]0.083[/C][C]0.068[/C][C]-0.032[/C][C]0.426[/C][C]0.362[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270280&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270280&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)
	AMS.I1	AMS.I2	AMS.I3	AMS.E1	AMS.E2	AMS.E3
AMS.I1	1	0.579	0.593	0.446	0.227	0.083
AMS.I2	0.579	1	0.521	0.228	0.503	0.068
AMS.I3	0.593	0.521	1	0.248	0.189	-0.032
AMS.E1	0.446	0.228	0.248	1	0.222	0.426
AMS.E2	0.227	0.503	0.189	0.222	1	0.362
AMS.E3	0.083	0.068	-0.032	0.426	0.362	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
AMS.I1;AMS.I2	0.5788	0.5711	0.4412
p-value	(0)	(0)	(0)
AMS.I1;AMS.I3	0.5931	0.5773	0.4459
p-value	(0)	(0)	(0)
AMS.I1;AMS.E1	0.446	0.3773	0.2832
p-value	(0)	(0)	(0)
AMS.I1;AMS.E2	0.2268	0.1786	0.1336
p-value	(1e-04)	(0.0028)	(0.002)
AMS.I1;AMS.E3	0.0825	0.0619	0.0465
p-value	(0.17)	(0.3038)	(0.2873)
AMS.I2;AMS.I3	0.5207	0.4746	0.3658
p-value	(0)	(0)	(0)
AMS.I2;AMS.E1	0.2282	0.2079	0.1532
p-value	(1e-04)	(5e-04)	(4e-04)
AMS.I2;AMS.E2	0.5032	0.4918	0.371
p-value	(0)	(0)	(0)
AMS.I2;AMS.E3	0.0681	0.1143	0.0842
p-value	(0.2576)	(0.0569)	(0.0527)
AMS.I3;AMS.E1	0.2481	0.2512	0.1847
p-value	(0)	(0)	(0)
AMS.I3;AMS.E2	0.1887	0.1704	0.1241
p-value	(0.0016)	(0.0044)	(0.0038)
AMS.I3;AMS.E3	-0.0321	-0.0135	-0.0093
p-value	(0.5939)	(0.823)	(0.8299)
AMS.E1;AMS.E2	0.2219	0.1765	0.1295
p-value	(2e-04)	(0.0032)	(0.0029)
AMS.E1;AMS.E3	0.4256	0.3715	0.2809
p-value	(0)	(0)	(0)
AMS.E2;AMS.E3	0.3618	0.3388	0.2512
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
AMS.I1;AMS.I2 & 0.5788 & 0.5711 & 0.4412 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AMS.I1;AMS.I3 & 0.5931 & 0.5773 & 0.4459 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AMS.I1;AMS.E1 & 0.446 & 0.3773 & 0.2832 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AMS.I1;AMS.E2 & 0.2268 & 0.1786 & 0.1336 \tabularnewline
p-value & (1e-04) & (0.0028) & (0.002) \tabularnewline
AMS.I1;AMS.E3 & 0.0825 & 0.0619 & 0.0465 \tabularnewline
p-value & (0.17) & (0.3038) & (0.2873) \tabularnewline
AMS.I2;AMS.I3 & 0.5207 & 0.4746 & 0.3658 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AMS.I2;AMS.E1 & 0.2282 & 0.2079 & 0.1532 \tabularnewline
p-value & (1e-04) & (5e-04) & (4e-04) \tabularnewline
AMS.I2;AMS.E2 & 0.5032 & 0.4918 & 0.371 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AMS.I2;AMS.E3 & 0.0681 & 0.1143 & 0.0842 \tabularnewline
p-value & (0.2576) & (0.0569) & (0.0527) \tabularnewline
AMS.I3;AMS.E1 & 0.2481 & 0.2512 & 0.1847 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AMS.I3;AMS.E2 & 0.1887 & 0.1704 & 0.1241 \tabularnewline
p-value & (0.0016) & (0.0044) & (0.0038) \tabularnewline
AMS.I3;AMS.E3 & -0.0321 & -0.0135 & -0.0093 \tabularnewline
p-value & (0.5939) & (0.823) & (0.8299) \tabularnewline
AMS.E1;AMS.E2 & 0.2219 & 0.1765 & 0.1295 \tabularnewline
p-value & (2e-04) & (0.0032) & (0.0029) \tabularnewline
AMS.E1;AMS.E3 & 0.4256 & 0.3715 & 0.2809 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AMS.E2;AMS.E3 & 0.3618 & 0.3388 & 0.2512 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270280&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]AMS.I1;AMS.I2[/C][C]0.5788[/C][C]0.5711[/C][C]0.4412[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AMS.I1;AMS.I3[/C][C]0.5931[/C][C]0.5773[/C][C]0.4459[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AMS.I1;AMS.E1[/C][C]0.446[/C][C]0.3773[/C][C]0.2832[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AMS.I1;AMS.E2[/C][C]0.2268[/C][C]0.1786[/C][C]0.1336[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0.0028)[/C][C](0.002)[/C][/ROW]
[ROW][C]AMS.I1;AMS.E3[/C][C]0.0825[/C][C]0.0619[/C][C]0.0465[/C][/ROW]
[ROW][C]p-value[/C][C](0.17)[/C][C](0.3038)[/C][C](0.2873)[/C][/ROW]
[ROW][C]AMS.I2;AMS.I3[/C][C]0.5207[/C][C]0.4746[/C][C]0.3658[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AMS.I2;AMS.E1[/C][C]0.2282[/C][C]0.2079[/C][C]0.1532[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](5e-04)[/C][C](4e-04)[/C][/ROW]
[ROW][C]AMS.I2;AMS.E2[/C][C]0.5032[/C][C]0.4918[/C][C]0.371[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AMS.I2;AMS.E3[/C][C]0.0681[/C][C]0.1143[/C][C]0.0842[/C][/ROW]
[ROW][C]p-value[/C][C](0.2576)[/C][C](0.0569)[/C][C](0.0527)[/C][/ROW]
[ROW][C]AMS.I3;AMS.E1[/C][C]0.2481[/C][C]0.2512[/C][C]0.1847[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AMS.I3;AMS.E2[/C][C]0.1887[/C][C]0.1704[/C][C]0.1241[/C][/ROW]
[ROW][C]p-value[/C][C](0.0016)[/C][C](0.0044)[/C][C](0.0038)[/C][/ROW]
[ROW][C]AMS.I3;AMS.E3[/C][C]-0.0321[/C][C]-0.0135[/C][C]-0.0093[/C][/ROW]
[ROW][C]p-value[/C][C](0.5939)[/C][C](0.823)[/C][C](0.8299)[/C][/ROW]
[ROW][C]AMS.E1;AMS.E2[/C][C]0.2219[/C][C]0.1765[/C][C]0.1295[/C][/ROW]
[ROW][C]p-value[/C][C](2e-04)[/C][C](0.0032)[/C][C](0.0029)[/C][/ROW]
[ROW][C]AMS.E1;AMS.E3[/C][C]0.4256[/C][C]0.3715[/C][C]0.2809[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AMS.E2;AMS.E3[/C][C]0.3618[/C][C]0.3388[/C][C]0.2512[/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=270280&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270280&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
AMS.I1;AMS.I2	0.5788	0.5711	0.4412
p-value	(0)	(0)	(0)
AMS.I1;AMS.I3	0.5931	0.5773	0.4459
p-value	(0)	(0)	(0)
AMS.I1;AMS.E1	0.446	0.3773	0.2832
p-value	(0)	(0)	(0)
AMS.I1;AMS.E2	0.2268	0.1786	0.1336
p-value	(1e-04)	(0.0028)	(0.002)
AMS.I1;AMS.E3	0.0825	0.0619	0.0465
p-value	(0.17)	(0.3038)	(0.2873)
AMS.I2;AMS.I3	0.5207	0.4746	0.3658
p-value	(0)	(0)	(0)
AMS.I2;AMS.E1	0.2282	0.2079	0.1532
p-value	(1e-04)	(5e-04)	(4e-04)
AMS.I2;AMS.E2	0.5032	0.4918	0.371
p-value	(0)	(0)	(0)
AMS.I2;AMS.E3	0.0681	0.1143	0.0842
p-value	(0.2576)	(0.0569)	(0.0527)
AMS.I3;AMS.E1	0.2481	0.2512	0.1847
p-value	(0)	(0)	(0)
AMS.I3;AMS.E2	0.1887	0.1704	0.1241
p-value	(0.0016)	(0.0044)	(0.0038)
AMS.I3;AMS.E3	-0.0321	-0.0135	-0.0093
p-value	(0.5939)	(0.823)	(0.8299)
AMS.E1;AMS.E2	0.2219	0.1765	0.1295
p-value	(2e-04)	(0.0032)	(0.0029)
AMS.E1;AMS.E3	0.4256	0.3715	0.2809
p-value	(0)	(0)	(0)
AMS.E2;AMS.E3	0.3618	0.3388	0.2512
p-value	(0)	(0)	(0)

