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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 computationSun, 16 Nov 2014 18:53:36 +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/Nov/16/t1416164035pvtcix8v1v5przn.htm/, Retrieved Sun, 19 May 2024 19:50:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=255274, Retrieved Sun, 19 May 2024 19:50:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75

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]
- RM D            [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [30mths] [2014-11-16 18:53:36] [d39ba099107fddb6b3ada692142e09dd] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6	36	88
8	56	94
8	48	90
7	32	73
5	44	68
7	39	80
8	34	86
9	41	86
9	50	91
3	39	79
9	62	96
7	52	92
9	37	72
8	50	96
6	41	70
7	55	86
8	41	87
9	56	88
7	39	79
6	52	90
8	46	95
7	44	85
8	41	90
9	50	115
9	50	84
7	44	79
4	52	94
7	54	97
7	44	86
9	52	111
7	37	87
9	52	98
10	50	87
5	36	68
6	50	88
9	52	82
9	55	111
8	31	75
6	36	94
6	49	95
5	42	80
8	37	95
8	41	68
5	30	94
6	52	88
9	30	84
4	44	101
8	66	98
9	48	78
7	43	109
7	57	102
6	46	81
9	54	97
9	48	75
8	48	97
6	62	101
10	58	101
8	58	95
7	62	95
8	46	95
3	34	90
8	66	107
10	52	92
7	55	86
5	55	70
10	57	95
5	56	96
8	55	91
9	56	87
6	54	92
9	55	97
8	46	102
5	52	91
8	32	68
3	44	88
7	46	97
8	59	90
10	46	101
9	46	94
10	54	101
9	66	109
8	56	100
8	59	103
8	57	94
9	52	97
4	48	85
6	44	75
7	41	77
4	50	87
9	48	78
7	48	108
8	59	97
8	46	106
7	54	107
7	55	95
9	54	107
8	59	115
8	44	101
9	54	85
9	52	90
10	66	115
7	44	95
8	57	97
5	39	112
9	60	97
8	45	77
7	41	90
8	50	94
8	39	103
7	43	77
6	48	98
7	37	90
7	58	111
6	46	77
6	43	88
7	44	75
9	34	92
6	30	78
10	50	106
4	39	80
8	37	87
7	55	92
5	39	86
9	36	85
8	43	90
9	50	101
8	55	94
8	43	86
9	60	86
8	48	90
9	30	75
7	43	86
6	39	91
8	52	97
6	39	91
5	39	70
3	56	98
6	59	96
8	46	95
7	57	100
8	50	95
6	54	97
9	50	97
9	60	92
10	59	115
7	41	88
5	48	87
8	59	100
9	60	98
8	56	102

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' @ 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255274&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255274&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255274&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' @ jenkins.wessa.net






ANOVA Model
MC30VRB ~ MWARM30
means54.667-11.417-8.067-11.03-8.351-7.632-5.642-4.364

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
MC30VRB  ~  MWARM30 \tabularnewline
means & 54.667 & -11.417 & -8.067 & -11.03 & -8.351 & -7.632 & -5.642 & -4.364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255274&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]MC30VRB  ~  MWARM30[/C][/ROW]
[ROW][C]means[/C][C]54.667[/C][C]-11.417[/C][C]-8.067[/C][C]-11.03[/C][C]-8.351[/C][C]-7.632[/C][C]-5.642[/C][C]-4.364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255274&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255274&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
MC30VRB ~ MWARM30
means54.667-11.417-8.067-11.03-8.351-7.632-5.642-4.364






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MWARM307995.162142.1662.0420.054
Residuals1429885.51169.616 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MWARM30 & 7 & 995.162 & 142.166 & 2.042 & 0.054 \tabularnewline
Residuals & 142 & 9885.511 & 69.616 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255274&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]995.162[/C][C]142.166[/C][C]2.042[/C][C]0.054[/C][/ROW]
[ROW][C]Residuals[/C][C]142[/C][C]9885.511[/C][C]69.616[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255274&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255274&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)
MWARM307995.162142.1662.0420.054
Residuals1429885.51169.616






