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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, 14 Dec 2011 09:56:45 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/14/t132387465359pdf4fivlw49c3.htm/, Retrieved Wed, 01 May 2024 16:23:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155020, Retrieved Wed, 01 May 2024 16:23:58 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [workshop 10: corr...] [2011-12-14 14:56:45] [d7127d50f40450f0f3837a0965e389eb] [Current]
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Dataseries X:
2050	2650	13	7	1	0	1639
2150	2664	6	5	1	0	1193
2150	2921	3	6	1	0	1635
1999	2580	4	4	1	0	1732
1900	2580	4	4	1	0	1534
1800	2774	2	4	1	0	1765
1560	1920	1	5	1	0	1161
1449	1710	1	3	1	0	1010
1375	1837	4	5	1	0	1191
1270	1880	8	6	1	0	930
1250	2150	15	3	1	0	984
1235	1894	14	5	1	0	1112
1170	1928	18	8	1	0	600
1155	1767	16	4	1	0	794
1110	1630	15	3	1	1	867
1139	1680	17	4	1	1	750
995	1500	15	4	1	0	743
900	1400	16	2	1	1	731
960	1573	17	6	1	0	768
1695	2931	28	3	1	1	1142
1553	2200	28	4	1	0	1035
1020	1478	53	3	1	1	626
1020	1713	30	4	1	1	600
850	1190	41	1	1	0	600
720	1121	46	4	1	0	398
749	1733	43	6	1	0	656
2150	2848	4	6	1	0	1487
1350	2253	23	4	1	0	939
1299	2743	25	5	1	1	1232
1250	2180	17	4	1	1	1141
1239	1706	14	4	1	0	810
1125	1710	16	4	1	0	800
1080	2200	26	4	1	0	1076
1050	1680	13	4	1	0	875
1049	1900	34	3	1	0	690
934	1543	20	3	1	0	820
875	1173	6	4	1	0	456
805	1258	7	4	1	1	821
759	997	4	4	1	0	461
729	1007	19	6	1	0	513
710	1083	22	4	1	0	504
690	1348	15	2	1	0	
975	1500	7	3	0	1	700
939	1428	40	2	0	0	701
2100	2116	25	3	0	0	1209
580	1051	15	2	0	0	426
1844	2250	40	6	0	0	915
699	1400	45	1	0	1	481
1160	1720	5	4	0	0	867
1109	1740	4	3	0	0	816
1129	1700	6	4	0	0	725
1050	1620	6	4	0	0	800
1045	1630	6	4	0	0	750
1050	1920	8	4	0	0	944
1020	1606	5	4	0	0	811
1000	1535	7	5	0	1	668
1030	1540	6	2	0	1	826
975	1739	13	3	0	0	880
940	1305	5	3	0	0	647
920	1415	7	4	0	0	866
945	1580	9	3	0	0	810
874	1236	3	4	0	0	707
872	1229	6	3	0	0	721
870	1273	4	4	0	0	638
869	1165	7	4	0	0	694
766	1200	7	4	0	1	634
739	970	4	4	0	1	541




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155020&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155020&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155020&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'Gertrude Mary Cox' @ cox.wessa.net







Correlations for all pairs of data series (method=kendall)
PRICESQFTAGEFEATSNECORTAX
PRICE10.083-0.378-0.15-0.3170.3290.491
SQFT0.08310.2880.6480.573-0.5480.143
AGE-0.3780.28810.3530.429-0.353-0.348
FEATS-0.150.6480.35310.642-0.698-0.036
NE-0.3170.5730.4290.6421-0.707-0.078
COR0.329-0.548-0.353-0.698-0.70710.072
TAX0.4910.143-0.348-0.036-0.0780.0721

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & PRICE & SQFT & AGE & FEATS & NE & COR & TAX \tabularnewline
PRICE & 1 & 0.083 & -0.378 & -0.15 & -0.317 & 0.329 & 0.491 \tabularnewline
SQFT & 0.083 & 1 & 0.288 & 0.648 & 0.573 & -0.548 & 0.143 \tabularnewline
AGE & -0.378 & 0.288 & 1 & 0.353 & 0.429 & -0.353 & -0.348 \tabularnewline
