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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 computationSat, 03 Dec 2016 15:14:52 +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/03/t1480776103qlnt4yl303kijit.htm/, Retrieved Sun, 05 May 2024 14:26:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297630, Retrieved Sun, 05 May 2024 14:26:01 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90

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-       [Kendall tau Correlation Matrix] [] [2016-12-03 14:14:52] [9b171b8beffcb53bb49a1e7c02b89c12] [Current]
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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	NA	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	18
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	NA	4	14
4	4	4	4	4	4	13
4	4	4	5	5	4	18
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	NA	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	NA	4	15
5	4	4	5	5	5	17
4	4	5	3	NA	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	14
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
4	4	4	3	4	4	16
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	14
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	14
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=297630&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=297630&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297630&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
SK110.261-0.0170.1230.106-0.010.316
SK20.26110.1150.1360.089-0.0170.392
SK3-0.0170.11510.125-0.0640.1140.029
SK40.1230.1360.12510.09-0.0270.199
SK50.1060.089-0.0640.0910.1450.163
SK6-0.01-0.0170.114-0.0270.14510.02
TVDC 0.3160.3920.0290.1990.1630.021

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & SK1 & SK2 & SK3 & SK4 & SK5 & SK6 & TVDC
 \tabularnewline
SK1 & 1 & 0.261 & -0.017 & 0.123 & 0.106 & -0.01 & 0.316 \tabularnewline
SK2 & 0.261 & 1 & 0.115 & 0.136 & 0.089 & -0.017 & 0.392 \tabularnewline
SK3 & -0.017 & 0.115 & 1 & 0.125 & -0.064 & 0.114 & 0.029 \tabularnewline
SK4 & 0.123 & 0.136 & 0.125 & 1 & 0.09 & -0.027 & 0.199 \tabularnewline
SK5 & 0.106 & 0.089 & -0.064 & 0.09 & 1 & 0.145 & 0.163 \tabularnewline
SK6 & -0.01 & -0.017 & 0.114 & -0.027 & 0.145 & 1 & 0.02 \tabularnewline
TVDC
 & 0.316 & 0.392 & 0.029 & 0.199 & 0.163 & 0.02 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297630&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]0.261[/C][C]-0.017[/C][C]0.123[/C][C]0.106[/C][C]-0.01[/C][C]0.316[/C][/ROW]
[ROW][C]SK2[/C][C]0.261[/C][C]1[/C][C]0.115[/C][C]0.136[/C][C]0.089[/C][C]-0.017[/C][C]0.392[/C][/ROW]
[ROW][C]SK3[/C][C]-0.017[/C][C]0.115[/C][C]1[/C][C]0.125[/C][C]-0.064[/C][C]0.114[/C][C]0.029[/C][/ROW]
[ROW][C]SK4[/C][C]0.123[/C][C]0.136[/C][C]0.125[/C][C]1[/C][C]0.09[/C][C]-0.027[/C][C]0.199[/C][/ROW]
[ROW][C]SK5[/C][C]0.106[/C][C]0.089[/C][C]-0.064[/C][C]0.09[/C][C]1[/C][C]0.145[/C][C]0.163[/C][/ROW]
[ROW][C]SK6[/C][C]-0.01[/C][C]-0.017[/C][C]0.114[/C][C]-0.027[/C][C]0.145[/C][C]1[/C][C]0.02[/C][/ROW]
[ROW][C]TVDC
[/C][C]0.316[/C][C]0.392[/C][C]0.029[/C][C]0.199[/C][C]0.163[/C][C]0.02[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297630&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297630&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
SK110.261-0.0170.1230.106-0.010.316
SK20.26110.1150.1360.089-0.0170.392
SK3-0.0170.11510.125-0.0640.1140.029
SK40.1230.1360.12510.09-0.0270.199
SK50.1060.089-0.0640.0910.1450.163
SK6-0.01-0.0170.114-0.0270.14510.02
TVDC 0.3160.3920.0290.1990.1630.021






