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Author*The author of this computation has been verified*
R Software ModulePatrick.Wessarwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationTue, 14 Dec 2010 09:50:22 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/14/t1292320487f5kcjkxtmxgpeap.htm/, Retrieved Thu, 02 May 2024 20:03:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109335, Retrieved Thu, 02 May 2024 20:03:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
-   PD    [Kendall tau Correlation Matrix] [WS 10 Pearson] [2010-12-14 09:50:22] [61e5ee05de011f44efa37f086a4e2271] [Current]
- R PD      [Kendall tau Correlation Matrix] [WS 10 Pearson] [2010-12-14 11:46:16] [1c68a339ea090fe045c8010fcdb839f1]
- R  D      [Kendall tau Correlation Matrix] [WS 10 Pearson] [2010-12-14 11:49:17] [1c68a339ea090fe045c8010fcdb839f1]
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Dataseries X:
1	24	24	13	13	13
1	25	25	12	12	13
1	17	30	15	10	16
0	18	19	12	9	12
1	18	22	10	10	11
1	16	22	12	12	12
1	20	25	15	13	18
1	16	23	9	12	11
1	18	17	12	12	14
1	17	21	11	6	9
0	23	19	11	5	14
1	30	19	11	12	12
0	23	15	15	11	11
1	18	16	7	14	12
1	15	23	11	14	13
0	12	27	11	12	11
0	21	22	10	12	12
1	15	14	14	11	16
0	20	22	10	11	9
1	31	23	6	7	11
0	27	23	11	9	13
1	34	21	15	11	15
1	21	19	11	11	10
1	31	18	12	12	11
0	19	20	14	12	13
1	16	23	15	11	16
0	20	25	9	11	15
1	21	19	13	8	14
1	22	24	13	9	14
0	17	22	16	12	14
1	24	25	13	10	8
0	25	26	12	10	13
1	26	29	14	12	15
1	25	32	11	8	13
0	17	25	9	12	11
0	32	29	16	11	15
0	33	28	12	12	15
0	13	17	10	7	9
1	32	28	13	11	13
0	25	29	16	11	16
0	29	26	14	12	13
1	22	25	15	9	11
0	18	14	5	15	12
0	17	25	8	11	12
1	20	26	11	11	12
1	15	20	16	11	14
1	20	18	17	11	14
1	33	32	9	15	8
1	29	25	9	11	13
0	23	25	13	12	16
1	26	23	10	12	13
0	18	21	6	9	11
0	20	20	12	12	14
1	11	15	8	12	13
0	28	30	14	13	13
1	26	24	12	11	13
1	22	26	11	9	12
1	17	24	16	9	16
0	12	22	8	11	15
1	14	14	15	11	15
0	17	24	7	12	12
0	21	24	16	12	14
1	19	24	14	9	12
1	18	24	16	11	15
1	10	19	9	9	12
0	29	31	14	12	13
1	31	22	11	12	12
0	19	27	13	12	12
1	9	19	15	12	13
0	20	25	5	14	5
0	28	20	15	11	13
1	19	21	13	12	13
1	30	27	11	11	14
0	29	23	11	6	17
0	26	25	12	10	13
1	23	20	12	12	13
1	13	21	12	13	12
1	21	22	12	8	13
0	19	23	14	12	14
0	28	25	6	12	11
0	23	25	7	12	12
1	18	17	14	6	12
0	21	19	14	11	16
1	20	25	10	10	12
1	23	19	13	12	12
0	21	20	12	13	12
1	21	26	9	11	10
1	15	23	12	7	15
1	28	27	16	11	15
1	19	17	10	11	12
1	26	17	14	11	16
1	10	19	10	11	15
1	16	17	16	12	16
1	22	22	15	10	13
1	19	21	12	11	12
1	31	32	10	12	11
1	31	21	8	7	13
0	29	21	8	13	10
0	19	18	11	8	15
1	22	18	13	12	13
0	23	23	16	11	16
1	15	19	16	12	15
0	20	20	14	14	18
1	18	21	11	10	13
0	23	20	4	10	10
1	25	17	14	13	16
0	21	18	9	10	13
0	24	19	14	11	15
1	25	22	8	10	14
1	17	15	8	7	15
1	13	14	11	10	14
1	28	18	12	8	13
1	21	24	11	12	13
0	25	35	14	12	15
1	9	29	15	12	16
1	16	21	16	11	14
1	19	25	16	12	14
1	17	20	11	12	16
0	25	22	14	12	14
1	20	13	14	11	12
1	29	26	12	12	13
1	14	17	14	11	12
1	22	25	8	11	12
1	15	20	13	13	14
0	19	19	16	12	14
1	20	21	12	12	14
1	15	22	16	12	16
1	20	24	12	12	13
0	18	21	11	8	14