FEATS & -0.15 & 0.648 & 0.353 & 1 & 0.642 & -0.698 & -0.036 \tabularnewline
NE & -0.317 & 0.573 & 0.429 & 0.642 & 1 & -0.707 & -0.078 \tabularnewline
COR & 0.329 & -0.548 & -0.353 & -0.698 & -0.707 & 1 & 0.072 \tabularnewline
TAX & 0.491 & 0.143 & -0.348 & -0.036 & -0.078 & 0.072 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155020&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]PRICE[/C][C]SQFT[/C][C]AGE[/C][C]FEATS[/C][C]NE[/C][C]COR[/C][C]TAX[/C][/ROW]
[ROW][C]PRICE[/C][C]1[/C][C]0.083[/C][C]-0.378[/C][C]-0.15[/C][C]-0.317[/C][C]0.329[/C][C]0.491[/C][/ROW]
[ROW][C]SQFT[/C][C]0.083[/C][C]1[/C][C]0.288[/C][C]0.648[/C][C]0.573[/C][C]-0.548[/C][C]0.143[/C][/ROW]
[ROW][C]AGE[/C][C]-0.378[/C][C]0.288[/C][C]1[/C][C]0.353[/C][C]0.429[/C][C]-0.353[/C][C]-0.348[/C][/ROW]
[ROW][C]FEATS[/C][C]-0.15[/C][C]0.648[/C][C]0.353[/C][C]1[/C][C]0.642[/C][C]-0.698[/C][C]-0.036[/C][/ROW]
[ROW][C]NE[/C][C]-0.317[/C][C]0.573[/C][C]0.429[/C][C]0.642[/C][C]1[/C][C]-0.707[/C][C]-0.078[/C][/ROW]
[ROW][C]COR[/C][C]0.329[/C][C]-0.548[/C][C]-0.353[/C][C]-0.698[/C][C]-0.707[/C][C]1[/C][C]0.072[/C][/ROW]
[ROW][C]TAX[/C][C]0.491[/C][C]0.143[/C][C]-0.348[/C][C]-0.036[/C][C]-0.078[/C][C]0.072[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155020&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155020&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)
PRICESQFTAGEFEATSNECORTAX
PRICE10.083-0.378-0.15-0.3170.3290.491
SQFT0.08310.2880.6480.573-0.5480.143
AGE-0.3780.28810.3530.429-0.353-0.348
FEATS-0.150.6480.35310.642-0.698-0.036
NE-0.3170.5730.4290.6421-0.707-0.078
COR0.329-0.548-0.353-0.698-0.70710.072
TAX0.4910.143-0.348-0.036-0.0780.0721







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
PRICE;SQFT0.03410.06460.0834
p-value(0.7842)(0.6036)(0.3217)
PRICE;AGE-0.4948-0.5496-0.3776
p-value(0)(0)(0)
PRICE;FEATS-0.1608-0.2007-0.1495
p-value(0.1937)(0.1034)(0.1029)
PRICE;NE-0.3382-0.3853-0.3173
p-value(0.0051)(0.0013)(0.0017)
PRICE;COR0.40440.3850.3295
p-value(7e-04)(0.0013)(3e-04)
PRICE;TAX0.48560.61630.4906
p-value(0)(0)(0)
SQFT;AGE0.4050.42110.2878
p-value(7e-04)(4e-04)(0.001)
SQFT;FEATS0.85990.810.6475
p-value(0)(0)(0)
SQFT;NE0.73440.69210.573
p-value(0)(0)(0)
SQFT;COR-0.8705-0.7501-0.5483
p-value(0)(0)(0)
SQFT;TAX0.15120.20640.1428
p-value(0.2221)(0.0937)(0.0901)
AGE;FEATS0.39950.4810.3526
p-value(8e-04)(0)(2e-04)
AGE;NE0.44830.5030.4288
p-value(1e-04)(0)(0)
AGE;COR-0.5338-0.4783-0.3525
p-value(0)(0)(2e-04)
AGE;TAX-0.421-0.4655-0.3483
p-value(4e-04)(1e-04)(1e-04)
FEATS;NE0.7240.71110.6422
p-value(0)(0)(0)
FEATS;COR-0.8586-0.8335-0.6983
p-value(0)(0)(0)
FEATS;TAX-0.0585-0.0404-0.0362
p-value(0.6381)(0.7457)(0.6928)
NE;COR-0.8464-0.7911-0.7066
p-value(0)(0)(0)
NE;TAX-0.0779-0.0942-0.0776
p-value(0.5311)(0.4484)(0.4442)
COR;TAX0.06550.08440.0724
p-value(0.5986)(0.4972)(0.4247)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
PRICE;SQFT & 0.0341 & 0.0646 & 0.0834 \tabularnewline
p-value & (0.7842) & (0.6036) & (0.3217) \tabularnewline
