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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 computationTue, 13 Dec 2011 14:10:52 -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/13/t1323803518nfvbscq7xgnhsi5.htm/, Retrieved Thu, 02 May 2024 20:18:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154647, Retrieved Thu, 02 May 2024 20:18:20 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact69
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...] [2011-12-13 19:10:52] [d7127d50f40450f0f3837a0965e389eb] [Current]
- R P     [Kendall tau Correlation Matrix] [workshop 10: corr...] [2011-12-13 19:13:05] [c6bc82f3e3d78f4e6e841dae94c52ed9]
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Dataseries X:
2050	2650	13	7	1	0	1639
2080	2600	NA	4	1	0	1088
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
1450	2150	NA	4	1	0	NA
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
1180	1830	NA	3	1	0	733
1155	1767	16	4	1	0	794
1110	1630	15	3	1	1	867
1139	1680	17	4	1	1	750
995	1725	NA	3	1	0	923
995	1500	15	4	1	0	743
975	1430	NA	3	1	0	752
975	1360	NA	4	1	0	696
900	1400	16	2	1	1	731
960	1573	17	6	1	0	768
860	1385	NA	2	1	0	653
1695	2931	28	3	1	1	1142
1553	2200	28	4	1	0	1035
1250	2277	NA	4	1	0	NA
1300	2000	NA	3	1	0	1076
1020	1478	53	3	1	1	626
1020	1713	30	4	1	1	600
922	1326	NA	4	1	0	668
925	1050	NA	2	1	1	553
899	1464	NA	2	1	0	566
850	1190	41	1	1	0	600
876	1156	NA	1	1	0	NA
890	1746	NA	2	1	0	591
870	1280	NA	1	1	0	599
700	1215	NA	3	1	0	477
720	1121	46	4	1	0	398
720	1050	NA	1	1	0	NA
749	1733	43	6	1	0	656
731	1299	NA	6	1	0	585
725	1140	NA	3	1	1	490
670	1181	NA	4	1	0	440
2150	2848	4	6	1	0	1487
1599	2440	NA	5	1	0	1265
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
1200	1948	NA	4	1	0	899
1125	1710	16	4	1	0	800
1100	1657	NA	4	1	0	865
1080	2200	26	4	1	0	1076
1050	1680	13	4	1	0	875
1049	1900	34	3	1	0	690
955	1565	NA	3	1	0	648
934	1543	20	3	1	0	820
875	1173	6	4	1	0	456
889	1549	NA	4	1	0	723
855	1900	NA	3	1	0	780
835	1560	NA	5	1	1	638
810	1365	NA	2	1	0	673
805	1258	7	4	1	1	821
799	1314	NA	2	1	0	671
750	1338	NA	3	1	1	649
759	997	4	4	1	0	461
755	1275	NA	5	1	0	NA
750	1030	NA	1	1	0	486
730	1027	NA	3	1	0	427
729	1007	19	6	1	0	513
710	1083	22	4	1	0	504
773	1320	NA	5	1	0	NA
690	1348	15	2	1	0	NA
670	1350	NA	2	1	0	622
619	837	NA	2	1	0	342
1295	3750	NA	4	0	1	1200
975	1500	7	3	0	1	700
939	1428	40	2	0	0	701
820	1375	NA	1	0	0	585
780	1080	NA	3	0	0	600
770	900	NA	3	0	0	391
700	1505	NA	2	0	1	591
620	1480	NA	4	0	0	NA
540	1142	NA	0	0	0	223
1070	1464	NA	2	0	0	376
2100	2116	25	3	0	0	1209
725	1280	NA	3	0	0	447
660	1159	NA	0	0	0	225
600	1198	NA	4	0	0	NA
580	1051	15	2	0	0	426
1844	2250	40	6	0	0	915
1580	2563	NA	2	0	0	1189
699	1400	45	1	0	1	481
1330	1850	5	5	0	1	NA
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
950	1715	NA	3	0	0	900
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 time2 seconds
R Server'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154647&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154647&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154647&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'AstonUniversity' @ aston.wessa.net







Correlations for all pairs of data series (method=kendall)
PRICESQFTAGEFEATSNECORTAX
