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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationThu, 22 Dec 2011 05:21:17 -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/22/t1324549372mv8mqr1uqq0wgg5.htm/, Retrieved Fri, 03 May 2024 09:23:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159250, Retrieved Fri, 03 May 2024 09:23:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102
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 20:06:20] [b98453cac15ba1066b407e146608df68]
-   P   [Recursive Partitioning (Regression Trees)] [WS 10 RP Happiness] [2011-12-15 20:40:18] [3dd791303389e75e672968b227170a72]
- RMPD      [Kendall tau Correlation Matrix] [Paper Pearson] [2011-12-22 10:21:17] [ef8d8c90df4ff8d053d0205bd6ba250c] [Current]
- RM          [Kendall tau Correlation Matrix] [Paper Pearson] [2011-12-22 10:23:05] [3dd791303389e75e672968b227170a72]
-   P         [Kendall tau Correlation Matrix] [Paper Kendall] [2011-12-22 10:25:11] [3dd791303389e75e672968b227170a72]
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Dataseries X:
4	30	115	79	1
NA	28	109	58	1
1	38	146	60	0
NA	30	116	108	NA
NA	22	68	49	NA
NA	26	101	0	NA
3	25	96	121	1
NA	18	67	1	NA
NA	11	44	20	NA
3	26	100	43	1
4	25	93	69	0
4	38	140	78	0
NA	44	166	86	NA
3	30	99	44	0
NA	40	139	104	NA
NA	34	130	63	NA
2	47	181	158	0
4	30	116	102	1
4	31	116	77	0
NA	23	88	82	1
NA	36	139	115	NA
4	36	135	101	1
5	30	108	80	0
NA	25	89	50	1
NA	39	156	83	NA
4	34	129	123	0
4	31	118	73	0
5	31	118	81	1
4	33	125	105	0
4	25	95	47	0
NA	33	126	105	NA
2	35	135	94	1
3	42	154	44	1
NA	43	165	114	NA
NA	30	113	38	NA
4	33	127	107	1
NA	13	52	30	NA
NA	32	121	71	NA
2	36	136	84	0
4	0	0	0	0
NA	28	108	59	NA
2	14	46	33	1
4	17	54	42	1
4	32	124	96	0
NA	30	115	106	NA
4	35	128	56	1
3	20	80	57	0
4	28	97	59	1
5	28	104	39	0
NA	39	59	34	NA
4	34	125	76	0
NA	26	82	20	NA
5	39	149	91	1
NA	39	149	115	NA
NA	33	122	85	NA
NA	28	118	76	0
NA	4	12	8	0
2	39	144	79	0
NA	18	67	21	NA
NA	14	52	30	NA
3	29	108	76	0
4	44	166	101	0
3	21	80	94	0
4	16	60	27	1
NA	28	107	92	NA
4	35	127	123	0
NA	28	107	75	NA
NA	38	146	128	NA
NA	23	84	105	1
NA	36	141	55	NA
NA	32	123	56	NA
4	29	111	41	0
NA	25	98	72	0
5	27	105	67	1
NA	36	135	75	0
4	28	107	114	1
NA	23	85	118	NA
NA	40	155	77	NA
2	23	88	22	0
NA	40	155	66	NA
4	28	104	69	1
4	34	132	105	1
NA	33	127	116	NA
4	28	108	88	1
NA	34	129	73	0
NA	30	116	99	NA
4	33	122	62	0
NA	22	85	53	NA
4	38	147	118	0
NA	26	99	30	NA
4	35	87	100	0
NA	8	28	49	NA
2	24	90	24	0
2	29	109	67	1
4	20	78	46	0
3	29	111	57	0
NA	45	158	75	NA
4	37	141	135	0
NA	33	122	68	NA
2	33	124	124	1
3	25	93	33	0
2	32	124	98	0
4	29	112	58	0
4	28	108	68	0
NA	28	99	81	NA
4	31	117	131	0
3	52	199	110	1
4	21	78	37	0
NA	24	91	130	1
2	41	158	93	1
5	33	126	118	0
NA	32	122	39	1
NA	19	71	13	NA
NA	20	75	74	NA
4	31	115	81	0
NA	31	119	109	NA
NA	32	124	151	NA
4	18	72	51	0
2	23	91	28	1
4	17	45	40	0
4	20	78	56	0
2	12	39	27	0
NA	17	68	37	NA
4	30	119	83	0
NA	31	117	54	NA
NA	10	39	27	NA
3	13	50	28	1
5	22	88	59	0
4	42	155	133	0
2	1	0	12	0
NA	9	36	0	NA
4	32	123	106	0
NA	11	32	23	NA
4	25	99	44	1
4	36	136	71	0
4	31	117	116	1
NA	0	0	4	0
NA	24	88	62	0
2	13	39	12	1
