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Author*Unverified author*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationSun, 23 Jun 2013 18:58:26 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Jun/23/t1372028348yn8hr0bn8bwo9jh.htm/, Retrieved Mon, 29 Apr 2024 15:21:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=210761, Retrieved Mon, 29 Apr 2024 15:21:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact191
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [] [2013-06-23 22:58:26] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
1.5 20 5 13 8 3 4 31
1.38 20 5 11 8 3 4 28
1.56 7 6 10 8 2 4 24
1.57 16 4 10 7 2 4 21
1.45 63 7 17 11 3 5 43
2.67 8 2 7 3 2 3 22
1.33 28 7 13 9 2 4 26
0.5 0 2 2 2 1 1 3
1.45 7 7 13 11 4 5 38
1.38 6 6 10 8 2 4 21




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210761&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210761&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210761&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'George Udny Yule' @ yule.wessa.net







Correlations for all pairs of data series (method=spearman)
CNCICNOANOACNOAJSDSMPSMALL
CNC10.104-0.249-0.094-0.1640.1520.0620.083
IC0.10410.3770.7440.4990.4680.4410.661
NOA-0.2490.37710.80.9620.5230.8410.668
NOAC-0.0940.7440.810.9070.7860.850.904
NOAJS-0.1640.4990.9620.90710.7260.9150.834
DSM0.1520.4680.5230.7860.72610.7930.901
PSM0.0620.4410.8410.850.9150.79310.791
ALL0.0830.6610.6680.9040.8340.9010.7911

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=spearman) \tabularnewline
  & CNC & IC & NOA & NOAC & NOAJS & DSM & PSM & ALL \tabularnewline
CNC & 1 & 0.104 & -0.249 & -0.094 & -0.164 & 0.152 & 0.062 & 0.083 \tabularnewline
IC & 0.104 & 1 & 0.377 & 0.744 & 0.499 & 0.468 & 0.441 & 0.661 \tabularnewline
NOA & -0.249 & 0.377 & 1 & 0.8 & 0.962 & 0.523 & 0.841 & 0.668 \tabularnewline
NOAC & -0.094 & 0.744 & 0.8 & 1 & 0.907 & 0.786 & 0.85 & 0.904 \tabularnewline
NOAJS & -0.164 & 0.499 & 0.962 & 0.907 & 1 & 0.726 & 0.915 & 0.834 \tabularnewline
DSM & 0.152 & 0.468 & 0.523 & 0.786 & 0.726 & 1 & 0.793 & 0.901 \tabularnewline
PSM & 0.062 & 0.441 & 0.841 & 0.85 & 0.915 & 0.793 & 1 & 0.791 \tabularnewline
ALL & 0.083 & 0.661 & 0.668 & 0.904 & 0.834 & 0.901 & 0.791 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210761&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=spearman)[/C][/ROW]
[ROW][C] [/C][C]CNC[/C][C]IC[/C][C]NOA[/C][C]NOAC[/C][C]NOAJS[/C][C]DSM[/C][C]PSM[/C][C]ALL[/C][/ROW]
[ROW][C]CNC[/C][C]1[/C][C]0.104[/C][C]-0.249[/C][C]-0.094[/C][C]-0.164[/C][C]0.152[/C][C]0.062[/C][C]0.083[/C][/ROW]
[ROW][C]IC[/C][C]0.104[/C][C]1[/C][C]0.377[/C][C]0.744[/C][C]0.499[/C][C]0.468[/C][C]0.441[/C][C]0.661[/C][/ROW]
[ROW][C]NOA[/C][C]-0.249[/C][C]0.377[/C][C]1[/C][C]0.8[/C][C]0.962[/C][C]0.523[/C][C]0.841[/C][C]0.668[/C][/ROW]
[ROW][C]NOAC[/C][C]-0.094[/C][C]0.744[/C][C]0.8[/C][C]1[/C][C]0.907[/C][C]0.786[/C][C]0.85[/C][C]0.904[/C][/ROW]
