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Author*The author of this computation has been verified*
R Software Modulerwasp_Two Factor ANOVA.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationMon, 07 Nov 2011 09:42:17 -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/07/t1320676962q4rswtz5f9midjt.htm/, Retrieved Wed, 24 Apr 2024 22:15:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=140212, Retrieved Wed, 24 Apr 2024 22:15:07 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [] [2011-11-07 14:42:17] [3627de22d386f4cb93d383ef7c1ade7f] [Current]
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Dataseries X:
'WWE'	0	0
'WWE'	0	0
'WWE'	0	1
'WWE'	0	0
'WWE'	0	1
'WWE'	0	0
'WWE'	0	0
'WWE'	0	1
'WWE'	0	0
'WWE'	0	0
'WWE'	0	0
'WWE'	0	0
'WWE'	0	0
'WWE'	0	0
'WWE'	0	0
'WWE'	1	1
'WWE'	1	0
'WWE'	0	0
'WWE'	0	0
'WWE'	0	1
'WWE'	0	0
'WWE'	1	1
'WWE'	0	0
'WWE'	0	1
'WWE'	0	1
'WWE'	0	1
'WWE'	0	0
'WWE'	1	1
'WWE'	0	1
'WWE'	0	1
'WWE'	0	0
'WWE'	0	1
'WWE'	0	1
'WWE'	0	0
'WWE'	0	0
'WWE'	1	1
'WWE'	1	1
'WWE'	0	0
'WWE'	0	0
'WWE'	0	0
'WWE'	0	0
'CSWE'	0	0
'CSWE'	1	0
'CSWE'	0	0
'CSWE'	0	1
'CSWE'	0	1
'CSWE'	0	0
'CSWE'	0	0
'CSWE'	0	0
'CSWE'	0	0
'CSWE'	1	1
'CSWE'	0	0
'CSWE'	0	1
'CSWE'	0	0
'CSWE'	0	1
'CSWE'	0	1
'CSWE'	0	1
'CSWE'	1	1
'CSWE'	0	1
'CSWE'	0	0
'CSWE'	0	1
'CSWE'	0	0
'CSWE'	0	0
'CSWE'	0	0
'CSWE'	0	1
'CSWE'	0	0
'CSWE'	0	0
'CSWE'	0	1
'CSWE'	0	1
'CSWE'	1	1
'CSWE'	0	0
'CSWE'	0	1
'CSWE'	0	1
'CSWE'	0	0
'CSWE'	0	0
'CSWE'	0	1
'CSWE'	0	0
'CSWE'	0	0
'CSWE'	0	0
'CSWE'	0	1
'CSWE'	0	1
'C'	0	0
'C'	0	0
'C'	1	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	1
'C'	1	1
'C'	0	0
'C'	0	0
'C'	0	1
'C'	0	1
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	1
'C'	1	1
'C'	0	1
'C'	0	0
'C'	0	0
'C'	1	1
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	1	1
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	0	0
'C'	1	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140212&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140212&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140212&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'Herman Ole Andreas Wold' @ wold.wessa.net







ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.0340.4660.0130.006-0.355-0.193

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 0.034 & 0.466 & 0.013 & 0.006 & -0.355 & -0.193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140212&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]0.034[/C][C]0.466[/C][C]0.013[/C][C]0.006[/C][C]-0.355[/C][C]-0.193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140212&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140212&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_A * Treatment_B
means0.0340.4660.0130.006-0.355-0.193







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A11.7421.74217.5120
Treatment_B10.2440.1221.2280.297
Treatment_A:Treatment_B10.5380.2692.7050.071
Residuals11411.3420.099

