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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_One Factor ANOVA.wasp
Title produced by softwareOne-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)
Date of computationMon, 28 Nov 2011 06:33:18 -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/28/t1322480013dpzkrgo5ir3ygde.htm/, Retrieved Fri, 19 Apr 2024 01:41:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147653, Retrieved Fri, 19 Apr 2024 01:41:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi Square Measure of Association- Free Statistics Software (Calculator)] [One Way ANOVA wit...] [2009-11-29 13:09:19] [98fd0e87c3eb04e0cc2efde01dbafab6]
-   PD  [Chi Square Measure of Association- Free Statistics Software (Calculator)] [One Way ANOVA for...] [2009-12-01 13:05:10] [3fdd735c61ad38cbc9b3393dc997cdb7]
- R P     [Chi Square Measure of Association- Free Statistics Software (Calculator)] [CARE date with Tu...] [2009-12-01 18:33:48] [98fd0e87c3eb04e0cc2efde01dbafab6]
-   P       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [CARE Data with Tu...] [2010-11-23 12:09:38] [3fdd735c61ad38cbc9b3393dc997cdb7]
- RM          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [IQ and Mothers Age] [2011-11-21 16:34:08] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R  D          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Q2] [2011-11-28 11:17:34] [dad037f44fabfe86d5fbc624d8e4b058]
-    D              [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Q3] [2011-11-28 11:33:18] [e57e243758c137c22281f3a9b4f93ddb] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
88	6
94	8
90	8
73	7
68	5
80	7
86	8
86	9
91	9
79	3
96	9
92	7
72	9
96	8
70	6
86	7
87	8
88	9
79	7
90	6
95	8
85	7
90	8
115	9
84	9
79	7
94	4
97	7
86	7
111	9
87	7
98	9
87	10
68	5
88	6
82	9
111	9
75	8
94	6
95	6
80	5
95	8
68	8
94	5
88	6
84	9
101	4
98	8
78	9
109	7
102	7
81	6
97	9
75	9
97	8
101	6
101	10
95	8
95	7
95	8
90	3
107	8
92	10
86	7
70	5
95	10
96	5
91	8
87	9
92	6
97	9
102	8
91	5
68	8
88	3
97	7
90	8
101	10
94	9
101	10
109	9
100	8
103	8
94	8
97	9
85	4
75	6
77	7
87	4
78	9
108	7
97	8
106	8
107	7
95	7
107	9
115	8
101	8
85	9
90	9
115	10
95	7
97	8
112	5
97	9
77	8
90	7
94	8
103	8
77	7
98	6
90	7
111	7
77	6
88	6
75	7
92	9
78	6
106	10
80	4
87	8
92	7
86	5
85	9
90	8
101	9
94	8
86	8
86	9
90	8
75	9
86	7
91	6
97	8
91	6
70	5
98	3
96	6
95	8
100	7
95	8
97	6
97	9
92	9
115	10
88	7
87	5
100	8
98	9
102	8
96	4

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'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147653&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147653&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147653&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'AstonUniversity' @ aston.wessa.net






ANOVA Model
WISCRY7V ~ MWARM30
means101.444-12.694-10.944-17.626-13.129-10.962-7.894-9.475

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
WISCRY7V  ~  MWARM30 \tabularnewline
means & 101.444 & -12.694 & -10.944 & -17.626 & -13.129 & -10.962 & -7.894 & -9.475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147653&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]WISCRY7V  ~  MWARM30[/C][/ROW]
[ROW][C]means[/C][C]101.444[/C][C]-12.694[/C][C]-10.944[/C][C]-17.626[/C][C]-13.129[/C][C]-10.962[/C][C]-7.894[/C][C]-9.475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147653&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147653&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
WISCRY7V ~ MWARM30
means101.444-12.694-10.944-17.626-13.129-10.962-7.894-9.475






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MWARM3071973.291281.8992.6760.012
Residuals14315066.325105.359 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MWARM30 & 7 & 1973.291 & 281.899 & 2.676 & 0.012 \tabularnewline
Residuals & 143 & 15066.325 & 105.359 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147653&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]MWARM30[/C][C]7[/C][C]1973.291[/C][C]281.899[/C][C]2.676[/C][C]0.012[/C][/ROW]
[ROW][C]Residuals[/C][C]143[/C][C]15066.325[/C][C]105.359[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147653&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147653&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)
MWARM3071973.291281.8992.6760.012
Residuals14315066.325105.359