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
SK1;SK20.27140.28580.2609
p-value(5e-04)(2e-04)(3e-04)
SK1;SK3-0.0218-0.0188-0.0169
p-value(0.7842)(0.8135)(0.8127)
SK1;SK40.08290.13040.1231
p-value(0.2972)(0.1004)(0.0924)
SK1;SK50.10910.11330.106
p-value(0.1698)(0.1536)(0.1478)
SK1;SK6-0.0114-0.0114-0.0101
p-value(0.8866)(0.8862)(0.8901)
SK1;TVDC 0.3760.37340.3165
p-value(0)(0)(0)
SK2;SK30.12550.12410.1149
p-value(0.1138)(0.1178)(0.1136)
SK2;SK40.18050.14440.1356
p-value(0.0224)(0.0686)(0.0679)
SK2;SK50.07140.09580.089
p-value(0.3697)(0.228)(0.2312)
SK2;SK6-0.0103-0.0187-0.0169
p-value(0.8967)(0.8141)(0.8204)
SK2;TVDC 0.50490.45220.3919
p-value(0)(0)(0)
SK3;SK40.06390.13460.1246
p-value(0.4224)(0.0897)(0.0909)
SK3;SK5-0.0808-0.0696-0.0643
p-value(0.3095)(0.3816)(0.3839)
SK3;SK60.10550.12330.1139
p-value(0.1844)(0.1204)(0.1223)
SK3;TVDC 0.07840.03480.0293
p-value(0.3246)(0.6626)(0.6607)
SK4;SK50.0750.09390.0899
p-value(0.3462)(0.2375)(0.2334)
SK4;SK6-0.0345-0.0285-0.027
p-value(0.6651)(0.7202)(0.7201)
SK4;TVDC 0.19770.22770.1992
p-value(0.0122)(0.0038)(0.0035)
SK5;SK60.12170.1530.145
p-value(0.1253)(0.0534)(0.0547)
SK5;TVDC 0.13040.18920.1628
p-value(0.1003)(0.0166)(0.0172)
SK6;TVDC -0.00930.02340.0204
p-value(0.9073)(0.7689)(0.7656)