[ROW][C]NOAJS[/C][C]-0.164[/C][C]0.499[/C][C]0.962[/C][C]0.907[/C][C]1[/C][C]0.726[/C][C]0.915[/C][C]0.834[/C][/ROW]
[ROW][C]DSM[/C][C]0.152[/C][C]0.468[/C][C]0.523[/C][C]0.786[/C][C]0.726[/C][C]1[/C][C]0.793[/C][C]0.901[/C][/ROW]
[ROW][C]PSM[/C][C]0.062[/C][C]0.441[/C][C]0.841[/C][C]0.85[/C][C]0.915[/C][C]0.793[/C][C]1[/C][C]0.791[/C][/ROW]
[ROW][C]ALL[/C][C]0.083[/C][C]0.661[/C][C]0.668[/C][C]0.904[/C][C]0.834[/C][C]0.901[/C][C]0.791[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210761&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210761&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=spearman)
CNCICNOANOACNOAJSDSMPSMALL
CNC10.104-0.249-0.094-0.1640.1520.0620.083
IC0.10410.3770.7440.4990.4680.4410.661
NOA-0.2490.37710.80.9620.5230.8410.668
NOAC-0.0940.7440.810.9070.7860.850.904
NOAJS-0.1640.4990.9620.90710.7260.9150.834
DSM0.1520.4680.5230.7860.72610.7930.901
PSM0.0620.4410.8410.850.9150.79310.791
ALL0.0830.6610.6680.9040.8340.9010.7911







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
CNC;IC0.03770.10430.0698
p-value(0.9176)(0.7743)(0.7851)
CNC;NOA-0.1295-0.2493-0.2442
p-value(0.7215)(0.4874)(0.3544)
CNC;NOAC0.1846-0.0938-0.1221
p-value(0.6097)(0.7967)(0.6424)
CNC;NOAJS-0.0358-0.1641-0.1979
p-value(0.9218)(0.6505)(0.453)
CNC;DSM0.20620.15170.1078
p-value(0.5677)(0.6757)(0.6938)
CNC;PSM0.31760.06210.0283
p-value(0.3712)(0.8648)(0.918)
CNC;ALL0.32860.08260.046
p-value(0.3539)(0.8206)(0.8563)
IC;NOA0.48680.3770.3419
p-value(0.1537)(0.2829)(0.1948)
IC;NOAC0.7570.74390.6593
p-value(0.0112)(0.0136)(0.0122)
IC;NOAJS0.56060.49860.47
p-value(0.0919)(0.1424)(0.0747)
IC;DSM0.34240.46830.4044
p-value(0.3328)(0.1722)(0.1399)
IC;PSM0.52490.44140.3965
p-value(0.1193)(0.2016)(0.1496)
IC;ALL0.67440.66060.5058
p-value(0.0324)(0.0376)(0.0464)
NOA;NOAC0.84150.80.6923
p-value(0.0023)(0.0055)(0.0107)
NOA;NOAJS0.95440.96160.9092
p-value(0)(0)(8e-04)
NOA;DSM0.59270.52260.4529
p-value(0.071)(0.1212)(0.1092)
NOA;PSM0.82930.84050.7731
p-value(0.003)(0.0023)(0.0065)
NOA;ALL0.73520.66780.5311
p-value(0.0154)(0.0348)(0.0424)
NOAC;NOAJS0.92350.90680.8572
p-value(1e-04)(3e-04)(0.0016)
NOAC;DSM0.73830.78640.7077
p-value(0.0147)(0.007)(0.0121)
NOAC;PSM0.9270.85020.8028
p-value(1e-04)(0.0018)(0.0046)
NOAC;ALL0.93870.90360.7966
p-value(1e-04)(3e-04)(0.0023)
NOAJS;DSM0.75840.72610.6596
p-value(0.011)(0.0174)(0.0197)
NOAJS;PSM0.92790.91520.8736
p-value(1e-04)(2e-04)(0.0021)
NOAJS;ALL0.86140.83370.7092
p-value(0.0014)(0.0027)(0.0067)
DSM;PSM0.78920.79340.755
p-value(0.0066)(0.0062)(0.0107)
DSM;ALL0.87650.90090.7995
p-value(9e-04)(4e-04)(0.0033)
PSM;ALL0.91440.79060.6999
p-value(2e-04)(0.0065)(0.0103)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
CNC;IC & 0.0377 & 0.1043 & 0.0698 \tabularnewline
p-value & (0.9176) & (0.7743) & (0.7851) \tabularnewline
CNC;NOA & -0.1295 & -0.2493 & -0.2442 \tabularnewline
