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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationTue, 06 Dec 2016 18:28:18 +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/2016/Dec/06/t1481045315vqtqg7uzhb8wlu2.htm/, Retrieved Sat, 04 May 2024 18:22:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297883, Retrieved Sat, 04 May 2024 18:22:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61

Tree of Dependent Computations

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4	2	4	3	5	4	13
5	3	3	4	5	4	16
4	4	5	4	5	4	17
3	4	3	3	4	4	15
4	4	5	4	5	4	16
3	4	4	4	5	5	16
3	4	4	3	3	4	18
3	4	5	4	4	4	16
4	5	4	4	5	5	17
4	5	5	4	5	5	17
4	4	2	4	5	4	17
4	4	5	3	5	4	15
4	4	4	3	4	5	16
3	3	5	4	4	5	14
4	4	5	4	2	5	16
3	4	5	4	4	5	17
3	4	5	4	4	5	16
5	5	4	3	4	4	15
4	4	4	4	5	4	17
3	4	5	3	4	5	16
4	4	4	4	5	5	15
4	4	5	4	4	5	16
4	4	5	4	4	4	15
4	4	5	4	4	5	17
3	4	4	4	4	4	14
3	4	4	3	5	5	16
4	4	4	4	4	4	15
2	4	5	4	5	5	16
5	4	4	4	4	4	16
4	3	5	4	4	4	13
4	5	5	4	5	5	15
5	4	5	4	4	5	17
4	3	5	4	4	5	15
2	3	5	4	5	4	13
4	5	2	4	4	4	17
3	4	5	4	4	4	15
4	3	5	3	4	5	14
4	3	3	4	4	4	14
4	4	5	4	4	4	18
5	4	4	4	4	4	15
4	5	5	4	5	5	17
3	3	4	4	4	4	13
5	5	5	3	5	5	16
5	4	5	3	4	4	15
4	4	4	3	4	5	15
4	4	4	4	4	4	16
3	5	5	3	3	4	15
4	4	4	4	5	4	13
4	5	5	4	4	4	17
5	5	2	4	5	4	18
5	5	5	4	4	4	17
4	3	5	4	5	5	11
4	3	4	3	4	5	14
4	4	5	4	4	4	13
3	4	4	3	3	4	15
3	4	4	4	4	3	17
4	4	4	3	5	4	16
4	4	4	4	5	4	15
5	5	3	4	5	5	17
2	4	4	4	5	5	16
4	4	4	4	5	5	16
3	4	4	4	2	4	16
4	4	5	4	5	5	15
4	2	4	4	4	4	12
4	4	4	3	5	3	17
4	4	4	3	5	4	14
5	4	5	3	3	5	14
3	4	4	3	5	5	16
3	4	4	3	4	5	15
4	5	5	5	5	4	15
4	4	3	4	4	4	13
4	4	4	4	4	4	13
4	4	4	5	5	4	17
3	4	3	4	4	4	15
4	4	4	4	5	4	16
3	4	5	3	5	5	14
3	3	5	4	4	5	15
4	3	5	4	4	4	17
4	4	5	4	4	5	16
3	3	3	4	4	4	10
4	4	4	4	5	4	16
4	4	3	4	5	5	17
4	4	4	4	5	5	17
5	4	4	4	4	4	20
5	4	3	5	4	5	17
4	4	5	4	5	5	18
3	4	5	4	4	5	15
3	4	4	4	4	4	17
4	2	3	3	4	4	14
4	4	5	4	4	3	15
4	4	5	4	4	5	17
4	4	4	4	5	4	16
4	5	4	4	5	3	17
3	4	4	3	5	5	15
4	4	5	4	4	5	16
5	4	3	4	4	5	18
5	4	5	5	4	5	18
4	5	4	4	5	5	16
5	3	4	4	5	5	17
4	4	5	4	4	5	15
5	4	4	4	4	5	13
3	4	4	3	4	4	15
5	4	4	5	5	5	17
4	4	5	3	4	5	16
4	4	3	3	4	3	16
4	4	5	4	4	4	15
4	4	5	4	4	4	16
3	4	5	4	5	3	16
4	4	4	4	4	4	13
4	4	4	3	4	5	15
3	3	4	3	5	5	12
4	4	4	3	4	4	19
3	4	5	4	4	4	16
4	4	5	4	3	4	16
5	4	5	1	5	5	17
5	4	5	4	5	5	16
4	4	4	4	4	3	14
4	4	5	3	4	4	15
3	4	4	3	4	5	14
4	4	4	4	4	4	16
4	4	4	4	5	4	15
4	5	3	4	4	4	17
3	4	4	4	4	4	15
4	4	4	3	4	4	16
4	4	4	4	4	5	16
3	4	3	3	4	4	15
4	4	4	3	4	3	15
3	2	4	2	4	4	11
4	4	4	3	5	4	16
5	4	4	3	5	4	18
2	4	4	3	3	5	13
3	3	4	4	4	4	11
5	5	4	4	5	4	18
4	5	5	4	4	4	15
5	5	5	5	5	4	19
4	5	5	4	5	5	17
4	4	4	3	4	5	13
3	4	5	4	5	4	14
4	4	5	4	4	4	16
4	4	2	4	4	4	13
4	4	3	4	5	5	17
4	4	4	4	5	5	14
5	4	5	3	5	4	19
4	3	5	4	4	4	14
4	4	5	4	4	4	16
3	3	2	3	4	4	12
4	5	5	4	4	3	16
4	4	4	3	4	4	16
4	4	4	4	4	5	15
3	4	5	3	5	5	12
4	4	5	4	4	5	15
5	4	5	4	5	4	17
4	4	5	4	3	4	13
2	3	5	4	4	4	15
4	4	4	4	4	5	18
4	3	4	3	5	5	15
4	4	4	4	4	3	18
4	5	5	5	4	4	15
5	4	3	4	4	4	15
5	4	4	3	4	4	16
3	3	1	4	5	5	13
4	4	4	4	4	5	16
4	4	4	4	5	4	13
2	3	4	5	5	4	16

