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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software ModulePatrick.Wessarwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationSun, 12 Dec 2010 17:09:17 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/12/t1292173632vpa4vmogl55oyvt.htm/, Retrieved Tue, 07 May 2024 08:27:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108561, Retrieved Tue, 07 May 2024 08:27:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
F   PD    [Kendall tau Correlation Matrix] [] [2010-12-12 17:09:17] [6b31f806e9ccc1f74a26091056f791cb] [Current]
-   PD      [Kendall tau Correlation Matrix] [] [2010-12-19 19:05:25] [de55ccbf69577500a5f46ed42a101114]
-   PD      [Kendall tau Correlation Matrix] [] [2010-12-19 19:07:05] [de55ccbf69577500a5f46ed42a101114]
-   PD      [Kendall tau Correlation Matrix] [] [2010-12-20 10:28:58] [74be16979710d4c4e7c6647856088456]
-   PD      [Kendall tau Correlation Matrix] [] [2010-12-21 17:10:58] [de55ccbf69577500a5f46ed42a101114]
-   PD      [Kendall tau Correlation Matrix] [] [2010-12-21 17:13:21] [de55ccbf69577500a5f46ed42a101114]
-    D        [Kendall tau Correlation Matrix] [] [2010-12-21 18:57:23] [de55ccbf69577500a5f46ed42a101114]
Feedback Forum
2010-12-19 08:31:39 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
De student maakt hier een correcte correlatiematrix en uit de interpretatie die hij geeft, blijkt duidelijk dat de student deze materie goed begrepen heeft. Positief vind ik dat de student hier - in tegenstelling tot de pearson correlatie - wel kijkt waar de correlatie het grootst is.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8	350	165	3693	11.5
8	318	150	3436	11
8	302	140	3449	10.5
8	429	198	4341	10
8	440	215	4312	8.5
8	455	225	4425	10
8	383	170	3563	10
8	340	160	3609	8
8	455	225	3086	10
4	113	95	2372	15
6	199	97	2774	15.5
4	97	46	1835	20.5
4	110	87	2672	17.5
4	104	95	2375	17.5
4	121	113	2234	12.5
8	360	215	4615	14
8	307	200	4376	15
8	304	193	4732	18.5
4	97	88	2130	14.5
4	113	95	2228	14
6	250	100	3329	15.5
6	232	100	3288	15.5
8	350	165	4209	12
8	318	150	4096	13
8	400	170	4746	12
8	400	175	5140	12
4	140	72	2408	19
6	250	100	3282	15
4	122	86	2220	14
4	116	90	2123	14
4	88	76	2065	14.5
4	71	65	1773	19
4	97	60	1834	19
4	91	70	1955	20.5
4	97.5	80	2126	17
4	122	86	2226	16.5
8	350	165	4274	12
8	318	150	4135	13.5
8	351	153	4129	13
8	429	208	4633	11
8	350	155	4502	13.5
8	400	190	4422	12.5
3	70	97	2330	13.5
8	307	130	4098	14
8	302	140	4294	16
4	121	112	2933	14.5
4	121	76	2511	18
4	122	86	2395	16
4	120	97	2506	14.5
4	98	80	2164	15
8	350	175	4100	13
8	304	150	3672	11.5
8	302	137	4042	14.5
8	318	150	3777	12.5
8	400	150	4464	12
8	351	158	4363	13
8	440	215	4735	11
8	455	225	4951	11
6	225	105	3121	16.5
6	250	100	3278	18
6	250	88	3021	16.5
6	198	95	2904	16
8	400	150	4997	14
8	350	180	4499	12.5
6	232	100	2789	15
4	140	72	2401	19.5
4	108	94	2379	16.5
4	122	85	2310	18.5
6	155	107	2472	14
8	350	145	4082	13
8	400	230	4278	9.5
4	116	75	2158	15.5
4	114	91	2582	14
8	318	150	3399	11
4	121	110	2660	14
8	350	180	3664	11
6	198	95	3102	16.5
6	232	100	2901	16
4	122	80	2451	16.5
4	71	65	1836	21
6	250	100	3781	17
