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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software ModuleIan.Hollidayrwasp_Two Factor ANOVA -V2.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationWed, 26 May 2010 17:02:24 +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/2010/May/26/t1274894195nc0qduhoo1uxtyk.htm/, Retrieved Thu, 20 Feb 2025 19:19:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=76523, Retrieved Thu, 20 Feb 2025 19:19:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact265

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [two-way anova wit...] [2010-05-26 17:02:24] [a9208f4f8d3b118336aae915785f2bd9] [Current]
- R PD    [Two-Way ANOVA] [ANOVA with good l...] [2010-05-28 23:09:47] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R         [Variability] [ANOVA with better...] [2010-05-29 09:47:12] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R           [Variability] [ANOVA with better...] [2010-05-29 09:54:40] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R  D          [Variability] [compendium] [2010-05-30 20:52:28] [4edce1892c378475bb20c4acd224a51d]
- R  D          [Variability] [Trimmed 10%] [2010-05-31 11:11:24] [856c65906cd78e3f7881668c6dfea87f]
- R  D          [Variability] [Trimmed date 10%] [2010-05-31 11:11:52] [885328d98a95a442af53d0763bccf325]
- RMPD          [] [] [1970-01-01 00:00:00] [166b3b50b14ad81e946931c96c6ff94f]
- RMP           [Two-Way ANOVA] [] [2010-06-01 23:03:14] [3a572644bd86164a067a2945d748afb7]
- R             [Variability] [blog 1] [2010-06-02 08:40:04] [c519646407a489a26f129bdc22b2e203]
- R             [Variability] [SectionA] [2010-06-02 08:40:35] [4edce1892c378475bb20c4acd224a51d]
- R             [Variability] [Test 1] [2010-06-02 08:40:25] [7d07ebb7f3978280240b500f174a2af2]
- R  D            [Variability] [test 2] [2010-06-02 09:11:16] [7d07ebb7f3978280240b500f174a2af2]
- RMPD            [Simple Linear Regression] [linear regression] [2010-06-02 09:42:39] [7d07ebb7f3978280240b500f174a2af2]
- R             [Variability] [Question one] [2010-06-02 08:40:45] [6754037f2a7547483397efade45eb176]
- R             [Variability] [Question 1] [2010-06-02 08:40:59] [153000c0b3bd367036e4d581452d08df]
- R  D            [Variability] [Question 1ii] [2010-06-02 08:52:01] [153000c0b3bd367036e4d581452d08df]
- R             [Variability] [Examcompendium] [2010-06-02 08:40:07] [175567c9546e50fd2412bc13fece161f]
- R             [Variability] [Section A 1 - 1] [2010-06-02 08:40:52] [5cb86ecb659a3920b562748ed004e500]
- R             [Variability] [adler] [2010-06-02 08:41:42] [f894c941edfbe86ae91b01acfc2a02b5]
- R             [Variability] [exam] [2010-06-02 08:41:23] [5bdc9e4bd4169daeceaf774f5b9d9a20]
- R             [Variability] [section A Q1] [2010-06-02 08:41:49] [256a42577f5eb7e9c8a1b74c73a90fa8]
- R             [Variability] [june exam section...] [2010-06-02 08:41:39] [a2ec18f77143ca7c2255feafca790c81]
- R             [Variability] [] [2010-06-02 08:41:42] [a6e410a5e6b2ff1e0abc686251520516]
- R  D          [Variability] [exam1] [2010-06-02 08:41:53] [86674042f568b97a0cb1393bb670625c]
- R             [Variability] [Adler Exam Reprod...] [2010-06-02 08:41:55] [814d7f27257f4c09e0e8a930c67f7fe6]
- R             [Variability] [] [2010-06-02 08:42:17] [74be16979710d4c4e7c6647856088456]
- R             [Variability] [Reproduced Analys...] [2010-06-02 08:42:28] [e92f8a4a2b7017be7b51e64bfca3a271]
- R             [Variability] [reproduction of a...] [2010-06-02 08:42:14] [c3b05d290fad0f2bad0901abbf20f20e]
- R             [Variability] [Adler Study] [2010-06-02 08:41:57] [74be16979710d4c4e7c6647856088456]
- R             [Variability] [June exam ANOVA] [2010-06-02 08:42:16] [012a64ac316c94a67eaef3285dac2cf7]
- R             [Variability] [] [2010-06-02 08:42:05] [a0fd591e63c5cd1c7fa328d28b50e124]
- R             [Variability] [Adler Study] [2010-06-02 08:41:55] [06b133101696c6b5dc677840451c9976]
- R  D            [Variability] [Adler Adjusted] [2010-06-02 09:17:20] [06b133101696c6b5dc677840451c9976]
- R             [Variability] [Exam QA1] [2010-06-02 08:43:04] [2cb75f3785cab2383fe897d8b1eb3abc]
- R  D            [Variability] [SUMMER EXAM QA ad...] [2010-06-02 09:21:13] [2cb75f3785cab2383fe897d8b1eb3abc]
- R             [Variability] [AdvancedStats] [2010-06-02 08:43:30] [7ee8584ae92dbbc2a823887b8397aaa8]
- R             [Variability] [Section A - ANOVA] [2010-06-02 08:43:24] [8431b2cca73e677c29fb8bfdfc230859]
- R             [Variability] [Section a q1] [2010-06-02 08:44:23] [d85e8cd4dd2ccdf2c3dfa3761837f774]
- R             [Variability] [] [2010-06-02 08:42:08] [5937f6f6887339314deaa19552a34d35]
- R             [Variability] [ANOVA] [2010-06-02 08:44:10] [01668bebb17c5d247271c36324d8beb6]
- R               [Variability] [ANOVA for attract...] [2010-06-02 09:08:14] [01668bebb17c5d247271c36324d8beb6]
- R             [Variability] [statsexam] [2010-06-02 08:44:40] [66f61a2d5ef80b1eafe31e5651ad0889]
- R             [Variability] [adler study] [2010-06-02 08:42:09] [74be16979710d4c4e7c6647856088456]
- R             [Variability] [adler study] [2010-06-02 08:42:09] [74be16979710d4c4e7c6647856088456]
- R             [Variability] [Adler] [2010-06-02 08:42:19] [f4cf89988ffad44ee0ca561abbab9122]
- R             [Variability] [] [2010-06-02 08:45:50] [36cf82ea4074b55afa05ece289b9dfca]
- R             [Variability] [] [2010-06-02 08:46:11] [bab33aa52cf59fc0c168bbba8dd04cc1]
- R             [Variability] [Nacy Adler (1973)] [2010-06-02 08:40:58] [166b3b50b14ad81e946931c96c6ff94f]