0	33	26	4	8	4
1	22	24	16	12	16
0	16	16	15	11	13
0	17	23	10	12	16
1	16	18	13	13	15
0	21	16	15	12	14
0	26	26	12	12	13
1	18	19	14	11	14
1	18	21	7	12	12
0	17	21	19	12	15
1	22	22	12	10	14
0	30	23	12	11	13
1	30	29	13	12	14
0	24	21	15	12	16
1	21	21	8	10	6
1	21	23	12	12	13
0	29	27	10	13	13
0	31	25	8	12	14
0	20	21	10	15	15
1	16	10	15	11	14
1	22	20	16	12	15
0	20	26	13	11	13
1	28	24	16	12	16
1	38	29	9	11	12
1	22	19	14	10	15
1	20	24	14	11	12
1	17	19	12	11	14




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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 Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \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=109335&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]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=109335&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109335&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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=pearson)
genderCoMPersStPopularityFindFrieLiked
gender1-0.121-0.1220.129-0.080.031
CoM-0.12110.443-0.109-0.002-0.124
PersSt-0.1220.4431-0.0410.103-0.076
Popularity0.129-0.109-0.04110.0940.567
FindFrie-0.08-0.0020.1030.09410.089
Liked 0.031-0.124-0.0760.5670.0891

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & gender & CoM & PersSt & Popularity & FindFrie & Liked
 \tabularnewline
gender & 1 & -0.121 & -0.122 & 0.129 & -0.08 & 0.031 \tabularnewline
CoM & -0.121 & 1 & 0.443 & -0.109 & -0.002 & -0.124 \tabularnewline
PersSt & -0.122 & 0.443 & 1 & -0.041 & 0.103 & -0.076 \tabularnewline
Popularity & 0.129 & -0.109 & -0.041 & 1 & 0.094 & 0.567 \tabularnewline
FindFrie & -0.08 & -0.002 & 0.103 & 0.094 & 1 & 0.089 \tabularnewline
Liked
 & 0.031 & -0.124 & -0.076 & 0.567 & 0.089 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109335&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]gender[/C][C]CoM[/C][C]PersSt[/C][C]Popularity[/C][C]FindFrie[/C][C]Liked
[/C][/ROW]
[ROW][C]gender[/C][C]1[/C][C]-0.121[/C][C]-0.122[/C][C]0.129[/C][C]-0.08[/C][C]0.031[/C][/ROW]
[ROW][C]CoM[/C][C]-0.121[/C][C]1[/C][C]0.443[/C][C]-0.109[/C][C]-0.002[/C][C]-0.124[/C][/ROW]
[ROW][C]PersSt[/C][C]-0.122[/C][C]0.443[/C][C]1[/C][C]-0.041[/C][C]0.103[/C][C]-0.076[/C][/ROW]
[ROW][C]Popularity[/C][C]0.129[/C][C]-0.109[/C][C]-0.041[/C][C]1[/C][C]0.094[/C][C]0.567[/C][/ROW]
[ROW][C]FindFrie[/C][C]-0.08[/C][C]-0.002[/C][C]0.103[/C][C]0.094[/C][C]1[/C][C]0.089[/C][/ROW]
[ROW][C]Liked
[/C][C]0.031[/C][C]-0.124[/C][C]-0.076[/C][C]0.567[/C][C]0.089[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109335&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109335&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)
genderCoMPersStPopularityFindFrieLiked
gender1-0.121-0.1220.129-0.080.031
CoM-0.12110.443-0.109-0.002-0.124
PersSt-0.1220.4431-0.0410.103-0.076
Popularity0.129-0.109-0.04110.0940.567
FindFrie-0.08-0.0020.1030.09410.089
Liked 0.031-0.124-0.0760.5670.0891







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
gender;CoM-0.1209-0.1372-0.115
p-value(0.1328)(0.0877)(0.0876)
