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R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Tue, 01 Dec 2015 12:00:57 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/01/t1448971336ml40lfaumlkefak.htm/, Retrieved Thu, 16 May 2024 13:20:06 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284687, Retrieved Thu, 16 May 2024 13:20:06 +0000

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-       [Kendall tau Correlation Matrix] [] [2015-12-01 12:00:57] [99e6c1fc4b516bf23f1fe004560f1c88] [Current]

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12.9 149 18 68 0
12.2 139 31 39 1
12.8 148 39 32 0
7.4 158 46 62 1
6.7 128 31 33 1
12.6 224 67 52 1
14.8 159 35 62 0
13.3 105 52 77 1
11.1 159 77 76 1
8.2 167 37 41 1
11.4 165 32 48 1
6.4 159 36 63 1
10.6 119 38 30 1
12 176 69 78 0
6.3 54 21 19 0
11.3 91 26 31 0
11.9 163 54 66 1
9.3 124 36 35 0
9.6 137 42 42 1
10 121 23 45 0
6.4 153 34 21 1
13.8 148 112 25 1
10.8 221 35 44 0
13.8 188 47 69 1
11.7 149 47 54 1
10.9 244 37 74 1
16.1 148 109 80 1
13.4 92 24 42 0
9.9 150 20 61 1
11.5 153 22 41 0
8.3 94 23 46 0
11.7 156 32 39 0
9 132 30 34 1
9.7 161 92 51 1
10.8 105 43 42 1
10.3 97 55 31 1
10.4 151 16 39 0
12.7 131 49 20 1
9.3 166 71 49 1
11.8 157 43 53 0
5.9 111 29 31 1
11.4 145 56 39 1
13 162 46 54 1
10.8 163 19 49 1
12.3 59 23 34 1
11.3 187 59 46 0
11.8 109 30 55 1
7.9 90 61 42 1
12.7 105 7 50 0
12.3 83 38 13 1
11.6 116 32 37 1
6.7 42 16 25 1
10.9 148 19 30 1
12.1 155 22 28 1
13.3 125 48 45 1
10.1 116 23 35 1
5.7 128 26 28 0
14.3 138 33 41 1
8 49 9 6 0
13.3 96 24 45 1
9.3 164 34 73 1
12.5 162 48 17 0
7.6 99 18 40 0
15.9 202 43 64 1
9.2 186 33 37 0
9.1 66 28 25 1
11.1 183 71 65 0
13 214 26 100 1
14.5 188 67 28 1
12.2 104 34 35 0
12.3 177 80 56 0
11.4 126 29 29 0
8.8 76 16 43 0
14.6 99 59 59 1
12.6 139 32 50 0
13 162 43 59 0
12.6 108 38 27 1
13.2 159 29 61 0
9.9 74 36 28 0
7.7 110 32 51 1
10.5 96 35 35 0
13.4 116 21 29 0
10.9 87 29 48 0
4.3 97 12 25 1
10.3 127 37 44 0
11.8 106 37 64 1
11.2 80 47 32 1
11.4 74 51 20 0
8.6 91 32 28 0
13.2 133 21 34 0
12.6 74 13 31 1
5.6 114 14 26 1
9.9 140 -2 58 1
8.8 95 20 23 0
7.7 98 24 21 1
9 121 11 21 0
7.3 126 23 33 1
11.4 98 24 16 1
13.6 95 14 20 1
7.9 110 52 37 1
10.7 70 15 35 1
10.3 102 23 33 0
8.3 86 19 27 1
9.6 130 35 41 1
14.2 96 24 40 1
8.5 102 39 35 0
13.5 100 29 28 0
4.9 94 13 32 0
6.4 52 8 22 0
9.6 98 18 44 0
11.6 118 24 27 0
11.1 99 19 17 1
4.35 48 23 12 1
12.7 50 16 45 1
18.1 150 33 37 1
17.85 154 32 37 1
16.6 109 37 108 0
12.6 68 14 10 1
17.1 194 52 68 1
19.1 158 75 72 0
