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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_twosampletests_mean.wasp
Title produced by softwarePaired and Unpaired Two Samples Tests about the Mean
Date of computationTue, 25 Nov 2014 14:03:25 +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/2014/Nov/25/t1416924226fm1iau83wy6jfqg.htm/, Retrieved Sun, 19 May 2024 10:26:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=258710, Retrieved Sun, 19 May 2024 10:26:23 +0000
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Original text written by user:
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User-defined keywords
Estimated Impact95

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-       [Paired and Unpaired Two Samples Tests about the Mean] [paper] [2014-11-25 14:03:25] [627bde65e5570be47fd7fc8a9f75ea40] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
50	62
68	71
54	54
73	65
73	52
75	84
72	42
70	66
81	65
71	78
61	66
76	61
70	71
60	69
70	72
76	68
67	70
76	68
75	67
63	77
70	72
75	69
60	71
73	62
64	64
59	58
64	52
60	59
78	68
67	76
66	65
68	59
66	69
73	63
72	75
59	63
78	60
68	73
73	63
65	70
71	66
76	63
63	64
59	61
73	62
66	61
62	66
69	56
57	66
63	53
65	59
47	71
68	71
71	64
59	66
68	62
48	65
59	68
60	60
59	65
79	68
59	64
71	74
57	69
66	68
63	72
58	67
48	66
73	51
61	56
68	67
62	69
62	56
69	55
58	67
58	76
72	64
62	68
65	64
69	65
66	71
66	63
55	60
72	72
62	70
64	61
64	61
68	62
70	71
69	51
73	56
74	70
78	73
75	76
50	52
65	57
78	60
78	60
70	62
63	59
63	61
67	69
66	59
62	66
73	67
69	75
84	69
58	58
57	60
68	74
69	55
60	63
66	68
81	62
72	72
74	62
65	75
51	58
80	47
74	19
70	50
69	79
55	71
71	48
69	66
63	74
39	66
68	71
68	53
67	60
70	70
66	69
59	59
62	72
75	71
73	74
73	80
61	73
63	67
78	61
65	74
77	32
69	69
68	64
76	60
67	59
69	78
59	60
73	68
78	73
68	67
68	65
67	74
55	55
73	49
66	53
75	64
77	57
75	67
57	70
66	75
60	65
64	65
74	69
59	48
69	68
63	74
73	67
55	65
77	74
70	69
66	63
77	68
78	70
72	78
50	74
72	62
71	69
80	65
64	67
69	52
75	53
79	76
60	63
53	41
69	72
68	52
73	65
64	63
79	56
76	56
66	64
57	75
58	65
74	61
62	71
66	72
66	62
65	66
66	63
77	71
65	64
67	78
84	60
58	68
75	68
72	60
75	65
72	64
75	69
72	74
72	76
75	73
66	76
73	55
70	62
64	78
	67
	75
	59
	70
	59
	63
	67
	58
	71
	53
	64
	67
	72
	57
	62
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	54
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	74
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	63
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	66
	78
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	58
	84
	67
	63
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	69
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	80
	67
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	79
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	81
	52
	76
	60
	77
	64
	67
	79
	40
	71
	73
	70
	66
	74
	58
	51
	75
	50
	77
	71

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' @ jenkins.wessa.net
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' @ jenkins.wessa.net \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=258710&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' @ jenkins.wessa.net[/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=258710&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=258710&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' @ jenkins.wessa.net
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'.






Two Sample t-test (unpaired)
Mean of Sample 167.5338078291815
Mean of Sample 265.0569395017794
t-stat3.65062111135851
df560
p-value0.000286111817937485
H0 value0
Alternativetwo.sided
CI Level0.95
CI[1.14419440627008,3.80954224853421]
F-test to compare two variances
F-stat0.853859490171263
df280
p-value0.186847415544894
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.675213201165475,1.07977158577036]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 67.5338078291815 \tabularnewline
Mean of Sample 2 & 65.0569395017794 \tabularnewline
t-stat & 3.65062111135851 \tabularnewline
df & 560 \tabularnewline
p-value & 0.000286111817937485 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [1.14419440627008,3.80954224853421] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.853859490171263 \tabularnewline
df & 280 \tabularnewline
p-value & 0.186847415544894 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.675213201165475,1.07977158577036] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=258710&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]67.5338078291815[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]65.0569395017794[/C][/ROW]
[ROW][C]t-stat[/C][C]3.65062111135851[/C][/ROW]
[ROW][C]df[/C][C]560[/C][/ROW]
[ROW][C]p-value[/C][C]0.000286111817937485[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][1.14419440627008,3.80954224853421][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.853859490171263[/C][/ROW]
[ROW][C]df[/C][C]280[/C][/ROW]
[ROW][C]p-value[/C][C]0.186847415544894[/C][/ROW]
[ROW][C]H0 value[/C][C]1[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][0.675213201165475,1.07977158577036][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=258710&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=258710&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:

Two Sample t-test (unpaired)
Mean of Sample 167.5338078291815
Mean of Sample 265.0569395017794
t-stat3.65062111135851
df560
p-value0.000286111817937485
H0 value0
Alternativetwo.sided
CI Level0.95
CI[1.14419440627008,3.80954224853421]
F-test to compare two variances
F-stat0.853859490171263
df280
p-value0.186847415544894
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.675213201165475,1.07977158577036]