6	258	110	3632	18
8	302	140	4141	14
8	350	150	4699	14.5
8	302	140	4638	16
8	304	150	4257	15.5
4	79	67	1963	15.5
4	97	78	2300	14.5
4	83	61	2003	19
4	90	75	2125	14.5
4	116	75	2246	14
4	120	97	2489	15
4	79	67	2000	16
6	225	95	3264	16
6	250	72	3158	19.5
8	400	170	4668	11.5
8	350	145	4440	14
8	351	148	4657	13.5
6	231	110	3907	21
6	258	110	3730	19
6	225	95	3785	19
8	262	110	3221	13.5
8	302	129	3169	12
4	140	83	2639	17
6	232	100	2914	16
4	134	96	2702	13.5
4	90	71	2223	16.5
6	171	97	2984	14.5
4	115	95	2694	15
4	120	88	2957	17
4	121	115	2671	13.5
4	91	53	1795	17.5
4	116	81	2220	16.9
4	140	92	2572	14.9
4	101	83	2202	15.3
8	305	140	4215	13
8	304	120	3962	13.9
8	351	152	4215	12.8
6	250	105	3353	14.5
6	200	81	3012	17.6
4	85	52	2035	22.2
4	98	60	2164	22.1
6	225	100	3651	17.7
6	250	110	3645	16.2
6	258	95	3193	17.8
4	85	70	1990	17
4	97	75	2155	16.4
4	130	102	3150	15.7
8	318	150	3940	13.2
6	168	120	3820	16.7
8	350	180	4380	12.1
8	302	130	3870	15
8	318	150	3755	14
4	111	80	2155	14.8
4	79	58	1825	18.6
4	85	70	1945	16.8
8	305	145	3880	12.5
8	318	145	4140	13.7
6	231	105	3425	16.9
6	225	100	3630	17.7
8	400	180	4220	11.1
8	350	170	4165	11.4
8	351	149	4335	14.5
4	97	78	1940	14.5
4	97	75	2265	18.2
4	140	89	2755	15.8
4	98	83	2075	15.9
4	97	67	1985	16.4
6	146	97	2815	14.5
4	121	110	2600	12.8
4	90	48	1985	21.5
4	98	66	1800	14.4
4	85	70	2070	18.6
8	318	140	3735	13.2
8	302	139	3570	12.8
6	200	95	3155	18.2
6	200	85	2965	15.8
6	225	100	3430	17.2
6	232	90	3210	17.2
6	200	85	3070	16.7
6	225	110	3620	18.7
8	305	145	3425	13.2
6	231	165	3445	13.4
8	318	140	4080	13.7
4	98	68	2155	16.5
4	119	97	2300	14.7
4	105	75	2230	14.5
4	151	85	2855	17.6
5	131	103	2830	15.9
6	163	125	3140	13.6
6	163	133	3410	15.8
4	89	71	1990	14.9
4	98	68	2135	16.6
6	200	85	2990	18.2
4	140	88	2890	17.3
6	225	110	3360	16.6
8	305	130	3840	15.4
8	351	138	3955	13.2
8	318	135	3830	15.2
8	351	142	4054	14.3
8	267	125	3605	15
4	89	71	1925	14
4	86	65	1975	15.2
4	121	80	2670	15
4	141	71	3190	24.8
8	260	90	3420	22.2
4	105	70	2150	14.9
4	85	65	2020	19.2
4	151	90	2670	16
6	173	115	2595	11.3
4	151	90	2556	13.2
4	98	76	2144	14.7
4	98	70	2120	15.5
4	86	65	2019	16.4
4	140	88	2870	18.1
4	151	90	3003	20.1
4	97	78	2188	15.8
4	134	90	2711	15.5
4	119	92	2434	15
4	108	75	2265	15.2
4	156	105	2800	14.4
4	85	65	2110	19.2
5	121	67	2950	19.9
4	91	67	1850	13.8
4	89	62	1845	15.3
4	122	88	2500	15.1
4	135	84	2490	15.7
4	151	84	2635	16.4
6	173	110	2725	12.6
4	135	84	2385	12.9
4	86	64	1875	16.4
4	81	60	1760	16.1
4	85	65	1975	19.4
4	89	62	2050	17.3
4	105	63	2215	14.9
4	98	65	2045	16.2
4	105	74	2190	14.2
4	119	100	2615	14.8
4	141	80	3230	20.4
6	146	120	2930	13.8
6	231	110	3415	15.8
6	200	88	3060	17.1
6	225	85	3465	16.6
4	112	88	2640	18.6
4	112	88	2395	18
4	135	84	2525	16
4	151	90	2735	18
4	105	74	1980	15.3
4	91	68	1970	17.6
4	105	63	2125	14.7
4	120	88	2160	14.5
4	107	75	2205	14.5
4	91	67	1965	15.7
6	181	110	2945	16.4
6	262	85	3015	17
4	144	96	2665	13.9
4	151	90	2950	17.3
4	140	86	2790	15.6
4	135	84	2295	11.6
4	120	79	2625	18.6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108561&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108561&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108561&T=0