PRICE10.695-0.1250.3220.124-0.0190.695
SQFT0.695100.3040.129-0.0120.685
AGE-0.12501-0.1390.2240.14-0.176
FEATS0.3220.304-0.13910.153-0.0310.332
NE0.1240.1290.2240.1531-0.0770.112
COR-0.019-0.0120.14-0.031-0.0771-0.047
TAX0.6950.685-0.1760.3320.112-0.0471

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & PRICE & SQFT & AGE & FEATS & NE & COR & TAX \tabularnewline
PRICE & 1 & 0.695 & -0.125 & 0.322 & 0.124 & -0.019 & 0.695 \tabularnewline
SQFT & 0.695 & 1 & 0 & 0.304 & 0.129 & -0.012 & 0.685 \tabularnewline
AGE & -0.125 & 0 & 1 & -0.139 & 0.224 & 0.14 & -0.176 \tabularnewline
FEATS & 0.322 & 0.304 & -0.139 & 1 & 0.153 & -0.031 & 0.332 \tabularnewline
NE & 0.124 & 0.129 & 0.224 & 0.153 & 1 & -0.077 & 0.112 \tabularnewline
COR & -0.019 & -0.012 & 0.14 & -0.031 & -0.077 & 1 & -0.047 \tabularnewline
TAX & 0.695 & 0.685 & -0.176 & 0.332 & 0.112 & -0.047 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154647&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]0.695[/C][C]-0.125[/C][C]0.322[/C][C]0.124[/C][C]-0.019[/C][C]0.695[/C][/ROW]
[ROW][C]SQFT[/C][C]0.695[/C][C]1[/C][C]0[/C][C]0.304[/C][C]0.129[/C][C]-0.012[/C][C]0.685[/C][/ROW]
[ROW][C]AGE[/C][C]-0.125[/C][C]0[/C][C]1[/C][C]-0.139[/C][C]0.224[/C][C]0.14[/C][C]-0.176[/C][/ROW]
[ROW][C]FEATS[/C][C]0.322[/C][C]0.304[/C][C]-0.139[/C][C]1[/C][C]0.153[/C][C]-0.031[/C][C]0.332[/C][/ROW]
[ROW][C]NE[/C][C]0.124[/C][C]0.129[/C][C]0.224[/C][C]0.153[/C][C]1[/C][C]-0.077[/C][C]0.112[/C][/ROW]
[ROW][C]COR[/C][C]-0.019[/C][C]-0.012[/C][C]0.14[/C][C]-0.031[/C][C]-0.077[/C][C]1[/C][C]-0.047[/C][/ROW]
[ROW][C]TAX[/C][C]0.695[/C][C]0.685[/C][C]-0.176[/C][C]0.332[/C][C]0.112[/C][C]-0.047[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154647&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154647&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
PRICE10.695-0.1250.3220.124-0.0190.695
SQFT0.695100.3040.129-0.0120.685
AGE-0.12501-0.1390.2240.14-0.176
FEATS0.3220.304-0.13910.153-0.0310.332
NE0.1240.1290.2240.1531-0.0770.112
COR-0.019-0.0120.14-0.031-0.0771-0.047
TAX0.6950.685-0.1760.3320.112-0.0471







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
PRICE;SQFT0.84480.87420.6954
p-value(0)(0)(0)
PRICE;AGE-0.1687-0.1995-0.1245
p-value(0.1691)(0.1029)(0.1415)
PRICE;FEATS0.42030.40720.3215
p-value(0)(0)(0)
PRICE;NE0.16780.15060.1237
p-value(0.0705)(0.1051)(0.1048)
PRICE;COR-0.0793-0.0233-0.0192
p-value(0.3954)(0.8029)(0.8017)
PRICE;TAX0.87570.86760.6946
p-value(0)(0)(0)
SQFT;AGE-0.0397-0.0145-4e-04
p-value(0.7482)(0.9069)(0.9958)
SQFT;FEATS0.39490.39580.3043
p-value(0)(0)(0)
SQFT;NE0.1450.15730.1291
p-value(0.1187)(0.0903)(0.0902)
SQFT;COR0.0405-0.0149-0.0122
p-value(0.6644)(0.8733)(0.8725)
SQFT;TAX0.85860.86140.6845
p-value(0)(0)(0)
AGE;FEATS-0.1878-0.1749-0.1394
p-value(0.1251)(0.1538)(0.1348)
AGE;NE0.22680.26650.2235
p-value(0.0629)(0.028)(0.0291)
AGE;COR0.13640.16640.1395
p-value(0.2674)(0.1751)(0.1732)
AGE;TAX-0.2918-0.2608-0.176
p-value(0.0174)(0.0344)(0.0406)
FEATS;NE0.190.17010.1527
p-value(0.0402)(0.0667)(0.067)
FEATS;COR-0.0415-0.0349-0.0313
p-value(0.6565)(0.709)(0.7072)
FEATS;TAX0.44170.42590.3315
p-value(0)(0)(0)
NE;COR-0.0773-0.0773-0.0773
p-value(0.4072)(0.4072)(0.4049)
NE;TAX0.19740.13610.1118
