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Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationWed, 17 Dec 2014 10:22:08 +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/2014/Dec/17/t1418811752f559xnr7rexge5t.htm/, Retrieved Thu, 16 May 2024 10:39:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270021, Retrieved Thu, 16 May 2024 10:39:11 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [pearson correlati...] [2014-12-17 10:22:08] [d71ad52285d92a63edfc83f9fb1da7a1] [Current]
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Dataseries X:
0 26 50 0 21 21 149 18 68
0 51 68 0 23 26 152 7 55
1 57 62 0 22 22 139 31 39
0 37 54 0 21 22 148 39 32
1 67 71 0 21 18 158 46 62
1 43 54 0 21 23 128 31 33
1 52 65 0 21 12 224 67 52
0 52 73 0 21 20 159 35 62
1 43 52 0 23 22 105 52 77
1 84 84 0 22 21 159 77 76
1 67 42 0 25 19 167 37 41
1 49 66 0 21 22 165 32 48
1 70 65 0 23 15 159 36 63
1 52 78 0 22 20 119 38 30
0 58 73 0 21 19 176 69 78
0 68 75 0 21 18 54 21 19
0 62 72 1 25 15 91 26 31
1 43 66 0 21 20 163 54 66
0 56 70 0 21 21 124 36 35
1 56 61 1 20 21 137 42 42
0 74 81 0 24 15 121 23 45
1 65 71 0 23 16 153 34 21
1 63 69 0 21 23 148 112 25
0 58 71 0 24 21 221 35 44
1 57 72 0 23 18 188 47 69
1 63 68 0 21 25 149 47 54
1 53 70 0 22 9 244 37 74
1 57 68 1 20 30 148 109 80
0 51 61 1 18 20 92 24 42
1 64 67 0 21 23 150 20 61
0 53 76 0 22 16 153 22 41
0 29 70 0 22 16 94 23 46
0 54 60 0 21 19 156 32 39
1 51 77 0 23 25 146 7 63
1 58 72 0 21 25 132 30 34
1 43 69 0 25 18 161 92 51
1 51 71 0 22 23 105 43 42
1 53 62 0 22 21 97 55 31
0 54 70 0 20 10 151 16 39
1 56 64 1 21 14 131 49 20
1 61 58 0 21 22 166 71 49
0 47 76 0 21 26 157 43 53
1 39 52 0 22 23 111 29 31
1 48 59 0 21 23 145 56 39
1 50 68 0 24 24 162 46 54
1 35 76 0 22 24 163 19 49
1 30 65 1 22 18 59 23 34
0 68 67 0 21 23 187 59 46
1 49 59 0 22 15 109 30 55
1 61 69 1 19 19 90 61 42
0 67 76 0 22 16 105 7 50
1 47 63 1 23 25 83 38 13
1 56 75 1 20 23 116 32 37
1 50 63 1 20 17 42 16 25
1 43 60 0 23 19 148 19 30
1 67 73 1 20 21 155 22 28
1 62 63 0 23 18 125 48 45
1 57 70 0 21 27 116 23 35
0 41 75 1 22 21 128 26 28
1 54 66 0 21 13 138 33 41
0 45 63 1 21 8 49 9 6
1 48 63 1 19 29 96 24 45
1 61 64 0 22 28 164 34 73
0 56 70 0 21 23 162 48 17
0 41 75 0 21 21 99 18 40
1 43 61 0 21 19 202 43 64
0 53 60 0 21 19 186 33 37
1 44 62 1 21 20 66 28 25
0 66 73 0 21 18 183 71 65
1 58 61 0 22 19 214 26 100
1 46 66 0 22 17 188 67 28
0 37 64 1 18 19 104 34 35
0 51 59 0 21 25 177 80 56
0 51 64 0 23 19 126 29 29
0 56 60 1 19 22 76 16 43
1 66 56 1 19 23 99 59 59
1 45 66 0 23 26 157 58 52
0 37 78 0 21 14 139 32 50
0 42 67 0 21 16 162 43 59
1 38 59 1 21 24 108 38 27
0 66 66 0 20 20 159 29 61
0 34 68 1 19 12 74 36 28
1 53 71 0 21 24 110 32 51
0 49 66 1 19 22 96 35 35
0 55 73 1 19 12 116 21 29
0 49 72 1 19 22 87 29 48
1 59 71 1 20 20 97 12 25
0 40 59 1 19 10 127 37 44
1 58 64 1 19 23 106 37 64
1 60 66 1 19 17 80 47 32
0 63 78 1 20 22 74 51 20
0 56 68 1 19 24 91 32 28
0 54 73 1 18 18 133 21 34
1 52 62 1 19 21 74 13 31
1 34 65 1 21 20 114 14 26
1 69 68 1 18 20 140 -2 58
0 32 65 1 18 22 95 20 23
1 48 60 1 19 19 98 24 21
0 67 71 1 21 20 121 11 21
1 58 65 1 20 26 126 23 33
1 57 68 1 24 23 98 24 16
1 42 64 1 22 24 95 14 20
1 64 74 1 21 21 110 52 37
1 58 69 1 21 21 70 15 35
0 66 76 1 19 19 102 23 33
1 26 68 1 19 8 86 19 27
1 61 72 1 20 17 130 35 41
1 52 67 1 18 20 96 24 40
0 51 63 1 19 11 102 39 35
0 55 59 1 19 8 100 29 28
