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Author's title

Author*Unverified author*
R Software Modulerwasp_Two Factor ANOVA.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationTue, 08 Nov 2011 12:29:17 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/08/t13207734001jdsn4trc6tzvmm.htm/, Retrieved Thu, 31 Oct 2024 22:48:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=140800, Retrieved Thu, 31 Oct 2024 22:48:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Two-Way ANOVA] [WS5 Q8 pre] [2011-11-08 10:12:47] [91ce4971c808115c699d50336245df56]
- R  D    [Two-Way ANOVA] [WS5 Q8 pre] [2011-11-08 17:29:17] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
0	'E'	'M'
0	'F'	'V'
0	'F'	'M'
0	'H'	'M'
0	'H'	'M'
0	'H'	'M'
0	'E'	'M'
0	'F'	'M'
0	'E'	'M'
0	'F'	'V'
0	'H'	'V'
0	'E'	'V'
0	'F'	'M'
0	'H'	'V'
0	'E'	'V'
0	'H'	'V'
0	'E'	'M'
0	'F'	'M'
0	'H'	'V'
0	'F'	'V'
0	'H'	'V'
0	'H'	'M'
0	'H'	'V'
0	'E'	'V'
0	'F'	'V'
0	'E'	'V'
0	'E'	'V'
1	'F'	'M'
0	'F'	'V'
0	'H'	'V'
0	'E'	'M'
0	'E'	'M'
0	'H'	'M'
0	'E'	'M'
0	'F'	'M'
0	'E'	'M'
0	'F'	'V'
0	'H'	'V'
0	'E'	'V'
0	'F'	'V'
0	'F'	'V'
0	'F'	'V'
0	'F'	'V'
0	'H'	'M'
0	'E'	'V'
0	'E'	'V'
0	'H'	'V'
0	'E'	'M'
0	'F'	'M'
0	'F'	'V'
0	'H'	'V'
0	'E'	'M'
0	'F'	'M'
0	'E'	'M'
0	'H'	'M'
0	'H'	'M'
0	'H'	'M'
0	'E'	'M'
0	'H'	'V'
0	'E'	'V'
0	'H'	'M'
0	'F'	'M'
0	'H'	'M'
0	'F'	'V'
0	'E'	'M'
0	'E'	'M'
0	'F'	'V'
0	'H'	'M'
0	'F'	'V'
0	'E'	'M'
1	'E'	'M'
0	'H'	'V'
0	'H'	'M'
0	'F'	'M'
0	'H'	'M'
0	'E'	'V'
0	'F'	'M'
0	'E'	'V'
0	'E'	'V'
0	'E'	'V'
0	'F'	'M'
0	'E'	'M'
0	'F'	'M'
0	'H'	'M'
1	'H'	'M'
0	'H'	'M'
0	'F'	'V'
0	'H'	'M'
0	'H'	'M'
0	'F'	'M'
0	'F'	'M'
0	'H'	'V'
0	'F'	'M'
0	'H'	'M'
0	'E'	'V'
0	'F'	'M'
0	'E'	'V'
0	'H'	'M'
0	'F'	'M'
1	'F'	'M'
0	'H'	'M'
0	'E'	'M'
0	'F'	'V'
0	'H'	'M'
0	'E'	'M'
0	'F'	'V'
0	'H'	'V'
0	'H'	'M'
0	'F'	'M'
0	'F'	'M'
0	'H'	'M'
0	'E'	'V'
0	'H'	'M'
0	'E'	'M'
0	'E'	'V'
0	'F'	'M'
0	'F'	'M'




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=140800&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=140800&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140800&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 time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







ANOVA Model
Response ~ Treatment * Gender
means0.050.037-0.012-0.05-0.0370.012

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment * Gender \tabularnewline
means & 0.05 & 0.037 & -0.012 & -0.05 & -0.037 & 0.012 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140800&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment * Gender[/C][/ROW]
[ROW][C]means[/C][C]0.05[/C][C]0.037[/C][C]-0.012[/C][C]-0.05[/C][C]-0.037[/C][C]0.012[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140800&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140800&T=1

As an alternative you can also use a QR Code:  