16.1 159 72 143 1
13.35 67 15 9 0
18.4 147 29 55 0
14.7 39 13 17 1
10.6 100 40 37 1
12.6 111 19 27 1
16.2 138 24 37 1
13.6 101 121 58 1
18.9 131 93 66 1
14.1 101 36 21 1
14.5 114 23 19 1
16.15 165 85 78 0
14.75 114 41 35 1
14.8 111 46 48 1
12.45 75 18 27 1
12.65 82 35 43 1
17.35 121 17 30 1
8.6 32 4 25 1
18.4 150 28 69 0
16.1 117 44 72 1
11.6 71 10 23 1
17.75 165 38 13 1
15.25 154 57 61 1
17.65 126 23 43 1
16.35 149 36 51 0
17.65 145 22 67 0
13.6 120 40 36 1
14.35 109 31 44 0
14.75 132 11 45 0
18.25 172 38 34 1
9.9 169 24 36 0
16 114 37 72 1
18.25 156 37 39 1
16.85 172 22 43 0
14.6 68 15 25 1
13.85 89 2 56 1
18.95 167 43 80 1
15.6 113 31 40 0
14.85 115 29 73 0
11.75 78 45 34 0
18.45 118 25 72 0
15.9 87 4 42 1
17.1 173 31 61 0
16.1 2 -4 23 1
19.9 162 66 74 0
10.95 49 61 16 1
18.45 122 32 66 0
15.1 96 31 9 1
15 100 39 41 0
11.35 82 19 57 0
15.95 100 31 48 1
18.1 115 36 51 0
14.6 141 42 53 1
15.4 165 21 29 1
15.4 165 21 29 1
17.6 110 25 55 1
13.35 118 32 54 1
19.1 158 26 43 0
15.35 146 28 51 1
7.6 49 32 20 0
13.4 90 41 79 0
13.9 121 29 39 0
19.1 155 33 61 1
15.25 104 17 55 0
12.9 147 13 30 1
16.1 110 32 55 0
17.35 108 30 22 0
13.15 113 34 37 0
12.15 115 59 2 0
12.6 61 13 38 1
10.35 60 23 27 1
15.4 109 10 56 1
9.6 68 5 25 1
18.2 111 31 39 0
13.6 77 19 33 0
14.85 73 32 43 1
14.75 151 30 57 0
14.1 89 25 43 0
14.9 78 48 23 0
16.25 110 35 44 0
19.25 220 67 54 1
13.6 65 15 28 1
13.6 141 22 36 0
15.65 117 18 39 0
12.75 122 33 16 1
14.6 63 46 23 0
9.85 44 24 40 1
12.65 52 14 24 1
19.2 131 12 78 0
16.6 101 38 57 1
11.2 42 12 37 1
15.25 152 28 27 1
11.9 107 41 61 0
13.2 77 12 27 0
16.35 154 31 69 0
12.4 103 33 34 1
15.85 96 34 44 1
18.15 175 21 34 1
11.15 57 20 39 1
15.65 112 44 51 0
17.75 143 52 34 0
7.65 49 7 31 0
12.35 110 29 13 1
15.6 131 11 12 1
19.3 167 26 51 0
15.2 56 24 24 0
17.1 137 7 19 0
15.6 86 60 30 1
18.4 121 13 81 1
19.05 149 20 42 0
18.55 168 52 22 0
19.1 140 28 85 0
13.1 88 25 27 1
12.85 168 39 25 1
9.5 94 9 22 1
4.5 51 19 19 1
11.85 48 13 14 0
13.6 145 60 45 1
11.7 66 19 45 1
12.4 85 34 28 1
13.35 109 14 51 0
11.4 63 17 41 0
14.9 102 45 31 1
19.9 162 66 74 0
11.2 86 48 19 1
14.6 114 29 51 1
17.6 164 -2 73 0
14.05 119 51 24 1
16.1 126 2 61 0
13.35 132 24 23 1
11.85 142 40 14 1
11.95 83 20 54 0
14.75 94 19 51 1
15.15 81 16 62 0
13.2 166 20 36 1
16.85 110 40 59 0
7.85 64 27 24 1
7.7 93 25 26 0
12.6 104 49 54 0
7.85 105 39 39 1
10.95 49 61 16 1
12.35 88 19 36 0
9.95 95 67 31 1
14.9 102 45 31 1
16.65 99 30 42 0
13.4 63 8 39 1
13.95 76 19 25 0
15.7 109 52 31 0
16.85 117 22 38 1
10.95 57 17 31 1
