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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 computationSun, 14 Dec 2014 08:14:03 +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/14/t1418544887hlxchc5zw4w85t6.htm/, Retrieved Thu, 31 Oct 2024 23:42:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267326, Retrieved Thu, 31 Oct 2024 23:42:34 +0000
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Dataseries X:
NA NA NA NA NA
139 70 31 39 70
NA NA NA NA NA
158 114 46 62 108
128 69 31 33 64
224 176 67 52 119
NA NA NA NA NA
105 121 52 77 129
159 110 77 76 153
167 158 37 41 78
165 116 32 48 80
159 181 36 63 99
119 77 38 30 68
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
163 152 54 66 120
NA NA NA NA NA
137 99 42 42 84
NA NA NA NA NA
153 68 34 21 55
148 101 112 25 137
NA NA NA NA NA
188 88 47 69 116
149 112 47 54 101
244 171 37 74 111
148 137 109 80 189
NA NA NA NA NA
150 66 20 61 81
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
132 102 30 34 64
161 161 92 51 143
105 120 43 42 85
97 127 55 31 86
NA NA NA NA NA
131 108 49 20 69
166 85 71 49 120
NA NA NA NA NA
111 48 29 31 60
145 152 56 39 95
162 75 46 54 100
163 107 19 49 68
59 62 23 34 57
NA NA NA NA NA
109 124 30 55 85
90 72 61 42 103
NA NA NA NA NA
83 58 38 13 51
116 97 32 37 69
42 88 16 25 41
148 126 19 30 49
155 104 22 28 50
125 148 48 45 93
116 146 23 35 58
NA NA NA NA NA
138 97 33 41 74
NA NA NA NA NA
96 99 24 45 69
164 118 34 73 107
NA NA NA NA NA
NA NA NA NA NA
202 139 43 64 107
NA NA NA NA NA
66 60 28 25 53
NA NA NA NA NA
214 142 26 100 126
188 94 67 28 95
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
99 75 59 59 118
NA NA NA NA NA
NA NA NA NA NA
108 69 38 27 65
NA NA NA NA NA
NA NA NA NA NA
110 85 32 51 83
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
97 34 12 25 37
NA NA NA NA NA
106 95 37 64 101
80 57 47 32 79
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
74 54 13 31 44
114 64 14 26 40
140 76 -2 58 56
NA NA NA NA NA
98 88 24 21 45
NA NA NA NA NA
126 102 23 33 56
98 61 24 16 40
95 80 14 20 34
110 49 52 37 89
70 78 15 35 50
NA NA NA NA NA
86 45 19 27 46
130 55 35 41 76
96 96 24 40 64
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
99 38 19 17 36
48 41 23 12 34
50 146 16 45 61
150 182 33 37 70
154 192 32 37 69
NA NA NA NA NA
68 35 14 10 23
194 439 52 68 120
NA NA NA NA NA
159 341 72 143 215
NA NA NA NA NA
NA NA NA NA NA
39 85 13 17 30
100 200 40 37 77
111 158 19 27 46
138 199 24 37 61
101 297 121 58 178
131 227 93 66 160
101 108 36 21 57
114 86 23 19 42
NA NA NA NA NA
114 148 41 35 75
111 178 46 48 94
75 120 18 27 45
82 207 35 43 78
121 157 17 30 47
32 128 4 25 29
NA NA NA NA NA
117 323 44 72 116
71 79 10 23 32
165 70 38 13 50
154 146 57 61 118
126 246 23 43 66
NA NA NA NA NA
NA NA NA NA NA
120 127 40 36 76
NA NA NA NA NA
NA NA NA NA NA
172 228 38 34 72
NA NA NA NA NA
114 180 37 72 109
156 212 37 39 76
NA NA NA NA NA
68 130 15 25 40
89 179 2 56 58
167 243 43 80 123
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
87 305 4 42 46
NA NA NA NA NA
2 96 -4 23 19
NA NA NA NA NA
49 52 61 16 78
NA NA NA NA NA
96 40 31 9 40
NA NA NA NA NA
NA NA NA NA NA
100 214 31 48 79
NA NA NA NA NA
141 119 42 53 95
165 222 21 29 49
165 222 21 29 49
110 159 25 55 80
118 165 32 54 86
NA NA NA NA NA
146 125 28 51 79
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
155 274 33 61 94
NA NA NA NA NA
147 84 13 30 43
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
61 139 13 38 51
60 95 23 27 50
109 130 10 56 67
68 72 5 25 30
NA NA NA NA NA
NA NA NA NA NA
73 206 32 43 75
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
220 255 67 54 121
65 111 15 28 43
NA NA NA NA NA
NA NA NA NA NA
122 92 33 16 50
NA NA NA NA NA
44 171 24 40 64
52 83 14 24 38
NA NA NA NA NA
101 186 38 57 96
42 50 12 37 49
152 117 28 27 56
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
103 148 33 34 67
96 137 34 44 78
175 181 21 34 55
57 98 20 39 59
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
110 85 29 13 43
131 66 11 12 23
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
86 122 60 30 91
121 218 13 81 94
NA NA NA NA NA
NA NA NA NA NA
NA NA NA NA NA
88 94 25 27 52
168 113 39 25 64
94 84 9 22 31
51 86 19 19 38
NA NA NA NA NA
145 222 60 45 105
66 167 19 45 64
85 82 34 28 62
NA NA NA NA NA
NA NA NA NA NA
102 83 45 31 76
NA NA NA NA NA
86 89 48 19 68
114 225 29 51 80
NA NA NA NA NA
119 102 51 24 76
NA NA NA NA NA
132 128 24 23 46
142 91 40 14 53
NA NA NA NA NA
94 204 19 51 70
NA NA NA NA NA
166 138 20 36 56
NA NA NA NA NA
64 44 27 24 51
NA NA NA NA NA
NA NA NA NA NA
105 79 39 39 78
49 52 61 16 78
NA NA NA NA NA
95 116 67 31 98
102 83 45 31 76
NA NA NA NA NA
63 153 8 39 47
NA NA NA NA NA
NA NA NA NA NA
117 106 22 38 60
57 58 17 31 48
NA NA NA NA NA
73 74 34 22 56
NA NA NA NA NA
NA NA NA NA NA
105 131 25 51 76
NA NA NA NA NA
NA NA NA NA NA