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

ANOVA Model
Response ~ Treatment * Gender
means0.050.037-0.012-0.05-0.0370.012







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment20.0150.0080.2270.797
Gender20.0980.0982.9140.091
Treatment:Gender20.0120.0060.1810.834
Residuals1113.7380.034

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 2 &  &  &  &  \tabularnewline
Treatment & 2 & 0.015 & 0.008 & 0.227 & 0.797 \tabularnewline
Gender & 2 & 0.098 & 0.098 & 2.914 & 0.091 \tabularnewline
Treatment:Gender & 2 & 0.012 & 0.006 & 0.181 & 0.834 \tabularnewline
Residuals & 111 & 3.738 & 0.034 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140800&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C][/C][C]2[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment[/C][C]2[/C][C]0.015[/C][C]0.008[/C][C]0.227[/C][C]0.797[/C][/ROW]
[ROW][C]Gender[/C][C]2[/C][C]0.098[/C][C]0.098[/C][C]2.914[/C][C]0.091[/C][/ROW]
[ROW][C]Treatment:Gender[/C][C]2[/C][C]0.012[/C][C]0.006[/C][C]0.181[/C][C]0.834[/C][/ROW]
[ROW][C]Residuals[/C][C]111[/C][C]3.738[/C][C]0.034[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140800&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140800&T=2

As an alternative you can also use a QR Code:  

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

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment20.0150.0080.2270.797
Gender20.0980.0982.9140.091
Treatment:Gender20.0120.0060.1810.834
Residuals1113.7380.034







Tukey Honest Significant Difference Comparisons
difflwruprp adj
F-E0.023-0.0760.1220.847
H-E-0.002-0.1010.0970.999
H-F-0.025-0.1220.0720.815
V-M-0.059-0.1270.010.092
F:M-E:M0.037-0.1260.20.986
H:M-E:M-0.012-0.170.1471
E:V-E:M-0.05-0.2260.1260.962
F:V-E:M-0.05-0.2260.1260.962
H:V-E:M-0.05-0.2350.1350.97
H:M-F:M-0.048-0.2010.1040.94
E:V-F:M-0.087-0.2570.0830.677
F:V-F:M-0.087-0.2570.0830.677
H:V-F:M-0.087-0.2670.0930.728
E:V-H:M-0.038-0.2040.1280.985
F:V-H:M-0.038-0.2040.1280.985
H:V-H:M-0.038-0.2150.1380.988
F:V-E:V0-0.1830.1831
H:V-E:V0-0.1920.1921
H:V-F:V0-0.1920.1921

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
F-E & 0.023 & -0.076 & 0.122 & 0.847 \tabularnewline
H-E & -0.002 & -0.101 & 0.097 & 0.999 \tabularnewline
H-F & -0.025 & -0.122 & 0.072 & 0.815 \tabularnewline
V-M & -0.059 & -0.127 & 0.01 & 0.092 \tabularnewline
F:M-E:M & 0.037 & -0.126 & 0.2 & 0.986 \tabularnewline
H:M-E:M & -0.012 & -0.17 & 0.147 & 1 \tabularnewline
E:V-E:M & -0.05 & -0.226 & 0.126 & 0.962 \tabularnewline
F:V-E:M & -0.05 & -0.226 & 0.126 & 0.962 \tabularnewline
H:V-E:M & -0.05 & -0.235 & 0.135 & 0.97 \tabularnewline
H:M-F:M & -0.048 & -0.201 & 0.104 & 0.94 \tabularnewline
E:V-F:M & -0.087 & -0.257 & 0.083 & 0.677 \tabularnewline
F:V-F:M & -0.087 & -0.257 & 0.083 & 0.677 \tabularnewline
H:V-F:M & -0.087 & -0.267 & 0.093 & 0.728 \tabularnewline
E:V-H:M & -0.038 & -0.204 & 0.128 & 0.985 \tabularnewline
F:V-H:M & -0.038 & -0.204 & 0.128 & 0.985 \tabularnewline
H:V-H:M & -0.038 & -0.215 & 0.138 & 0.988 \tabularnewline
F:V-E:V & 0 & -0.183 & 0.183 & 1 \tabularnewline
H:V-E:V & 0 & -0.192 & 0.192 & 1 \tabularnewline
H:V-F:V & 0 & -0.192 & 0.192 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140800&T=3