Tables (Output of Computation)





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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297883&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] [ROW]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297883&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297883&T=0

As an alternative you can also use a QR Code:  
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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Correlations for all pairs of data series (method=kendall)
SK1SK2SK3SK4SK5SK6TVDC
SK110.253-0.0070.1290.1130.0020.298
SK20.25310.10.1280.093-0.0280.384
SK3-0.0070.110.109-0.0640.1330.037
SK40.1290.1280.10910.089-0.0370.189
SK50.1130.093-0.0640.08910.1390.166
SK60.002-0.0280.133-0.0370.13910.03
TVDC 0.2980.3840.0370.1890.1660.031

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & SK1 & SK2 & SK3 & SK4 & SK5 & SK6 & TVDC
 \tabularnewline
SK1 & 1 & 0.253 & -0.007 & 0.129 & 0.113 & 0.002 & 0.298 \tabularnewline
SK2 & 0.253 & 1 & 0.1 & 0.128 & 0.093 & -0.028 & 0.384 \tabularnewline
SK3 & -0.007 & 0.1 & 1 & 0.109 & -0.064 & 0.133 & 0.037 \tabularnewline
SK4 & 0.129 & 0.128 & 0.109 & 1 & 0.089 & -0.037 & 0.189 \tabularnewline
SK5 & 0.113 & 0.093 & -0.064 & 0.089 & 1 & 0.139 & 0.166 \tabularnewline
SK6 & 0.002 & -0.028 & 0.133 & -0.037 & 0.139 & 1 & 0.03 \tabularnewline
TVDC
 & 0.298 & 0.384 & 0.037 & 0.189 & 0.166 & 0.03 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297883&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]SK1[/C][C]SK2[/C][C]SK3[/C][C]SK4[/C][C]SK5[/C][C]SK6[/C][C]TVDC
[/C][/ROW]
[ROW][C]SK1[/C][C]1[/C][C]0.253[/C][C]-0.007[/C][C]0.129[/C][C]0.113[/C][C]0.002[/C][C]0.298[/C][/ROW]
[ROW][C]SK2[/C][C]0.253[/C][C]1[/C][C]0.1[/C][C]0.128[/C][C]0.093[/C][C]-0.028[/C][C]0.384[/C][/ROW]
[ROW][C]SK3[/C][C]-0.007[/C][C]0.1[/C][C]1[/C][C]0.109[/C][C]-0.064[/C][C]0.133[/C][C]0.037[/C][/ROW]
[ROW][C]SK4[/C][C]0.129[/C][C]0.128[/C][C]0.109[/C][C]1[/C][C]0.089[/C][C]-0.037[/C][C]0.189[/C][/ROW]
[ROW][C]SK5[/C][C]0.113[/C][C]0.093[/C][C]-0.064[/C][C]0.089[/C][C]1[/C][C]0.139[/C][C]0.166[/C][/ROW]
[ROW][C]SK6[/C][C]0.002[/C][C]-0.028[/C][C]0.133[/C][C]-0.037[/C][C]0.139[/C][C]1[/C][C]0.03[/C][/ROW]
[ROW][C]TVDC
[/C][C]0.298[/C][C]0.384[/C][C]0.037[/C][C]0.189[/C][C]0.166[/C][C]0.03[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297883&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297883&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Correlations for all pairs of data series (method=kendall)
SK1SK2SK3SK4SK5SK6TVDC
SK110.253-0.0070.1290.1130.0020.298
SK20.25310.10.1280.093-0.0280.384
SK3-0.0070.110.109-0.0640.1330.037
SK40.1290.1280.10910.089-0.0370.189
SK50.1130.093-0.0640.08910.1390.166
SK60.002-0.0280.133-0.0370.13910.03
TVDC 0.2980.3840.0370.1890.1660.031