15.35 120 33 17 0
12.2 73 34 22 1
15.1 91 22 55 0
17.75 108 30 62 0
15.2 105 25 51 1
14.6 117 38 30 0
16.65 119 26 49 0
8.1 31 13 16 1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'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 & 1 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284687&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284687&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284687&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Correlations for all pairs of data series (method=pearson)
	TOT	LFM	PRH	CH	gender
TOT	1	0.303	0.136	0.366	-0.131
LFM	0.303	1	0.378	0.446	-0.039
PRH	0.136	0.378	1	0.277	0.09
CH	0.366	0.446	0.277	1	-0.115
gender	-0.131	-0.039	0.09	-0.115	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & TOT & LFM & PRH & CH & gender \tabularnewline
TOT & 1 & 0.303 & 0.136 & 0.366 & -0.131 \tabularnewline
LFM & 0.303 & 1 & 0.378 & 0.446 & -0.039 \tabularnewline
PRH & 0.136 & 0.378 & 1 & 0.277 & 0.09 \tabularnewline
CH & 0.366 & 0.446 & 0.277 & 1 & -0.115 \tabularnewline
gender & -0.131 & -0.039 & 0.09 & -0.115 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284687&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]TOT[/C][C]LFM[/C][C]PRH[/C][C]CH[/C][C]gender[/C][/ROW]
[ROW][C]TOT[/C][C]1[/C][C]0.303[/C][C]0.136[/C][C]0.366[/C][C]-0.131[/C][/ROW]
[ROW][C]LFM[/C][C]0.303[/C][C]1[/C][C]0.378[/C][C]0.446[/C][C]-0.039[/C][/ROW]
[ROW][C]PRH[/C][C]0.136[/C][C]0.378[/C][C]1[/C][C]0.277[/C][C]0.09[/C][/ROW]
[ROW][C]CH[/C][C]0.366[/C][C]0.446[/C][C]0.277[/C][C]1[/C][C]-0.115[/C][/ROW]
[ROW][C]gender[/C][C]-0.131[/C][C]-0.039[/C][C]0.09[/C][C]-0.115[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284687&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284687&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)
	TOT	LFM	PRH	CH	gender
TOT	1	0.303	0.136	0.366	-0.131
LFM	0.303	1	0.378	0.446	-0.039
PRH	0.136	0.378	1	0.277	0.09
CH	0.366	0.446	0.277	1	-0.115
gender	-0.131	-0.039	0.09	-0.115	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
TOT;LFM	0.3027	0.3008	0.2116
p-value	(0)	(0)	(0)
TOT;PRH	0.1362	0.1179	0.078
p-value	(0.0232)	(0.0495)	(0.0554)
TOT;CH	0.3657	0.3456	0.2389
p-value	(0)	(0)	(0)
TOT;gender	-0.1314	-0.1255	-0.103
p-value	(0.0285)	(0.0365)	(0.0368)
LFM;PRH	0.3782	0.3858	0.2675
p-value	(0)	(0)	(0)
LFM;CH	0.4465	0.4389	0.3087
p-value	(0)	(0)	(0)
LFM;gender	-0.0392	-0.0429	-0.0352
p-value	(0.5151)	(0.4758)	(0.4748)
PRH;CH	0.2765	0.2425	0.1726
p-value	(0)	(0)	(0)
PRH;gender	0.0901	0.072	0.0594