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I error	Pearson r	Spearman rho	Kendall tau
0.01	0.8	0.8	0.8
0.02	0.8	0.8	0.8
0.03	0.8	0.8	0.8
0.04	0.8	0.8	0.8
0.05	0.8	0.8	0.8
0.06	0.8	0.87	0.87
0.07	0.8	0.87	0.87
0.08	0.8	0.87	0.87
0.09	0.8	0.87	0.87
0.1	0.8	0.87	0.87

\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 & 0.8 & 0.8 & 0.8 \tabularnewline
0.02 & 0.8 & 0.8 & 0.8 \tabularnewline
0.03 & 0.8 & 0.8 & 0.8 \tabularnewline
0.04 & 0.8 & 0.8 & 0.8 \tabularnewline
0.05 & 0.8 & 0.8 & 0.8 \tabularnewline
0.06 & 0.8 & 0.87 & 0.87 \tabularnewline
0.07 & 0.8 & 0.87 & 0.87 \tabularnewline
0.08 & 0.8 & 0.87 & 0.87 \tabularnewline
0.09 & 0.8 & 0.87 & 0.87 \tabularnewline
0.1 & 0.8 & 0.87 & 0.87 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270280&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]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[ROW][C]0.02[/C][C]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[ROW][C]0.03[/C][C]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[ROW][C]0.04[/C][C]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[ROW][C]0.05[/C][C]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[ROW][C]0.06[/C][C]0.8[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.07[/C][C]0.8[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.08[/C][C]0.8[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.09[/C][C]0.8[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.1[/C][C]0.8[/C][C]0.87[/C][C]0.87[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270280&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270280&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	0.8	0.8	0.8
0.02	0.8	0.8	0.8
0.03	0.8	0.8	0.8
0.04	0.8	0.8	0.8
0.05	0.8	0.8	0.8
0.06	0.8	0.87	0.87
0.07	0.8	0.87	0.87
0.08	0.8	0.87	0.87
0.09	0.8	0.87	0.87
0.1	0.8	0.87	0.87

Figure 1

PNG link

Postscript link

PDF link

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')
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