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
SK1;SK20.2640.27730.2531
p-value(6e-04)(3e-04)(4e-04)
SK1;SK3-0.0162-0.008-0.007
p-value(0.8366)(0.9194)(0.921)
SK1;SK40.08880.1370.1295
p-value(0.2584)(0.0802)(0.0733)
SK1;SK50.11420.12090.1131
p-value(0.1455)(0.123)(0.118)
SK1;SK6-1e-040.00210.0021
p-value(0.9991)(0.9786)(0.977)
SK1;TVDC 0.35290.35220.2982
p-value(0)(0)(0)
SK2;SK30.11320.10870.1004
p-value(0.1489)(0.1661)(0.1614)
SK2;SK40.17340.13590.1277
p-value(0.0264)(0.0827)(0.0819)
SK2;SK50.07490.10020.0932
p-value(0.3404)(0.2018)(0.2051)
SK2;SK6-0.0209-0.0306-0.028
p-value(0.7907)(0.6972)(0.7025)
SK2;TVDC 0.48930.4430.3837
p-value(0)(0)(0)
SK3;SK40.05270.11820.1094
p-value(0.5029)(0.1317)(0.1329)
SK3;SK5-0.0797-0.0689-0.0635
p-value(0.3104)(0.3809)(0.3834)
SK3;SK60.12260.14440.1335
p-value(0.1177)(0.065)(0.0668)
SK3;TVDC 0.0860.04310.0366
p-value(0.2735)(0.5835)(0.5792)
SK4;SK50.07410.09290.0891
p-value(0.346)(0.2366)(0.2324)
SK4;SK6-0.0427-0.039-0.037
p-value(0.5868)(0.6201)(0.62)
SK4;TVDC 0.17960.21690.1891
p-value(0.0214)(0.0053)(0.0051)
SK5;SK60.11680.14650.139
p-value(0.1364)(0.0612)(0.0625)
SK5;TVDC 0.13530.19260.1663
p-value(0.0841)(0.0135)(0.0139)
SK6;TVDC 0.00560.03470.0303
p-value(0.9429)(0.659)(0.6538)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
SK1;SK2 & 0.264 & 0.2773 & 0.2531 \tabularnewline
p-value & (6e-04) & (3e-04) & (4e-04) \tabularnewline
SK1;SK3 & -0.0162 & -0.008 & -0.007 \tabularnewline
p-value & (0.8366) & (0.9194) & (0.921) \tabularnewline
SK1;SK4 & 0.0888 & 0.137 & 0.1295 \tabularnewline
p-value & (0.2584) & (0.0802) & (0.0733) \tabularnewline
SK1;SK5 & 0.1142 & 0.1209 & 0.1131 \tabularnewline
p-value & (0.1455) & (0.123) & (0.118) \tabularnewline
SK1;SK6 & -1e-04 & 0.0021 & 0.0021 \tabularnewline
p-value & (0.9991) & (0.9786) & (0.977) \tabularnewline
SK1;TVDC
 & 0.3529 & 0.3522 & 0.2982 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SK2;SK3 & 0.1132 & 0.1087 & 0.1004 \tabularnewline
p-value & (0.1489) & (0.1661) & (0.1614) \tabularnewline
SK2;SK4 & 0.1734 & 0.1359 & 0.1277 \tabularnewline
p-value & (0.0264) & (0.0827) & (0.0819) \tabularnewline
SK2;SK5 & 0.0749 & 0.1002 & 0.0932 \tabularnewline
p-value & (0.3404) & (0.2018) & (0.2051) \tabularnewline
SK2;SK6 & -0.0209 & -0.0306 & -0.028 \tabularnewline
p-value & (0.7907) & (0.6972) & (0.7025) \tabularnewline
SK2;TVDC
 & 0.4893 & 0.443 & 0.3837 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SK3;SK4 & 0.0527 & 0.1182 & 0.1094 \tabularnewline
p-value & (0.5029) & (0.1317) & (0.1329) \tabularnewline
SK3;SK5 & -0.0797 & -0.0689 & -0.0635 \tabularnewline
p-value & (0.3104) & (0.3809) & (0.3834) \tabularnewline
SK3;SK6 & 0.1226 & 0.1444 & 0.1335 \tabularnewline
p-value & (0.1177) & (0.065) & (0.0668) \tabularnewline
SK3;TVDC
 & 0.086 & 0.0431 & 0.0366 \tabularnewline
p-value & (0.2735) & (0.5835) & (0.5792) \tabularnewline
SK4;SK5 & 0.0741 & 0.0929 & 0.0891 \tabularnewline
p-value & (0.346) & (0.2366) & (0.2324) \tabularnewline
SK4;SK6 & -0.0427 & -0.039 & -0.037 \tabularnewline
p-value & (0.5868) & (0.6201) & (0.62) \tabularnewline
SK4;TVDC
 & 0.1796 & 0.2169 & 0.1891 \tabularnewline
p-value & (0.0214) & (0.0053) & (0.0051) \tabularnewline
SK5;SK6 & 0.1168 & 0.1465 & 0.139 \tabularnewline