0 50 73 1 20 15 94 13 32
0 60 66 1 21 18 52 8 22
0 56 62 1 18 18 98 18 44
0 63 69 1 19 19 118 24 27
1 61 66 1 19 19 99 19 17
1 52 51 0 22 23 48 23 12
1 16 56 0 22 22 50 16 45
1 46 67 0 22 21 150 33 37
1 56 69 0 20 25 154 32 37
0 52 57 1 19 30 109 37 108
1 55 56 1 20 17 68 14 10
1 50 55 0 22 27 194 52 68
0 59 63 0 21 23 158 75 72
1 60 67 0 21 23 159 72 143
0 52 65 0 21 18 67 15 9
0 44 47 0 21 18 147 29 55
1 67 76 0 21 23 39 13 17
1 52 64 0 21 19 100 40 37
1 55 68 0 21 15 111 19 27
1 37 64 0 22 20 138 24 37
1 54 65 0 24 16 101 121 58
1 72 71 1 21 24 131 93 66
1 51 63 0 22 25 101 36 21
1 48 60 0 20 25 114 23 19
0 60 68 0 21 19 165 85 78
1 50 72 0 24 19 114 41 35
1 63 70 0 25 16 111 46 48
1 33 61 0 22 19 75 18 27
1 67 61 0 21 19 82 35 43
1 46 62 0 21 23 121 17 30
1 54 71 0 22 21 32 4 25
0 59 71 0 23 22 150 28 69
1 61 51 0 24 19 117 44 72
1 33 56 1 20 20 71 10 23
1 47 70 0 22 20 165 38 13
1 69 73 0 25 3 154 57 61
1 52 76 0 22 23 126 23 43
0 55 59 0 22 14 138 26 22
0 55 68 0 21 23 149 36 51
0 41 48 0 21 20 145 22 67
1 73 52 0 21 15 120 40 36
0 51 59 0 22 13 138 18 21
0 52 60 0 22 16 109 31 44
0 50 59 0 22 7 132 11 45
1 51 57 0 21 24 172 38 34
0 60 79 0 22 17 169 24 36
1 56 60 0 23 24 114 37 72
1 56 60 0 21 24 156 37 39
0 29 59 0 21 19 172 22 43
1 66 62 1 21 25 68 15 25
1 66 59 1 19 20 89 2 56
1 73 61 0 21 28 167 43 80
0 55 71 0 21 23 113 31 40
0 64 57 1 19 27 115 29 73
0 40 66 1 18 18 78 45 34
0 46 63 1 19 28 118 25 72
1 58 69 1 21 21 87 4 42
0 43 58 0 22 19 173 31 61
1 61 59 0 22 23 2 -4 23
0 51 48 1 19 27 162 66 74
1 50 66 1 20 22 49 61 16
0 52 73 1 19 28 122 32 66
1 54 67 1 21 25 96 31 9
0 66 61 1 19 21 100 39 41
0 61 68 1 20 22 82 19 57
1 80 75 1 21 28 100 31 48
0 51 62 1 19 20 115 36 51
1 56 69 1 21 29 141 42 53
1 56 58 0 21 25 165 21 29
1 56 60 0 21 25 165 21 29
1 53 74 1 19 20 110 25 55
1 47 55 0 25 20 118 32 54
0 25 62 0 21 16 158 26 43
1 47 63 1 20 20 146 28 51
0 46 69 0 25 20 49 32 20
0 50 58 1 19 23 90 41 79
0 39 58 1 20 18 121 29 39
1 51 68 0 22 25 155 33 61
0 58 72 1 19 18 104 17 55
1 35 62 1 20 19 147 13 30
0 58 62 1 19 25 110 32 55
0 60 65 1 19 25 108 30 22
0 62 69 1 18 25 113 34 37
0 63 66 1 19 24 115 59 2
1 53 72 1 21 19 61 13 38
1 46 62 1 19 26 60 23 27
1 67 75 1 20 10 109 10 56
1 59 58 1 20 17 68 5 25
0 64 66 1 19 13 111 31 39
0 38 55 1 19 17 77 19 33
1 50 47 1 22 30 73 32 43
0 48 72 0 21 25 151 30 57
0 48 62 1 19 4 89 25 43
0 47 64 1 19 16 78 48 23
0 66 64 1 19 21 110 35 44
1 47 19 0 23 23 220 67 54
1 63 50 1 19 22 65 15 28
0 58 68 0 20 17 141 22 36
0 44 70 1 19 20 117 18 39
1 51 79 0 22 20 122 33 16
0 43 69 1 19 22 63 46 23
1 55 71 0 25 16 44 24 40
1 38 48 1 19 23 52 14 24
1 56 66 1 20 16 62 23 29
0 45 73 1 19 0 131 12 78
1 50 74 1 19 18 101 38 57
1 54 66 1 20 25 42 12 37
1 57 71 0 20 23 152 28 27
0 60 74 0 21 12 107 41 61
0 55 78 1 19 18 77 12 27
0 56 75 0 21 24 154 31 69
1 49 53 0 23 11 103 33 34
1 37 60 1 19 18 96 34 44
0 43 50 0 21 14 154 41 21
1 59 70 0 22 23 175 21 34
1 46 69 1 20 24 57 20 39
0 51 65 1 18 29 112 44 51
0 58 78 0 21 18 143 52 34
0 64 78 1 20 15 49 7 31
1 53 59 0 21 29 110 29 13
1 48 72 0 21 16 131 11 12
0 51 70 0 21 19 167 26 51
0 47 63 1 19 22 56 24 24
0 59 63 0 21 16 137 7 19
1 62 71 1 19 23 86 60 30
1 62 74 0 21 23 121 13 81
0 51 67 0 21 19 149 20 42
0 64 66 0 22 4 168 52 22
0 52 62 0 21 20 140 28 85
1 67 80 1 22 24 88 25 27
1 50 73 0 22 20 168 39 25
1 54 67 0 22 4 94 9 22
1 58 61 0 22 24 51 19 19