gender;PersSt-0.1217-0.1246-0.1053
p-value(0.1301)(0.1212)(0.1209)
gender;Popularity0.12920.09940.0852
p-value(0.1081)(0.2171)(0.216)
gender;FindFrie-0.0798-0.1098-0.0984
p-value(0.3218)(0.1723)(0.1716)
gender;Liked 0.0313-7e-04-6e-04
p-value(0.698)(0.9927)(0.9926)
CoM;PersSt0.44270.42370.3127
p-value(0)(0)(0)
CoM;Popularity-0.1091-0.0849-0.0624
p-value(0.175)(0.2919)(0.2806)
CoM;FindFrie-0.00180.00350.0023
p-value(0.9818)(0.9658)(0.9695)
CoM;Liked -0.1242-0.0968-0.0706
p-value(0.1223)(0.2294)(0.2315)
PersSt;Popularity-0.0411-0.0712-0.0508
p-value(0.6107)(0.3773)(0.3835)
PersSt;FindFrie0.10330.09280.071
p-value(0.1995)(0.2494)(0.2435)
PersSt;Liked -0.0755-0.0717-0.0537
p-value(0.3488)(0.3739)(0.3664)
Popularity;FindFrie0.0940.08910.0671
p-value(0.2432)(0.2688)(0.2772)
Popularity;Liked 0.56720.55120.433
p-value(0)(0)(0)
FindFrie;Liked 0.08860.07750.0612
p-value(0.2714)(0.3361)(0.3316)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
gender;CoM & -0.1209 & -0.1372 & -0.115 \tabularnewline
p-value & (0.1328) & (0.0877) & (0.0876) \tabularnewline
gender;PersSt & -0.1217 & -0.1246 & -0.1053 \tabularnewline
p-value & (0.1301) & (0.1212) & (0.1209) \tabularnewline
gender;Popularity & 0.1292 & 0.0994 & 0.0852 \tabularnewline
p-value & (0.1081) & (0.2171) & (0.216) \tabularnewline
gender;FindFrie & -0.0798 & -0.1098 & -0.0984 \tabularnewline
p-value & (0.3218) & (0.1723) & (0.1716) \tabularnewline
gender;Liked
 & 0.0313 & -7e-04 & -6e-04 \tabularnewline
p-value & (0.698) & (0.9927) & (0.9926) \tabularnewline
CoM;PersSt & 0.4427 & 0.4237 & 0.3127 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
CoM;Popularity & -0.1091 & -0.0849 & -0.0624 \tabularnewline
p-value & (0.175) & (0.2919) & (0.2806) \tabularnewline
CoM;FindFrie & -0.0018 & 0.0035 & 0.0023 \tabularnewline
p-value & (0.9818) & (0.9658) & (0.9695) \tabularnewline
CoM;Liked
 & -0.1242 & -0.0968 & -0.0706 \tabularnewline
p-value & (0.1223) & (0.2294) & (0.2315) \tabularnewline
PersSt;Popularity & -0.0411 & -0.0712 & -0.0508 \tabularnewline
p-value & (0.6107) & (0.3773) & (0.3835) \tabularnewline
PersSt;FindFrie & 0.1033 & 0.0928 & 0.071 \tabularnewline
p-value & (0.1995) & (0.2494) & (0.2435) \tabularnewline
PersSt;Liked
 & -0.0755 & -0.0717 & -0.0537 \tabularnewline
p-value & (0.3488) & (0.3739) & (0.3664) \tabularnewline
Popularity;FindFrie & 0.094 & 0.0891 & 0.0671 \tabularnewline
p-value & (0.2432) & (0.2688) & (0.2772) \tabularnewline
Popularity;Liked
 & 0.5672 & 0.5512 & 0.433 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
FindFrie;Liked
 & 0.0886 & 0.0775 & 0.0612 \tabularnewline
p-value & (0.2714) & (0.3361) & (0.3316) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109335&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]gender;CoM[/C][C]-0.1209[/C][C]-0.1372[/C][C]-0.115[/C][/ROW]
[ROW][C]p-value[/C][C](0.1328)[/C][C](0.0877)[/C][C](0.0876)[/C][/ROW]
[ROW][C]gender;PersSt[/C][C]-0.1217[/C][C]-0.1246[/C][C]-0.1053[/C][/ROW]
[ROW][C]p-value[/C][C](0.1301)[/C][C](0.1212)[/C][C](0.1209)[/C][/ROW]