Tukey Honest Significant Difference Comparisons
difflwruprp adj
3-10-12.694-31.676.2810.447
4-10-10.944-27.5875.6980.47
5-10-17.626-31.819-3.4340.005
6-10-13.129-25.906-0.3510.039
7-10-10.962-23.011.0870.103
8-10-7.894-19.5443.7550.43
9-10-9.475-21.3492.40.224
4-31.75-18.63322.1331
5-3-4.932-23.36913.5050.992
6-3-0.434-17.80516.9371
7-31.733-15.10918.5751
8-34.8-11.75921.3590.986
9-33.22-13.49819.9380.999
5-4-6.682-22.7089.3440.904
6-4-2.184-16.97112.6031
7-4-0.017-14.17914.1451
8-43.05-10.77416.8740.997
9-41.47-12.54415.4841
6-54.498-7.46616.4610.943
7-56.665-4.51717.8460.598
8-59.732-1.01920.4820.107
9-58.152-2.84219.1450.311
7-62.167-7.15311.4870.996
8-65.234-3.56414.0320.601
9-63.654-5.4412.7480.92
8-73.067-4.63410.7690.923
9-71.487-6.559.5240.999
9-8-1.58-9.0065.8450.998

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
3-10 & -12.694 & -31.67 & 6.281 & 0.447 \tabularnewline
4-10 & -10.944 & -27.587 & 5.698 & 0.47 \tabularnewline
5-10 & -17.626 & -31.819 & -3.434 & 0.005 \tabularnewline
6-10 & -13.129 & -25.906 & -0.351 & 0.039 \tabularnewline
7-10 & -10.962 & -23.01 & 1.087 & 0.103 \tabularnewline
8-10 & -7.894 & -19.544 & 3.755 & 0.43 \tabularnewline
9-10 & -9.475 & -21.349 & 2.4 & 0.224 \tabularnewline
4-3 & 1.75 & -18.633 & 22.133 & 1 \tabularnewline
5-3 & -4.932 & -23.369 & 13.505 & 0.992 \tabularnewline
6-3 & -0.434 & -17.805 & 16.937 & 1 \tabularnewline
7-3 & 1.733 & -15.109 & 18.575 & 1 \tabularnewline
8-3 & 4.8 & -11.759 & 21.359 & 0.986 \tabularnewline
9-3 & 3.22 & -13.498 & 19.938 & 0.999 \tabularnewline
5-4 & -6.682 & -22.708 & 9.344 & 0.904 \tabularnewline
6-4 & -2.184 & -16.971 & 12.603 & 1 \tabularnewline
7-4 & -0.017 & -14.179 & 14.145 & 1 \tabularnewline
8-4 & 3.05 & -10.774 & 16.874 & 0.997 \tabularnewline
9-4 & 1.47 & -12.544 & 15.484 & 1 \tabularnewline
6-5 & 4.498 & -7.466 & 16.461 & 0.943 \tabularnewline
7-5 & 6.665 & -4.517 & 17.846 & 0.598 \tabularnewline
8-5 & 9.732 & -1.019 & 20.482 & 0.107 \tabularnewline
9-5 & 8.152 & -2.842 & 19.145 & 0.311 \tabularnewline
7-6 & 2.167 & -7.153 & 11.487 & 0.996 \tabularnewline
8-6 & 5.234 & -3.564 & 14.032 & 0.601 \tabularnewline
9-6 & 3.654 & -5.44 & 12.748 & 0.92 \tabularnewline
8-7 & 3.067 & -4.634 & 10.769 & 0.923 \tabularnewline
9-7 & 1.487 & -6.55 & 9.524 & 0.999 \tabularnewline
9-8 & -1.58 & -9.006 & 5.845 & 0.998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147653&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]3-10[/C][C]-12.694[/C][C]-31.67[/C][C]6.281[/C][C]0.447[/C][/ROW]
[ROW][C]4-10[/C][C]-10.944[/C][C]-27.587[/C][C]5.698[/C][C]0.47[/C][/ROW]
[ROW][C]5-10[/C][C]-17.626[/C][C]-31.819[/C][C]-3.434[/C][C]0.005[/C][/ROW]
[ROW][C]6-10[/C][C]-13.129[/C][C]-25.906[/C][C]-0.351[/C][C]0.039[/C][/ROW]
[ROW][C]7-10[/C][C]-10.962[/C][C]-23.01[/C][C]1.087[/C][C]0.103[/C][/ROW]
[ROW][C]8-10[/C][C]-7.894[/C][C]-19.544[/C][C]3.755[/C][C]0.43[/C][/ROW]
[ROW][C]9-10[/C][C]-9.475[/C][C]-21.349[/C][C]2.4[/C][C]0.224[/C][/ROW]
[ROW][C]4-3[/C][C]1.75[/C][C]-18.633[/C][C]22.133[/C][C]1[/C][/ROW]
[ROW][C]5-3[/C][C]-4.932[/C][C]-23.369[/C][C]13.505[/C][C]0.992[/C][/ROW]
[ROW][C]6-3[/C][C]-0.434[/C][C]-17.805[/C][C]16.937[/C][C]1[/C][/ROW]
[ROW][C]7-3[/C][C]1.733[/C][C]-15.109[/C][C]18.575[/C][C]1[/C][/ROW]
[ROW][C]8-3[/C][C]4.8[/C][C]-11.759[/C][C]21.359[/C][C]0.986[/C][/ROW]
[ROW][C]9-3[/C][C]3.22[/C][C]-13.498[/C][C]19.938[/C][C]0.999[/C][/ROW]
[ROW][C]5-4[/C][C]-6.682[/C][C]-22.708[/C][C]9.344[/C][C]0.904[/C][/ROW]
[ROW][C]6-4[/C][C]-2.184[/C][C]-16.971[/C][C]12.603[/C][C]1[/C][/ROW]
[ROW][C]7-4[/C][C]-0.017[/C][C]-14.179[/C][C]14.145[/C][C]1[/C][/ROW]
[ROW][C]8-4[/C][C]3.05[/C][C]-10.774[/C][C]16.874[/C][C]0.997[/C][/ROW]