0 56 73 1 21 22 48 13 14
1 63 74 0 22 16 145 60 45
1 31 32 0 23 3 66 19 45
1 65 69 1 19 15 85 34 28
0 71 69 0 22 24 109 14 51
0 50 84 1 21 17 63 17 41
1 57 64 1 19 20 102 45 31
0 47 58 1 19 27 162 66 74
1 54 60 0 20 23 128 24 24
1 47 59 1 20 26 86 48 19
1 57 78 1 18 23 114 29 51
0 43 57 0 21 17 164 -2 73
1 41 60 0 21 20 119 51 24
0 63 68 0 20 22 126 2 61
1 63 68 0 20 19 132 24 23
1 56 73 0 21 24 142 40 14
0 51 69 0 21 19 83 20 54
1 50 67 1 19 23 94 19 51
0 22 60 1 19 15 81 16 62
1 41 65 0 21 27 166 20 36
0 59 66 1 19 26 110 40 59
1 56 74 1 19 22 64 27 24
0 66 81 0 24 22 93 25 26
0 53 72 1 19 18 104 49 54
1 42 55 1 19 15 105 39 39
1 52 49 1 20 22 49 61 16
0 54 74 1 19 27 88 19 36
1 44 53 1 19 10 95 67 31
1 62 64 1 19 20 102 45 31
0 53 65 1 19 17 99 30 42
1 50 57 1 19 23 63 8 39
0 36 51 1 19 19 76 19 25
0 76 80 1 20 13 109 52 31
1 66 67 1 20 27 117 22 38
1 62 70 1 19 23 57 17 31
0 59 74 1 21 16 120 33 17
1 47 75 1 19 25 73 34 22
0 55 70 1 19 2 91 22 55
0 58 69 1 19 26 108 30 62
1 60 65 1 21 20 105 25 51
0 44 55 0 22 23 117 38 30
0 57 71 1 19 22 119 26 49
1 45 65 1 19 24 31 13 16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270021&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]3 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=270021&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270021&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Correlations for all pairs of data series (method=pearson)
genderAMS.IAMS.EstudieprogrammaageNUMERACYTOTLFMPRHCH
gender10.056-0.126-0.0790.2290.182-0.0480.093-0.101
AMS.I0.05610.3490.0070.0410.0840.0720.1430.093
AMS.E-0.1260.34910.044-0.006-0.026-0.007-0.044-0.03
studieprogramma-0.0790.0070.0441-0.6740.038-0.525-0.144-0.181
age0.2290.041-0.006-0.6741-0.0740.2760.1330.053
NUMERACYTOT0.1820.084-0.0260.038-0.07410.0160.0970.098
LFM-0.0480.072-0.007-0.5250.2760.01610.3680.439
PRH0.0930.143-0.044-0.1440.1330.0970.36810.269
CH-0.1010.093-0.03-0.1810.0530.0980.4390.2691

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & gender & AMS.I & AMS.E & studieprogramma & age & NUMERACYTOT & LFM & PRH & CH \tabularnewline
gender & 1 & 0.056 & -0.126 & -0.079 & 0.229 & 0.182 & -0.048 & 0.093 & -0.101 \tabularnewline
AMS.I & 0.056 & 1 & 0.349 & 0.007 & 0.041 & 0.084 & 0.072 & 0.143 & 0.093 \tabularnewline
AMS.E & -0.126 & 0.349 & 1 & 0.044 & -0.006 & -0.026 & -0.007 & -0.044 & -0.03 \tabularnewline
studieprogramma & -0.079 & 0.007 & 0.044 & 1 & -0.674 & 0.038 & -0.525 & -0.144 & -0.181 \tabularnewline
age & 0.229 & 0.041 & -0.006 & -0.674 & 1 & -0.074 & 0.276 & 0.133 & 0.053 \tabularnewline
NUMERACYTOT & 0.182 & 0.084 & -0.026 & 0.038 & -0.074 & 1 & 0.016 & 0.097 & 0.098 \tabularnewline
LFM & -0.048 & 0.072 & -0.007 & -0.525 & 0.276 & 0.016 & 1 & 0.368 & 0.439 \tabularnewline
PRH & 0.093 & 0.143 & -0.044 & -0.144 & 0.133 & 0.097 & 0.368 & 1 & 0.269 \tabularnewline
CH & -0.101 & 0.093 & -0.03 & -0.181 & 0.053 & 0.098 & 0.439 & 0.269 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270021&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]gender[/C][C]AMS.I[/C][C]AMS.E[/C][C]studieprogramma[/C][C]age[/C][C]NUMERACYTOT[/C][C]LFM[/C][C]PRH[/C][C]CH[/C][/ROW]
[ROW][C]gender[/C][C]1[/C][C]0.056[/C][C]-0.126[/C][C]-0.079[/C][C]0.229[/C][C]0.182[/C][C]-0.048[/C][C]0.093[/C][C]-0.101[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.056[/C][C]1[/C][C]0.349[/C][C]0.007[/C][C]0.041[/C][C]0.084[/C][C]0.072[/C][C]0.143[/C][C]0.093[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.126[/C][C]0.349[/C][C]1[/C][C]0.044[/C][C]-0.006[/C][C]-0.026[/C][C]-0.007[/C][C]-0.044[/C][C]-0.03[/C][/ROW]