As an alternative you can also use a QR Code:  
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'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Correlations for all pairs of data series (method=kendall)
cylindersengine.displacementhorsepowerweightacceleration
cylinders10.7960.7070.75-0.377
engine.displacement0.79610.7390.808-0.361
horsepower0.7070.73910.722-0.48
weight0.750.8080.7221-0.278
acceleration -0.377-0.361-0.48-0.2781

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & cylinders & engine.displacement & horsepower & weight & acceleration
 \tabularnewline
cylinders & 1 & 0.796 & 0.707 & 0.75 & -0.377 \tabularnewline
engine.displacement & 0.796 & 1 & 0.739 & 0.808 & -0.361 \tabularnewline
horsepower & 0.707 & 0.739 & 1 & 0.722 & -0.48 \tabularnewline
weight & 0.75 & 0.808 & 0.722 & 1 & -0.278 \tabularnewline
acceleration
 & -0.377 & -0.361 & -0.48 & -0.278 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108561&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]cylinders[/C][C]engine.displacement[/C][C]horsepower[/C][C]weight[/C][C]acceleration
[/C][/ROW]
[ROW][C]cylinders[/C][C]1[/C][C]0.796[/C][C]0.707[/C][C]0.75[/C][C]-0.377[/C][/ROW]
[ROW][C]engine.displacement[/C][C]0.796[/C][C]1[/C][C]0.739[/C][C]0.808[/C][C]-0.361[/C][/ROW]
[ROW][C]horsepower[/C][C]0.707[/C][C]0.739[/C][C]1[/C][C]0.722[/C][C]-0.48[/C][/ROW]
[ROW][C]weight[/C][C]0.75[/C][C]0.808[/C][C]0.722[/C][C]1[/C][C]-0.278[/C][/ROW]
[ROW][C]acceleration
[/C][C]-0.377[/C][C]-0.361[/C][C]-0.48[/C][C]-0.278[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108561&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108561&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Correlations for all pairs of data series (method=kendall)
cylindersengine.displacementhorsepowerweightacceleration
cylinders10.7960.7070.75-0.377
engine.displacement0.79610.7390.808-0.361
horsepower0.7070.73910.722-0.48
weight0.750.8080.7221-0.278
acceleration -0.377-0.361-0.48-0.2781






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
cylinders;engine.displacement0.94930.91510.7963
p-value(0)(0)(0)
cylinders;horsepower0.84810.83870.7074
p-value(0)(0)(0)
cylinders;weight0.90060.88610.7502
p-value(0)(0)(0)
cylinders;acceleration -0.5133-0.4912-0.3766
p-value(0)(0)(0)
engine.displacement;horsepower0.90780.89550.7393
p-value(0)(0)(0)
engine.displacement;weight0.9320.94990.8075
p-value(0)(0)(0)
engine.displacement;acceleration -0.5592-0.5133-0.361
p-value(0)(0)(0)
horsepower;weight0.86580.89390.7221
p-value(0)(0)(0)
horsepower;acceleration -0.6819-0.651-0.4803
p-value(0)(0)(0)
weight;acceleration -0.4252-0.4177-0.278
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
cylinders;engine.displacement & 0.9493 & 0.9151 & 0.7963 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
cylinders;horsepower & 0.8481 & 0.8387 & 0.7074 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
cylinders;weight & 0.9006 & 0.8861 & 0.7502 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
cylinders;acceleration
 & -0.5133 & -0.4912 & -0.3766 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
engine.displacement;horsepower & 0.9078 & 0.8955 & 0.7393 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
engine.displacement;weight & 0.932 & 0.9499 & 0.8075 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
engine.displacement;acceleration
 & -0.5592 & -0.5133 & -0.361 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
horsepower;weight & 0.8658 & 0.8939 & 0.7221 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
horsepower;acceleration
 & -0.6819 & -0.651 & -0.4803 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
weight;acceleration
 & -0.4252 & -0.4177 & -0.278 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108561&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]cylinders;engine.displacement[/C][C]0.9493[/C][C]0.9151[/C][C]0.7963[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]cylinders;horsepower[/C][C]0.8481[/C][C]0.8387[/C][C]0.7074[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]cylinders;weight[/C][C]0.9006[/C][C]0.8861[/C][C]0.7502[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]cylinders;acceleration
[/C][C]-0.5133[/C][C]-0.4912[/C][C]-0.3766[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]engine.displacement;horsepower[/C][C]0.9078[/C][C]0.8955[/C][C]0.7393[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]engine.displacement;weight[/C][C]0.932[/C][C]0.9499[/C][C]0.8075[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]engine.displacement;acceleration
[/C][C]-0.5592[/C][C]-0.5133[/C][C]-0.361[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]horsepower;weight[/C][C]0.8658[/C][C]0.8939[/C][C]0.7221[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]horsepower;acceleration
[/C][C]-0.6819[/C][C]-0.651[/C][C]-0.4803[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]weight;acceleration
[/C][C]-0.4252[/C][C]-0.4177[/C][C]-0.278[/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=108561&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108561&T=2

As an alternative you can also use a QR Code:  
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
cylinders;engine.displacement0.94930.91510.7963
p-value(0)(0)(0)
cylinders;horsepower0.84810.83870.7074
p-value(0)(0)(0)
cylinders;weight0.90060.88610.7502
p-value(0)(0)(0)
cylinders;acceleration -0.5133-0.4912-0.3766
p-value(0)(0)(0)
engine.displacement;horsepower0.90780.89550.7393
p-value(0)(0)(0)
engine.displacement;weight0.9320.94990.8075
p-value(0)(0)(0)
engine.displacement;acceleration -0.5592-0.5133-0.361
p-value(0)(0)(0)
horsepower;weight0.86580.89390.7221
p-value(0)(0)(0)
horsepower;acceleration -0.6819-0.651-0.4803
p-value(0)(0)(0)
weight;acceleration -0.4252-0.4177-0.278
p-value(0)(0)(0)


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = no ;
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')