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*The author of this computation has been verified*

R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Thu, 06 Dec 2012 08:08:31 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/06/t1354799324fz27h09qmgf3nh2.htm/, Retrieved Thu, 25 Apr 2024 09:02:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197064, Retrieved Thu, 25 Apr 2024 09:02:29 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

169

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 20:21:33] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [] [2012-12-06 12:48:07] [8ab8078357d7493428921287469fd527]
- RM        [Kendall tau Correlation Matrix] [] [2012-12-06 13:08:31] [eace0511beeaae09dbb51bfebd62c02b] [Current]

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Dataseries X:

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Histogram

Boxplots

41	38	7	53	145	56
39	32	5	86	101	56
30	35	5	66	98	54
31	33	5	67	132	89
34	37	8	76	60	40
35	29	6	78	38	25
39	31	5	53	144	92
34	36	6	80	5	18
36	35	5	74	28	63
37	38	4	76	84	44
38	31	6	79	79	33
36	34	5	54	127	84
38	35	5	67	78	88
39	38	6	54	60	55
33	37	7	87	131	60
32	33	6	58	84	66
36	32	7	75	133	154
38	38	6	88	150	53
39	38	8	64	91	119
32	32	7	57	132	41
32	33	5	66	136	61
31	31	5	68	124	58
39	38	7	54	118	75
37	39	7	56	70	33
39	32	5	86	107	40
41	32	4	80	119	92
36	35	10	76	89	100
33	37	6	69	112	112
33	33	5	78	108	73
34	33	5	67	52	40
31	28	5	80	112	45
27	32	5	54	116	60
37	31	6	71	123	62
34	37	5	84	125	75
34	30	5	74	27	31
32	33	5	71	162	77
29	31	5	63	32	34
36	33	5	71	64	46
29	31	5	76	92	99
35	33	5	69	0	17
37	32	5	74	83	66
34	33	7	75	41	30
38	32	5	54	47	76
35	33	6	52	120	146
38	28	7	69	105	67
37	35	7	68	79	56
38	39	5	65	65	107
33	34	5	75	70	58
36	38	4	74	55	34
38	32	5	75	39	61
32	38	4	72	67	119
32	30	5	67	21	42
32	33	5	63	127	66
34	38	7	62	152	89
32	32	5	63	113	44
37	32	5	76	99	66
39	34	6	74	7	24
29	34	4	67	141	259
37	36	6	73	21	17
35	34	6	70	35	64
30	28	5	53	109	41
38	34	7	77	133	68
34	35	6	77	123	168
31	35	8	52	26	43
34	31	7	54	230	132
35	37	5	80	166	105
36	35	6	66	68	71
30	27	6	73	147	112
39	40	5	63	179	94
35	37	5	69	61	82
38	36	5	67	101	70
31	38	5	54	108	57
34	39	4	81	90	53
38	41	6	69	114	103
34	27	6	84	103	121
39	30	6	80	142	62
37	37	6	70	79	52
34	31	7	69	88	52
28	31	5	77	25	32
37	27	7	54	83	62
33	36	6	79	113	45
37	38	5	30	118	46
35	37	5	71	110	63
37	33	4	73	129	75
32	34	8	72	51	88
33	31	8	77	93	46
38	39	5	75	76	53
33	34	5	69	49	37
29	32	6	54	118	90
33	33	4	70	38	63
31	36	5	73	141	78
36	32	5	54	58	25
35	41	5	77	27	45
32	28	5	82	91	46
29	30	6	80	48	41
39	36	6	80	63	144
37	35	5	69	56	82
35	31	6	78	144	91
37	34	5	81	73	71
32	36	7	76	168	63
38	36	5	76	64	53
37	35	6	73	97	62
36	37	6	85	117	63
32	28	6	66	100	32
33	39	4	79	149	39
40	32	5	68	187	62
38	35	5	76	127	117
41	39	7	71	37	34
36	35	6	54	245	92
43	42	9	46	87	93
30	34	6	82	177	54
31	33	6	74	49	144
32	41	5	88	49	14
32	33	6	38	73	61
37	34	5	76	177	109
37	32	8	86	94	38
33	40	7	54	117	73
34	40	5	70	60	75
33	35	7	69	55	50
38	36	6	90	39	61
33	37	6	54	64	55
31	27	9	76	26	77
38	39	7	89	64	75
37	38	6	76	58	72
33	31	5	73	95	50
31	33	5	79	25	32
39	32	6	90	26	53
44	39	6	74	76	42
33	36	7	81	129	71
35	33	5	72	11	10
32	33	5	71	2	35
28	32	5	66	101	65
40	37	6	77	28	25
27	30	4	65	36	66
37	38	5	74	89	41
32	29	7	82	193	86
28	22	5	54	4	16
34	35	7	63	84	42
30	35	7	54	23	19
35	34	6	64	39	19
31	35	5	69	14	45
32	34	8	54	78	65
30	34	5	84	14	35
30	35	5	86	101	95
31	23	5	77	82	49
40	31	6	89	24	37
32	27	4	76	36	64
36	36	5	60	75	38
32	31	5	75	16	34
35	32	7	73	55	32
38	39	6	85	131	65
42	37	7	79	131	52
34	38	10	71	39	62
35	39	6	72	144	65
35	34	8	69	139	83
33	31	4	78	211	95
36	32	5	54	78	29
32	37	6	69	50	18
33	36	7	81	39	33
34	32	7	84	90	247
32	35	6	84	166	139
34	36	6	69	12	29