NA	8	25	18	1
4	13	52	14	0
4	19	75	60	0
NA	18	71	7	NA
4	33	124	98	0
NA	40	151	64	NA
NA	22	71	29	NA
3	38	145	32	1
2	24	87	25	1
NA	8	27	16	NA
NA	35	131	48	NA
4	43	162	100	0
3	43	165	46	NA
4	14	54	45	0
5	41	159	129	1
NA	38	147	130	NA
4	45	170	136	0
NA	31	119	59	1
NA	13	49	25	NA
NA	28	104	32	NA
4	31	120	63	0
NA	40	150	95	NA
NA	30	112	14	0
4	16	59	36	1
4	37	136	113	1
4	30	107	47	1
2	35	130	92	1
NA	32	115	70	NA
NA	27	107	19	NA
2	20	75	50	1
3	18	71	41	0
NA	31	120	91	0
1	31	116	111	1
3	21	79	41	0
3	39	150	120	1
NA	41	156	135	NA
NA	13	51	27	NA
NA	32	118	87	NA
3	18	71	25	0
4	39	144	131	1
2	14	47	45	1
4	7	28	29	0
3	17	68	58	1
NA	0	0	4	NA
4	30	110	47	0
4	37	147	109	0
NA	0	0	7	NA
NA	5	15	12	NA
NA	1	4	0	NA
NA	16	64	37	NA
2	32	111	37	0
NA	24	85	46	NA
4	17	68	15	1
NA	11	40	42	NA
4	24	80	7	1
4	22	88	54	0
4	12	48	54	1
4	19	76	14	0
3	13	51	16	0
NA	17	67	33	NA
NA	15	59	32	0
NA	16	61	21	NA
NA	24	76	15	NA
2	15	60	38	1
5	17	68	22	1
NA	18	71	28	NA
NA	20	76	10	NA
NA	16	62	31	NA
4	16	61	32	0
4	18	67	32	0
NA	22	88	43	NA
NA	8	30	27	NA
2	17	64	37	1
NA	18	68	20	NA
3	16	64	32	1
3	23	91	0	1
4	22	88	5	1
NA	13	52	26	NA
3	13	49	10	0
NA	16	62	27	0
NA	16	61	11	NA
2	20	76	29	0
5	22	88	25	0
4	17	66	55	1
NA	18	71	23	NA
NA	17	68	5	0
NA	12	48	43	1
NA	7	25	23	NA
4	17	68	34	0
NA	14	41	36	NA
5	23	90	35	0
4	17	66	0	1
4	14	54	37	0
NA	15	59	28	NA
NA	17	60	16	NA
4	21	77	26	0
2	18	68	38	0
2	18	72	23	0
NA	17	67	22	NA
4	17	64	30	0
NA	16	63	16	NA
NA	15	59	18	0
NA	21	84	28	0
NA	16	64	32	NA
2	14	56	21	1
NA	15	54	23	NA
NA	17	67	29	NA
4	15	58	50	1
2	15	59	12	0
NA	10	40	21	NA
NA	6	22	18	NA
4	22	83	27	0
4	21	81	41	0
NA	1	2	13	NA
4	18	72	12	1
4	17	61	21	1
4	4	15	8	1
5	10	32	26	0
NA	16	62	27	0
NA	16	58	13	NA
NA	9	36	16	NA
NA	16	59	2	NA
NA	17	68	42	NA
NA	7	21	5	NA
4	15	55	37	NA
NA	14	54	17	NA
NA	14	55	38	NA
4	18	72	37	1
2	12	41	29	1
2	16	61	32	1
NA	21	67	35	1
NA	19	76	17	NA
NA	16	64	20	NA
NA	1	3	7	NA
NA	16	63	46	NA
NA	10	40	24	NA
NA	19	69	40	NA
NA	12	48	3	NA
NA	2	8	10	0
NA	14	52	37	NA
4	17	66	17	1
NA	19	76	28	NA
NA	14	43	19	NA
NA	11	39	29	NA
NA	4	14	8	NA
2	16	61	10	0
NA	20	71	15	NA
NA	12	44	15	NA
NA	15	60	28	NA
NA	16	64	17	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159250&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159250&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159250&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'Gertrude Mary Cox' @ cox.wessa.net







Correlations for all pairs of data series (method=pearson)
LearningCompetenceCompendiumsReviewedLongFeedbackMessagesBloggedComputationsGeslacht
LearningCompetence10.0720.0770.134-0.116
CompendiumsReviewed0.07210.9840.7620.008
LongFeedbackMessages0.0770.98410.7680.006
BloggedComputations0.1340.7620.76810.034
Geslacht-0.1160.0080.0060.0341

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & LearningCompetence & CompendiumsReviewed & LongFeedbackMessages & BloggedComputations & Geslacht \tabularnewline
LearningCompetence & 1 & 0.072 & 0.077 & 0.134 & -0.116 \tabularnewline
CompendiumsReviewed & 0.072 & 1 & 0.984 & 0.762 & 0.008 \tabularnewline
LongFeedbackMessages & 0.077 & 0.984 & 1 & 0.768 & 0.006 \tabularnewline