Tukey Honest Significant Difference Comparisons
difflwruprp adj
3-10-11.417-26.8434.0090.314
4-10-8.067-22.3856.2520.666
5-10-11.03-22.5680.5080.072
6-10-8.351-18.7382.0370.216
7-10-7.632-17.4272.1630.251
8-10-5.642-15.1123.8290.599
9-10-4.364-14.0175.290.86
4-33.35-13.8720.570.999
5-30.386-14.60215.3751
6-33.066-11.05617.1880.998
7-33.784-9.90717.4760.99
8-35.775-7.68719.2370.89
9-37.053-6.53820.6440.752
5-4-2.964-16.80910.8820.998
6-4-0.284-13.18712.6181
7-40.434-11.99612.8651
8-42.425-9.75214.6020.999
9-43.703-8.61616.0220.983
6-52.679-7.04612.4050.99
7-53.398-5.69212.4880.944
8-55.389-3.35114.1280.555
9-56.667-2.27115.6040.304
7-60.719-6.8588.2951
8-62.709-4.4439.8620.94
9-63.987-3.40511.380.713
8-71.991-4.278.2510.977
9-73.269-3.2659.8020.785
9-81.278-4.7597.3150.998

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
3-10 & -11.417 & -26.843 & 4.009 & 0.314 \tabularnewline
4-10 & -8.067 & -22.385 & 6.252 & 0.666 \tabularnewline
5-10 & -11.03 & -22.568 & 0.508 & 0.072 \tabularnewline
6-10 & -8.351 & -18.738 & 2.037 & 0.216 \tabularnewline
7-10 & -7.632 & -17.427 & 2.163 & 0.251 \tabularnewline
8-10 & -5.642 & -15.112 & 3.829 & 0.599 \tabularnewline
9-10 & -4.364 & -14.017 & 5.29 & 0.86 \tabularnewline
4-3 & 3.35 & -13.87 & 20.57 & 0.999 \tabularnewline
5-3 & 0.386 & -14.602 & 15.375 & 1 \tabularnewline
6-3 & 3.066 & -11.056 & 17.188 & 0.998 \tabularnewline
7-3 & 3.784 & -9.907 & 17.476 & 0.99 \tabularnewline
8-3 & 5.775 & -7.687 & 19.237 & 0.89 \tabularnewline
9-3 & 7.053 & -6.538 & 20.644 & 0.752 \tabularnewline
5-4 & -2.964 & -16.809 & 10.882 & 0.998 \tabularnewline
6-4 & -0.284 & -13.187 & 12.618 & 1 \tabularnewline
7-4 & 0.434 & -11.996 & 12.865 & 1 \tabularnewline
8-4 & 2.425 & -9.752 & 14.602 & 0.999 \tabularnewline
9-4 & 3.703 & -8.616 & 16.022 & 0.983 \tabularnewline
6-5 & 2.679 & -7.046 & 12.405 & 0.99 \tabularnewline
7-5 & 3.398 & -5.692 & 12.488 & 0.944 \tabularnewline
8-5 & 5.389 & -3.351 & 14.128 & 0.555 \tabularnewline
9-5 & 6.667 & -2.271 & 15.604 & 0.304 \tabularnewline
7-6 & 0.719 & -6.858 & 8.295 & 1 \tabularnewline
8-6 & 2.709 & -4.443 & 9.862 & 0.94 \tabularnewline
9-6 & 3.987 & -3.405 & 11.38 & 0.713 \tabularnewline
8-7 & 1.991 & -4.27 & 8.251 & 0.977 \tabularnewline
9-7 & 3.269 & -3.265 & 9.802 & 0.785 \tabularnewline
9-8 & 1.278 & -4.759 & 7.315 & 0.998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255274&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]-11.417[/C][C]-26.843[/C][C]4.009[/C][C]0.314[/C][/ROW]
[ROW][C]4-10[/C][C]-8.067[/C][C]-22.385[/C][C]6.252[/C][C]0.666[/C][/ROW]
[ROW][C]5-10[/C][C]-11.03[/C][C]-22.568[/C][C]0.508[/C][C]0.072[/C][/ROW]
[ROW][C]6-10[/C][C]-8.351[/C][C]-18.738[/C][C]2.037[/C][C]0.216[/C][/ROW]
[ROW][C]7-10[/C][C]-7.632[/C][C]-17.427[/C][C]2.163[/C][C]0.251[/C][/ROW]
[ROW][C]8-10[/C][C]-5.642[/C][C]-15.112[/C][C]3.829[/C][C]0.599[/C][/ROW]
[ROW][C]9-10[/C][C]-4.364[/C][C]-14.017[/C][C]5.29[/C][C]0.86[/C][/ROW]
[ROW][C]4-3[/C][C]3.35[/C][C]-13.87[/C][C]20.57[/C][C]0.999[/C][/ROW]