[TABLE]
[ROW][C]Tukey Honest Significant Difference Comparisons[/C][/ROW]
[ROW][C] [/C][C]diff[/C][C]lwr[/C][C]upr[/C][C]p adj[/C][/ROW]
[ROW][C]F-E[/C][C]0.023[/C][C]-0.076[/C][C]0.122[/C][C]0.847[/C][/ROW]
[ROW][C]H-E[/C][C]-0.002[/C][C]-0.101[/C][C]0.097[/C][C]0.999[/C][/ROW]
[ROW][C]H-F[/C][C]-0.025[/C][C]-0.122[/C][C]0.072[/C][C]0.815[/C][/ROW]
[ROW][C]V-M[/C][C]-0.059[/C][C]-0.127[/C][C]0.01[/C][C]0.092[/C][/ROW]
[ROW][C]F:M-E:M[/C][C]0.037[/C][C]-0.126[/C][C]0.2[/C][C]0.986[/C][/ROW]
[ROW][C]H:M-E:M[/C][C]-0.012[/C][C]-0.17[/C][C]0.147[/C][C]1[/C][/ROW]
[ROW][C]E:V-E:M[/C][C]-0.05[/C][C]-0.226[/C][C]0.126[/C][C]0.962[/C][/ROW]
[ROW][C]F:V-E:M[/C][C]-0.05[/C][C]-0.226[/C][C]0.126[/C][C]0.962[/C][/ROW]
[ROW][C]H:V-E:M[/C][C]-0.05[/C][C]-0.235[/C][C]0.135[/C][C]0.97[/C][/ROW]
[ROW][C]H:M-F:M[/C][C]-0.048[/C][C]-0.201[/C][C]0.104[/C][C]0.94[/C][/ROW]
[ROW][C]E:V-F:M[/C][C]-0.087[/C][C]-0.257[/C][C]0.083[/C][C]0.677[/C][/ROW]
[ROW][C]F:V-F:M[/C][C]-0.087[/C][C]-0.257[/C][C]0.083[/C][C]0.677[/C][/ROW]
[ROW][C]H:V-F:M[/C][C]-0.087[/C][C]-0.267[/C][C]0.093[/C][C]0.728[/C][/ROW]
[ROW][C]E:V-H:M[/C][C]-0.038[/C][C]-0.204[/C][C]0.128[/C][C]0.985[/C][/ROW]
[ROW][C]F:V-H:M[/C][C]-0.038[/C][C]-0.204[/C][C]0.128[/C][C]0.985[/C][/ROW]
[ROW][C]H:V-H:M[/C][C]-0.038[/C][C]-0.215[/C][C]0.138[/C][C]0.988[/C][/ROW]
[ROW][C]F:V-E:V[/C][C]0[/C][C]-0.183[/C][C]0.183[/C][C]1[/C][/ROW]
[ROW][C]H:V-E:V[/C][C]0[/C][C]-0.192[/C][C]0.192[/C][C]1[/C][/ROW]
[ROW][C]H:V-F:V[/C][C]0[/C][C]-0.192[/C][C]0.192[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140800&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140800&T=3

As an alternative you can also use a QR Code:  

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

Tukey Honest Significant Difference Comparisons
difflwruprp adj
F-E0.023-0.0760.1220.847
H-E-0.002-0.1010.0970.999
H-F-0.025-0.1220.0720.815
V-M-0.059-0.1270.010.092
F:M-E:M0.037-0.1260.20.986
H:M-E:M-0.012-0.170.1471
E:V-E:M-0.05-0.2260.1260.962
F:V-E:M-0.05-0.2260.1260.962
H:V-E:M-0.05-0.2350.1350.97
H:M-F:M-0.048-0.2010.1040.94
E:V-F:M-0.087-0.2570.0830.677
F:V-F:M-0.087-0.2570.0830.677
H:V-F:M-0.087-0.2670.0930.728
E:V-H:M-0.038-0.2040.1280.985
F:V-H:M-0.038-0.2040.1280.985
H:V-H:M-0.038-0.2150.1380.988
F:V-E:V0-0.1830.1831
H:V-E:V0-0.1920.1921
H:V-F:V0-0.1920.1921