p-value(0.0415)(0.1622)(0.1612)
COR;TAX-0.06-0.0571-0.0469
p-value(0.5392)(0.5588)(0.5563)

\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 & 0.8448 & 0.8742 & 0.6954 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
PRICE;AGE & -0.1687 & -0.1995 & -0.1245 \tabularnewline
p-value & (0.1691) & (0.1029) & (0.1415) \tabularnewline
PRICE;FEATS & 0.4203 & 0.4072 & 0.3215 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
PRICE;NE & 0.1678 & 0.1506 & 0.1237 \tabularnewline
p-value & (0.0705) & (0.1051) & (0.1048) \tabularnewline
PRICE;COR & -0.0793 & -0.0233 & -0.0192 \tabularnewline
p-value & (0.3954) & (0.8029) & (0.8017) \tabularnewline
PRICE;TAX & 0.8757 & 0.8676 & 0.6946 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SQFT;AGE & -0.0397 & -0.0145 & -4e-04 \tabularnewline
p-value & (0.7482) & (0.9069) & (0.9958) \tabularnewline
SQFT;FEATS & 0.3949 & 0.3958 & 0.3043 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SQFT;NE & 0.145 & 0.1573 & 0.1291 \tabularnewline
p-value & (0.1187) & (0.0903) & (0.0902) \tabularnewline
SQFT;COR & 0.0405 & -0.0149 & -0.0122 \tabularnewline
p-value & (0.6644) & (0.8733) & (0.8725) \tabularnewline
SQFT;TAX & 0.8586 & 0.8614 & 0.6845 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AGE;FEATS & -0.1878 & -0.1749 & -0.1394 \tabularnewline
p-value & (0.1251) & (0.1538) & (0.1348) \tabularnewline
AGE;NE & 0.2268 & 0.2665 & 0.2235 \tabularnewline
p-value & (0.0629) & (0.028) & (0.0291) \tabularnewline
AGE;COR & 0.1364 & 0.1664 & 0.1395 \tabularnewline
p-value & (0.2674) & (0.1751) & (0.1732) \tabularnewline
AGE;TAX & -0.2918 & -0.2608 & -0.176 \tabularnewline
p-value & (0.0174) & (0.0344) & (0.0406) \tabularnewline
FEATS;NE & 0.19 & 0.1701 & 0.1527 \tabularnewline
p-value & (0.0402) & (0.0667) & (0.067) \tabularnewline
FEATS;COR & -0.0415 & -0.0349 & -0.0313 \tabularnewline
p-value & (0.6565) & (0.709) & (0.7072) \tabularnewline
FEATS;TAX & 0.4417 & 0.4259 & 0.3315 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
NE;COR & -0.0773 & -0.0773 & -0.0773 \tabularnewline
p-value & (0.4072) & (0.4072) & (0.4049) \tabularnewline
NE;TAX & 0.1974 & 0.1361 & 0.1118 \tabularnewline
p-value & (0.0415) & (0.1622) & (0.1612) \tabularnewline
COR;TAX & -0.06 & -0.0571 & -0.0469 \tabularnewline
p-value & (0.5392) & (0.5588) & (0.5563) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154647&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]0.8448[/C][C]0.8742[/C][C]0.6954[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]PRICE;AGE[/C][C]-0.1687[/C][C]-0.1995[/C][C]-0.1245[/C][/ROW]
[ROW][C]p-value[/C][C](0.1691)[/C][C](0.1029)[/C][C](0.1415)[/C][/ROW]
[ROW][C]PRICE;FEATS[/C][C]0.4203[/C][C]0.4072[/C][C]0.3215[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]PRICE;NE[/C][C]0.1678[/C][C]0.1506[/C][C]0.1237[/C][/ROW]
[ROW][C]p-value[/C][C](0.0705)[/C][C](0.1051)[/C][C](0.1048)[/C][/ROW]
[ROW][C]PRICE;COR[/C][C]-0.0793[/C][C]-0.0233[/C][C]-0.0192[/C][/ROW]
[ROW][C]p-value[/C][C](0.3954)[/C][C](0.8029)[/C][C](0.8017)[/C][/ROW]
[ROW][C]PRICE;TAX[/C][C]0.8757[/C][C]0.8676[/C][C]0.6946[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SQFT;AGE[/C][C]-0.0397[/C][C]-0.0145[/C][C]-4e-04[/C][/ROW]
[ROW][C]p-value[/C][C](0.7482)[/C][C](0.9069)[/C][C](0.9958)[/C][/ROW]