[ROW][C]studieprogramma[/C][C]-0.079[/C][C]0.007[/C][C]0.044[/C][C]1[/C][C]-0.674[/C][C]0.038[/C][C]-0.525[/C][C]-0.144[/C][C]-0.181[/C][/ROW]
[ROW][C]age[/C][C]0.229[/C][C]0.041[/C][C]-0.006[/C][C]-0.674[/C][C]1[/C][C]-0.074[/C][C]0.276[/C][C]0.133[/C][C]0.053[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.182[/C][C]0.084[/C][C]-0.026[/C][C]0.038[/C][C]-0.074[/C][C]1[/C][C]0.016[/C][C]0.097[/C][C]0.098[/C][/ROW]
[ROW][C]LFM[/C][C]-0.048[/C][C]0.072[/C][C]-0.007[/C][C]-0.525[/C][C]0.276[/C][C]0.016[/C][C]1[/C][C]0.368[/C][C]0.439[/C][/ROW]
[ROW][C]PRH[/C][C]0.093[/C][C]0.143[/C][C]-0.044[/C][C]-0.144[/C][C]0.133[/C][C]0.097[/C][C]0.368[/C][C]1[/C][C]0.269[/C][/ROW]
[ROW][C]CH[/C][C]-0.101[/C][C]0.093[/C][C]-0.03[/C][C]-0.181[/C][C]0.053[/C][C]0.098[/C][C]0.439[/C][C]0.269[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270021&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270021&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)
genderAMS.IAMS.EstudieprogrammaageNUMERACYTOTLFMPRHCH
gender10.056-0.126-0.0790.2290.182-0.0480.093-0.101
AMS.I0.05610.3490.0070.0410.0840.0720.1430.093
AMS.E-0.1260.34910.044-0.006-0.026-0.007-0.044-0.03
studieprogramma-0.0790.0070.0441-0.6740.038-0.525-0.144-0.181
age0.2290.041-0.006-0.6741-0.0740.2760.1330.053
NUMERACYTOT0.1820.084-0.0260.038-0.07410.0160.0970.098
LFM-0.0480.072-0.007-0.5250.2760.01610.3680.439
PRH0.0930.143-0.044-0.1440.1330.0970.36810.269
CH-0.1010.093-0.03-0.1810.0530.0980.4390.2691







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
gender;AMS.I0.05610.04460.0371
p-value(0.3441)(0.452)(0.4511)
gender;AMS.E-0.1261-0.1131-0.0942
p-value(0.033)(0.056)(0.0562)
gender;studieprogramma-0.0791-0.0791-0.0791
p-value(0.1822)(0.1822)(0.1817)
gender;age0.22860.23660.2112
p-value(1e-04)(1e-04)(1e-04)
gender;NUMERACYTOT0.18220.20430.1723
p-value(0.002)(5e-04)(6e-04)
gender;LFM-0.0479-0.0509-0.0418
p-value(0.42)(0.3908)(0.3898)
gender;PRH0.09290.07310.0603
p-value(0.1171)(0.2178)(0.2172)
gender;CH-0.1013-0.1253-0.1033
p-value(0.0871)(0.0341)(0.0344)
AMS.I;AMS.E0.34890.350.247
p-value(0)(0)(0)
AMS.I;studieprogramma0.00690.01460.0121
p-value(0.908)(0.8054)(0.8049)
AMS.I;age0.04155e-04-3e-04
p-value(0.4848)(0.9929)(0.9949)
AMS.I;NUMERACYTOT0.0840.05030.0327
p-value(0.1563)(0.3968)(0.431)
AMS.I;LFM0.07210.06160.0433
p-value(0.2239)(0.2994)(0.2841)
AMS.I;PRH0.1430.08380.0568
p-value(0.0155)(0.1576)(0.1622)
AMS.I;CH0.09340.0780.051
p-value(0.1151)(0.1884)(0.2089)
AMS.E;studieprogramma0.04390.0130.0108
p-value(0.46)(0.827)(0.8265)
AMS.E;age-0.0060.04550.0348
p-value(0.9202)(0.4429)(0.4294)
AMS.E;NUMERACYTOT-0.0264-0.0573-0.0406
p-value(0.6568)(0.3342)(0.3292)
AMS.E;LFM-0.00690.03480.0224
p-value(0.907)(0.5582)(0.5798)
AMS.E;PRH-0.0435-0.0452-0.0318
p-value(0.4633)(0.4462)(0.4343)
AMS.E;CH-0.030.00890.0063
p-value(0.6133)(0.8811)(0.8774)
studieprogramma;age-0.6735-0.7235-0.646
p-value(0)(0)(0)
studieprogramma;NUMERACYTOT0.03820.04410.0372
p-value(0.5204)(0.4573)(0.4563)
studieprogramma;LFM-0.5252-0.5657-0.4643
p-value(0)(0)(0)
studieprogramma;PRH-0.1444-0.1533-0.1265
p-value(0.0145)(0.0094)(0.0097)
studieprogramma;CH-0.1813-0.1809-0.1492
p-value(0.0021)(0.0021)(0.0023)