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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=267326&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=267326&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267326&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 time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Correlations for all pairs of data series (method=kendall)
LFM_mannenBLOGS_mannenPRH_mannenCOMP_HOURS_mannenRFC_HOURS_mannen
LFM_mannen10.2530.2860.2910.352
BLOGS_mannen0.25310.1190.3970.303
PRH_mannen0.2860.11910.2230.629
COMP_HOURS_mannen0.2910.3970.22310.608
RFC_HOURS_mannen0.3520.3030.6290.6081

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & LFM_mannen & BLOGS_mannen & PRH_mannen & COMP_HOURS_mannen & RFC_HOURS_mannen \tabularnewline
LFM_mannen & 1 & 0.253 & 0.286 & 0.291 & 0.352 \tabularnewline
BLOGS_mannen & 0.253 & 1 & 0.119 & 0.397 & 0.303 \tabularnewline
PRH_mannen & 0.286 & 0.119 & 1 & 0.223 & 0.629 \tabularnewline
COMP_HOURS_mannen & 0.291 & 0.397 & 0.223 & 1 & 0.608 \tabularnewline
RFC_HOURS_mannen & 0.352 & 0.303 & 0.629 & 0.608 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267326&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]LFM_mannen[/C][C]BLOGS_mannen[/C][C]PRH_mannen[/C][C]COMP_HOURS_mannen[/C][C]RFC_HOURS_mannen[/C][/ROW]
[ROW][C]LFM_mannen[/C][C]1[/C][C]0.253[/C][C]0.286[/C][C]0.291[/C][C]0.352[/C][/ROW]
[ROW][C]BLOGS_mannen[/C][C]0.253[/C][C]1[/C][C]0.119[/C][C]0.397[/C][C]0.303[/C][/ROW]
[ROW][C]PRH_mannen[/C][C]0.286[/C][C]0.119[/C][C]1[/C][C]0.223[/C][C]0.629[/C][/ROW]
[ROW][C]COMP_HOURS_mannen[/C][C]0.291[/C][C]0.397[/C][C]0.223[/C][C]1[/C][C]0.608[/C][/ROW]
[ROW][C]RFC_HOURS_mannen[/C][C]0.352[/C][C]0.303[/C][C]0.629[/C][C]0.608[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267326&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267326&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=kendall)
LFM_mannenBLOGS_mannenPRH_mannenCOMP_HOURS_mannenRFC_HOURS_mannen
LFM_mannen10.2530.2860.2910.352
BLOGS_mannen0.25310.1190.3970.303
PRH_mannen0.2860.11910.2230.629
COMP_HOURS_mannen0.2910.3970.22310.608
RFC_HOURS_mannen0.3520.3030.6290.6081