[ROW][C]5-3[/C][C]0.386[/C][C]-14.602[/C][C]15.375[/C][C]1[/C][/ROW]
[ROW][C]6-3[/C][C]3.066[/C][C]-11.056[/C][C]17.188[/C][C]0.998[/C][/ROW]
[ROW][C]7-3[/C][C]3.784[/C][C]-9.907[/C][C]17.476[/C][C]0.99[/C][/ROW]
[ROW][C]8-3[/C][C]5.775[/C][C]-7.687[/C][C]19.237[/C][C]0.89[/C][/ROW]
[ROW][C]9-3[/C][C]7.053[/C][C]-6.538[/C][C]20.644[/C][C]0.752[/C][/ROW]
[ROW][C]5-4[/C][C]-2.964[/C][C]-16.809[/C][C]10.882[/C][C]0.998[/C][/ROW]
[ROW][C]6-4[/C][C]-0.284[/C][C]-13.187[/C][C]12.618[/C][C]1[/C][/ROW]
[ROW][C]7-4[/C][C]0.434[/C][C]-11.996[/C][C]12.865[/C][C]1[/C][/ROW]
[ROW][C]8-4[/C][C]2.425[/C][C]-9.752[/C][C]14.602[/C][C]0.999[/C][/ROW]
[ROW][C]9-4[/C][C]3.703[/C][C]-8.616[/C][C]16.022[/C][C]0.983[/C][/ROW]
[ROW][C]6-5[/C][C]2.679[/C][C]-7.046[/C][C]12.405[/C][C]0.99[/C][/ROW]
[ROW][C]7-5[/C][C]3.398[/C][C]-5.692[/C][C]12.488[/C][C]0.944[/C][/ROW]
[ROW][C]8-5[/C][C]5.389[/C][C]-3.351[/C][C]14.128[/C][C]0.555[/C][/ROW]
[ROW][C]9-5[/C][C]6.667[/C][C]-2.271[/C][C]15.604[/C][C]0.304[/C][/ROW]
[ROW][C]7-6[/C][C]0.719[/C][C]-6.858[/C][C]8.295[/C][C]1[/C][/ROW]
[ROW][C]8-6[/C][C]2.709[/C][C]-4.443[/C][C]9.862[/C][C]0.94[/C][/ROW]
[ROW][C]9-6[/C][C]3.987[/C][C]-3.405[/C][C]11.38[/C][C]0.713[/C][/ROW]
[ROW][C]8-7[/C][C]1.991[/C][C]-4.27[/C][C]8.251[/C][C]0.977[/C][/ROW]
[ROW][C]9-7[/C][C]3.269[/C][C]-3.265[/C][C]9.802[/C][C]0.785[/C][/ROW]
[ROW][C]9-8[/C][C]1.278[/C][C]-4.759[/C][C]7.315[/C][C]0.998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255274&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255274&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
3-10-11.417-26.8434.0090.314
4-10-8.067-22.3856.2520.666
5-10-11.03-22.5680.5080.072
6-10-8.351-18.7382.0370.216
7-10-7.632-17.4272.1630.251
8-10-5.642-15.1123.8290.599
9-10-4.364-14.0175.290.86
4-33.35-13.8720.570.999
5-30.386-14.60215.3751
6-33.066-11.05617.1880.998
7-33.784-9.90717.4760.99
8-35.775-7.68719.2370.89
9-37.053-6.53820.6440.752
5-4-2.964-16.80910.8820.998
6-4-0.284-13.18712.6181
7-40.434-11.99612.8651
8-42.425-9.75214.6020.999
9-43.703-8.61616.0220.983
6-52.679-7.04612.4050.99
7-53.398-5.69212.4880.944
8-55.389-3.35114.1280.555
9-56.667-2.27115.6040.304
7-60.719-6.8588.2951
8-62.709-4.4439.8620.94
9-63.987-3.40511.380.713
8-71.991-4.278.2510.977
9-73.269-3.2659.8020.785
9-81.278-4.7597.3150.998






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group70.4930.838
142 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 7 & 0.493 & 0.838 \tabularnewline
  & 142 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255274&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]0.493[/C][C]0.838[/C][/ROW]
[ROW][C] [/C][C]142[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255274&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255274&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)
Group70.4930.838
142


Figures (Output of Computation)

Input Parameters & R Code

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