p-value & (0.7215) & (0.4874) & (0.3544) \tabularnewline
CNC;NOAC & 0.1846 & -0.0938 & -0.1221 \tabularnewline
p-value & (0.6097) & (0.7967) & (0.6424) \tabularnewline
CNC;NOAJS & -0.0358 & -0.1641 & -0.1979 \tabularnewline
p-value & (0.9218) & (0.6505) & (0.453) \tabularnewline
CNC;DSM & 0.2062 & 0.1517 & 0.1078 \tabularnewline
p-value & (0.5677) & (0.6757) & (0.6938) \tabularnewline
CNC;PSM & 0.3176 & 0.0621 & 0.0283 \tabularnewline
p-value & (0.3712) & (0.8648) & (0.918) \tabularnewline
CNC;ALL & 0.3286 & 0.0826 & 0.046 \tabularnewline
p-value & (0.3539) & (0.8206) & (0.8563) \tabularnewline
IC;NOA & 0.4868 & 0.377 & 0.3419 \tabularnewline
p-value & (0.1537) & (0.2829) & (0.1948) \tabularnewline
IC;NOAC & 0.757 & 0.7439 & 0.6593 \tabularnewline
p-value & (0.0112) & (0.0136) & (0.0122) \tabularnewline
IC;NOAJS & 0.5606 & 0.4986 & 0.47 \tabularnewline
p-value & (0.0919) & (0.1424) & (0.0747) \tabularnewline
IC;DSM & 0.3424 & 0.4683 & 0.4044 \tabularnewline
p-value & (0.3328) & (0.1722) & (0.1399) \tabularnewline
IC;PSM & 0.5249 & 0.4414 & 0.3965 \tabularnewline
p-value & (0.1193) & (0.2016) & (0.1496) \tabularnewline
IC;ALL & 0.6744 & 0.6606 & 0.5058 \tabularnewline
p-value & (0.0324) & (0.0376) & (0.0464) \tabularnewline
NOA;NOAC & 0.8415 & 0.8 & 0.6923 \tabularnewline
p-value & (0.0023) & (0.0055) & (0.0107) \tabularnewline
NOA;NOAJS & 0.9544 & 0.9616 & 0.9092 \tabularnewline
p-value & (0) & (0) & (8e-04) \tabularnewline
NOA;DSM & 0.5927 & 0.5226 & 0.4529 \tabularnewline
p-value & (0.071) & (0.1212) & (0.1092) \tabularnewline
NOA;PSM & 0.8293 & 0.8405 & 0.7731 \tabularnewline
p-value & (0.003) & (0.0023) & (0.0065) \tabularnewline
NOA;ALL & 0.7352 & 0.6678 & 0.5311 \tabularnewline
p-value & (0.0154) & (0.0348) & (0.0424) \tabularnewline
NOAC;NOAJS & 0.9235 & 0.9068 & 0.8572 \tabularnewline
p-value & (1e-04) & (3e-04) & (0.0016) \tabularnewline
NOAC;DSM & 0.7383 & 0.7864 & 0.7077 \tabularnewline
p-value & (0.0147) & (0.007) & (0.0121) \tabularnewline
NOAC;PSM & 0.927 & 0.8502 & 0.8028 \tabularnewline
p-value & (1e-04) & (0.0018) & (0.0046) \tabularnewline
NOAC;ALL & 0.9387 & 0.9036 & 0.7966 \tabularnewline
p-value & (1e-04) & (3e-04) & (0.0023) \tabularnewline
NOAJS;DSM & 0.7584 & 0.7261 & 0.6596 \tabularnewline
p-value & (0.011) & (0.0174) & (0.0197) \tabularnewline
NOAJS;PSM & 0.9279 & 0.9152 & 0.8736 \tabularnewline
p-value & (1e-04) & (2e-04) & (0.0021) \tabularnewline
NOAJS;ALL & 0.8614 & 0.8337 & 0.7092 \tabularnewline
p-value & (0.0014) & (0.0027) & (0.0067) \tabularnewline
DSM;PSM & 0.7892 & 0.7934 & 0.755 \tabularnewline
p-value & (0.0066) & (0.0062) & (0.0107) \tabularnewline
DSM;ALL & 0.8765 & 0.9009 & 0.7995 \tabularnewline
p-value & (9e-04) & (4e-04) & (0.0033) \tabularnewline
PSM;ALL & 0.9144 & 0.7906 & 0.6999 \tabularnewline
