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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, 16 Dec 2014 13:14:44 +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/Dec/16/t14187357133akdshs0ykvpwlh.htm/, Retrieved Thu, 31 Oct 2024 23:13:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269496, Retrieved Thu, 31 Oct 2024 23:13:41 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Notched Boxplots] [1.1 AMSI - boxplot] [2014-12-09 11:18:21] [4d39cf209776852399955073f9d0ee7a]
- R  D  [Notched Boxplots] [1.1 AMSI : Notche...] [2014-12-10 13:41:48] [765bd0d5d4a0c852014c120c6930661d]
- RMPD      [Paired and Unpaired Two Samples Tests about the Mean] [1.1 Two sample T-...] [2014-12-16 13:14:44] [706bcb1d0c5210dc074174906fafd7a3] [Current]
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Dataseries X:
51	69
56	48
67	NA
69	68
NA	74
56	67
55	65
NA	NA
67	74
NA	NA
NA	NA
76	69
64	NA
68	63
64	NA
65	NA
71	68
63	NA
60	70
NA	78
72	NA
70	NA
61	NA
61	74
62	NA
71	62
NA	69
51	65
56	67
70	NA
73	52
76	NA
NA	53
NA	NA
NA	NA
52	NA
NA	NA
NA	NA
NA	NA
57	76
NA	63
60	41
60	NA
NA	NA
62	NA
59	NA
61	NA
NA	72
NA	52
NA	65
NA	63
69	NA
NA	56
59	NA
NA	56
66	64
NA	NA
67	75
NA	NA
NA	NA
75	NA
NA	NA
69	NA
58	NA
60	65
74	NA
55	NA
NA	61
63	71
NA	72
NA	62
NA	NA
68	66
NA	63
62	NA
NA	NA
NA	NA
NA	NA
NA	71
72	NA
62	NA
75	NA
58	NA
NA	NA
NA	NA
47	64
NA	78
NA	60
NA	NA
NA	NA
19	68
50	NA
NA	68
NA	60
79	65
NA	64
71	69
48	NA
66	NA
NA	NA
74	NA
66	NA
71	74
NA	NA
NA	NA
NA	76
53	NA
60	NA
NA	73
70	NA
69	76
NA	55
NA	NA
NA	62
59	NA
72	78
NA	NA
NA	67
NA	75
71	59
74	NA
NA	70
NA	59
NA	NA
80	63
73	67
67	58
61	71
NA	NA
74	53
32	NA
69	NA
NA	64
NA	NA
64	67
NA	72
60	NA
59	NA
78	57
NA	62
60	74
NA	54
68	NA
73	66
NA	64
67	74
NA	71
65	NA
NA	NA
74	63
NA	NA
NA	70
55	66
49	NA
NA	78
53	NA
64	72
NA	NA
57	NA
NA	72
NA	58
67	84
70	67
NA	NA
75	NA
NA	63
NA	NA
65	55
NA	NA
NA	58
65	69
	54
	58
	67
	77
	80
	67
	NA
	71
	NA
	NA
	79
	76
	72
	81
	52
	76
	60
	NA
	77
	64
	67
	NA
	79
	40
	71
	73
	NA
	70
	66
	NA
	NA
	74
	58
	51
	75
	NA
	50
	NA
	77
	NA
	NA
	NA
	NA
	71
	NA
	NA
	NA
	NA
	NA
	NA
	NA
	NA
	NA
	NA
	NA
	NA




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269496&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]1 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=269496&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269496&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 time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Two Sample t-test (unpaired)
Mean of Sample 164.2481203007519
Mean of Sample 266.3813559322034
t-stat-1.94290665791975
df249
p-value0.0531545938473179
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.29571019315088,0.0292389302478546]
F-test to compare two variances
F-stat1.33416590241456
df132
p-value0.111528530772824
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.935028484731291,1.89644028535688]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 64.2481203007519 \tabularnewline
Mean of Sample 2 & 66.3813559322034 \tabularnewline
t-stat & -1.94290665791975 \tabularnewline
df & 249 \tabularnewline
p-value & 0.0531545938473179 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-4.29571019315088,0.0292389302478546] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 1.33416590241456 \tabularnewline
df & 132 \tabularnewline
p-value & 0.111528530772824 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.935028484731291,1.89644028535688] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269496&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]64.2481203007519[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]66.3813559322034[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.94290665791975[/C][/ROW]
[ROW][C]df[/C][C]249[/C][/ROW]
[ROW][C]p-value[/C][C]0.0531545938473179[/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][-4.29571019315088,0.0292389302478546][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]1.33416590241456[/C][/ROW]
[ROW][C]df[/C][C]132[/C][/ROW]
[ROW][C]p-value[/C][C]0.111528530772824[/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][0.935028484731291,1.89644028535688][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269496&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269496&T=1

As an alternative you can also use a QR Code:  

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

Two Sample t-test (unpaired)
Mean of Sample 164.2481203007519
Mean of Sample 266.3813559322034
t-stat-1.94290665791975
df249
p-value0.0531545938473179
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.29571019315088,0.0292389302478546]
F-test to compare two variances
F-stat1.33416590241456
df132
p-value0.111528530772824
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.935028484731291,1.89644028535688]







Welch Two Sample t-test (unpaired)
Mean of Sample 164.2481203007519
Mean of Sample 266.3813559322034
t-stat-1.959667772694
df248.857563108743
p-value0.0511508053839117
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.27722045705479,0.0107491941517678]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 64.2481203007519 \tabularnewline
Mean of Sample 2 & 66.3813559322034 \tabularnewline
t-stat & -1.959667772694 \tabularnewline
df & 248.857563108743 \tabularnewline
p-value & 0.0511508053839117 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-4.27722045705479,0.0107491941517678] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269496&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]64.2481203007519[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]66.3813559322034[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.959667772694[/C][/ROW]
[ROW][C]df[/C][C]248.857563108743[/C][/ROW]
[ROW][C]p-value[/C][C]0.0511508053839117[/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][-4.27722045705479,0.0107491941517678][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269496&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269496&T=2

As an alternative you can also use a QR Code:  

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

Welch Two Sample t-test (unpaired)
Mean of Sample 164.2481203007519
Mean of Sample 266.3813559322034
t-stat-1.959667772694
df248.857563108743
p-value0.0511508053839117
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.27722045705479,0.0107491941517678]







Wicoxon rank sum test with continuity correction (unpaired)
W6755.5
p-value0.0571595653300879
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.165031222123104
p-value0.0663565386939269
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0942398368803365
p-value0.635276546177908

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 6755.5 \tabularnewline
p-value & 0.0571595653300879 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.165031222123104 \tabularnewline
p-value & 0.0663565386939269 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.0942398368803365 \tabularnewline
p-value & 0.635276546177908 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269496&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]6755.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.0571595653300879[/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]0.165031222123104[/C][/ROW]
[ROW][C]p-value[/C][C]0.0663565386939269[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.0942398368803365[/C][/ROW]
[ROW][C]p-value[/C][C]0.635276546177908[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269496&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269496&T=3

As an alternative you can also use a QR Code:  

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

Wicoxon rank sum test with continuity correction (unpaired)
W6755.5
p-value0.0571595653300879
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.165031222123104
p-value0.0663565386939269
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0942398368803365
p-value0.635276546177908



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0.0 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0.0 ;
R code (references can be found in the software module):
par6 <- '0.0'
par5 <- 'unpaired'
par4 <- 'two.sided'
par3 <- '0.95'
par2 <- '2'
par1 <- '1'
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')