[ROW][C]gender;Popularity[/C][C]0.1292[/C][C]0.0994[/C][C]0.0852[/C][/ROW]
[ROW][C]p-value[/C][C](0.1081)[/C][C](0.2171)[/C][C](0.216)[/C][/ROW]
[ROW][C]gender;FindFrie[/C][C]-0.0798[/C][C]-0.1098[/C][C]-0.0984[/C][/ROW]
[ROW][C]p-value[/C][C](0.3218)[/C][C](0.1723)[/C][C](0.1716)[/C][/ROW]
[ROW][C]gender;Liked
[/C][C]0.0313[/C][C]-7e-04[/C][C]-6e-04[/C][/ROW]
[ROW][C]p-value[/C][C](0.698)[/C][C](0.9927)[/C][C](0.9926)[/C][/ROW]
[ROW][C]CoM;PersSt[/C][C]0.4427[/C][C]0.4237[/C][C]0.3127[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CoM;Popularity[/C][C]-0.1091[/C][C]-0.0849[/C][C]-0.0624[/C][/ROW]
[ROW][C]p-value[/C][C](0.175)[/C][C](0.2919)[/C][C](0.2806)[/C][/ROW]
[ROW][C]CoM;FindFrie[/C][C]-0.0018[/C][C]0.0035[/C][C]0.0023[/C][/ROW]
[ROW][C]p-value[/C][C](0.9818)[/C][C](0.9658)[/C][C](0.9695)[/C][/ROW]
[ROW][C]CoM;Liked
[/C][C]-0.1242[/C][C]-0.0968[/C][C]-0.0706[/C][/ROW]
[ROW][C]p-value[/C][C](0.1223)[/C][C](0.2294)[/C][C](0.2315)[/C][/ROW]
[ROW][C]PersSt;Popularity[/C][C]-0.0411[/C][C]-0.0712[/C][C]-0.0508[/C][/ROW]
[ROW][C]p-value[/C][C](0.6107)[/C][C](0.3773)[/C][C](0.3835)[/C][/ROW]
[ROW][C]PersSt;FindFrie[/C][C]0.1033[/C][C]0.0928[/C][C]0.071[/C][/ROW]
[ROW][C]p-value[/C][C](0.1995)[/C][C](0.2494)[/C][C](0.2435)[/C][/ROW]
[ROW][C]PersSt;Liked
[/C][C]-0.0755[/C][C]-0.0717[/C][C]-0.0537[/C][/ROW]
[ROW][C]p-value[/C][C](0.3488)[/C][C](0.3739)[/C][C](0.3664)[/C][/ROW]
[ROW][C]Popularity;FindFrie[/C][C]0.094[/C][C]0.0891[/C][C]0.0671[/C][/ROW]
[ROW][C]p-value[/C][C](0.2432)[/C][C](0.2688)[/C][C](0.2772)[/C][/ROW]
[ROW][C]Popularity;Liked
[/C][C]0.5672[/C][C]0.5512[/C][C]0.433[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]FindFrie;Liked
[/C][C]0.0886[/C][C]0.0775[/C][C]0.0612[/C][/ROW]
[ROW][C]p-value[/C][C](0.2714)[/C][C](0.3361)[/C][C](0.3316)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109335&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109335&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
gender;CoM-0.1209-0.1372-0.115
p-value(0.1328)(0.0877)(0.0876)
gender;PersSt-0.1217-0.1246-0.1053
p-value(0.1301)(0.1212)(0.1209)
gender;Popularity0.12920.09940.0852
p-value(0.1081)(0.2171)(0.216)
gender;FindFrie-0.0798-0.1098-0.0984
p-value(0.3218)(0.1723)(0.1716)
gender;Liked 0.0313-7e-04-6e-04
p-value(0.698)(0.9927)(0.9926)
CoM;PersSt0.44270.42370.3127
p-value(0)(0)(0)
CoM;Popularity-0.1091-0.0849-0.0624
p-value(0.175)(0.2919)(0.2806)
CoM;FindFrie-0.00180.00350.0023
p-value(0.9818)(0.9658)(0.9695)
CoM;Liked -0.1242-0.0968-0.0706
p-value(0.1223)(0.2294)(0.2315)
PersSt;Popularity-0.0411-0.0712-0.0508
p-value(0.6107)(0.3773)(0.3835)
PersSt;FindFrie0.10330.09280.071
p-value(0.1995)(0.2494)(0.2435)
PersSt;Liked -0.0755-0.0717-0.0537
p-value(0.3488)(0.3739)(0.3664)
Popularity;FindFrie0.0940.08910.0671
p-value(0.2432)(0.2688)(0.2772)
Popularity;Liked 0.56720.55120.433
p-value(0)(0)(0)
FindFrie;Liked 0.08860.07750.0612
p-value(0.2714)(0.3361)(0.3316)



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')
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)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')