[Truncated]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.28	'A'	'M'
0.95	'A'	'M'
0.96	'A'	'M'
0.97	'A'	'M'
0.40	'A'	'M'
0.18	'A'	'M'
0.12	'A'	'M'
0.62	'A'	'M'
1.81	'A'	'F'
1.51	'A'	'F'
1.41	'A'	'F'
1.39	'A'	'F'
1.20	'A'	'F'
1.55	'A'	'F'
1.48	'A'	'F'
1.25	'A'	'F'
0.95	'B'	'M'
1.33	'B'	'M'
0.92	'B'	'M'
0.85	'B'	'M'
1.06	'B'	'M'
0.69	'B'	'M'
0.70	'B'	'M'
0.79	'B'	'M'
2.93	'B'	'F'
3.24	'B'	'F'
3.42	'B'	'F'
2.79	'B'	'F'
2.54	'B'	'F'
3.28	'B'	'F'
2.80	'B'	'F'
3.40	'B'	'F'

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76523&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76523&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76523&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'Gwilym Jenkins' @ 72.249.127.135






ANOVA Model
xdf2$Value ~ xdf2$Cake * xdf2$Curry
means1.451.6-0.89-1.249

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
xdf2$Value ~ xdf2$Cake * xdf2$Curry \tabularnewline
means & 1.45 & 1.6 & -0.89 & -1.249 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76523&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]xdf2$Value ~ xdf2$Cake * xdf2$Curry[/C][/ROW]
[ROW][C]means[/C][C]1.45[/C][C]1.6[/C][C]-0.89[/C][C]-1.249[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76523&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76523&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
xdf2$Value ~ xdf2$Cake * xdf2$Curry
means1.451.6-0.89-1.249






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
xdf2$Cake17.6157.61595.1130
xdf2$Curry118.34718.347229.1610
xdf2$Cake:xdf2$Curry13.1193.11938.9550
Residuals282.2420.08 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
xdf2$Cake & 1 & 7.615 & 7.615 & 95.113 & 0 \tabularnewline
xdf2$Curry & 1 & 18.347 & 18.347 & 229.161 & 0 \tabularnewline
xdf2$Cake:xdf2$Curry & 1 & 3.119 & 3.119 & 38.955 & 0 \tabularnewline
Residuals & 28 & 2.242 & 0.08 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76523&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]xdf2$Cake[/C][C]1[/C][C]7.615[/C][C]7.615[/C][C]95.113[/C][C]0[/C][/ROW]
[ROW][C]xdf2$Curry[/C][C]1[/C][C]18.347[/C][C]18.347[/C][C]229.161[/C][C]0[/C][/ROW]
[ROW][C]xdf2$Cake:xdf2$Curry[/C][C]1[/C][C]3.119[/C][C]3.119[/C][C]38.955[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]28[/C][C]2.242[/C][C]0.08[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76523&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76523&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)
1
xdf2$Cake17.6157.61595.1130
xdf2$Curry118.34718.347229.1610
xdf2$Cake:xdf2$Curry13.1193.11938.9550
Residuals282.2420.08