p-value & (2e-04) & (0.0065) & (0.0103) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210761&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]CNC;IC[/C][C]0.0377[/C][C]0.1043[/C][C]0.0698[/C][/ROW]
[ROW][C]p-value[/C][C](0.9176)[/C][C](0.7743)[/C][C](0.7851)[/C][/ROW]
[ROW][C]CNC;NOA[/C][C]-0.1295[/C][C]-0.2493[/C][C]-0.2442[/C][/ROW]
[ROW][C]p-value[/C][C](0.7215)[/C][C](0.4874)[/C][C](0.3544)[/C][/ROW]
[ROW][C]CNC;NOAC[/C][C]0.1846[/C][C]-0.0938[/C][C]-0.1221[/C][/ROW]
[ROW][C]p-value[/C][C](0.6097)[/C][C](0.7967)[/C][C](0.6424)[/C][/ROW]
[ROW][C]CNC;NOAJS[/C][C]-0.0358[/C][C]-0.1641[/C][C]-0.1979[/C][/ROW]
[ROW][C]p-value[/C][C](0.9218)[/C][C](0.6505)[/C][C](0.453)[/C][/ROW]
[ROW][C]CNC;DSM[/C][C]0.2062[/C][C]0.1517[/C][C]0.1078[/C][/ROW]
[ROW][C]p-value[/C][C](0.5677)[/C][C](0.6757)[/C][C](0.6938)[/C][/ROW]
[ROW][C]CNC;PSM[/C][C]0.3176[/C][C]0.0621[/C][C]0.0283[/C][/ROW]
[ROW][C]p-value[/C][C](0.3712)[/C][C](0.8648)[/C][C](0.918)[/C][/ROW]
[ROW][C]CNC;ALL[/C][C]0.3286[/C][C]0.0826[/C][C]0.046[/C][/ROW]
[ROW][C]p-value[/C][C](0.3539)[/C][C](0.8206)[/C][C](0.8563)[/C][/ROW]
[ROW][C]IC;NOA[/C][C]0.4868[/C][C]0.377[/C][C]0.3419[/C][/ROW]
[ROW][C]p-value[/C][C](0.1537)[/C][C](0.2829)[/C][C](0.1948)[/C][/ROW]
[ROW][C]IC;NOAC[/C][C]0.757[/C][C]0.7439[/C][C]0.6593[/C][/ROW]
[ROW][C]p-value[/C][C](0.0112)[/C][C](0.0136)[/C][C](0.0122)[/C][/ROW]
[ROW][C]IC;NOAJS[/C][C]0.5606[/C][C]0.4986[/C][C]0.47[/C][/ROW]
[ROW][C]p-value[/C][C](0.0919)[/C][C](0.1424)[/C][C](0.0747)[/C][/ROW]
[ROW][C]IC;DSM[/C][C]0.3424[/C][C]0.4683[/C][C]0.4044[/C][/ROW]
[ROW][C]p-value[/C][C](0.3328)[/C][C](0.1722)[/C][C](0.1399)[/C][/ROW]
[ROW][C]IC;PSM[/C][C]0.5249[/C][C]0.4414[/C][C]0.3965[/C][/ROW]
[ROW][C]p-value[/C][C](0.1193)[/C][C](0.2016)[/C][C](0.1496)[/C][/ROW]
[ROW][C]IC;ALL[/C][C]0.6744[/C][C]0.6606[/C][C]0.5058[/C][/ROW]
[ROW][C]p-value[/C][C](0.0324)[/C][C](0.0376)[/C][C](0.0464)[/C][/ROW]
[ROW][C]NOA;NOAC[/C][C]0.8415[/C][C]0.8[/C][C]0.6923[/C][/ROW]
[ROW][C]p-value[/C][C](0.0023)[/C][C](0.0055)[/C][C](0.0107)[/C][/ROW]
[ROW][C]NOA;NOAJS[/C][C]0.9544[/C][C]0.9616[/C][C]0.9092[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](8e-04)[/C][/ROW]
[ROW][C]NOA;DSM[/C][C]0.5927[/C][C]0.5226[/C][C]0.4529[/C][/ROW]
[ROW][C]p-value[/C][C](0.071)[/C][C](0.1212)[/C][C](0.1092)[/C][/ROW]
[ROW][C]NOA;PSM[/C][C]0.8293[/C][C]0.8405[/C][C]0.7731[/C][/ROW]
[ROW][C]p-value[/C][C](0.003)[/C][C](0.0023)[/C][C](0.0065)[/C][/ROW]
[ROW][C]NOA;ALL[/C][C]0.7352[/C][C]0.6678[/C][C]0.5311[/C][/ROW]
[ROW][C]p-value[/C][C](0.0154)[/C][C](0.0348)[/C][C](0.0424)[/C][/ROW]
[ROW][C]NOAC;NOAJS[/C][C]0.9235[/C][C]0.9068[/C][C]0.8572[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](3e-04)[/C][C](0.0016)[/C][/ROW]
[ROW][C]NOAC;DSM[/C][C]0.7383[/C][C]0.7864[/C][C]0.7077[/C][/ROW]
[ROW][C]p-value[/C][C](0.0147)[/C][C](0.007)[/C][C](0.0121)[/C][/ROW]
[ROW][C]NOAC;PSM[/C][C]0.927[/C][C]0.8502[/C][C]0.8028[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0.0018)[/C][C](0.0046)[/C][/ROW]