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group50.7460.591
111

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 0.746 & 0.591 \tabularnewline
  & 111 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140800&T=4

[TABLE]
[ROW][C]Levenes Test for Homogeneity of Variance[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]Group[/C][C]5[/C][C]0.746[/C][C]0.591[/C][/ROW]
[ROW][C] [/C][C]111[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140800&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140800&T=4

As an alternative you can also use a QR Code:  

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

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group50.7460.591
111



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
R code (references can be found in the software module):
cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
cat3 <- as.numeric(par3)
intercept<-as.logical(par4)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
f2 <- as.character(x[,cat3])
xdf<-data.frame(x1,f1, f2)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
(V3 <-dimnames(y)[[1]][cat3])
names(xdf)<-c('Response', 'Treatment', 'Gender')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment * Gender- 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment * Gender, data = xdf) )
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Model', length(lmxdf$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],length(lmxdf$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'means',,TRUE)
for(i in 1:length(lmxdf$coefficients)){
a<-table.element(a, round(lmxdf$coefficients[i], digits=3),,FALSE)
}
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',,TRUE)
a<-table.element(a, 'Df',,FALSE)
a<-table.element(a, 'Sum Sq',,FALSE)
a<-table.element(a, 'Mean Sq',,FALSE)
a<-table.element(a, 'F value',,FALSE)
a<-table.element(a, 'Pr(>F)',,FALSE)
a<-table.row.end(a)
for(i in 1 : length(rownames(anova.xdf))-1){
a<-table.row.start(a)
a<-table.element(a,rownames(anova.xdf)[i] ,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[i], digits=3),,FALSE)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$'Df'[i+1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='anovaplot.png')
boxplot(Response ~ Treatment + Gender, data=xdf, xlab=V2, ylab=V1, main='Boxplots of ANOVA Groups')
dev.off()
bitmap(file='designplot.png')
xdf2 <- xdf # to preserve xdf make copy for function
names(xdf2) <- c(V1, V2, V3)
plot.design(xdf2, main='Design Plot of Group Means')
dev.off()
bitmap(file='interactionplot.png')
interaction.plot(xdf$Treatment, xdf$Gender, xdf$Response, xlab=V2, ylab=V1, trace.label=V3, main='Possible Interactions Between Anova Groups')
dev.off()
if(intercept==TRUE){
thsd<-TukeyHSD(aov.xdf)
names(thsd) <- c(V2, V3, paste(V2, ':', V3, sep=''))
bitmap(file='TukeyHSDPlot.png')
layout(matrix(c(1,2,3,3), 2,2))
plot(thsd, las=1)
dev.off()
}
if(intercept==TRUE){
ntables<-length(names(thsd))
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Tukey Honest Significant Difference Comparisons', 5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ', 1, TRUE)
for(i in 1:4){
a<-table.element(a,colnames(thsd[[1]])[i], 1, TRUE)
}
a<-table.row.end(a)
for(nt in 1:ntables){
for(i in 1:length(rownames(thsd[[nt]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[nt]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[nt]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
} # end nt
a<-table.end(a)
table.save(a,file='hsdtable.tab')
}#end if hsd tables
if(intercept==FALSE){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'TukeyHSD Message', 1,TRUE)
a<-table.row.end(a)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Must Include Intercept to use Tukey Test ', 1, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
library(car)
lt.lmxdf<-levene.test(lmxdf)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Levenes Test for Homogeneity of Variance', 4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ', 1, TRUE)
for (i in 1:3){
a<-table.element(a,names(lt.lmxdf)[i], 1, FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Group', 1, TRUE)
for (i in 1:3){
a<-table.element(a,round(lt.lmxdf[[i]][1], digits=3), 1, FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ', 1, TRUE)
a<-table.element(a,lt.lmxdf[[1]][2], 1, FALSE)
a<-table.element(a,' ', 1, FALSE)
a<-table.element(a,' ', 1, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')