p-value & (0.1364) & (0.0612) & (0.0625) \tabularnewline
SK5;TVDC
 & 0.1353 & 0.1926 & 0.1663 \tabularnewline
p-value & (0.0841) & (0.0135) & (0.0139) \tabularnewline
SK6;TVDC
 & 0.0056 & 0.0347 & 0.0303 \tabularnewline
p-value & (0.9429) & (0.659) & (0.6538) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297883&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]SK1;SK2[/C][C]0.264[/C][C]0.2773[/C][C]0.2531[/C][/ROW]
[ROW][C]p-value[/C][C](6e-04)[/C][C](3e-04)[/C][C](4e-04)[/C][/ROW]
[ROW][C]SK1;SK3[/C][C]-0.0162[/C][C]-0.008[/C][C]-0.007[/C][/ROW]
[ROW][C]p-value[/C][C](0.8366)[/C][C](0.9194)[/C][C](0.921)[/C][/ROW]
[ROW][C]SK1;SK4[/C][C]0.0888[/C][C]0.137[/C][C]0.1295[/C][/ROW]
[ROW][C]p-value[/C][C](0.2584)[/C][C](0.0802)[/C][C](0.0733)[/C][/ROW]
[ROW][C]SK1;SK5[/C][C]0.1142[/C][C]0.1209[/C][C]0.1131[/C][/ROW]
[ROW][C]p-value[/C][C](0.1455)[/C][C](0.123)[/C][C](0.118)[/C][/ROW]
[ROW][C]SK1;SK6[/C][C]-1e-04[/C][C]0.0021[/C][C]0.0021[/C][/ROW]
[ROW][C]p-value[/C][C](0.9991)[/C][C](0.9786)[/C][C](0.977)[/C][/ROW]
[ROW][C]SK1;TVDC
[/C][C]0.3529[/C][C]0.3522[/C][C]0.2982[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SK2;SK3[/C][C]0.1132[/C][C]0.1087[/C][C]0.1004[/C][/ROW]
[ROW][C]p-value[/C][C](0.1489)[/C][C](0.1661)[/C][C](0.1614)[/C][/ROW]
[ROW][C]SK2;SK4[/C][C]0.1734[/C][C]0.1359[/C][C]0.1277[/C][/ROW]
[ROW][C]p-value[/C][C](0.0264)[/C][C](0.0827)[/C][C](0.0819)[/C][/ROW]
[ROW][C]SK2;SK5[/C][C]0.0749[/C][C]0.1002[/C][C]0.0932[/C][/ROW]
[ROW][C]p-value[/C][C](0.3404)[/C][C](0.2018)[/C][C](0.2051)[/C][/ROW]
[ROW][C]SK2;SK6[/C][C]-0.0209[/C][C]-0.0306[/C][C]-0.028[/C][/ROW]
[ROW][C]p-value[/C][C](0.7907)[/C][C](0.6972)[/C][C](0.7025)[/C][/ROW]
[ROW][C]SK2;TVDC
[/C][C]0.4893[/C][C]0.443[/C][C]0.3837[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SK3;SK4[/C][C]0.0527[/C][C]0.1182[/C][C]0.1094[/C][/ROW]
[ROW][C]p-value[/C][C](0.5029)[/C][C](0.1317)[/C][C](0.1329)[/C][/ROW]
[ROW][C]SK3;SK5[/C][C]-0.0797[/C][C]-0.0689[/C][C]-0.0635[/C][/ROW]
[ROW][C]p-value[/C][C](0.3104)[/C][C](0.3809)[/C][C](0.3834)[/C][/ROW]
[ROW][C]SK3;SK6[/C][C]0.1226[/C][C]0.1444[/C][C]0.1335[/C][/ROW]
[ROW][C]p-value[/C][C](0.1177)[/C][C](0.065)[/C][C](0.0668)[/C][/ROW]
[ROW][C]SK3;TVDC
[/C][C]0.086[/C][C]0.0431[/C][C]0.0366[/C][/ROW]
[ROW][C]p-value[/C][C](0.2735)[/C][C](0.5835)[/C][C](0.5792)[/C][/ROW]
[ROW][C]SK4;SK5[/C][C]0.0741[/C][C]0.0929[/C][C]0.0891[/C][/ROW]
[ROW][C]p-value[/C][C](0.346)[/C][C](0.2366)[/C][C](0.2324)[/C][/ROW]
[ROW][C]SK4;SK6[/C][C]-0.0427[/C][C]-0.039[/C][C]-0.037[/C][/ROW]
[ROW][C]p-value[/C][C](0.5868)[/C][C](0.6201)[/C][C](0.62)[/C][/ROW]
[ROW][C]SK4;TVDC
[/C][C]0.1796[/C][C]0.2169[/C][C]0.1891[/C][/ROW]
[ROW][C]p-value[/C][C](0.0214)[/C][C](0.0053)[/C][C](0.0051)[/C][/ROW]
[ROW][C]SK5;SK6[/C][C]0.1168[/C][C]0.1465[/C][C]0.139[/C][/ROW]
[ROW][C]p-value[/C][C](0.1364)[/C][C](0.0612)[/C][C](0.0625)[/C][/ROW]
[ROW][C]SK5;TVDC
[/C][C]0.1353[/C][C]0.1926[/C][C]0.1663[/C][/ROW]
[ROW][C]p-value[/C][C](0.0841)[/C][C](0.0135)[/C][C](0.0139)[/C][/ROW]
[ROW][C]SK6;TVDC
[/C][C]0.0056[/C][C]0.0347[/C][C]0.0303[/C][/ROW]
[ROW][C]p-value[/C][C](0.9429)[/C][C](0.659)[/C][C](0.6538)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297883&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297883&T=2