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 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 & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197064&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197064&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197064&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	2 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Correlations for all pairs of data series (method=pearson)
	Connected	Separate	age	beloning	totblogs	Login
Connected	1	0.368	0.148	0.081	0.064	-0.004
Separate	0.368	1	0.106	-0.023	0.04	0.031
age	0.148	0.106	1	-0.068	0.007	0.053
beloning	0.081	-0.023	-0.068	1	-0.053	0.022
totblogs	0.064	0.04	0.007	-0.053	1	0.459
Login	-0.004	0.031	0.053	0.022	0.459	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Connected & Separate & age & beloning & totblogs & Login \tabularnewline
Connected & 1 & 0.368 & 0.148 & 0.081 & 0.064 & -0.004 \tabularnewline
Separate & 0.368 & 1 & 0.106 & -0.023 & 0.04 & 0.031 \tabularnewline
age & 0.148 & 0.106 & 1 & -0.068 & 0.007 & 0.053 \tabularnewline
beloning & 0.081 & -0.023 & -0.068 & 1 & -0.053 & 0.022 \tabularnewline
totblogs & 0.064 & 0.04 & 0.007 & -0.053 & 1 & 0.459 \tabularnewline
Login & -0.004 & 0.031 & 0.053 & 0.022 & 0.459 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197064&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Connected[/C][C]Separate[/C][C]age[/C][C]beloning[/C][C]totblogs[/C][C]Login[/C][/ROW]
[ROW][C]Connected[/C][C]1[/C][C]0.368[/C][C]0.148[/C][C]0.081[/C][C]0.064[/C][C]-0.004[/C][/ROW]
[ROW][C]Separate[/C][C]0.368[/C][C]1[/C][C]0.106[/C][C]-0.023[/C][C]0.04[/C][C]0.031[/C][/ROW]
[ROW][C]age[/C][C]0.148[/C][C]0.106[/C][C]1[/C][C]-0.068[/C][C]0.007[/C][C]0.053[/C][/ROW]
[ROW][C]beloning[/C][C]0.081[/C][C]-0.023[/C][C]-0.068[/C][C]1[/C][C]-0.053[/C][C]0.022[/C][/ROW]
[ROW][C]totblogs[/C][C]0.064[/C][C]0.04[/C][C]0.007[/C][C]-0.053[/C][C]1[/C][C]0.459[/C][/ROW]
[ROW][C]Login[/C][C]-0.004[/C][C]0.031[/C][C]0.053[/C][C]0.022[/C][C]0.459[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197064&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197064&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)
	Connected	Separate	age	beloning	totblogs	Login
Connected	1	0.368	0.148	0.081	0.064	-0.004
Separate	0.368	1	0.106	-0.023	0.04	0.031
age	0.148	0.106	1	-0.068	0.007	0.053
beloning	0.081	-0.023	-0.068	1	-0.053	0.022
totblogs	0.064	0.04	0.007	-0.053	1	0.459
Login	-0.004	0.031	0.053	0.022	0.459	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
Connected;Separate	0.3682	0.3243	0.2413
p-value	(0)	(0)	(0)
Connected;age	0.1478	0.1481	0.1122
p-value	(0.0606)	(0.0599)	(0.0675)
Connected;beloning	0.0805	0.0983	0.0718
p-value	(0.3083)	(0.2131)	(0.1997)
Connected;totblogs	0.0644	0.0605	0.0456
p-value	(0.4159)	(0.4446)	(0.4077)
Connected;Login	-0.0035	0.0702	0.0474
p-value	(0.9645)	(0.3745)	(0.3914)
Separate;age	0.1057	0.1124	0.0898