p-value	(0.1339)	(0.2317)	(0.231)
CH;gender	-0.1145	-0.1431	-0.118
p-value	(0.0564)	(0.017)	(0.0173)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
TOT;LFM & 0.3027 & 0.3008 & 0.2116 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
TOT;PRH & 0.1362 & 0.1179 & 0.078 \tabularnewline
p-value & (0.0232) & (0.0495) & (0.0554) \tabularnewline
TOT;CH & 0.3657 & 0.3456 & 0.2389 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
TOT;gender & -0.1314 & -0.1255 & -0.103 \tabularnewline
p-value & (0.0285) & (0.0365) & (0.0368) \tabularnewline
LFM;PRH & 0.3782 & 0.3858 & 0.2675 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
LFM;CH & 0.4465 & 0.4389 & 0.3087 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
LFM;gender & -0.0392 & -0.0429 & -0.0352 \tabularnewline
p-value & (0.5151) & (0.4758) & (0.4748) \tabularnewline
PRH;CH & 0.2765 & 0.2425 & 0.1726 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
PRH;gender & 0.0901 & 0.072 & 0.0594 \tabularnewline
p-value & (0.1339) & (0.2317) & (0.231) \tabularnewline
CH;gender & -0.1145 & -0.1431 & -0.118 \tabularnewline
p-value & (0.0564) & (0.017) & (0.0173) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284687&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]TOT;LFM[/C][C]0.3027[/C][C]0.3008[/C][C]0.2116[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]TOT;PRH[/C][C]0.1362[/C][C]0.1179[/C][C]0.078[/C][/ROW]
[ROW][C]p-value[/C][C](0.0232)[/C][C](0.0495)[/C][C](0.0554)[/C][/ROW]
[ROW][C]TOT;CH[/C][C]0.3657[/C][C]0.3456[/C][C]0.2389[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]TOT;gender[/C][C]-0.1314[/C][C]-0.1255[/C][C]-0.103[/C][/ROW]
[ROW][C]p-value[/C][C](0.0285)[/C][C](0.0365)[/C][C](0.0368)[/C][/ROW]
[ROW][C]LFM;PRH[/C][C]0.3782[/C][C]0.3858[/C][C]0.2675[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]LFM;CH[/C][C]0.4465[/C][C]0.4389[/C][C]0.3087[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]LFM;gender[/C][C]-0.0392[/C][C]-0.0429[/C][C]-0.0352[/C][/ROW]
[ROW][C]p-value[/C][C](0.5151)[/C][C](0.4758)[/C][C](0.4748)[/C][/ROW]
[ROW][C]PRH;CH[/C][C]0.2765[/C][C]0.2425[/C][C]0.1726[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]PRH;gender[/C][C]0.0901[/C][C]0.072[/C][C]0.0594[/C][/ROW]
[ROW][C]p-value[/C][C](0.1339)[/C][C](0.2317)[/C][C](0.231)[/C][/ROW]
[ROW][C]CH;gender[/C][C]-0.1145[/C][C]-0.1431[/C][C]-0.118[/C][/ROW]
[ROW][C]p-value[/C][C](0.0564)[/C][C](0.017)[/C][C](0.0173)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284687&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284687&T=2