As an alternative you can also use a QR Code:  
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
SK1;SK20.2640.27730.2531
p-value(6e-04)(3e-04)(4e-04)
SK1;SK3-0.0162-0.008-0.007
p-value(0.8366)(0.9194)(0.921)
SK1;SK40.08880.1370.1295
p-value(0.2584)(0.0802)(0.0733)
SK1;SK50.11420.12090.1131
p-value(0.1455)(0.123)(0.118)
SK1;SK6-1e-040.00210.0021
p-value(0.9991)(0.9786)(0.977)
SK1;TVDC 0.35290.35220.2982
p-value(0)(0)(0)
SK2;SK30.11320.10870.1004
p-value(0.1489)(0.1661)(0.1614)
SK2;SK40.17340.13590.1277
p-value(0.0264)(0.0827)(0.0819)
SK2;SK50.07490.10020.0932
p-value(0.3404)(0.2018)(0.2051)
SK2;SK6-0.0209-0.0306-0.028
p-value(0.7907)(0.6972)(0.7025)
SK2;TVDC 0.48930.4430.3837
p-value(0)(0)(0)
SK3;SK40.05270.11820.1094
p-value(0.5029)(0.1317)(0.1329)
SK3;SK5-0.0797-0.0689-0.0635
p-value(0.3104)(0.3809)(0.3834)
SK3;SK60.12260.14440.1335
p-value(0.1177)(0.065)(0.0668)
SK3;TVDC 0.0860.04310.0366
p-value(0.2735)(0.5835)(0.5792)
SK4;SK50.07410.09290.0891
p-value(0.346)(0.2366)(0.2324)
SK4;SK6-0.0427-0.039-0.037
p-value(0.5868)(0.6201)(0.62)
SK4;TVDC 0.17960.21690.1891
p-value(0.0214)(0.0053)(0.0051)
SK5;SK60.11680.14650.139
p-value(0.1364)(0.0612)(0.0625)
SK5;TVDC 0.13530.19260.1663
p-value(0.0841)(0.0135)(0.0139)
SK6;TVDC 0.00560.03470.0303
p-value(0.9429)(0.659)(0.6538)






Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.140.190.19
0.020.140.240.24
0.030.240.240.24
0.040.240.240.24
0.050.240.240.24
0.060.240.240.24
0.070.240.330.33
0.080.240.330.38
0.090.290.430.43
0.10.290.430.43

\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 & 0.14 & 0.19 & 0.19 \tabularnewline
0.02 & 0.14 & 0.24 & 0.24 \tabularnewline
0.03 & 0.24 & 0.24 & 0.24 \tabularnewline
0.04 & 0.24 & 0.24 & 0.24 \tabularnewline
0.05 & 0.24 & 0.24 & 0.24 \tabularnewline
0.06 & 0.24 & 0.24 & 0.24 \tabularnewline
0.07 & 0.24 & 0.33 & 0.33 \tabularnewline
0.08 & 0.24 & 0.33 & 0.38 \tabularnewline
0.09 & 0.29 & 0.43 & 0.43 \tabularnewline
0.1 & 0.29 & 0.43 & 0.43 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297883&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]0.14[/C][C]0.19[/C][C]0.19[/C][/ROW]
[ROW][C]0.02[/C][C]0.14[/C][C]0.24[/C][C]0.24[/C][/ROW]
[ROW][C]0.03[/C][C]0.24[/C][C]0.24[/C][C]0.24[/C][/ROW]
[ROW][C]0.04[/C][C]0.24[/C][C]0.24[/C][C]0.24[/C][/ROW]
[ROW][C]0.05[/C][C]0.24[/C][C]0.24[/C][C]0.24[/C][/ROW]
[ROW][C]0.06[/C][C]0.24[/C][C]0.24[/C][C]0.24[/C][/ROW]
[ROW][C]0.07[/C][C]0.24[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.08[/C][C]0.24[/C][C]0.33[/C][C]0.38[/C][/ROW]
[ROW][C]0.09[/C][C]0.29[/C][C]0.43[/C][C]0.43[/C][/ROW]
[ROW][C]0.1[/C][C]0.29[/C][C]0.43[/C][C]0.43[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297883&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297883&T=3

As an alternative you can also use a QR Code:  
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
0.010.140.190.19
0.020.140.240.24
0.030.240.240.24
0.040.240.240.24
0.050.240.240.24
0.060.240.240.24
0.070.240.330.33
0.080.240.330.38
0.090.290.430.43
0.10.290.430.43


Figures (Output of Computation)

Input Parameters & R Code

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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
x <- na.omit(x)
y <- t(na.omit(t(y)))
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
print(n)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$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')