[ROW][C]9-4[/C][C]1.47[/C][C]-12.544[/C][C]15.484[/C][C]1[/C][/ROW]
[ROW][C]6-5[/C][C]4.498[/C][C]-7.466[/C][C]16.461[/C][C]0.943[/C][/ROW]
[ROW][C]7-5[/C][C]6.665[/C][C]-4.517[/C][C]17.846[/C][C]0.598[/C][/ROW]
[ROW][C]8-5[/C][C]9.732[/C][C]-1.019[/C][C]20.482[/C][C]0.107[/C][/ROW]
[ROW][C]9-5[/C][C]8.152[/C][C]-2.842[/C][C]19.145[/C][C]0.311[/C][/ROW]
[ROW][C]7-6[/C][C]2.167[/C][C]-7.153[/C][C]11.487[/C][C]0.996[/C][/ROW]
[ROW][C]8-6[/C][C]5.234[/C][C]-3.564[/C][C]14.032[/C][C]0.601[/C][/ROW]
[ROW][C]9-6[/C][C]3.654[/C][C]-5.44[/C][C]12.748[/C][C]0.92[/C][/ROW]
[ROW][C]8-7[/C][C]3.067[/C][C]-4.634[/C][C]10.769[/C][C]0.923[/C][/ROW]
[ROW][C]9-7[/C][C]1.487[/C][C]-6.55[/C][C]9.524[/C][C]0.999[/C][/ROW]
[ROW][C]9-8[/C][C]-1.58[/C][C]-9.006[/C][C]5.845[/C][C]0.998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147653&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147653&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
3-10-12.694-31.676.2810.447
4-10-10.944-27.5875.6980.47
5-10-17.626-31.819-3.4340.005
6-10-13.129-25.906-0.3510.039
7-10-10.962-23.011.0870.103
8-10-7.894-19.5443.7550.43
9-10-9.475-21.3492.40.224
4-31.75-18.63322.1331
5-3-4.932-23.36913.5050.992
6-3-0.434-17.80516.9371
7-31.733-15.10918.5751
8-34.8-11.75921.3590.986
9-33.22-13.49819.9380.999
5-4-6.682-22.7089.3440.904
6-4-2.184-16.97112.6031
7-4-0.017-14.17914.1451
8-43.05-10.77416.8740.997
9-41.47-12.54415.4841
6-54.498-7.46616.4610.943
7-56.665-4.51717.8460.598
8-59.732-1.01920.4820.107
9-58.152-2.84219.1450.311
7-62.167-7.15311.4870.996
8-65.234-3.56414.0320.601
9-63.654-5.4412.7480.92
8-73.067-4.63410.7690.923
9-71.487-6.559.5240.999
9-8-1.58-9.0065.8450.998






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group71.1210.353
143 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 7 & 1.121 & 0.353 \tabularnewline
  & 143 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147653&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]7[/C][C]1.121[/C][C]0.353[/C][/ROW]
[ROW][C] [/C][C]143[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147653&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147653&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)
Group71.1210.353
143


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
R code (references can be found in the software module):
cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
intercept<-as.logical(par3)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
xdf<-data.frame(x1,f1)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
names(xdf)<-c('Response', 'Treatment')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment - 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment, 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$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, paste(V1, ' ~ ', V2), length(lmxdf$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)
a<-table.row.start(a)
a<-table.element(a, V2,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3),,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$Df[2],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], 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, data=xdf, xlab=V2, ylab=V1)
dev.off()
if(intercept==TRUE){
thsd<-TukeyHSD(aov.xdf)
bitmap(file='TukeyHSDPlot.png')
plot(thsd)
dev.off()
}
if(intercept==TRUE){
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(i in 1:length(rownames(thsd[[1]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[1]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[1]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
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')