p-value	(0.1807)	(0.1544)	(0.1433)
Separate;beloning	-0.0226	-0.0219	-0.0142
p-value	(0.7752)	(0.7824)	(0.7999)
Separate;totblogs	0.0399	0.0343	0.0255
p-value	(0.6142)	(0.6644)	(0.6436)
Separate;Login	0.0305	0.0618	0.0404
p-value	(0.6997)	(0.435)	(0.4642)
age;beloning	-0.0678	-0.0234	-0.0188
p-value	(0.3913)	(0.7675)	(0.7554)
age;totblogs	0.0071	0.0405	0.0313
p-value	(0.9288)	(0.6091)	(0.5978)
age;Login	0.0534	0.0394	0.0314
p-value	(0.4996)	(0.6184)	(0.5978)
beloning;totblogs	-0.053	-0.0047	-0.0057
p-value	(0.5031)	(0.9531)	(0.9157)
beloning;Login	0.0222	-0.0239	-0.0161
p-value	(0.779)	(0.7629)	(0.7661)
totblogs;Login	0.4589	0.5614	0.3979
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
Connected;Separate & 0.3682 & 0.3243 & 0.2413 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Connected;age & 0.1478 & 0.1481 & 0.1122 \tabularnewline
p-value & (0.0606) & (0.0599) & (0.0675) \tabularnewline
Connected;beloning & 0.0805 & 0.0983 & 0.0718 \tabularnewline
p-value & (0.3083) & (0.2131) & (0.1997) \tabularnewline
Connected;totblogs & 0.0644 & 0.0605 & 0.0456 \tabularnewline
p-value & (0.4159) & (0.4446) & (0.4077) \tabularnewline
Connected;Login & -0.0035 & 0.0702 & 0.0474 \tabularnewline
p-value & (0.9645) & (0.3745) & (0.3914) \tabularnewline
Separate;age & 0.1057 & 0.1124 & 0.0898 \tabularnewline
p-value & (0.1807) & (0.1544) & (0.1433) \tabularnewline
Separate;beloning & -0.0226 & -0.0219 & -0.0142 \tabularnewline
p-value & (0.7752) & (0.7824) & (0.7999) \tabularnewline
Separate;totblogs & 0.0399 & 0.0343 & 0.0255 \tabularnewline
p-value & (0.6142) & (0.6644) & (0.6436) \tabularnewline
Separate;Login & 0.0305 & 0.0618 & 0.0404 \tabularnewline
p-value & (0.6997) & (0.435) & (0.4642) \tabularnewline
age;beloning & -0.0678 & -0.0234 & -0.0188 \tabularnewline
p-value & (0.3913) & (0.7675) & (0.7554) \tabularnewline
age;totblogs & 0.0071 & 0.0405 & 0.0313 \tabularnewline
p-value & (0.9288) & (0.6091) & (0.5978) \tabularnewline
age;Login & 0.0534 & 0.0394 & 0.0314 \tabularnewline
p-value & (0.4996) & (0.6184) & (0.5978) \tabularnewline
beloning;totblogs & -0.053 & -0.0047 & -0.0057 \tabularnewline
p-value & (0.5031) & (0.9531) & (0.9157) \tabularnewline
beloning;Login & 0.0222 & -0.0239 & -0.0161 \tabularnewline
p-value & (0.779) & (0.7629) & (0.7661) \tabularnewline
totblogs;Login & 0.4589 & 0.5614 & 0.3979 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197064&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]Connected;Separate[/C][C]0.3682[/C][C]0.3243[/C][C]0.2413[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Connected;age[/C][C]0.1478[/C][C]0.1481[/C][C]0.1122[/C][/ROW]
[ROW][C]p-value[/C][C](0.0606)[/C][C](0.0599)[/C][C](0.0675)[/C][/ROW]
[ROW][C]Connected;beloning[/C][C]0.0805[/C][C]0.0983[/C][C]0.0718[/C][/ROW]
[ROW][C]p-value[/C][C](0.3083)[/C][C](0.2131)[/C][C](0.1997)[/C][/ROW]
[ROW][C]Connected;totblogs[/C][C]0.0644[/C][C]0.0605[/C][C]0.0456[/C][/ROW]