\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 & 0.2714 & 0.2858 & 0.2609 \tabularnewline
p-value & (5e-04) & (2e-04) & (3e-04) \tabularnewline
SK1;SK3 & -0.0218 & -0.0188 & -0.0169 \tabularnewline
p-value & (0.7842) & (0.8135) & (0.8127) \tabularnewline
SK1;SK4 & 0.0829 & 0.1304 & 0.1231 \tabularnewline
p-value & (0.2972) & (0.1004) & (0.0924) \tabularnewline
SK1;SK5 & 0.1091 & 0.1133 & 0.106 \tabularnewline
p-value & (0.1698) & (0.1536) & (0.1478) \tabularnewline
SK1;SK6 & -0.0114 & -0.0114 & -0.0101 \tabularnewline
p-value & (0.8866) & (0.8862) & (0.8901) \tabularnewline
SK1;TVDC
 & 0.376 & 0.3734 & 0.3165 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SK2;SK3 & 0.1255 & 0.1241 & 0.1149 \tabularnewline
p-value & (0.1138) & (0.1178) & (0.1136) \tabularnewline
SK2;SK4 & 0.1805 & 0.1444 & 0.1356 \tabularnewline
p-value & (0.0224) & (0.0686) & (0.0679) \tabularnewline
SK2;SK5 & 0.0714 & 0.0958 & 0.089 \tabularnewline
p-value & (0.3697) & (0.228) & (0.2312) \tabularnewline
SK2;SK6 & -0.0103 & -0.0187 & -0.0169 \tabularnewline
p-value & (0.8967) & (0.8141) & (0.8204) \tabularnewline
SK2;TVDC
 & 0.5049 & 0.4522 & 0.3919 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SK3;SK4 & 0.0639 & 0.1346 & 0.1246 \tabularnewline
p-value & (0.4224) & (0.0897) & (0.0909) \tabularnewline
SK3;SK5 & -0.0808 & -0.0696 & -0.0643 \tabularnewline
p-value & (0.3095) & (0.3816) & (0.3839) \tabularnewline
SK3;SK6 & 0.1055 & 0.1233 & 0.1139 \tabularnewline
p-value & (0.1844) & (0.1204) & (0.1223) \tabularnewline
SK3;TVDC
 & 0.0784 & 0.0348 & 0.0293 \tabularnewline
p-value & (0.3246) & (0.6626) & (0.6607) \tabularnewline
SK4;SK5 & 0.075 & 0.0939 & 0.0899 \tabularnewline
p-value & (0.3462) & (0.2375) & (0.2334) \tabularnewline
SK4;SK6 & -0.0345 & -0.0285 & -0.027 \tabularnewline
p-value & (0.6651) & (0.7202) & (0.7201) \tabularnewline
SK4;TVDC
 & 0.1977 & 0.2277 & 0.1992 \tabularnewline
p-value & (0.0122) & (0.0038) & (0.0035) \tabularnewline
SK5;SK6 & 0.1217 & 0.153 & 0.145 \tabularnewline
p-value & (0.1253) & (0.0534) & (0.0547) \tabularnewline
SK5;TVDC
 & 0.1304 & 0.1892 & 0.1628 \tabularnewline
p-value & (0.1003) & (0.0166) & (0.0172) \tabularnewline
SK6;TVDC
 & -0.0093 & 0.0234 & 0.0204 \tabularnewline
p-value & (0.9073) & (0.7689) & (0.7656) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297630&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]0.2714[/C][C]0.2858[/C][C]0.2609[/C][/ROW]
[ROW][C]p-value[/C][C](5e-04)[/C][C](2e-04)[/C][C](3e-04)[/C][/ROW]
[ROW][C]SK1;SK3[/C][C]-0.0218[/C][C]-0.0188[/C][C]-0.0169[/C][/ROW]
[ROW][C]p-value[/C][C](0.7842)[/C][C](0.8135)[/C][C](0.8127)[/C][/ROW]
[ROW][C]SK1;SK4[/C][C]0.0829[/C][C]0.1304[/C][C]0.1231[/C][/ROW]
[ROW][C]p-value[/C][C](0.2972)[/C][C](0.1004)[/C][C](0.0924)[/C][/ROW]
[ROW][C]SK1;SK5[/C][C]0.1091[/C][C]0.1133[/C][C]0.106[/C][/ROW]
[ROW][C]p-value[/C][C](0.1698)[/C][C](0.1536)[/C][C](0.1478)[/C][/ROW]
[ROW][C]SK1;SK6[/C][C]-0.0114[/C][C]-0.0114[/C][C]-0.0101[/C][/ROW]
[ROW][C]p-value[/C][C](0.8866)[/C][C](0.8862)[/C][C](0.8901)[/C][/ROW]
[ROW][C]SK1;TVDC
[/C][C]0.376[/C][C]0.3734[/C][C]0.3165[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SK2;SK3[/C][C]0.1255[/C][C]0.1241[/C][C]0.1149[/C][/ROW]
[ROW][C]p-value[/C][C](0.1138)[/C][C](0.1178)[/C][C](0.1136)[/C][/ROW]
[ROW][C]SK2;SK4[/C][C]0.1805[/C][C]0.1444[/C][C]0.1356[/C][/ROW]
[ROW][C]p-value[/C][C](0.0224)[/C][C](0.0686)[/C][C](0.0679)[/C][/ROW]
[ROW][C]SK2;SK5[/C][C]0.0714[/C][C]0.0958[/C][C]0.089[/C][/ROW]
[ROW][C]p-value[/C][C](0.3697)[/C][C](0.228)[/C][C](0.2312)[/C][/ROW]
[ROW][C]SK2;SK6[/C][C]-0.0103[/C][C]-0.0187[/C][C]-0.0169[/C][/ROW]
[ROW][C]p-value[/C][C](0.8967)[/C][C](0.8141)[/C][C](0.8204)[/C][/ROW]
[ROW][C]SK2;TVDC
[/C][C]0.5049[/C][C]0.4522[/C][C]0.3919[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SK3;SK4[/C][C]0.0639[/C][C]0.1346[/C][C]0.1246[/C][/ROW]
[ROW][C]p-value[/C][C](0.4224)[/C][C](0.0897)[/C][C](0.0909)[/C][/ROW]