age;NUMERACYTOT-0.0741-0.0566-0.0429
p-value(0.2113)(0.3404)(0.3366)
age;LFM0.27620.33310.2421
p-value(0)(0)(0)
age;PRH0.13340.10.0714
p-value(0.0241)(0.0914)(0.1018)
age;CH0.05290.04670.0326
p-value(0.3723)(0.4313)(0.4554)
NUMERACYTOT;LFM0.01630.04650.0301
p-value(0.7837)(0.4331)(0.4625)
NUMERACYTOT;PRH0.09740.09160.0643
p-value(0.1004)(0.1223)(0.1191)
NUMERACYTOT;CH0.09810.07990.0537
p-value(0.0978)(0.1776)(0.1928)
LFM;PRH0.36830.37360.2591
p-value(0)(0)(0)
LFM;CH0.43860.42740.3
p-value(0)(0)(0)
PRH;CH0.26880.23020.1633
p-value(0)(1e-04)(1e-04)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
gender;AMS.I & 0.0561 & 0.0446 & 0.0371 \tabularnewline
p-value & (0.3441) & (0.452) & (0.4511) \tabularnewline
gender;AMS.E & -0.1261 & -0.1131 & -0.0942 \tabularnewline
p-value & (0.033) & (0.056) & (0.0562) \tabularnewline
gender;studieprogramma & -0.0791 & -0.0791 & -0.0791 \tabularnewline
p-value & (0.1822) & (0.1822) & (0.1817) \tabularnewline
gender;age & 0.2286 & 0.2366 & 0.2112 \tabularnewline
p-value & (1e-04) & (1e-04) & (1e-04) \tabularnewline
gender;NUMERACYTOT & 0.1822 & 0.2043 & 0.1723 \tabularnewline
p-value & (0.002) & (5e-04) & (6e-04) \tabularnewline
gender;LFM & -0.0479 & -0.0509 & -0.0418 \tabularnewline
p-value & (0.42) & (0.3908) & (0.3898) \tabularnewline
gender;PRH & 0.0929 & 0.0731 & 0.0603 \tabularnewline
p-value & (0.1171) & (0.2178) & (0.2172) \tabularnewline
gender;CH & -0.1013 & -0.1253 & -0.1033 \tabularnewline
p-value & (0.0871) & (0.0341) & (0.0344) \tabularnewline
AMS.I;AMS.E & 0.3489 & 0.35 & 0.247 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AMS.I;studieprogramma & 0.0069 & 0.0146 & 0.0121 \tabularnewline
p-value & (0.908) & (0.8054) & (0.8049) \tabularnewline
AMS.I;age & 0.0415 & 5e-04 & -3e-04 \tabularnewline
p-value & (0.4848) & (0.9929) & (0.9949) \tabularnewline
AMS.I;NUMERACYTOT & 0.084 & 0.0503 & 0.0327 \tabularnewline
p-value & (0.1563) & (0.3968) & (0.431) \tabularnewline
AMS.I;LFM & 0.0721 & 0.0616 & 0.0433 \tabularnewline
p-value & (0.2239) & (0.2994) & (0.2841) \tabularnewline
AMS.I;PRH & 0.143 & 0.0838 & 0.0568 \tabularnewline
p-value & (0.0155) & (0.1576) & (0.1622) \tabularnewline
AMS.I;CH & 0.0934 & 0.078 & 0.051 \tabularnewline
p-value & (0.1151) & (0.1884) & (0.2089) \tabularnewline
AMS.E;studieprogramma & 0.0439 & 0.013 & 0.0108 \tabularnewline
p-value & (0.46) & (0.827) & (0.8265) \tabularnewline
AMS.E;age & -0.006 & 0.0455 & 0.0348 \tabularnewline
p-value & (0.9202) & (0.4429) & (0.4294) \tabularnewline
AMS.E;NUMERACYTOT & -0.0264 & -0.0573 & -0.0406 \tabularnewline
p-value & (0.6568) & (0.3342) & (0.3292) \tabularnewline
AMS.E;LFM & -0.0069 & 0.0348 & 0.0224 \tabularnewline
p-value & (0.907) & (0.5582) & (0.5798) \tabularnewline
AMS.E;PRH & -0.0435 & -0.0452 & -0.0318 \tabularnewline
p-value & (0.4633) & (0.4462) & (0.4343) \tabularnewline
AMS.E;CH & -0.03 & 0.0089 & 0.0063 \tabularnewline
p-value & (0.6133) & (0.8811) & (0.8774) \tabularnewline
studieprogramma;age & -0.6735 & -0.7235 & -0.646 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
studieprogramma;NUMERACYTOT & 0.0382 & 0.0441 & 0.0372 \tabularnewline
p-value & (0.5204) & (0.4573) & (0.4563) \tabularnewline
studieprogramma;LFM & -0.5252 & -0.5657 & -0.4643 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
studieprogramma;PRH & -0.1444 & -0.1533 & -0.1265 \tabularnewline
p-value & (0.0145) & (0.0094) & (0.0097) \tabularnewline