PRICE;AGE & -0.4948 & -0.5496 & -0.3776 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
PRICE;FEATS & -0.1608 & -0.2007 & -0.1495 \tabularnewline
p-value & (0.1937) & (0.1034) & (0.1029) \tabularnewline
PRICE;NE & -0.3382 & -0.3853 & -0.3173 \tabularnewline
p-value & (0.0051) & (0.0013) & (0.0017) \tabularnewline
PRICE;COR & 0.4044 & 0.385 & 0.3295 \tabularnewline
p-value & (7e-04) & (0.0013) & (3e-04) \tabularnewline
PRICE;TAX & 0.4856 & 0.6163 & 0.4906 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SQFT;AGE & 0.405 & 0.4211 & 0.2878 \tabularnewline
p-value & (7e-04) & (4e-04) & (0.001) \tabularnewline
SQFT;FEATS & 0.8599 & 0.81 & 0.6475 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SQFT;NE & 0.7344 & 0.6921 & 0.573 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SQFT;COR & -0.8705 & -0.7501 & -0.5483 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SQFT;TAX & 0.1512 & 0.2064 & 0.1428 \tabularnewline
p-value & (0.2221) & (0.0937) & (0.0901) \tabularnewline
AGE;FEATS & 0.3995 & 0.481 & 0.3526 \tabularnewline
p-value & (8e-04) & (0) & (2e-04) \tabularnewline
AGE;NE & 0.4483 & 0.503 & 0.4288 \tabularnewline
p-value & (1e-04) & (0) & (0) \tabularnewline
AGE;COR & -0.5338 & -0.4783 & -0.3525 \tabularnewline
p-value & (0) & (0) & (2e-04) \tabularnewline
AGE;TAX & -0.421 & -0.4655 & -0.3483 \tabularnewline
p-value & (4e-04) & (1e-04) & (1e-04) \tabularnewline
FEATS;NE & 0.724 & 0.7111 & 0.6422 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
FEATS;COR & -0.8586 & -0.8335 & -0.6983 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
FEATS;TAX & -0.0585 & -0.0404 & -0.0362 \tabularnewline
p-value & (0.6381) & (0.7457) & (0.6928) \tabularnewline
NE;COR & -0.8464 & -0.7911 & -0.7066 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
NE;TAX & -0.0779 & -0.0942 & -0.0776 \tabularnewline
p-value & (0.5311) & (0.4484) & (0.4442) \tabularnewline
COR;TAX & 0.0655 & 0.0844 & 0.0724 \tabularnewline
p-value & (0.5986) & (0.4972) & (0.4247) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155020&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]PRICE;SQFT[/C][C]0.0341[/C][C]0.0646[/C][C]0.0834[/C][/ROW]
[ROW][C]p-value[/C][C](0.7842)[/C][C](0.6036)[/C][C](0.3217)[/C][/ROW]
[ROW][C]PRICE;AGE[/C][C]-0.4948[/C][C]-0.5496[/C][C]-0.3776[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]PRICE;FEATS[/C][C]-0.1608[/C][C]-0.2007[/C][C]-0.1495[/C][/ROW]
[ROW][C]p-value[/C][C](0.1937)[/C][C](0.1034)[/C][C](0.1029)[/C][/ROW]
[ROW][C]PRICE;NE[/C][C]-0.3382[/C][C]-0.3853[/C][C]-0.3173[/C][/ROW]
[ROW][C]p-value[/C][C](0.0051)[/C][C](0.0013)[/C][C](0.0017)[/C][/ROW]
[ROW][C]PRICE;COR[/C][C]0.4044[/C][C]0.385[/C][C]0.3295[/C][/ROW]
[ROW][C]p-value[/C][C](7e-04)[/C][C](0.0013)[/C][C](3e-04)[/C][/ROW]
[ROW][C]PRICE;TAX[/C][C]0.4856[/C][C]0.6163[/C][C]0.4906[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SQFT;AGE[/C][C]0.405[/C][C]0.4211[/C][C]0.2878[/C][/ROW]
[ROW][C]p-value[/C][C](7e-04)[/C][C](4e-04)[/C][C](0.001)[/C][/ROW]