[ROW][C]SK3;SK5[/C][C]-0.0808[/C][C]-0.0696[/C][C]-0.0643[/C][/ROW]
[ROW][C]p-value[/C][C](0.3095)[/C][C](0.3816)[/C][C](0.3839)[/C][/ROW]
[ROW][C]SK3;SK6[/C][C]0.1055[/C][C]0.1233[/C][C]0.1139[/C][/ROW]
[ROW][C]p-value[/C][C](0.1844)[/C][C](0.1204)[/C][C](0.1223)[/C][/ROW]
[ROW][C]SK3;TVDC
[/C][C]0.0784[/C][C]0.0348[/C][C]0.0293[/C][/ROW]
[ROW][C]p-value[/C][C](0.3246)[/C][C](0.6626)[/C][C](0.6607)[/C][/ROW]
[ROW][C]SK4;SK5[/C][C]0.075[/C][C]0.0939[/C][C]0.0899[/C][/ROW]
[ROW][C]p-value[/C][C](0.3462)[/C][C](0.2375)[/C][C](0.2334)[/C][/ROW]
[ROW][C]SK4;SK6[/C][C]-0.0345[/C][C]-0.0285[/C][C]-0.027[/C][/ROW]
[ROW][C]p-value[/C][C](0.6651)[/C][C](0.7202)[/C][C](0.7201)[/C][/ROW]
[ROW][C]SK4;TVDC
[/C][C]0.1977[/C][C]0.2277[/C][C]0.1992[/C][/ROW]
[ROW][C]p-value[/C][C](0.0122)[/C][C](0.0038)[/C][C](0.0035)[/C][/ROW]
[ROW][C]SK5;SK6[/C][C]0.1217[/C][C]0.153[/C][C]0.145[/C][/ROW]
[ROW][C]p-value[/C][C](0.1253)[/C][C](0.0534)[/C][C](0.0547)[/C][/ROW]
[ROW][C]SK5;TVDC
[/C][C]0.1304[/C][C]0.1892[/C][C]0.1628[/C][/ROW]
[ROW][C]p-value[/C][C](0.1003)[/C][C](0.0166)[/C][C](0.0172)[/C][/ROW]
[ROW][C]SK6;TVDC
[/C][C]-0.0093[/C][C]0.0234[/C][C]0.0204[/C][/ROW]
[ROW][C]p-value[/C][C](0.9073)[/C][C](0.7689)[/C][C](0.7656)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297630&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297630&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;SK20.27140.28580.2609
p-value(5e-04)(2e-04)(3e-04)
SK1;SK3-0.0218-0.0188-0.0169
p-value(0.7842)(0.8135)(0.8127)
SK1;SK40.08290.13040.1231
p-value(0.2972)(0.1004)(0.0924)
SK1;SK50.10910.11330.106
p-value(0.1698)(0.1536)(0.1478)
SK1;SK6-0.0114-0.0114-0.0101
p-value(0.8866)(0.8862)(0.8901)
SK1;TVDC 0.3760.37340.3165
p-value(0)(0)(0)
SK2;SK30.12550.12410.1149
p-value(0.1138)(0.1178)(0.1136)
SK2;SK40.18050.14440.1356
p-value(0.0224)(0.0686)(0.0679)
SK2;SK50.07140.09580.089
p-value(0.3697)(0.228)(0.2312)
SK2;SK6-0.0103-0.0187-0.0169
p-value(0.8967)(0.8141)(0.8204)
SK2;TVDC 0.50490.45220.3919
p-value(0)(0)(0)
SK3;SK40.06390.13460.1246
p-value(0.4224)(0.0897)(0.0909)
SK3;SK5-0.0808-0.0696-0.0643
p-value(0.3095)(0.3816)(0.3839)
SK3;SK60.10550.12330.1139
p-value(0.1844)(0.1204)(0.1223)
SK3;TVDC 0.07840.03480.0293
p-value(0.3246)(0.6626)(0.6607)
SK4;SK50.0750.09390.0899
p-value(0.3462)(0.2375)(0.2334)
SK4;SK6-0.0345-0.0285-0.027
p-value(0.6651)(0.7202)(0.7201)
SK4;TVDC 0.19770.22770.1992
p-value(0.0122)(0.0038)(0.0035)
SK5;SK60.12170.1530.145
p-value(0.1253)(0.0534)(0.0547)
SK5;TVDC 0.13040.18920.1628
p-value(0.1003)(0.0166)(0.0172)
SK6;TVDC -0.00930.02340.0204
p-value(0.9073)(0.7689)(0.7656)






Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.140.190.19
0.020.190.240.24
0.030.240.240.24
0.040.240.240.24
0.050.240.240.24
0.060.240.290.29
0.070.240.330.33
0.080.240.330.33
0.090.240.380.33
0.10.240.380.43

\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 & 0.19 & 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 & 0.29 & 0.29 \tabularnewline
0.07 & 0.24 & 0.33 & 0.33 \tabularnewline
0.08 & 0.24 & 0.33 & 0.33 \tabularnewline
0.09 & 0.24 & 0.38 & 0.33 \tabularnewline
0.1 & 0.24 & 0.38 & 0.43 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297630&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]0.19[/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]0.29[/C][C]0.29[/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]0.33[/C][/ROW]
[ROW][C]0.09[/C][C]0.24[/C][C]0.38[/C][C]0.33[/C][/ROW]
[ROW][C]0.1[/C][C]0.24[/C][C]0.38[/C][C]0.43[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297630&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297630&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
0.020.190.240.24
0.030.240.240.24
0.040.240.240.24
0.050.240.240.24
0.060.240.290.29
0.070.240.330.33
0.080.240.330.33
0.090.240.380.33
0.10.240.380.43


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')