FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSun, 14 Dec 2014 18:26:59 +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/14/t1418581738m18c7bsu8m8cxni.htm/, Retrieved Thu, 16 May 2024 13:45:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267801, Retrieved Thu, 16 May 2024 13:45:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Paired and Unpaired Two Samples Tests about the Mean] [two sample t gend...] [2014-12-14 18:26:59] [26b3f07cb5f54f7efd4618e9d9764016] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
149 NA
148 NA
NA 158
NA 128
NA 224
159 NA
NA 105
NA 159
NA 167
NA 165
NA 159
176 NA
54 NA
91 NA
NA 163
124 NA
121 NA
NA 148
221 NA
NA 149
NA 244
NA 148
NA 150
153 NA
94 NA
156 NA
NA 132
NA 105
151 NA
NA 131
157 NA
NA 162
NA 163
NA 59
187 NA
NA 116
NA 148
NA 155
NA 125
NA 116
NA 138
NA 164
162 NA
99 NA
186 NA
NA 188
177 NA
139 NA
162 NA
NA 108
159 NA
NA 110
96 NA
87 NA
NA 97
127 NA
74 NA
NA 114
95 NA
121 NA
NA 130
52 NA
118 NA
NA 48
NA 50
NA 150
NA 154
109 NA
NA 68
NA 194
158 NA
NA 159
67 NA
147 NA
NA 39
NA 100
NA 111
NA 138
NA 101
NA 131
NA 101
NA 114
165 NA
NA 114
NA 111
NA 75
NA 82
NA 121
NA 32
150 NA
NA 117
NA 71
NA 165
NA 154
NA 126
149 NA
145 NA
NA 120
109 NA
132 NA
NA 172
169 NA
NA 114
NA 156
172 NA
NA 68
NA 89
NA 167
113 NA
115 NA
78 NA
118 NA
NA 87
173 NA
NA 2
162 NA
NA 49
122 NA
NA 96
100 NA
82 NA
NA 100
115 NA
NA 141
NA 165
NA 165
NA 110
NA 118
158 NA
NA 146
49 NA
90 NA
121 NA
NA 155
104 NA
NA 147
110 NA
108 NA
113 NA
115 NA
NA 61
NA 60
NA 109
NA 68
111 NA
77 NA
NA 73
151 NA
89 NA
78 NA
110 NA
NA 220
NA 65
141 NA
117 NA
NA 122
63 NA
NA 44
NA 52
131 NA
NA 101
NA 42
NA 152
107 NA
77 NA
154 NA
NA 103
NA 96
NA 175
NA 57
112 NA
143 NA
49 NA
NA 110
NA 131
167 NA
56 NA
137 NA
NA 86
NA 121
149 NA
168 NA
140 NA
NA 88
NA 168
NA 94
NA 51
48 NA
NA 145
NA 66
NA 85
109 NA
63 NA
NA 102
162 NA
NA 86
NA 114
164 NA
NA 119
126 NA
NA 132
NA 142
83 NA
NA 94
81 NA
NA 166
110 NA
NA 64
93 NA
104 NA
NA 105
NA 49
88 NA
NA 95
NA 102
99 NA
NA 63
76 NA
109 NA
NA 117
NA 57
120 NA
NA 73
91 NA
108 NA
NA 105
117 NA
119 NA
NA 31

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'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=267801&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=267801&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267801&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'Gertrude Mary Cox' @ cox.wessa.net






Two Sample t-test (unpaired)
Mean of Sample 1120.485436893204
Mean of Sample 2114.936507936508
t-stat1.03170218004568
df227
p-value0.303309605299689
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-5.04908619584951,16.1469441092414]
F-test to compare two variances
F-stat0.69335759241869
df102
p-value0.0557544696328297
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.479788550451133,1.00911874573364]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 120.485436893204 \tabularnewline
Mean of Sample 2 & 114.936507936508 \tabularnewline
t-stat & 1.03170218004568 \tabularnewline
df & 227 \tabularnewline
p-value & 0.303309605299689 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-5.04908619584951,16.1469441092414] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.69335759241869 \tabularnewline
df & 102 \tabularnewline
p-value & 0.0557544696328297 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.479788550451133,1.00911874573364] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267801&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]120.485436893204[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]114.936507936508[/C][/ROW]
[ROW][C]t-stat[/C][C]1.03170218004568[/C][/ROW]
[ROW][C]df[/C][C]227[/C][/ROW]
[ROW][C]p-value[/C][C]0.303309605299689[/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][-5.04908619584951,16.1469441092414][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.69335759241869[/C][/ROW]
[ROW][C]df[/C][C]102[/C][/ROW]
[ROW][C]p-value[/C][C]0.0557544696328297[/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][0.479788550451133,1.00911874573364][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267801&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267801&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 1120.485436893204
Mean of Sample 2114.936507936508
t-stat1.03170218004568
df227
p-value0.303309605299689
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-5.04908619584951,16.1469441092414]
F-test to compare two variances
F-stat0.69335759241869
df102
p-value0.0557544696328297
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.479788550451133,1.00911874573364]






Welch Two Sample t-test (unpaired)
Mean of Sample 1120.485436893204
Mean of Sample 2114.936507936508
t-stat1.050722844956
df226.915668292491
p-value0.294503599620141
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.85725681167462,15.9551147250665]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 120.485436893204 \tabularnewline
Mean of Sample 2 & 114.936507936508 \tabularnewline
t-stat & 1.050722844956 \tabularnewline
df & 226.915668292491 \tabularnewline
p-value & 0.294503599620141 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-4.85725681167462,15.9551147250665] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267801&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]120.485436893204[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]114.936507936508[/C][/ROW]
[ROW][C]t-stat[/C][C]1.050722844956[/C][/ROW]
[ROW][C]df[/C][C]226.915668292491[/C][/ROW]
[ROW][C]p-value[/C][C]0.294503599620141[/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][-4.85725681167462,15.9551147250665][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267801&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267801&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 1120.485436893204
Mean of Sample 2114.936507936508
t-stat1.050722844956
df226.915668292491
p-value0.294503599620141
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.85725681167462,15.9551147250665]






Wicoxon rank sum test with continuity correction (unpaired)
W6977
p-value0.328305981786551
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.126907073509015
p-value0.320950065980071
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.103097549699491
p-value0.58342024048945

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 6977 \tabularnewline
p-value & 0.328305981786551 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.126907073509015 \tabularnewline
p-value & 0.320950065980071 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.103097549699491 \tabularnewline
p-value & 0.58342024048945 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267801&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]6977[/C][/ROW]
[ROW][C]p-value[/C][C]0.328305981786551[/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]0.126907073509015[/C][/ROW]
[ROW][C]p-value[/C][C]0.320950065980071[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.103097549699491[/C][/ROW]
[ROW][C]p-value[/C][C]0.58342024048945[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267801&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267801&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)
W6977
p-value0.328305981786551
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.126907073509015
p-value0.320950065980071
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.103097549699491
p-value0.58342024048945


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 = 0.0 ;
R code (references can be found in the software module):
par6 <- '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')