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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 18:57:47 +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/t1416164280xnc528cqvnfbi3r.htm/, Retrieved Sun, 19 May 2024 21:34:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=255277, Retrieved Sun, 19 May 2024 21:34:03 +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)
-     [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)] [yr7] [2014-11-16 18:57:47] [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 time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255277&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]1 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=255277&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255277&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 time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






ANOVA Model
WISCRY7V ~ MWARM30
means101.444-12.694-12.044-17.626-13.129-10.962-7.894-9.475

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
WISCRY7V  ~  MWARM30 \tabularnewline
means & 101.444 & -12.694 & -12.044 & -17.626 & -13.129 & -10.962 & -7.894 & -9.475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255277&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]-12.044[/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=255277&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255277&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
means101.444-12.694-12.044-17.626-13.129-10.962-7.894-9.475






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MWARM3071988.935284.1342.6840.012
Residuals14215030.025105.845 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MWARM30 & 7 & 1988.935 & 284.134 & 2.684 & 0.012 \tabularnewline
Residuals & 142 & 15030.025 & 105.845 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255277&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]1988.935[/C][C]284.134[/C][C]2.684[/C][C]0.012[/C][/ROW]
[ROW][C]Residuals[/C][C]142[/C][C]15030.025[/C][C]105.845[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255277&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255277&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)
MWARM3071988.935284.1342.6840.012
Residuals14215030.025105.845






Tukey Honest Significant Difference Comparisons
difflwruprp adj
3-10-12.694-31.7156.3270.45
4-10-12.044-29.75.6110.421
5-10-17.626-31.853-3.3990.005
6-10-13.129-25.937-0.320.04
7-10-10.962-23.0391.1160.105
8-10-7.894-19.5723.7830.433
9-10-9.475-21.3782.4280.226
4-30.65-20.58321.8831
5-3-4.932-23.41313.550.992
6-3-0.434-17.84716.9791
7-31.733-15.1518.6151
8-34.8-11.79921.3990.987
9-33.22-13.53919.9780.999
5-4-5.582-22.65411.4910.973
6-4-1.084-16.99414.8251
7-41.083-14.24516.411
8-44.15-10.86419.1640.99
9-42.57-12.62117.761
6-54.498-7.49516.490.943
7-56.665-4.54417.8730.601
8-59.732-1.04520.5080.109
9-58.152-2.86919.1720.314
7-62.167-7.17511.5090.996
8-65.234-3.58514.0530.604
9-63.654-5.46212.7690.921
8-73.067-4.65310.7870.924
9-71.487-6.579.5440.999
9-8-1.58-9.0245.8630.998