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 1.742 & 1.742 & 17.512 & 0 \tabularnewline
Treatment_B & 1 & 0.244 & 0.122 & 1.228 & 0.297 \tabularnewline
Treatment_A:Treatment_B & 1 & 0.538 & 0.269 & 2.705 & 0.071 \tabularnewline
Residuals & 114 & 11.342 & 0.099 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140212&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]1[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]1[/C][C]1.742[/C][C]1.742[/C][C]17.512[/C][C]0[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]0.244[/C][C]0.122[/C][C]1.228[/C][C]0.297[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]0.538[/C][C]0.269[/C][C]2.705[/C][C]0.071[/C][/ROW]
[ROW][C]Residuals[/C][C]114[/C][C]11.342[/C][C]0.099[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140212&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140212&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)
1
Treatment_A11.7421.74217.5120
Treatment_B10.2440.1221.2280.297
Treatment_A:Treatment_B10.5380.2692.7050.071
Residuals11411.3420.099







Tukey Honest Significant Difference Comparisons
difflwruprp adj
1-00.2490.1310.3670
CSWE-C-0.108-0.2770.060.283
WWE-C-0.041-0.2080.1270.832
WWE-CSWE0.067-0.0990.2340.602
1:C-0:C0.4660.130.8010.001
0:CSWE-0:C0.013-0.2490.2751
1:CSWE-0:C0.123-0.1460.3930.77
0:WWE-0:C0.006-0.2440.2551
1:WWE-0:C0.278-0.0070.5630.06
0:CSWE-1:C-0.452-0.804-0.1010.004
1:CSWE-1:C-0.342-0.6990.0150.069
0:WWE-1:C-0.46-0.802-0.1180.002
1:WWE-1:C-0.188-0.5560.1810.681
1:CSWE-0:CSWE0.11-0.1790.40.879
0:WWE-0:CSWE-0.008-0.2780.2631
1:WWE-0:CSWE0.265-0.0390.5680.124
0:WWE-1:CSWE-0.118-0.3960.160.822
1:WWE-1:CSWE0.155-0.1560.4650.7
1:WWE-0:WWE0.272-0.020.5650.083