BloggedComputations & 0.134 & 0.762 & 0.768 & 1 & 0.034 \tabularnewline
Geslacht & -0.116 & 0.008 & 0.006 & 0.034 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159250&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]LearningCompetence[/C][C]CompendiumsReviewed[/C][C]LongFeedbackMessages[/C][C]BloggedComputations[/C][C]Geslacht[/C][/ROW]
[ROW][C]LearningCompetence[/C][C]1[/C][C]0.072[/C][C]0.077[/C][C]0.134[/C][C]-0.116[/C][/ROW]
[ROW][C]CompendiumsReviewed[/C][C]0.072[/C][C]1[/C][C]0.984[/C][C]0.762[/C][C]0.008[/C][/ROW]
[ROW][C]LongFeedbackMessages[/C][C]0.077[/C][C]0.984[/C][C]1[/C][C]0.768[/C][C]0.006[/C][/ROW]
[ROW][C]BloggedComputations[/C][C]0.134[/C][C]0.762[/C][C]0.768[/C][C]1[/C][C]0.034[/C][/ROW]
[ROW][C]Geslacht[/C][C]-0.116[/C][C]0.008[/C][C]0.006[/C][C]0.034[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159250&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159250&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)
LearningCompetenceCompendiumsReviewedLongFeedbackMessagesBloggedComputationsGeslacht
LearningCompetence10.0720.0770.134-0.116
CompendiumsReviewed0.07210.9840.7620.008
LongFeedbackMessages0.0770.98410.7680.006
BloggedComputations0.1340.7620.76810.034
Geslacht-0.1160.0080.0060.0341







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
LearningCompetence;CompendiumsReviewed0.07190.09840.0793
p-value(0.3868)(0.2357)(0.2179)
LearningCompetence;LongFeedbackMessages0.07720.10330.0829
p-value(0.353)(0.2133)(0.193)
LearningCompetence;BloggedComputations0.13380.16950.1338
p-value(0.1061)(0.0401)(0.0356)
LearningCompetence;Geslacht-0.1158-0.1148-0.1073
p-value(0.1655)(0.1692)(0.1684)
CompendiumsReviewed;LongFeedbackMessages0.9840.98110.9389
p-value(0)(0)(0)
CompendiumsReviewed;BloggedComputations0.76230.77220.574
p-value(0)(0)(0)
CompendiumsReviewed;Geslacht0.0077-0.0213-0.0177
p-value(0.9201)(0.7819)(0.781)
LongFeedbackMessages;BloggedComputations0.76760.77670.573
p-value(0)(0)(0)
LongFeedbackMessages;Geslacht0.0058-0.0236-0.0194
p-value(0.9396)(0.7584)(0.7574)
BloggedComputations;Geslacht0.03430.03510.0289
p-value(0.6553)(0.6473)(0.6459)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
LearningCompetence;CompendiumsReviewed & 0.0719 & 0.0984 & 0.0793 \tabularnewline
p-value & (0.3868) & (0.2357) & (0.2179) \tabularnewline
LearningCompetence;LongFeedbackMessages & 0.0772 & 0.1033 & 0.0829 \tabularnewline
p-value & (0.353) & (0.2133) & (0.193) \tabularnewline
LearningCompetence;BloggedComputations & 0.1338 & 0.1695 & 0.1338 \tabularnewline
p-value & (0.1061) & (0.0401) & (0.0356) \tabularnewline
LearningCompetence;Geslacht & -0.1158 & -0.1148 & -0.1073 \tabularnewline
p-value & (0.1655) & (0.1692) & (0.1684) \tabularnewline
CompendiumsReviewed;LongFeedbackMessages & 0.984 & 0.9811 & 0.9389 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
CompendiumsReviewed;BloggedComputations & 0.7623 & 0.7722 & 0.574 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
CompendiumsReviewed;Geslacht & 0.0077 & -0.0213 & -0.0177 \tabularnewline
p-value & (0.9201) & (0.7819) & (0.781) \tabularnewline