studieprogramma;CH & -0.1813 & -0.1809 & -0.1492 \tabularnewline
p-value & (0.0021) & (0.0021) & (0.0023) \tabularnewline
age;NUMERACYTOT & -0.0741 & -0.0566 & -0.0429 \tabularnewline
p-value & (0.2113) & (0.3404) & (0.3366) \tabularnewline
age;LFM & 0.2762 & 0.3331 & 0.2421 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
age;PRH & 0.1334 & 0.1 & 0.0714 \tabularnewline
p-value & (0.0241) & (0.0914) & (0.1018) \tabularnewline
age;CH & 0.0529 & 0.0467 & 0.0326 \tabularnewline
p-value & (0.3723) & (0.4313) & (0.4554) \tabularnewline
NUMERACYTOT;LFM & 0.0163 & 0.0465 & 0.0301 \tabularnewline
p-value & (0.7837) & (0.4331) & (0.4625) \tabularnewline
NUMERACYTOT;PRH & 0.0974 & 0.0916 & 0.0643 \tabularnewline
p-value & (0.1004) & (0.1223) & (0.1191) \tabularnewline
NUMERACYTOT;CH & 0.0981 & 0.0799 & 0.0537 \tabularnewline
p-value & (0.0978) & (0.1776) & (0.1928) \tabularnewline
LFM;PRH & 0.3683 & 0.3736 & 0.2591 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
LFM;CH & 0.4386 & 0.4274 & 0.3 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
PRH;CH & 0.2688 & 0.2302 & 0.1633 \tabularnewline
p-value & (0) & (1e-04) & (1e-04) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270021&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;AMS.I[/C][C]0.0561[/C][C]0.0446[/C][C]0.0371[/C][/ROW]
[ROW][C]p-value[/C][C](0.3441)[/C][C](0.452)[/C][C](0.4511)[/C][/ROW]
[ROW][C]gender;AMS.E[/C][C]-0.1261[/C][C]-0.1131[/C][C]-0.0942[/C][/ROW]
[ROW][C]p-value[/C][C](0.033)[/C][C](0.056)[/C][C](0.0562)[/C][/ROW]
[ROW][C]gender;studieprogramma[/C][C]-0.0791[/C][C]-0.0791[/C][C]-0.0791[/C][/ROW]
[ROW][C]p-value[/C][C](0.1822)[/C][C](0.1822)[/C][C](0.1817)[/C][/ROW]
[ROW][C]gender;age[/C][C]0.2286[/C][C]0.2366[/C][C]0.2112[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]gender;NUMERACYTOT[/C][C]0.1822[/C][C]0.2043[/C][C]0.1723[/C][/ROW]
[ROW][C]p-value[/C][C](0.002)[/C][C](5e-04)[/C][C](6e-04)[/C][/ROW]
[ROW][C]gender;LFM[/C][C]-0.0479[/C][C]-0.0509[/C][C]-0.0418[/C][/ROW]
[ROW][C]p-value[/C][C](0.42)[/C][C](0.3908)[/C][C](0.3898)[/C][/ROW]
[ROW][C]gender;PRH[/C][C]0.0929[/C][C]0.0731[/C][C]0.0603[/C][/ROW]
[ROW][C]p-value[/C][C](0.1171)[/C][C](0.2178)[/C][C](0.2172)[/C][/ROW]
[ROW][C]gender;CH[/C][C]-0.1013[/C][C]-0.1253[/C][C]-0.1033[/C][/ROW]
[ROW][C]p-value[/C][C](0.0871)[/C][C](0.0341)[/C][C](0.0344)[/C][/ROW]
[ROW][C]AMS.I;AMS.E[/C][C]0.3489[/C][C]0.35[/C][C]0.247[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AMS.I;studieprogramma[/C][C]0.0069[/C][C]0.0146[/C][C]0.0121[/C][/ROW]
[ROW][C]p-value[/C][C](0.908)[/C][C](0.8054)[/C][C](0.8049)[/C][/ROW]
[ROW][C]AMS.I;age[/C][C]0.0415[/C][C]5e-04[/C][C]-3e-04[/C][/ROW]
[ROW][C]p-value[/C][C](0.4848)[/C][C](0.9929)[/C][C](0.9949)[/C][/ROW]
[ROW][C]AMS.I;NUMERACYTOT[/C][C]0.084[/C][C]0.0503[/C][C]0.0327[/C][/ROW]
[ROW][C]p-value[/C][C](0.1563)[/C][C](0.3968)[/C][C](0.431)[/C][/ROW]
[ROW][C]AMS.I;LFM[/C][C]0.0721[/C][C]0.0616[/C][C]0.0433[/C][/ROW]
[ROW][C]p-value[/C][C](0.2239)[/C][C](0.2994)[/C][C](0.2841)[/C][/ROW]
[ROW][C]AMS.I;PRH[/C][C]0.143[/C][C]0.0838[/C][C]0.0568[/C][/ROW]
[ROW][C]p-value[/C][C](0.0155)[/C][C](0.1576)[/C][C](0.1622)[/C][/ROW]
[ROW][C]AMS.I;CH[/C][C]0.0934[/C][C]0.078[/C][C]0.051[/C][/ROW]
[ROW][C]p-value[/C][C](0.1151)[/C][C](0.1884)[/C][C](0.2089)[/C][/ROW]
[ROW][C]AMS.E;studieprogramma[/C][C]0.0439[/C][C]0.013[/C][C]0.0108[/C][/ROW]
