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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_Two Factor ANOVA.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationSun, 07 Dec 2014 16:22:10 +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/07/t1417969362i9udq8nc8dpl7df.htm/, Retrieved Thu, 16 May 2024 07:19:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263846, Retrieved Thu, 16 May 2024 07:19:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-07 12:05:48] [8d160a85bfd9526a7d0e42afc5fb569b]
-    D  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-07 12:31:37] [8d160a85bfd9526a7d0e42afc5fb569b]
-    D    [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-07 12:52:27] [8d160a85bfd9526a7d0e42afc5fb569b]
-    D      [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-07 13:04:05] [8d160a85bfd9526a7d0e42afc5fb569b]
-    D        [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-07 13:08:43] [8d160a85bfd9526a7d0e42afc5fb569b]
-    D          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-07 13:16:58] [8d160a85bfd9526a7d0e42afc5fb569b]
-   PD            [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-07 13:26:34] [8d160a85bfd9526a7d0e42afc5fb569b]
- RM D              [Two-Way ANOVA] [] [2014-12-07 15:34:35] [8d160a85bfd9526a7d0e42afc5fb569b]
-    D                  [Two-Way ANOVA] [] [2014-12-07 16:22:10] [1d338d9433eb3ecdb4d9d35f41140a45] [Current]
-    D                    [Two-Way ANOVA] [] [2014-12-07 17:48:21] [8d160a85bfd9526a7d0e42afc5fb569b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263846&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263846&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263846&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






ANOVA Model
Response ~ Treatment_A * Treatment_B
means68.298-2.270.039-2.9490.6991.112

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 68.298 & -2.27 & 0.039 & -2.949 & 0.699 & 1.112 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263846&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]68.298[/C][C]-2.27[/C][C]0.039[/C][C]-2.949[/C][C]0.699[/C][C]1.112[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263846&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263846&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:

ANOVA Model
Response ~ Treatment_A * Treatment_B
means68.298-2.270.039-2.9490.6991.112






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A2617.117308.5584.760.009
Treatment_B2597.946597.9469.2250.003
Treatment_A:Treatment_B222.14911.0740.1710.843
Residuals48231242.62364.819 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 2 &  &  &  &  \tabularnewline
Treatment_A & 2 & 617.117 & 308.558 & 4.76 & 0.009 \tabularnewline
Treatment_B & 2 & 597.946 & 597.946 & 9.225 & 0.003 \tabularnewline
Treatment_A:Treatment_B & 2 & 22.149 & 11.074 & 0.171 & 0.843 \tabularnewline
Residuals & 482 & 31242.623 & 64.819 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263846&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_A[/C][C]2[/C][C]617.117[/C][C]308.558[/C][C]4.76[/C][C]0.009[/C][/ROW]
[ROW][C]Treatment_B[/C][C]2[/C][C]597.946[/C][C]597.946[/C][C]9.225[/C][C]0.003[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]2[/C][C]22.149[/C][C]11.074[/C][C]0.171[/C][C]0.843[/C][/ROW]
[ROW][C]Residuals[/C][C]482[/C][C]31242.623[/C][C]64.819[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263846&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263846&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:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A2617.117308.5584.760.009
Treatment_B2597.946597.9469.2250.003
Treatment_A:Treatment_B222.14911.0740.1710.843
Residuals48231242.62364.819






Tukey Honest Significant Difference Comparisons
difflwruprp adj
2012-2011-1.815-4.120.4910.154
2014-20110.737-1.4752.9490.713
2014-20122.5520.5854.5190.007
'Male'-'Female'-2.233-3.677-0.7880.003
2012:'Female'-2011:'Female'-2.27-6.5792.0380.659
2014:'Female'-2011:'Female'0.039-4.0914.1691
2011:'Male'-2011:'Female'-2.949-7.3461.4470.391
2012:'Male'-2011:'Female'-4.521-8.637-0.4060.022
2014:'Male'-2011:'Female'-1.798-5.7812.1850.79
2014:'Female'-2012:'Female'2.31-1.3015.920.447
2011:'Male'-2012:'Female'-0.679-4.5923.2340.996
2012:'Male'-2012:'Female'-2.251-5.8441.3430.472
2014:'Male'-2012:'Female'0.473-2.9693.9140.999
2011:'Male'-2014:'Female'-2.988-6.7040.7270.196
2012:'Male'-2014:'Female'-4.56-7.939-1.1820.002
2014:'Male'-2014:'Female'-1.837-5.0531.3790.576
2012:'Male'-2011:'Male'-1.572-5.2712.1270.829
2014:'Male'-2011:'Male'1.152-2.44.7030.939
2014:'Male'-2012:'Male'2.723-0.4735.920.146