[ROW][C]SQFT;FEATS[/C][C]0.3949[/C][C]0.3958[/C][C]0.3043[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SQFT;NE[/C][C]0.145[/C][C]0.1573[/C][C]0.1291[/C][/ROW]
[ROW][C]p-value[/C][C](0.1187)[/C][C](0.0903)[/C][C](0.0902)[/C][/ROW]
[ROW][C]SQFT;COR[/C][C]0.0405[/C][C]-0.0149[/C][C]-0.0122[/C][/ROW]
[ROW][C]p-value[/C][C](0.6644)[/C][C](0.8733)[/C][C](0.8725)[/C][/ROW]
[ROW][C]SQFT;TAX[/C][C]0.8586[/C][C]0.8614[/C][C]0.6845[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AGE;FEATS[/C][C]-0.1878[/C][C]-0.1749[/C][C]-0.1394[/C][/ROW]
[ROW][C]p-value[/C][C](0.1251)[/C][C](0.1538)[/C][C](0.1348)[/C][/ROW]
[ROW][C]AGE;NE[/C][C]0.2268[/C][C]0.2665[/C][C]0.2235[/C][/ROW]
[ROW][C]p-value[/C][C](0.0629)[/C][C](0.028)[/C][C](0.0291)[/C][/ROW]
[ROW][C]AGE;COR[/C][C]0.1364[/C][C]0.1664[/C][C]0.1395[/C][/ROW]
[ROW][C]p-value[/C][C](0.2674)[/C][C](0.1751)[/C][C](0.1732)[/C][/ROW]
[ROW][C]AGE;TAX[/C][C]-0.2918[/C][C]-0.2608[/C][C]-0.176[/C][/ROW]
[ROW][C]p-value[/C][C](0.0174)[/C][C](0.0344)[/C][C](0.0406)[/C][/ROW]
[ROW][C]FEATS;NE[/C][C]0.19[/C][C]0.1701[/C][C]0.1527[/C][/ROW]
[ROW][C]p-value[/C][C](0.0402)[/C][C](0.0667)[/C][C](0.067)[/C][/ROW]
[ROW][C]FEATS;COR[/C][C]-0.0415[/C][C]-0.0349[/C][C]-0.0313[/C][/ROW]
[ROW][C]p-value[/C][C](0.6565)[/C][C](0.709)[/C][C](0.7072)[/C][/ROW]
[ROW][C]FEATS;TAX[/C][C]0.4417[/C][C]0.4259[/C][C]0.3315[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]NE;COR[/C][C]-0.0773[/C][C]-0.0773[/C][C]-0.0773[/C][/ROW]
[ROW][C]p-value[/C][C](0.4072)[/C][C](0.4072)[/C][C](0.4049)[/C][/ROW]
[ROW][C]NE;TAX[/C][C]0.1974[/C][C]0.1361[/C][C]0.1118[/C][/ROW]
[ROW][C]p-value[/C][C](0.0415)[/C][C](0.1622)[/C][C](0.1612)[/C][/ROW]
[ROW][C]COR;TAX[/C][C]-0.06[/C][C]-0.0571[/C][C]-0.0469[/C][/ROW]
[ROW][C]p-value[/C][C](0.5392)[/C][C](0.5588)[/C][C](0.5563)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154647&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154647&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;SQFT0.84480.87420.6954
p-value(0)(0)(0)
PRICE;AGE-0.1687-0.1995-0.1245
p-value(0.1691)(0.1029)(0.1415)
PRICE;FEATS0.42030.40720.3215
p-value(0)(0)(0)
PRICE;NE0.16780.15060.1237
p-value(0.0705)(0.1051)(0.1048)
PRICE;COR-0.0793-0.0233-0.0192
p-value(0.3954)(0.8029)(0.8017)
PRICE;TAX0.87570.86760.6946
p-value(0)(0)(0)
SQFT;AGE-0.0397-0.0145-4e-04
p-value(0.7482)(0.9069)(0.9958)
SQFT;FEATS0.39490.39580.3043
p-value(0)(0)(0)
SQFT;NE0.1450.15730.1291
p-value(0.1187)(0.0903)(0.0902)
SQFT;COR0.0405-0.0149-0.0122
p-value(0.6644)(0.8733)(0.8725)
SQFT;TAX0.85860.86140.6845
p-value(0)(0)(0)
AGE;FEATS-0.1878-0.1749-0.1394
p-value(0.1251)(0.1538)(0.1348)
AGE;NE0.22680.26650.2235
p-value(0.0629)(0.028)(0.0291)
AGE;COR0.13640.16640.1395
p-value(0.2674)(0.1751)(0.1732)
AGE;TAX-0.2918-0.2608-0.176
p-value(0.0174)(0.0344)(0.0406)
FEATS;NE0.190.17010.1527
p-value(0.0402)(0.0667)(0.067)
FEATS;COR-0.0415-0.0349-0.0313
p-value(0.6565)(0.709)(0.7072)
FEATS;TAX0.44170.42590.3315
p-value(0)(0)(0)
NE;COR-0.0773-0.0773-0.0773
p-value(0.4072)(0.4072)(0.4049)
NE;TAX0.19740.13610.1118
p-value(0.0415)(0.1622)(0.1612)
COR;TAX-0.06-0.0571-0.0469
p-value(0.5392)(0.5588)(0.5563)



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')