[ROW][C]p-value[/C][C](0.46)[/C][C](0.827)[/C][C](0.8265)[/C][/ROW]
[ROW][C]AMS.E;age[/C][C]-0.006[/C][C]0.0455[/C][C]0.0348[/C][/ROW]
[ROW][C]p-value[/C][C](0.9202)[/C][C](0.4429)[/C][C](0.4294)[/C][/ROW]
[ROW][C]AMS.E;NUMERACYTOT[/C][C]-0.0264[/C][C]-0.0573[/C][C]-0.0406[/C][/ROW]
[ROW][C]p-value[/C][C](0.6568)[/C][C](0.3342)[/C][C](0.3292)[/C][/ROW]
[ROW][C]AMS.E;LFM[/C][C]-0.0069[/C][C]0.0348[/C][C]0.0224[/C][/ROW]
[ROW][C]p-value[/C][C](0.907)[/C][C](0.5582)[/C][C](0.5798)[/C][/ROW]
[ROW][C]AMS.E;PRH[/C][C]-0.0435[/C][C]-0.0452[/C][C]-0.0318[/C][/ROW]
[ROW][C]p-value[/C][C](0.4633)[/C][C](0.4462)[/C][C](0.4343)[/C][/ROW]
[ROW][C]AMS.E;CH[/C][C]-0.03[/C][C]0.0089[/C][C]0.0063[/C][/ROW]
[ROW][C]p-value[/C][C](0.6133)[/C][C](0.8811)[/C][C](0.8774)[/C][/ROW]
[ROW][C]studieprogramma;age[/C][C]-0.6735[/C][C]-0.7235[/C][C]-0.646[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]studieprogramma;NUMERACYTOT[/C][C]0.0382[/C][C]0.0441[/C][C]0.0372[/C][/ROW]
[ROW][C]p-value[/C][C](0.5204)[/C][C](0.4573)[/C][C](0.4563)[/C][/ROW]
[ROW][C]studieprogramma;LFM[/C][C]-0.5252[/C][C]-0.5657[/C][C]-0.4643[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]studieprogramma;PRH[/C][C]-0.1444[/C][C]-0.1533[/C][C]-0.1265[/C][/ROW]
[ROW][C]p-value[/C][C](0.0145)[/C][C](0.0094)[/C][C](0.0097)[/C][/ROW]
[ROW][C]studieprogramma;CH[/C][C]-0.1813[/C][C]-0.1809[/C][C]-0.1492[/C][/ROW]
[ROW][C]p-value[/C][C](0.0021)[/C][C](0.0021)[/C][C](0.0023)[/C][/ROW]
[ROW][C]age;NUMERACYTOT[/C][C]-0.0741[/C][C]-0.0566[/C][C]-0.0429[/C][/ROW]
[ROW][C]p-value[/C][C](0.2113)[/C][C](0.3404)[/C][C](0.3366)[/C][/ROW]
[ROW][C]age;LFM[/C][C]0.2762[/C][C]0.3331[/C][C]0.2421[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]age;PRH[/C][C]0.1334[/C][C]0.1[/C][C]0.0714[/C][/ROW]
[ROW][C]p-value[/C][C](0.0241)[/C][C](0.0914)[/C][C](0.1018)[/C][/ROW]
[ROW][C]age;CH[/C][C]0.0529[/C][C]0.0467[/C][C]0.0326[/C][/ROW]
[ROW][C]p-value[/C][C](0.3723)[/C][C](0.4313)[/C][C](0.4554)[/C][/ROW]
[ROW][C]NUMERACYTOT;LFM[/C][C]0.0163[/C][C]0.0465[/C][C]0.0301[/C][/ROW]
[ROW][C]p-value[/C][C](0.7837)[/C][C](0.4331)[/C][C](0.4625)[/C][/ROW]
[ROW][C]NUMERACYTOT;PRH[/C][C]0.0974[/C][C]0.0916[/C][C]0.0643[/C][/ROW]
[ROW][C]p-value[/C][C](0.1004)[/C][C](0.1223)[/C][C](0.1191)[/C][/ROW]
[ROW][C]NUMERACYTOT;CH[/C][C]0.0981[/C][C]0.0799[/C][C]0.0537[/C][/ROW]
[ROW][C]p-value[/C][C](0.0978)[/C][C](0.1776)[/C][C](0.1928)[/C][/ROW]
[ROW][C]LFM;PRH[/C][C]0.3683[/C][C]0.3736[/C][C]0.2591[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]LFM;CH[/C][C]0.4386[/C][C]0.4274[/C][C]0.3[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]PRH;CH[/C][C]0.2688[/C][C]0.2302[/C][C]0.1633[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270021&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270021&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;AMS.I0.05610.04460.0371
p-value(0.3441)(0.452)(0.4511)
gender;AMS.E-0.1261-0.1131-0.0942
p-value(0.033)(0.056)(0.0562)
gender;studieprogramma-0.0791-0.0791-0.0791
p-value(0.1822)(0.1822)(0.1817)
gender;age0.22860.23660.2112
p-value(1e-04)(1e-04)(1e-04)
gender;NUMERACYTOT0.18220.20430.1723
p-value(0.002)(5e-04)(6e-04)
gender;LFM-0.0479-0.0509-0.0418
p-value(0.42)(0.3908)(0.3898)
gender;PRH0.09290.07310.0603
p-value(0.1171)(0.2178)(0.2172)
gender;CH-0.1013-0.1253-0.1033
p-value(0.0871)(0.0341)(0.0344)
AMS.I;AMS.E0.34890.350.247
p-value(0)(0)(0)
AMS.I;studieprogramma0.00690.01460.0121