[ROW][C]p-value[/C][C](0.4159)[/C][C](0.4446)[/C][C](0.4077)[/C][/ROW]
[ROW][C]Connected;Login[/C][C]-0.0035[/C][C]0.0702[/C][C]0.0474[/C][/ROW]
[ROW][C]p-value[/C][C](0.9645)[/C][C](0.3745)[/C][C](0.3914)[/C][/ROW]
[ROW][C]Separate;age[/C][C]0.1057[/C][C]0.1124[/C][C]0.0898[/C][/ROW]
[ROW][C]p-value[/C][C](0.1807)[/C][C](0.1544)[/C][C](0.1433)[/C][/ROW]
[ROW][C]Separate;beloning[/C][C]-0.0226[/C][C]-0.0219[/C][C]-0.0142[/C][/ROW]
[ROW][C]p-value[/C][C](0.7752)[/C][C](0.7824)[/C][C](0.7999)[/C][/ROW]
[ROW][C]Separate;totblogs[/C][C]0.0399[/C][C]0.0343[/C][C]0.0255[/C][/ROW]
[ROW][C]p-value[/C][C](0.6142)[/C][C](0.6644)[/C][C](0.6436)[/C][/ROW]
[ROW][C]Separate;Login[/C][C]0.0305[/C][C]0.0618[/C][C]0.0404[/C][/ROW]
[ROW][C]p-value[/C][C](0.6997)[/C][C](0.435)[/C][C](0.4642)[/C][/ROW]
[ROW][C]age;beloning[/C][C]-0.0678[/C][C]-0.0234[/C][C]-0.0188[/C][/ROW]
[ROW][C]p-value[/C][C](0.3913)[/C][C](0.7675)[/C][C](0.7554)[/C][/ROW]
[ROW][C]age;totblogs[/C][C]0.0071[/C][C]0.0405[/C][C]0.0313[/C][/ROW]
[ROW][C]p-value[/C][C](0.9288)[/C][C](0.6091)[/C][C](0.5978)[/C][/ROW]
[ROW][C]age;Login[/C][C]0.0534[/C][C]0.0394[/C][C]0.0314[/C][/ROW]
[ROW][C]p-value[/C][C](0.4996)[/C][C](0.6184)[/C][C](0.5978)[/C][/ROW]
[ROW][C]beloning;totblogs[/C][C]-0.053[/C][C]-0.0047[/C][C]-0.0057[/C][/ROW]
[ROW][C]p-value[/C][C](0.5031)[/C][C](0.9531)[/C][C](0.9157)[/C][/ROW]
[ROW][C]beloning;Login[/C][C]0.0222[/C][C]-0.0239[/C][C]-0.0161[/C][/ROW]
[ROW][C]p-value[/C][C](0.779)[/C][C](0.7629)[/C][C](0.7661)[/C][/ROW]
[ROW][C]totblogs;Login[/C][C]0.4589[/C][C]0.5614[/C][C]0.3979[/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=197064&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197064&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
Connected;Separate	0.3682	0.3243	0.2413
p-value	(0)	(0)	(0)
Connected;age	0.1478	0.1481	0.1122
p-value	(0.0606)	(0.0599)	(0.0675)
Connected;beloning	0.0805	0.0983	0.0718
p-value	(0.3083)	(0.2131)	(0.1997)
Connected;totblogs	0.0644	0.0605	0.0456
p-value	(0.4159)	(0.4446)	(0.4077)
Connected;Login	-0.0035	0.0702	0.0474
p-value	(0.9645)	(0.3745)	(0.3914)
Separate;age	0.1057	0.1124	0.0898
p-value	(0.1807)	(0.1544)	(0.1433)
Separate;beloning	-0.0226	-0.0219	-0.0142
p-value	(0.7752)	(0.7824)	(0.7999)
Separate;totblogs	0.0399	0.0343	0.0255
p-value	(0.6142)	(0.6644)	(0.6436)
Separate;Login	0.0305	0.0618	0.0404
p-value	(0.6997)	(0.435)	(0.4642)
age;beloning	-0.0678	-0.0234	-0.0188
p-value	(0.3913)	(0.7675)	(0.7554)
age;totblogs	0.0071	0.0405	0.0313
p-value	(0.9288)	(0.6091)	(0.5978)
age;Login	0.0534	0.0394	0.0314
p-value	(0.4996)	(0.6184)	(0.5978)
beloning;totblogs	-0.053	-0.0047	-0.0057
p-value	(0.5031)	(0.9531)	(0.9157)
beloning;Login	0.0222	-0.0239	-0.0161
p-value	(0.779)	(0.7629)	(0.7661)
totblogs;Login	0.4589	0.5614	0.3979
p-value	(0)	(0)	(0)

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 6 ; par2 = none ; par3 = 5 ; par4 = no ;

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code