[ROW][C]SQFT;FEATS[/C][C]0.8599[/C][C]0.81[/C][C]0.6475[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SQFT;NE[/C][C]0.7344[/C][C]0.6921[/C][C]0.573[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SQFT;COR[/C][C]-0.8705[/C][C]-0.7501[/C][C]-0.5483[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SQFT;TAX[/C][C]0.1512[/C][C]0.2064[/C][C]0.1428[/C][/ROW]
[ROW][C]p-value[/C][C](0.2221)[/C][C](0.0937)[/C][C](0.0901)[/C][/ROW]
[ROW][C]AGE;FEATS[/C][C]0.3995[/C][C]0.481[/C][C]0.3526[/C][/ROW]
[ROW][C]p-value[/C][C](8e-04)[/C][C](0)[/C][C](2e-04)[/C][/ROW]
[ROW][C]AGE;NE[/C][C]0.4483[/C][C]0.503[/C][C]0.4288[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AGE;COR[/C][C]-0.5338[/C][C]-0.4783[/C][C]-0.3525[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](2e-04)[/C][/ROW]
[ROW][C]AGE;TAX[/C][C]-0.421[/C][C]-0.4655[/C][C]-0.3483[/C][/ROW]
[ROW][C]p-value[/C][C](4e-04)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]FEATS;NE[/C][C]0.724[/C][C]0.7111[/C][C]0.6422[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]FEATS;COR[/C][C]-0.8586[/C][C]-0.8335[/C][C]-0.6983[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]FEATS;TAX[/C][C]-0.0585[/C][C]-0.0404[/C][C]-0.0362[/C][/ROW]
[ROW][C]p-value[/C][C](0.6381)[/C][C](0.7457)[/C][C](0.6928)[/C][/ROW]
[ROW][C]NE;COR[/C][C]-0.8464[/C][C]-0.7911[/C][C]-0.7066[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]NE;TAX[/C][C]-0.0779[/C][C]-0.0942[/C][C]-0.0776[/C][/ROW]
[ROW][C]p-value[/C][C](0.5311)[/C][C](0.4484)[/C][C](0.4442)[/C][/ROW]
[ROW][C]COR;TAX[/C][C]0.0655[/C][C]0.0844[/C][C]0.0724[/C][/ROW]
[ROW][C]p-value[/C][C](0.5986)[/C][C](0.4972)[/C][C](0.4247)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155020&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155020&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
PRICE;SQFT0.03410.06460.0834
p-value(0.7842)(0.6036)(0.3217)
PRICE;AGE-0.4948-0.5496-0.3776
p-value(0)(0)(0)
PRICE;FEATS-0.1608-0.2007-0.1495
p-value(0.1937)(0.1034)(0.1029)
PRICE;NE-0.3382-0.3853-0.3173
p-value(0.0051)(0.0013)(0.0017)
PRICE;COR0.40440.3850.3295
p-value(7e-04)(0.0013)(3e-04)
PRICE;TAX0.48560.61630.4906
p-value(0)(0)(0)
SQFT;AGE0.4050.42110.2878
p-value(7e-04)(4e-04)(0.001)
SQFT;FEATS0.85990.810.6475
p-value(0)(0)(0)
SQFT;NE0.73440.69210.573
p-value(0)(0)(0)
SQFT;COR-0.8705-0.7501-0.5483
p-value(0)(0)(0)
SQFT;TAX0.15120.20640.1428
p-value(0.2221)(0.0937)(0.0901)
AGE;FEATS0.39950.4810.3526
p-value(8e-04)(0)(2e-04)
AGE;NE0.44830.5030.4288
p-value(1e-04)(0)(0)
AGE;COR-0.5338-0.4783-0.3525
p-value(0)(0)(2e-04)
AGE;TAX-0.421-0.4655-0.3483
p-value(4e-04)(1e-04)(1e-04)
FEATS;NE0.7240.71110.6422
p-value(0)(0)(0)
FEATS;COR-0.8586-0.8335-0.6983
p-value(0)(0)(0)
FEATS;TAX-0.0585-0.0404-0.0362
p-value(0.6381)(0.7457)(0.6928)
NE;COR-0.8464-0.7911-0.7066
p-value(0)(0)(0)
NE;TAX-0.0779-0.0942-0.0776
p-value(0.5311)(0.4484)(0.4442)
COR;TAX0.06550.08440.0724
p-value(0.5986)(0.4972)(0.4247)



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')
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')