[ROW][C]NOAC;ALL[/C][C]0.9387[/C][C]0.9036[/C][C]0.7966[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](3e-04)[/C][C](0.0023)[/C][/ROW]
[ROW][C]NOAJS;DSM[/C][C]0.7584[/C][C]0.7261[/C][C]0.6596[/C][/ROW]
[ROW][C]p-value[/C][C](0.011)[/C][C](0.0174)[/C][C](0.0197)[/C][/ROW]
[ROW][C]NOAJS;PSM[/C][C]0.9279[/C][C]0.9152[/C][C]0.8736[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](2e-04)[/C][C](0.0021)[/C][/ROW]
[ROW][C]NOAJS;ALL[/C][C]0.8614[/C][C]0.8337[/C][C]0.7092[/C][/ROW]
[ROW][C]p-value[/C][C](0.0014)[/C][C](0.0027)[/C][C](0.0067)[/C][/ROW]
[ROW][C]DSM;PSM[/C][C]0.7892[/C][C]0.7934[/C][C]0.755[/C][/ROW]
[ROW][C]p-value[/C][C](0.0066)[/C][C](0.0062)[/C][C](0.0107)[/C][/ROW]
[ROW][C]DSM;ALL[/C][C]0.8765[/C][C]0.9009[/C][C]0.7995[/C][/ROW]
[ROW][C]p-value[/C][C](9e-04)[/C][C](4e-04)[/C][C](0.0033)[/C][/ROW]
[ROW][C]PSM;ALL[/C][C]0.9144[/C][C]0.7906[/C][C]0.6999[/C][/ROW]
[ROW][C]p-value[/C][C](2e-04)[/C][C](0.0065)[/C][C](0.0103)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210761&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210761&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
CNC;IC0.03770.10430.0698
p-value(0.9176)(0.7743)(0.7851)
CNC;NOA-0.1295-0.2493-0.2442
p-value(0.7215)(0.4874)(0.3544)
CNC;NOAC0.1846-0.0938-0.1221
p-value(0.6097)(0.7967)(0.6424)
CNC;NOAJS-0.0358-0.1641-0.1979
p-value(0.9218)(0.6505)(0.453)
CNC;DSM0.20620.15170.1078
p-value(0.5677)(0.6757)(0.6938)
CNC;PSM0.31760.06210.0283
p-value(0.3712)(0.8648)(0.918)
CNC;ALL0.32860.08260.046
p-value(0.3539)(0.8206)(0.8563)
IC;NOA0.48680.3770.3419
p-value(0.1537)(0.2829)(0.1948)
IC;NOAC0.7570.74390.6593
p-value(0.0112)(0.0136)(0.0122)
IC;NOAJS0.56060.49860.47
p-value(0.0919)(0.1424)(0.0747)
IC;DSM0.34240.46830.4044
p-value(0.3328)(0.1722)(0.1399)
IC;PSM0.52490.44140.3965
p-value(0.1193)(0.2016)(0.1496)
IC;ALL0.67440.66060.5058
p-value(0.0324)(0.0376)(0.0464)
NOA;NOAC0.84150.80.6923
p-value(0.0023)(0.0055)(0.0107)
NOA;NOAJS0.95440.96160.9092
p-value(0)(0)(8e-04)
NOA;DSM0.59270.52260.4529
p-value(0.071)(0.1212)(0.1092)
NOA;PSM0.82930.84050.7731
p-value(0.003)(0.0023)(0.0065)
NOA;ALL0.73520.66780.5311
p-value(0.0154)(0.0348)(0.0424)
NOAC;NOAJS0.92350.90680.8572
p-value(1e-04)(3e-04)(0.0016)
NOAC;DSM0.73830.78640.7077
p-value(0.0147)(0.007)(0.0121)
NOAC;PSM0.9270.85020.8028
p-value(1e-04)(0.0018)(0.0046)
NOAC;ALL0.93870.90360.7966
p-value(1e-04)(3e-04)(0.0023)
NOAJS;DSM0.75840.72610.6596
p-value(0.011)(0.0174)(0.0197)
NOAJS;PSM0.92790.91520.8736
p-value(1e-04)(2e-04)(0.0021)
NOAJS;ALL0.86140.83370.7092
p-value(0.0014)(0.0027)(0.0067)
DSM;PSM0.78920.79340.755
p-value(0.0066)(0.0062)(0.0107)
DSM;ALL0.87650.90090.7995
p-value(9e-04)(4e-04)(0.0033)
PSM;ALL0.91440.79060.6999
p-value(2e-04)(0.0065)(0.0103)



Parameters (Session):
par1 = spearman ;
Parameters (R input):
par1 = spearman ;
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')