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
1-0 & 0.249 & 0.131 & 0.367 & 0 \tabularnewline
CSWE-C & -0.108 & -0.277 & 0.06 & 0.283 \tabularnewline
WWE-C & -0.041 & -0.208 & 0.127 & 0.832 \tabularnewline
WWE-CSWE & 0.067 & -0.099 & 0.234 & 0.602 \tabularnewline
1:C-0:C & 0.466 & 0.13 & 0.801 & 0.001 \tabularnewline
0:CSWE-0:C & 0.013 & -0.249 & 0.275 & 1 \tabularnewline
1:CSWE-0:C & 0.123 & -0.146 & 0.393 & 0.77 \tabularnewline
0:WWE-0:C & 0.006 & -0.244 & 0.255 & 1 \tabularnewline
1:WWE-0:C & 0.278 & -0.007 & 0.563 & 0.06 \tabularnewline
0:CSWE-1:C & -0.452 & -0.804 & -0.101 & 0.004 \tabularnewline
1:CSWE-1:C & -0.342 & -0.699 & 0.015 & 0.069 \tabularnewline
0:WWE-1:C & -0.46 & -0.802 & -0.118 & 0.002 \tabularnewline
1:WWE-1:C & -0.188 & -0.556 & 0.181 & 0.681 \tabularnewline
1:CSWE-0:CSWE & 0.11 & -0.179 & 0.4 & 0.879 \tabularnewline
0:WWE-0:CSWE & -0.008 & -0.278 & 0.263 & 1 \tabularnewline
1:WWE-0:CSWE & 0.265 & -0.039 & 0.568 & 0.124 \tabularnewline
0:WWE-1:CSWE & -0.118 & -0.396 & 0.16 & 0.822 \tabularnewline
1:WWE-1:CSWE & 0.155 & -0.156 & 0.465 & 0.7 \tabularnewline
1:WWE-0:WWE & 0.272 & -0.02 & 0.565 & 0.083 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140212&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]1-0[/C][C]0.249[/C][C]0.131[/C][C]0.367[/C][C]0[/C][/ROW]
[ROW][C]CSWE-C[/C][C]-0.108[/C][C]-0.277[/C][C]0.06[/C][C]0.283[/C][/ROW]
[ROW][C]WWE-C[/C][C]-0.041[/C][C]-0.208[/C][C]0.127[/C][C]0.832[/C][/ROW]
[ROW][C]WWE-CSWE[/C][C]0.067[/C][C]-0.099[/C][C]0.234[/C][C]0.602[/C][/ROW]
[ROW][C]1:C-0:C[/C][C]0.466[/C][C]0.13[/C][C]0.801[/C][C]0.001[/C][/ROW]
[ROW][C]0:CSWE-0:C[/C][C]0.013[/C][C]-0.249[/C][C]0.275[/C][C]1[/C][/ROW]
[ROW][C]1:CSWE-0:C[/C][C]0.123[/C][C]-0.146[/C][C]0.393[/C][C]0.77[/C][/ROW]
[ROW][C]0:WWE-0:C[/C][C]0.006[/C][C]-0.244[/C][C]0.255[/C][C]1[/C][/ROW]
[ROW][C]1:WWE-0:C[/C][C]0.278[/C][C]-0.007[/C][C]0.563[/C][C]0.06[/C][/ROW]
[ROW][C]0:CSWE-1:C[/C][C]-0.452[/C][C]-0.804[/C][C]-0.101[/C][C]0.004[/C][/ROW]
[ROW][C]1:CSWE-1:C[/C][C]-0.342[/C][C]-0.699[/C][C]0.015[/C][C]0.069[/C][/ROW]
[ROW][C]0:WWE-1:C[/C][C]-0.46[/C][C]-0.802[/C][C]-0.118[/C][C]0.002[/C][/ROW]
[ROW][C]1:WWE-1:C[/C][C]-0.188[/C][C]-0.556[/C][C]0.181[/C][C]0.681[/C][/ROW]
[ROW][C]1:CSWE-0:CSWE[/C][C]0.11[/C][C]-0.179[/C][C]0.4[/C][C]0.879[/C][/ROW]
[ROW][C]0:WWE-0:CSWE[/C][C]-0.008[/C][C]-0.278[/C][C]0.263[/C][C]1[/C][/ROW]
[ROW][C]1:WWE-0:CSWE[/C][C]0.265[/C][C]-0.039[/C][C]0.568[/C][C]0.124[/C][/ROW]
[ROW][C]0:WWE-1:CSWE[/C][C]-0.118[/C][C]-0.396[/C][C]0.16[/C][C]0.822[/C][/ROW]
[ROW][C]1:WWE-1:CSWE[/C][C]0.155[/C][C]-0.156[/C][C]0.465[/C][C]0.7[/C][/ROW]
[ROW][C]1:WWE-0:WWE[/C][C]0.272[/C][C]-0.02[/C][C]0.565[/C][C]0.083[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140212&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140212&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
1-00.2490.1310.3670
CSWE-C-0.108-0.2770.060.283
WWE-C-0.041-0.2080.1270.832
WWE-CSWE0.067-0.0990.2340.602
1:C-0:C0.4660.130.8010.001
0:CSWE-0:C0.013-0.2490.2751
1:CSWE-0:C0.123-0.1460.3930.77
0:WWE-0:C0.006-0.2440.2551
1:WWE-0:C0.278-0.0070.5630.06
0:CSWE-1:C-0.452-0.804-0.1010.004
1:CSWE-1:C-0.342-0.6990.0150.069
0:WWE-1:C-0.46-0.802-0.1180.002
1:WWE-1:C-0.188-0.5560.1810.681
1:CSWE-0:CSWE0.11-0.1790.40.879
0:WWE-0:CSWE-0.008-0.2780.2631
1:WWE-0:CSWE0.265-0.0390.5680.124
0:WWE-1:CSWE-0.118-0.3960.160.822
1:WWE-1:CSWE0.155-0.1560.4650.7
1:WWE-0:WWE0.272-0.020.5650.083







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group56.5110
114

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 6.511 & 0 \tabularnewline
  & 114 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140212&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]6.511[/C][C]0[/C][/ROW]
[ROW][C] [/C][C]114[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140212&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140212&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)
Group56.5110
114



Parameters (Session):
par1 = 2 ; par2 = 3 ; par3 = 1 ; par4 = TRUE ;
Parameters (R input):
par1 = 2 ; par2 = 3 ; par3 = 1 ; 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')