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
3-10 & -12.694 & -31.715 & 6.327 & 0.45 \tabularnewline
4-10 & -12.044 & -29.7 & 5.611 & 0.421 \tabularnewline
5-10 & -17.626 & -31.853 & -3.399 & 0.005 \tabularnewline
6-10 & -13.129 & -25.937 & -0.32 & 0.04 \tabularnewline
7-10 & -10.962 & -23.039 & 1.116 & 0.105 \tabularnewline
8-10 & -7.894 & -19.572 & 3.783 & 0.433 \tabularnewline
9-10 & -9.475 & -21.378 & 2.428 & 0.226 \tabularnewline
4-3 & 0.65 & -20.583 & 21.883 & 1 \tabularnewline
5-3 & -4.932 & -23.413 & 13.55 & 0.992 \tabularnewline
6-3 & -0.434 & -17.847 & 16.979 & 1 \tabularnewline
7-3 & 1.733 & -15.15 & 18.615 & 1 \tabularnewline
8-3 & 4.8 & -11.799 & 21.399 & 0.987 \tabularnewline
9-3 & 3.22 & -13.539 & 19.978 & 0.999 \tabularnewline
5-4 & -5.582 & -22.654 & 11.491 & 0.973 \tabularnewline
6-4 & -1.084 & -16.994 & 14.825 & 1 \tabularnewline
7-4 & 1.083 & -14.245 & 16.41 & 1 \tabularnewline
8-4 & 4.15 & -10.864 & 19.164 & 0.99 \tabularnewline
9-4 & 2.57 & -12.621 & 17.76 & 1 \tabularnewline
6-5 & 4.498 & -7.495 & 16.49 & 0.943 \tabularnewline
7-5 & 6.665 & -4.544 & 17.873 & 0.601 \tabularnewline
8-5 & 9.732 & -1.045 & 20.508 & 0.109 \tabularnewline
9-5 & 8.152 & -2.869 & 19.172 & 0.314 \tabularnewline
7-6 & 2.167 & -7.175 & 11.509 & 0.996 \tabularnewline
8-6 & 5.234 & -3.585 & 14.053 & 0.604 \tabularnewline
9-6 & 3.654 & -5.462 & 12.769 & 0.921 \tabularnewline
8-7 & 3.067 & -4.653 & 10.787 & 0.924 \tabularnewline
9-7 & 1.487 & -6.57 & 9.544 & 0.999 \tabularnewline
9-8 & -1.58 & -9.024 & 5.863 & 0.998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255277&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]-31.715[/C][C]6.327[/C][C]0.45[/C][/ROW]
[ROW][C]4-10[/C][C]-12.044[/C][C]-29.7[/C][C]5.611[/C][C]0.421[/C][/ROW]
[ROW][C]5-10[/C][C]-17.626[/C][C]-31.853[/C][C]-3.399[/C][C]0.005[/C][/ROW]
[ROW][C]6-10[/C][C]-13.129[/C][C]-25.937[/C][C]-0.32[/C][C]0.04[/C][/ROW]
[ROW][C]7-10[/C][C]-10.962[/C][C]-23.039[/C][C]1.116[/C][C]0.105[/C][/ROW]
[ROW][C]8-10[/C][C]-7.894[/C][C]-19.572[/C][C]3.783[/C][C]0.433[/C][/ROW]
[ROW][C]9-10[/C][C]-9.475[/C][C]-21.378[/C][C]2.428[/C][C]0.226[/C][/ROW]
[ROW][C]4-3[/C][C]0.65[/C][C]-20.583[/C][C]21.883[/C][C]1[/C][/ROW]
[ROW][C]5-3[/C][C]-4.932[/C][C]-23.413[/C][C]13.55[/C][C]0.992[/C][/ROW]
[ROW][C]6-3[/C][C]-0.434[/C][C]-17.847[/C][C]16.979[/C][C]1[/C][/ROW]
[ROW][C]7-3[/C][C]1.733[/C][C]-15.15[/C][C]18.615[/C][C]1[/C][/ROW]
[ROW][C]8-3[/C][C]4.8[/C][C]-11.799[/C][C]21.399[/C][C]0.987[/C][/ROW]
[ROW][C]9-3[/C][C]3.22[/C][C]-13.539[/C][C]19.978[/C][C]0.999[/C][/ROW]
[ROW][C]5-4[/C][C]-5.582[/C][C]-22.654[/C][C]11.491[/C][C]0.973[/C][/ROW]
[ROW][C]6-4[/C][C]-1.084[/C][C]-16.994[/C][C]14.825[/C][C]1[/C][/ROW]
[ROW][C]7-4[/C][C]1.083[/C][C]-14.245[/C][C]16.41[/C][C]1[/C][/ROW]
[ROW][C]8-4[/C][C]4.15[/C][C]-10.864[/C][C]19.164[/C][C]0.99[/C][/ROW]
[ROW][C]9-4[/C][C]2.57[/C][C]-12.621[/C][C]17.76[/C][C]1[/C][/ROW]
[ROW][C]6-5[/C][C]4.498[/C][C]-7.495[/C][C]16.49[/C][C]0.943[/C][/ROW]
[ROW][C]7-5[/C][C]6.665[/C][C]-4.544[/C][C]17.873[/C][C]0.601[/C][/ROW]
[ROW][C]8-5[/C][C]9.732[/C][C]-1.045[/C][C]20.508[/C][C]0.109[/C][/ROW]
[ROW][C]9-5[/C][C]8.152[/C][C]-2.869[/C][C]19.172[/C][C]0.314[/C][/ROW]
[ROW][C]7-6[/C][C]2.167[/C][C]-7.175[/C][C]11.509[/C][C]0.996[/C][/ROW]
[ROW][C]8-6[/C][C]5.234[/C][C]-3.585[/C][C]14.053[/C][C]0.604[/C][/ROW]
[ROW][C]9-6[/C][C]3.654[/C][C]-5.462[/C][C]12.769[/C][C]0.921[/C][/ROW]
[ROW][C]8-7[/C][C]3.067[/C][C]-4.653[/C][C]10.787[/C][C]0.924[/C][/ROW]
[ROW][C]9-7[/C][C]1.487[/C][C]-6.57[/C][C]9.544[/C][C]0.999[/C][/ROW]
[ROW][C]9-8[/C][C]-1.58[/C][C]-9.024[/C][C]5.863[/C][C]0.998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255277&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255277&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
3-10-12.694-31.7156.3270.45
4-10-12.044-29.75.6110.421
5-10-17.626-31.853-3.3990.005
6-10-13.129-25.937-0.320.04
7-10-10.962-23.0391.1160.105
8-10-7.894-19.5723.7830.433
9-10-9.475-21.3782.4280.226
4-30.65-20.58321.8831
5-3-4.932-23.41313.550.992
6-3-0.434-17.84716.9791
7-31.733-15.1518.6151
8-34.8-11.79921.3990.987
9-33.22-13.53919.9780.999
5-4-5.582-22.65411.4910.973
6-4-1.084-16.99414.8251
7-41.083-14.24516.411
8-44.15-10.86419.1640.99
9-42.57-12.62117.761
6-54.498-7.49516.490.943
7-56.665-4.54417.8730.601
8-59.732-1.04520.5080.109
9-58.152-2.86919.1720.314
7-62.167-7.17511.5090.996
8-65.234-3.58514.0530.604
9-63.654-5.46212.7690.921
8-73.067-4.65310.7870.924
9-71.487-6.579.5440.999
9-8-1.58-9.0245.8630.998






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group71.1210.353
142 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 7 & 1.121 & 0.353 \tabularnewline
  & 142 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255277&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]142[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255277&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255277&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
142


Figures (Output of Computation)

Input Parameters & R Code

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