As an alternative you can also use a QR Code:

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

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
TOT;LFM	0.3027	0.3008	0.2116
p-value	(0)	(0)	(0)
TOT;PRH	0.1362	0.1179	0.078
p-value	(0.0232)	(0.0495)	(0.0554)
TOT;CH	0.3657	0.3456	0.2389
p-value	(0)	(0)	(0)
TOT;gender	-0.1314	-0.1255	-0.103
p-value	(0.0285)	(0.0365)	(0.0368)
LFM;PRH	0.3782	0.3858	0.2675
p-value	(0)	(0)	(0)
LFM;CH	0.4465	0.4389	0.3087
p-value	(0)	(0)	(0)
LFM;gender	-0.0392	-0.0429	-0.0352
p-value	(0.5151)	(0.4758)	(0.4748)
PRH;CH	0.2765	0.2425	0.1726
p-value	(0)	(0)	(0)
PRH;gender	0.0901	0.072	0.0594
p-value	(0.1339)	(0.2317)	(0.231)
CH;gender	-0.1145	-0.1431	-0.118
p-value	(0.0564)	(0.017)	(0.0173)

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I error	Pearson r	Spearman rho	Kendall tau
0.01	0.5	0.5	0.5
0.02	0.5	0.6	0.6
0.03	0.7	0.6	0.6
0.04	0.7	0.7	0.7
0.05	0.7	0.8	0.7
0.06	0.8	0.8	0.8
0.07	0.8	0.8	0.8
0.08	0.8	0.8	0.8
0.09	0.8	0.8	0.8
0.1	0.8	0.8	0.8

\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 & 0.5 & 0.5 & 0.5 \tabularnewline
0.02 & 0.5 & 0.6 & 0.6 \tabularnewline
0.03 & 0.7 & 0.6 & 0.6 \tabularnewline
0.04 & 0.7 & 0.7 & 0.7 \tabularnewline
0.05 & 0.7 & 0.8 & 0.7 \tabularnewline
0.06 & 0.8 & 0.8 & 0.8 \tabularnewline
0.07 & 0.8 & 0.8 & 0.8 \tabularnewline
0.08 & 0.8 & 0.8 & 0.8 \tabularnewline
0.09 & 0.8 & 0.8 & 0.8 \tabularnewline
0.1 & 0.8 & 0.8 & 0.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284687&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]0.5[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]0.02[/C][C]0.5[/C][C]0.6[/C][C]0.6[/C][/ROW]
[ROW][C]0.03[/C][C]0.7[/C][C]0.6[/C][C]0.6[/C][/ROW]
[ROW][C]0.04[/C][C]0.7[/C][C]0.7[/C][C]0.7[/C][/ROW]
[ROW][C]0.05[/C][C]0.7[/C][C]0.8[/C][C]0.7[/C][/ROW]
[ROW][C]0.06[/C][C]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[ROW][C]0.07[/C][C]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[ROW][C]0.08[/C][C]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[ROW][C]0.09[/C][C]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[ROW][C]0.1[/C][C]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284687&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284687&T=3

As an alternative you can also use a QR Code:

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

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I error	Pearson r	Spearman rho	Kendall tau
0.01	0.5	0.5	0.5
0.02	0.5	0.6	0.6
0.03	0.7	0.6	0.6
0.04	0.7	0.7	0.7
0.05	0.7	0.8	0.7
0.06	0.8	0.8	0.8
0.07	0.8	0.8	0.8
0.08	0.8	0.8	0.8
0.09	0.8	0.8	0.8
0.1	0.8	0.8	0.8

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
x <- na.omit(x)
y <- t(na.omit(t(y)))
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
ncorrs <- (n*n -n)/2
mycorrs <- array(0, dim=c(10,3))
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
cor.test(y[1,],y[2,],method=par1)
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
a<-table.element(a,dum,header=TRUE)
rp <- cor.test(y[i,],y[j,],method='pearson')
a<-table.element(a,round(rp$estimate,4))
rs <- cor.test(y[i,],y[j,],method='spearman')
a<-table.element(a,round(rs$estimate,4))
rk <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,round(rk$estimate,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=T)
a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
a<-table.row.end(a)
for (iii in 1:10) {
iiid100 <- iii / 100
if (rp$p.value < iiid100) mycorrs[iii, 1] = mycorrs[iii, 1] + 1
if (rs$p.value < iiid100) mycorrs[iii, 2] = mycorrs[iii, 2] + 1
if (rk$p.value < iiid100) mycorrs[iii, 3] = mycorrs[iii, 3] + 1
}
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Correlation Tests',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of significant by total number of Correlations',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Type I error',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
for (iii in 1:10) {
iiid100 <- iii / 100
a<-table.row.start(a)
a<-table.element(a,round(iiid100,2),header=T)
a<-table.element(a,round(mycorrs[iii,1]/ncorrs,2))
a<-table.element(a,round(mycorrs[iii,2]/ncorrs,2))
a<-table.element(a,round(mycorrs[iii,3]/ncorrs,2))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code