LongFeedbackMessages;BloggedComputations & 0.7676 & 0.7767 & 0.573 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
LongFeedbackMessages;Geslacht & 0.0058 & -0.0236 & -0.0194 \tabularnewline
p-value & (0.9396) & (0.7584) & (0.7574) \tabularnewline
BloggedComputations;Geslacht & 0.0343 & 0.0351 & 0.0289 \tabularnewline
p-value & (0.6553) & (0.6473) & (0.6459) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159250&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]LearningCompetence;CompendiumsReviewed[/C][C]0.0719[/C][C]0.0984[/C][C]0.0793[/C][/ROW]
[ROW][C]p-value[/C][C](0.3868)[/C][C](0.2357)[/C][C](0.2179)[/C][/ROW]
[ROW][C]LearningCompetence;LongFeedbackMessages[/C][C]0.0772[/C][C]0.1033[/C][C]0.0829[/C][/ROW]
[ROW][C]p-value[/C][C](0.353)[/C][C](0.2133)[/C][C](0.193)[/C][/ROW]
[ROW][C]LearningCompetence;BloggedComputations[/C][C]0.1338[/C][C]0.1695[/C][C]0.1338[/C][/ROW]
[ROW][C]p-value[/C][C](0.1061)[/C][C](0.0401)[/C][C](0.0356)[/C][/ROW]
[ROW][C]LearningCompetence;Geslacht[/C][C]-0.1158[/C][C]-0.1148[/C][C]-0.1073[/C][/ROW]
[ROW][C]p-value[/C][C](0.1655)[/C][C](0.1692)[/C][C](0.1684)[/C][/ROW]
[ROW][C]CompendiumsReviewed;LongFeedbackMessages[/C][C]0.984[/C][C]0.9811[/C][C]0.9389[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CompendiumsReviewed;BloggedComputations[/C][C]0.7623[/C][C]0.7722[/C][C]0.574[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CompendiumsReviewed;Geslacht[/C][C]0.0077[/C][C]-0.0213[/C][C]-0.0177[/C][/ROW]
[ROW][C]p-value[/C][C](0.9201)[/C][C](0.7819)[/C][C](0.781)[/C][/ROW]
[ROW][C]LongFeedbackMessages;BloggedComputations[/C][C]0.7676[/C][C]0.7767[/C][C]0.573[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]LongFeedbackMessages;Geslacht[/C][C]0.0058[/C][C]-0.0236[/C][C]-0.0194[/C][/ROW]
[ROW][C]p-value[/C][C](0.9396)[/C][C](0.7584)[/C][C](0.7574)[/C][/ROW]
[ROW][C]BloggedComputations;Geslacht[/C][C]0.0343[/C][C]0.0351[/C][C]0.0289[/C][/ROW]
[ROW][C]p-value[/C][C](0.6553)[/C][C](0.6473)[/C][C](0.6459)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159250&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159250&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
LearningCompetence;CompendiumsReviewed0.07190.09840.0793
p-value(0.3868)(0.2357)(0.2179)
LearningCompetence;LongFeedbackMessages0.07720.10330.0829
p-value(0.353)(0.2133)(0.193)
LearningCompetence;BloggedComputations0.13380.16950.1338
p-value(0.1061)(0.0401)(0.0356)
LearningCompetence;Geslacht-0.1158-0.1148-0.1073
p-value(0.1655)(0.1692)(0.1684)
CompendiumsReviewed;LongFeedbackMessages0.9840.98110.9389
p-value(0)(0)(0)
CompendiumsReviewed;BloggedComputations0.76230.77220.574
p-value(0)(0)(0)
CompendiumsReviewed;Geslacht0.0077-0.0213-0.0177
p-value(0.9201)(0.7819)(0.781)
LongFeedbackMessages;BloggedComputations0.76760.77670.573
p-value(0)(0)(0)
LongFeedbackMessages;Geslacht0.0058-0.0236-0.0194
p-value(0.9396)(0.7584)(0.7574)
BloggedComputations;Geslacht0.03430.03510.0289
p-value(0.6553)(0.6473)(0.6459)



Parameters (Session):
par1 = pearson ;
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')