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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, 02 Nov 2010 20:03:56 +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/Nov/02/t1288728193hdqpdo7g9pztxxt.htm/, Retrieved Sat, 27 Apr 2024 21:47:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=92026, Retrieved Sat, 27 Apr 2024 21:47:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Factor Analysis] [Sleep in Mammals ...] [2010-03-21 11:39:53] [b98453cac15ba1066b407e146608df68]
- RMPD  [Testing Mean with unknown Variance - Critical Value] [Hypothesis Test a...] [2010-10-19 11:45:26] [b98453cac15ba1066b407e146608df68]
F RMPD    [Paired and Unpaired Two Samples Tests about the Mean] [Q1, W5] [2010-11-02 19:21:54] [b3140021f9a1a3896de9ecbfce0f1101]
-    D      [Paired and Unpaired Two Samples Tests about the Mean] [Q2, W5] [2010-11-02 19:28:48] [b3140021f9a1a3896de9ecbfce0f1101]
F    D        [Paired and Unpaired Two Samples Tests about the Mean] [Q3, W5] [2010-11-02 19:32:03] [b3140021f9a1a3896de9ecbfce0f1101]
F    D            [Paired and Unpaired Two Samples Tests about the Mean] [Q5 a, W5] [2010-11-02 20:03:56] [0605ea080d54454c99180f574351b8e4] [Current]
F    D              [Paired and Unpaired Two Samples Tests about the Mean] [Q5 b,W5] [2010-11-02 20:06:39] [b3140021f9a1a3896de9ecbfce0f1101]
F    D                [Paired and Unpaired Two Samples Tests about the Mean] [Q5 c , WW5] [2010-11-02 20:10:07] [b3140021f9a1a3896de9ecbfce0f1101]
F RM D                [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Q6, KT] [2010-11-02 20:27:53] [b3140021f9a1a3896de9ecbfce0f1101]
-    D                  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Q7, KT] [2010-11-02 20:53:17] [b3140021f9a1a3896de9ecbfce0f1101]
F    D                  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Q7, KT] [2010-11-02 20:53:17] [b3140021f9a1a3896de9ecbfce0f1101]
F RM D                  [Two-Way ANOVA] [Q8] [2010-11-02 21:01:13] [b3140021f9a1a3896de9ecbfce0f1101]
Feedback Forum
2010-11-03 16:25:04 [Pascal Wijnen] [reply] 
De student heeft unpaired gebruikt in de plaats van paired. Er is ook geen interpretatie. Hier kunnen we normaal gezien de H0 aanvaarden en dus is er geen significant verschil aan te merken op lange termijn.
2010-11-07 22:46:01 [] [reply] 
Als je paired gebruikt, kom je voor treatment T tot volgend resultaat: http://www.freestatistics.org/blog/index.php?v=date/2010/Oct/29/t1288343058iuvzirzp23a2oj4.htm/. Ik heb kolom 'post 3' treatment T en kolom 'pre' treatment T gebruikt. We kunnen voor treatment 'T' concluderen dat er geen significant verschil is tussen de resultaten van de post-test 3 en die van de pre-test.

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Dataset

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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92026&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92026&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92026&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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Two Sample t-test (unpaired)
Mean of Sample 10.486486486486487
Mean of Sample 20.702702702702703
t-stat-1.91540852272229
df72
p-value0.0594122540199829
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.441243485561307,0.00881105312887502]
F-test to compare two variances
F-stat1.19580419580420
df36
p-value0.594453834955101
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.61572230708542,2.32239056186175]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 0.486486486486487 \tabularnewline
Mean of Sample 2 & 0.702702702702703 \tabularnewline
t-stat & -1.91540852272229 \tabularnewline
df & 72 \tabularnewline
p-value & 0.0594122540199829 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.441243485561307,0.00881105312887502] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 1.19580419580420 \tabularnewline
df & 36 \tabularnewline
p-value & 0.594453834955101 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.61572230708542,2.32239056186175] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92026&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]0.486486486486487[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]0.702702702702703[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.91540852272229[/C][/ROW]
[ROW][C]df[/C][C]72[/C][/ROW]
[ROW][C]p-value[/C][C]0.0594122540199829[/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][-0.441243485561307,0.00881105312887502][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]1.19580419580420[/C][/ROW]
[ROW][C]df[/C][C]36[/C][/ROW]
[ROW][C]p-value[/C][C]0.594453834955101[/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][0.61572230708542,2.32239056186175][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92026&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92026&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 10.486486486486487
Mean of Sample 20.702702702702703
t-stat-1.91540852272229
df72
p-value0.0594122540199829
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.441243485561307,0.00881105312887502]
F-test to compare two variances
F-stat1.19580419580420
df36
p-value0.594453834955101
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.61572230708542,2.32239056186175]






Welch Two Sample t-test (unpaired)
Mean of Sample 10.486486486486487
Mean of Sample 20.702702702702703
t-stat-1.91540852272229
df71.4319983900181
p-value0.0594439493142076
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.441274060039924,0.00884162760749109]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 0.486486486486487 \tabularnewline
Mean of Sample 2 & 0.702702702702703 \tabularnewline
t-stat & -1.91540852272229 \tabularnewline
df & 71.4319983900181 \tabularnewline
p-value & 0.0594439493142076 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.441274060039924,0.00884162760749109] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92026&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]0.486486486486487[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]0.702702702702703[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.91540852272229[/C][/ROW]
[ROW][C]df[/C][C]71.4319983900181[/C][/ROW]
[ROW][C]p-value[/C][C]0.0594439493142076[/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][-0.441274060039924,0.00884162760749109][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92026&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92026&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 10.486486486486487
Mean of Sample 20.702702702702703
t-stat-1.91540852272229
df71.4319983900181
p-value0.0594439493142076
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.441274060039924,0.00884162760749109]






Wicoxon rank sum test with continuity correction (unpaired)
W536.5
p-value0.0607965935906568
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.216216216216216
p-value0.352694374639084
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.486486486486487
p-value0.000314788499061414

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 536.5 \tabularnewline
p-value & 0.0607965935906568 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.216216216216216 \tabularnewline
p-value & 0.352694374639084 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.486486486486487 \tabularnewline
p-value & 0.000314788499061414 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92026&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]536.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.0607965935906568[/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]0.216216216216216[/C][/ROW]
[ROW][C]p-value[/C][C]0.352694374639084[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.486486486486487[/C][/ROW]
[ROW][C]p-value[/C][C]0.000314788499061414[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92026&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92026&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)
W536.5
p-value0.0607965935906568
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.216216216216216
p-value0.352694374639084
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.486486486486487
p-value0.000314788499061414


Figures (Output of Computation)

Input Parameters & R Code

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):
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')