Tukey Honest Significant Difference Comparisons
difflwruprp adj
B-A0.9760.7711.1810
M-F-1.514-1.719-1.3090
B:F-A:F1.61.2141.9860
A:M-A:F-0.89-1.276-0.5040
B:M-A:F-0.539-0.925-0.1520.004
A:M-B:F-2.49-2.876-2.1040
B:M-B:F-2.139-2.525-1.7520
B:M-A:M0.351-0.0350.7380.085

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
B-A & 0.976 & 0.771 & 1.181 & 0 \tabularnewline
M-F & -1.514 & -1.719 & -1.309 & 0 \tabularnewline
B:F-A:F & 1.6 & 1.214 & 1.986 & 0 \tabularnewline
A:M-A:F & -0.89 & -1.276 & -0.504 & 0 \tabularnewline
B:M-A:F & -0.539 & -0.925 & -0.152 & 0.004 \tabularnewline
A:M-B:F & -2.49 & -2.876 & -2.104 & 0 \tabularnewline
B:M-B:F & -2.139 & -2.525 & -1.752 & 0 \tabularnewline
B:M-A:M & 0.351 & -0.035 & 0.738 & 0.085 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76523&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]B-A[/C][C]0.976[/C][C]0.771[/C][C]1.181[/C][C]0[/C][/ROW]
[ROW][C]M-F[/C][C]-1.514[/C][C]-1.719[/C][C]-1.309[/C][C]0[/C][/ROW]
[ROW][C]B:F-A:F[/C][C]1.6[/C][C]1.214[/C][C]1.986[/C][C]0[/C][/ROW]
[ROW][C]A:M-A:F[/C][C]-0.89[/C][C]-1.276[/C][C]-0.504[/C][C]0[/C][/ROW]
[ROW][C]B:M-A:F[/C][C]-0.539[/C][C]-0.925[/C][C]-0.152[/C][C]0.004[/C][/ROW]
[ROW][C]A:M-B:F[/C][C]-2.49[/C][C]-2.876[/C][C]-2.104[/C][C]0[/C][/ROW]
[ROW][C]B:M-B:F[/C][C]-2.139[/C][C]-2.525[/C][C]-1.752[/C][C]0[/C][/ROW]
[ROW][C]B:M-A:M[/C][C]0.351[/C][C]-0.035[/C][C]0.738[/C][C]0.085[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76523&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76523&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
B-A0.9760.7711.1810
M-F-1.514-1.719-1.3090
B:F-A:F1.61.2141.9860
A:M-A:F-0.89-1.276-0.5040
B:M-A:F-0.539-0.925-0.1520.004
A:M-B:F-2.49-2.876-2.1040
B:M-B:F-2.139-2.525-1.7520
B:M-A:M0.351-0.0350.7380.085






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group33.6960.023
28 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 3.696 & 0.023 \tabularnewline
  & 28 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76523&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]3[/C][C]3.696[/C][C]0.023[/C][/ROW]
[ROW][C] [/C][C]28[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76523&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76523&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)
Group33.6960.023
28


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
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])
mynames<- c(V1, V2, V3)
xdf2<-xdf
names(xdf2)<-mynames
names(xdf)<-c('R', 'A', 'B')
mynames <- c(V1, V2, V3)
if(intercept == FALSE)eval (substitute(lmout<-lm(xdf$R ~ xdf$A * xdf$B- 1, data = xdf), list(xdf=quote(xdf2),R=mynames[1],A=mynames[2],B=mynames[3]) ))else eval(substitute(lmout<-lm(xdf$R ~ xdf$A * xdf$B, data = xdf), list(xdf=quote(xdf2),R=mynames[1],A=mynames[2],B=mynames[3]) ))
(aov.xdf<-aov(lmout) )
(anova.xdf<-anova(lmout) )
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Model', length(lmout$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmout$call['formula'],length(lmout$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'means',,TRUE)
for(i in 1:length(lmout$coefficients)){
a<-table.element(a, round(lmout$coefficients[i], digits=3),,FALSE)
}
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',,TRUE)
a<-table.element(a, 'Df',,FALSE)
a<-table.element(a, 'Sum Sq',,FALSE)
a<-table.element(a, 'Mean Sq',,FALSE)
a<-table.element(a, 'F value',,FALSE)
a<-table.element(a, 'Pr(>F)',,FALSE)
a<-table.row.end(a)
for(i in 1 : length(rownames(anova.xdf))-1){
a<-table.row.start(a)
a<-table.element(a,rownames(anova.xdf)[i] ,,TRUE)
a<-table.element(a, anova.xdf$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$'Mean Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='anovaplot.png')
boxplot(R ~ A + B, data=xdf, xlab=V2, ylab=V1, main='Boxplots of ANOVA Groups')
dev.off()
bitmap(file='designplot.png')
xdf2 <- xdf # to preserve xdf make copy for function
names(xdf2) <- c(V1, V2, V3)
plot.design(xdf2, main='Design Plot of Group Means')
dev.off()
bitmap(file='interactionplot.png')
interaction.plot(xdf$A, xdf$B, xdf$R, 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(lmout)
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')