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
LFM_mannen;BLOGS_mannen0.34750.36780.2527
p-value(0)(0)(0)
LFM_mannen;PRH_mannen0.37610.41330.286
p-value(0)(0)(0)
LFM_mannen;COMP_HOURS_mannen0.45030.42050.2912
p-value(0)(0)(0)
LFM_mannen;RFC_HOURS_mannen0.50390.50390.3522
p-value(0)(0)(0)
BLOGS_mannen;PRH_mannen0.22050.17370.1193
p-value(0.0055)(0.0296)(0.0281)
BLOGS_mannen;COMP_HOURS_mannen0.54990.56610.3973
p-value(0)(0)(0)
BLOGS_mannen;RFC_HOURS_mannen0.4640.43570.3034
p-value(0)(0)(0)
PRH_mannen;COMP_HOURS_mannen0.32760.31390.223
p-value(0)(1e-04)(0)
PRH_mannen;RFC_HOURS_mannen0.830.8080.6289
p-value(0)(0)(0)
COMP_HOURS_mannen;RFC_HOURS_mannen0.79880.78210.6076
p-value(0)(0)(0)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
LFM_mannen;BLOGS_mannen & 0.3475 & 0.3678 & 0.2527 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
LFM_mannen;PRH_mannen & 0.3761 & 0.4133 & 0.286 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
LFM_mannen;COMP_HOURS_mannen & 0.4503 & 0.4205 & 0.2912 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
LFM_mannen;RFC_HOURS_mannen & 0.5039 & 0.5039 & 0.3522 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
BLOGS_mannen;PRH_mannen & 0.2205 & 0.1737 & 0.1193 \tabularnewline
p-value & (0.0055) & (0.0296) & (0.0281) \tabularnewline
BLOGS_mannen;COMP_HOURS_mannen & 0.5499 & 0.5661 & 0.3973 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
BLOGS_mannen;RFC_HOURS_mannen & 0.464 & 0.4357 & 0.3034 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
PRH_mannen;COMP_HOURS_mannen & 0.3276 & 0.3139 & 0.223 \tabularnewline
p-value & (0) & (1e-04) & (0) \tabularnewline
PRH_mannen;RFC_HOURS_mannen & 0.83 & 0.808 & 0.6289 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
COMP_HOURS_mannen;RFC_HOURS_mannen & 0.7988 & 0.7821 & 0.6076 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267326&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]LFM_mannen;BLOGS_mannen[/C][C]0.3475[/C][C]0.3678[/C][C]0.2527[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]LFM_mannen;PRH_mannen[/C][C]0.3761[/C][C]0.4133[/C][C]0.286[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]LFM_mannen;COMP_HOURS_mannen[/C][C]0.4503[/C][C]0.4205[/C][C]0.2912[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]LFM_mannen;RFC_HOURS_mannen[/C][C]0.5039[/C][C]0.5039[/C][C]0.3522[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]BLOGS_mannen;PRH_mannen[/C][C]0.2205[/C][C]0.1737[/C][C]0.1193[/C][/ROW]
[ROW][C]p-value[/C][C](0.0055)[/C][C](0.0296)[/C][C](0.0281)[/C][/ROW]
[ROW][C]BLOGS_mannen;COMP_HOURS_mannen[/C][C]0.5499[/C][C]0.5661[/C][C]0.3973[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]BLOGS_mannen;RFC_HOURS_mannen[/C][C]0.464[/C][C]0.4357[/C][C]0.3034[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]PRH_mannen;COMP_HOURS_mannen[/C][C]0.3276[/C][C]0.3139[/C][C]0.223[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](1e-04)[/C][C](0)[/C][/ROW]
[ROW][C]PRH_mannen;RFC_HOURS_mannen[/C][C]0.83[/C][C]0.808[/C][C]0.6289[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]COMP_HOURS_mannen;RFC_HOURS_mannen[/C][C]0.7988[/C][C]0.7821[/C][C]0.6076[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267326&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267326&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
LFM_mannen;BLOGS_mannen0.34750.36780.2527
p-value(0)(0)(0)
LFM_mannen;PRH_mannen0.37610.41330.286
p-value(0)(0)(0)
LFM_mannen;COMP_HOURS_mannen0.45030.42050.2912
p-value(0)(0)(0)
LFM_mannen;RFC_HOURS_mannen0.50390.50390.3522
p-value(0)(0)(0)
BLOGS_mannen;PRH_mannen0.22050.17370.1193
p-value(0.0055)(0.0296)(0.0281)
BLOGS_mannen;COMP_HOURS_mannen0.54990.56610.3973
p-value(0)(0)(0)
BLOGS_mannen;RFC_HOURS_mannen0.4640.43570.3034
p-value(0)(0)(0)
PRH_mannen;COMP_HOURS_mannen0.32760.31390.223
p-value(0)(1e-04)(0)
PRH_mannen;RFC_HOURS_mannen0.830.8080.6289
p-value(0)(0)(0)
COMP_HOURS_mannen;RFC_HOURS_mannen0.79880.78210.6076
p-value(0)(0)(0)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.0110.90.9
0.0210.90.9
0.03111
0.04111
0.05111
0.06111
0.07111
0.08111
0.09111
0.1111

\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 & 1 & 0.9 & 0.9 \tabularnewline
0.02 & 1 & 0.9 & 0.9 \tabularnewline
0.03 & 1 & 1 & 1 \tabularnewline
0.04 & 1 & 1 & 1 \tabularnewline
0.05 & 1 & 1 & 1 \tabularnewline
0.06 & 1 & 1 & 1 \tabularnewline
0.07 & 1 & 1 & 1 \tabularnewline
0.08 & 1 & 1 & 1 \tabularnewline
0.09 & 1 & 1 & 1 \tabularnewline
0.1 & 1 & 1 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267326&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]1[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.02[/C][C]1[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.03[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.04[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.05[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.06[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.07[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.08[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.09[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.1[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267326&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267326&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
0.0110.90.9
0.0210.90.9
0.03111
0.04111
0.05111
0.06111
0.07111
0.08111
0.09111
0.1111



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