p-value(0.908)(0.8054)(0.8049)
AMS.I;age0.04155e-04-3e-04
p-value(0.4848)(0.9929)(0.9949)
AMS.I;NUMERACYTOT0.0840.05030.0327
p-value(0.1563)(0.3968)(0.431)
AMS.I;LFM0.07210.06160.0433
p-value(0.2239)(0.2994)(0.2841)
AMS.I;PRH0.1430.08380.0568
p-value(0.0155)(0.1576)(0.1622)
AMS.I;CH0.09340.0780.051
p-value(0.1151)(0.1884)(0.2089)
AMS.E;studieprogramma0.04390.0130.0108
p-value(0.46)(0.827)(0.8265)
AMS.E;age-0.0060.04550.0348
p-value(0.9202)(0.4429)(0.4294)
AMS.E;NUMERACYTOT-0.0264-0.0573-0.0406
p-value(0.6568)(0.3342)(0.3292)
AMS.E;LFM-0.00690.03480.0224
p-value(0.907)(0.5582)(0.5798)
AMS.E;PRH-0.0435-0.0452-0.0318
p-value(0.4633)(0.4462)(0.4343)
AMS.E;CH-0.030.00890.0063
p-value(0.6133)(0.8811)(0.8774)
studieprogramma;age-0.6735-0.7235-0.646
p-value(0)(0)(0)
studieprogramma;NUMERACYTOT0.03820.04410.0372
p-value(0.5204)(0.4573)(0.4563)
studieprogramma;LFM-0.5252-0.5657-0.4643
p-value(0)(0)(0)
studieprogramma;PRH-0.1444-0.1533-0.1265
p-value(0.0145)(0.0094)(0.0097)
studieprogramma;CH-0.1813-0.1809-0.1492
p-value(0.0021)(0.0021)(0.0023)
age;NUMERACYTOT-0.0741-0.0566-0.0429
p-value(0.2113)(0.3404)(0.3366)
age;LFM0.27620.33310.2421
p-value(0)(0)(0)
age;PRH0.13340.10.0714
p-value(0.0241)(0.0914)(0.1018)
age;CH0.05290.04670.0326
p-value(0.3723)(0.4313)(0.4554)
NUMERACYTOT;LFM0.01630.04650.0301
p-value(0.7837)(0.4331)(0.4625)
NUMERACYTOT;PRH0.09740.09160.0643
p-value(0.1004)(0.1223)(0.1191)
NUMERACYTOT;CH0.09810.07990.0537
p-value(0.0978)(0.1776)(0.1928)
LFM;PRH0.36830.37360.2591
p-value(0)(0)(0)
LFM;CH0.43860.42740.3
p-value(0)(0)(0)
PRH;CH0.26880.23020.1633
p-value(0)(1e-04)(1e-04)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.280.310.31
0.020.330.310.31
0.030.360.310.31
0.040.390.330.33
0.050.390.330.33
0.060.390.360.36
0.070.390.360.36
0.080.390.360.36
0.090.420.360.36
0.10.440.390.36

\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 & 0.28 & 0.31 & 0.31 \tabularnewline
0.02 & 0.33 & 0.31 & 0.31 \tabularnewline
0.03 & 0.36 & 0.31 & 0.31 \tabularnewline
0.04 & 0.39 & 0.33 & 0.33 \tabularnewline
0.05 & 0.39 & 0.33 & 0.33 \tabularnewline
0.06 & 0.39 & 0.36 & 0.36 \tabularnewline
0.07 & 0.39 & 0.36 & 0.36 \tabularnewline
0.08 & 0.39 & 0.36 & 0.36 \tabularnewline
0.09 & 0.42 & 0.36 & 0.36 \tabularnewline
0.1 & 0.44 & 0.39 & 0.36 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270021&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]0.28[/C][C]0.31[/C][C]0.31[/C][/ROW]
[ROW][C]0.02[/C][C]0.33[/C][C]0.31[/C][C]0.31[/C][/ROW]
[ROW][C]0.03[/C][C]0.36[/C][C]0.31[/C][C]0.31[/C][/ROW]
[ROW][C]0.04[/C][C]0.39[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.05[/C][C]0.39[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.06[/C][C]0.39[/C][C]0.36[/C][C]0.36[/C][/ROW]
[ROW][C]0.07[/C][C]0.39[/C][C]0.36[/C][C]0.36[/C][/ROW]
[ROW][C]0.08[/C][C]0.39[/C][C]0.36[/C][C]0.36[/C][/ROW]
[ROW][C]0.09[/C][C]0.42[/C][C]0.36[/C][C]0.36[/C][/ROW]
[ROW][C]0.1[/C][C]0.44[/C][C]0.39[/C][C]0.36[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270021&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270021&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 errorPearson rSpearman rhoKendall tau
0.010.280.310.31
0.020.330.310.31
0.030.360.310.31
0.040.390.330.33
0.050.390.330.33
0.060.390.360.36
0.070.390.360.36
0.080.390.360.36
0.090.420.360.36
0.10.440.390.36



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')