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
2012-2011 & -1.815 & -4.12 & 0.491 & 0.154 \tabularnewline
2014-2011 & 0.737 & -1.475 & 2.949 & 0.713 \tabularnewline
2014-2012 & 2.552 & 0.585 & 4.519 & 0.007 \tabularnewline
'Male'-'Female' & -2.233 & -3.677 & -0.788 & 0.003 \tabularnewline
2012:'Female'-2011:'Female' & -2.27 & -6.579 & 2.038 & 0.659 \tabularnewline
2014:'Female'-2011:'Female' & 0.039 & -4.091 & 4.169 & 1 \tabularnewline
2011:'Male'-2011:'Female' & -2.949 & -7.346 & 1.447 & 0.391 \tabularnewline
2012:'Male'-2011:'Female' & -4.521 & -8.637 & -0.406 & 0.022 \tabularnewline
2014:'Male'-2011:'Female' & -1.798 & -5.781 & 2.185 & 0.79 \tabularnewline
2014:'Female'-2012:'Female' & 2.31 & -1.301 & 5.92 & 0.447 \tabularnewline
2011:'Male'-2012:'Female' & -0.679 & -4.592 & 3.234 & 0.996 \tabularnewline
2012:'Male'-2012:'Female' & -2.251 & -5.844 & 1.343 & 0.472 \tabularnewline
2014:'Male'-2012:'Female' & 0.473 & -2.969 & 3.914 & 0.999 \tabularnewline
2011:'Male'-2014:'Female' & -2.988 & -6.704 & 0.727 & 0.196 \tabularnewline
2012:'Male'-2014:'Female' & -4.56 & -7.939 & -1.182 & 0.002 \tabularnewline
2014:'Male'-2014:'Female' & -1.837 & -5.053 & 1.379 & 0.576 \tabularnewline
2012:'Male'-2011:'Male' & -1.572 & -5.271 & 2.127 & 0.829 \tabularnewline
2014:'Male'-2011:'Male' & 1.152 & -2.4 & 4.703 & 0.939 \tabularnewline
2014:'Male'-2012:'Male' & 2.723 & -0.473 & 5.92 & 0.146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263846&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]2012-2011[/C][C]-1.815[/C][C]-4.12[/C][C]0.491[/C][C]0.154[/C][/ROW]
[ROW][C]2014-2011[/C][C]0.737[/C][C]-1.475[/C][C]2.949[/C][C]0.713[/C][/ROW]
[ROW][C]2014-2012[/C][C]2.552[/C][C]0.585[/C][C]4.519[/C][C]0.007[/C][/ROW]
[ROW][C]'Male'-'Female'[/C][C]-2.233[/C][C]-3.677[/C][C]-0.788[/C][C]0.003[/C][/ROW]
[ROW][C]2012:'Female'-2011:'Female'[/C][C]-2.27[/C][C]-6.579[/C][C]2.038[/C][C]0.659[/C][/ROW]
[ROW][C]2014:'Female'-2011:'Female'[/C][C]0.039[/C][C]-4.091[/C][C]4.169[/C][C]1[/C][/ROW]
[ROW][C]2011:'Male'-2011:'Female'[/C][C]-2.949[/C][C]-7.346[/C][C]1.447[/C][C]0.391[/C][/ROW]
[ROW][C]2012:'Male'-2011:'Female'[/C][C]-4.521[/C][C]-8.637[/C][C]-0.406[/C][C]0.022[/C][/ROW]
[ROW][C]2014:'Male'-2011:'Female'[/C][C]-1.798[/C][C]-5.781[/C][C]2.185[/C][C]0.79[/C][/ROW]
[ROW][C]2014:'Female'-2012:'Female'[/C][C]2.31[/C][C]-1.301[/C][C]5.92[/C][C]0.447[/C][/ROW]
[ROW][C]2011:'Male'-2012:'Female'[/C][C]-0.679[/C][C]-4.592[/C][C]3.234[/C][C]0.996[/C][/ROW]
[ROW][C]2012:'Male'-2012:'Female'[/C][C]-2.251[/C][C]-5.844[/C][C]1.343[/C][C]0.472[/C][/ROW]
[ROW][C]2014:'Male'-2012:'Female'[/C][C]0.473[/C][C]-2.969[/C][C]3.914[/C][C]0.999[/C][/ROW]
[ROW][C]2011:'Male'-2014:'Female'[/C][C]-2.988[/C][C]-6.704[/C][C]0.727[/C][C]0.196[/C][/ROW]
[ROW][C]2012:'Male'-2014:'Female'[/C][C]-4.56[/C][C]-7.939[/C][C]-1.182[/C][C]0.002[/C][/ROW]
[ROW][C]2014:'Male'-2014:'Female'[/C][C]-1.837[/C][C]-5.053[/C][C]1.379[/C][C]0.576[/C][/ROW]
[ROW][C]2012:'Male'-2011:'Male'[/C][C]-1.572[/C][C]-5.271[/C][C]2.127[/C][C]0.829[/C][/ROW]
[ROW][C]2014:'Male'-2011:'Male'[/C][C]1.152[/C][C]-2.4[/C][C]4.703[/C][C]0.939[/C][/ROW]
[ROW][C]2014:'Male'-2012:'Male'[/C][C]2.723[/C][C]-0.473[/C][C]5.92[/C][C]0.146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263846&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263846&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:

Tukey Honest Significant Difference Comparisons
difflwruprp adj
2012-2011-1.815-4.120.4910.154
2014-20110.737-1.4752.9490.713
2014-20122.5520.5854.5190.007
'Male'-'Female'-2.233-3.677-0.7880.003
2012:'Female'-2011:'Female'-2.27-6.5792.0380.659
2014:'Female'-2011:'Female'0.039-4.0914.1691
2011:'Male'-2011:'Female'-2.949-7.3461.4470.391
2012:'Male'-2011:'Female'-4.521-8.637-0.4060.022
2014:'Male'-2011:'Female'-1.798-5.7812.1850.79
2014:'Female'-2012:'Female'2.31-1.3015.920.447
2011:'Male'-2012:'Female'-0.679-4.5923.2340.996
2012:'Male'-2012:'Female'-2.251-5.8441.3430.472
2014:'Male'-2012:'Female'0.473-2.9693.9140.999
2011:'Male'-2014:'Female'-2.988-6.7040.7270.196
2012:'Male'-2014:'Female'-4.56-7.939-1.1820.002
2014:'Male'-2014:'Female'-1.837-5.0531.3790.576
2012:'Male'-2011:'Male'-1.572-5.2712.1270.829
2014:'Male'-2011:'Male'1.152-2.44.7030.939
2014:'Male'-2012:'Male'2.723-0.4735.920.146






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group51.8280.106
482 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 1.828 & 0.106 \tabularnewline
  & 482 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263846&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]1.828[/C][C]0.106[/C][/ROW]
[ROW][C] [/C][C]482[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263846&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263846&T=4

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


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

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group51.8280.106
482


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 2 ; par2 = 1 ; 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_A', 'Treatment_B')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment_A * Treatment_B- 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment_A * Treatment_B, 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$'Mean Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='anovaplot.png')
boxplot(Response ~ Treatment_A + Treatment_B, data=xdf, xlab=V2, ylab=V1, main='Boxplots of ANOVA Groups')
dev.off()
bitmap(file='designplot.png')
xdf2 <- xdf # to preserve xdf make copy for function
names(xdf2) <- c(V1, V2, V3)
plot.design(xdf2, main='Design Plot of Group Means')
dev.off()
bitmap(file='interactionplot.png')
interaction.plot(xdf$Treatment_A, xdf$Treatment_B, xdf$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')