Welch Two Sample t-test (unpaired)
Mean of Sample 167.5338078291815
Mean of Sample 265.0569395017794
t-stat3.65062111135851
df556.541520724654
p-value0.000286268641049089
H0 value0
Alternativetwo.sided
CI Level0.95
CI[1.14417646918805,3.80956018561624]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 67.5338078291815 \tabularnewline
Mean of Sample 2 & 65.0569395017794 \tabularnewline
t-stat & 3.65062111135851 \tabularnewline
df & 556.541520724654 \tabularnewline
p-value & 0.000286268641049089 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [1.14417646918805,3.80956018561624] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=258710&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]67.5338078291815[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]65.0569395017794[/C][/ROW]
[ROW][C]t-stat[/C][C]3.65062111135851[/C][/ROW]
[ROW][C]df[/C][C]556.541520724654[/C][/ROW]
[ROW][C]p-value[/C][C]0.000286268641049089[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][1.14417646918805,3.80956018561624][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=258710&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=258710&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:

Welch Two Sample t-test (unpaired)
Mean of Sample 167.5338078291815
Mean of Sample 265.0569395017794
t-stat3.65062111135851
df556.541520724654
p-value0.000286268641049089
H0 value0
Alternativetwo.sided
CI Level0.95
CI[1.14417646918805,3.80956018561624]






Wicoxon rank sum test with continuity correction (unpaired)
W46286
p-value0.000402394378517136
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.135231316725979
p-value0.0117295608319357
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0462633451957295
p-value0.924442530643977

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 46286 \tabularnewline
p-value & 0.000402394378517136 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.135231316725979 \tabularnewline
p-value & 0.0117295608319357 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.0462633451957295 \tabularnewline
p-value & 0.924442530643977 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=258710&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]46286[/C][/ROW]
[ROW][C]p-value[/C][C]0.000402394378517136[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributions of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.135231316725979[/C][/ROW]
[ROW][C]p-value[/C][C]0.0117295608319357[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.0462633451957295[/C][/ROW]
[ROW][C]p-value[/C][C]0.924442530643977[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=258710&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=258710&T=3

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


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

Wicoxon rank sum test with continuity correction (unpaired)
W46286
p-value0.000402394378517136
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.135231316725979
p-value0.0117295608319357
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0462633451957295
p-value0.924442530643977


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #column number of first sample
par2 <- as.numeric(par2) #column number of second sample
par3 <- as.numeric(par3) #confidence (= 1 - alpha)
if (par5 == 'unpaired') paired <- FALSE else paired <- TRUE
par6 <- as.numeric(par6) #H0
z <- t(y)
if (par1 == par2) stop('Please, select two different column numbers')
if (par1 < 1) stop('Please, select a column number greater than zero for the first sample')
if (par2 < 1) stop('Please, select a column number greater than zero for the second sample')
if (par1 > length(z[1,])) stop('The column number for the first sample should be smaller')
if (par2 > length(z[1,])) stop('The column number for the second sample should be smaller')
if (par3 <= 0) stop('The confidence level should be larger than zero')
if (par3 >= 1) stop('The confidence level should be smaller than zero')
(r.t <- t.test(z[,par1],z[,par2],var.equal=TRUE,alternative=par4,paired=paired,mu=par6,conf.level=par3))
(v.t <- var.test(z[,par1],z[,par2],conf.level=par3))
(r.w <- t.test(z[,par1],z[,par2],var.equal=FALSE,alternative=par4,paired=paired,mu=par6,conf.level=par3))
(w.t <- wilcox.test(z[,par1],z[,par2],alternative=par4,paired=paired,mu=par6,conf.level=par3))
(ks.t <- ks.test(z[,par1],z[,par2],alternative=par4))
m1 <- mean(z[,par1],na.rm=T)
m2 <- mean(z[,par2],na.rm=T)
mdiff <- m1 - m2
newsam1 <- z[!is.na(z[,par1]),par1]
newsam2 <- z[,par2]+mdiff
newsam2 <- newsam2[!is.na(newsam2)]
(ks1.t <- ks.test(newsam1,newsam2,alternative=par4))
mydf <- data.frame(cbind(z[,par1],z[,par2]))
colnames(mydf) <- c('Variable 1','Variable 2')
bitmap(file='test1.png')
boxplot(mydf, notch=TRUE, ylab='value',main=main)
dev.off()
bitmap(file='test2.png')
qqnorm(z[,par1],main='Normal QQplot - Variable 1')
qqline(z[,par1])
dev.off()
bitmap(file='test3.png')
qqnorm(z[,par2],main='Normal QQplot - Variable 2')
qqline(z[,par2])
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Two Sample t-test (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
if(!paired){
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 1',header=TRUE)
a<-table.element(a,r.t$estimate[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 2',header=TRUE)
a<-table.element(a,r.t$estimate[[2]])
a<-table.row.end(a)
} else {
a<-table.row.start(a)
a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE)
a<-table.element(a,r.t$estimate)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,r.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,r.t$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,r.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,r.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,r.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(r.t$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',r.t$conf.int[1],',',r.t$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'F-test to compare two variances',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'F-stat',header=TRUE)
a<-table.element(a,v.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,v.t$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,v.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,v.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,v.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(v.t$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',v.t$conf.int[1],',',v.t$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Welch Two Sample t-test (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
if(!paired){
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 1',header=TRUE)
a<-table.element(a,r.w$estimate[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 2',header=TRUE)
a<-table.element(a,r.w$estimate[[2]])
a<-table.row.end(a)
} else {
a<-table.row.start(a)
a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE)
a<-table.element(a,r.w$estimate)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,r.w$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,r.w$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,r.w$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,r.w$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,r.w$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(r.w$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',r.w$conf.int[1],',',r.w$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Wicoxon rank sum test with continuity correction (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'W',header=TRUE)
a<-table.element(a,w.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,w.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,w.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,w.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Kolmogorov-Smirnov Test to compare Distributions of two Samples',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'KS Statistic',header=TRUE)
a<-table.element(a,ks.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,ks.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'KS Statistic',header=TRUE)
a<-table.element(a,ks1.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,ks1.t$p.value)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')