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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_Two Factor ANOVA.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationWed, 17 Dec 2014 15:56:26 +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/Dec/17/t1418831803zyhqra8i6v6puvp.htm/, Retrieved Thu, 16 May 2024 14:20:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270450, Retrieved Thu, 16 May 2024 14:20:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61

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] [] [2014-12-17 15:56:26] [61a57b1a717662ce9f6e819e563a5fa9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
50 "'S'" "'Female'"
54 "'S'" "'Female'"
71 "'S'" "'Male'"
54 "'S'" "'Male'"
65 "'S'" "'Male'"
73 "'S'" "'Female'"
52 "'S'" "'Male'"
84 "'S'" "'Male'"
42 "'S'" "'Male'"
66 "'S'" "'Male'"
65 "'S'" "'Male'"
73 "'S'" "'Female'"
75 "'S'" "'Female'"
72 "'B'" "'Female'"
66 "'S'" "'Male'"
70 "'S'" "'Female'"
81 "'S'" "'Female'"
69 "'S'" "'Male'"
71 "'S'" "'Female'"
68 "'S'" "'Male'"
70 "'S'" "'Male'"
68 "'B'" "'Male'"
67 "'S'" "'Male'"
76 "'S'" "'Female'"
70 "'S'" "'Female'"
60 "'S'" "'Female'"
72 "'S'" "'Male'"
71 "'S'" "'Male'"
70 "'S'" "'Female'"
64 "'B'" "'Male'"
76 "'S'" "'Female'"
68 "'S'" "'Male'"
76 "'S'" "'Male'"
65 "'B'" "'Male'"
67 "'S'" "'Female'"
75 "'B'" "'Male'"
60 "'S'" "'Male'"
73 "'B'" "'Male'"
63 "'S'" "'Male'"
70 "'S'" "'Male'"
66 "'S'" "'Male'"
64 "'S'" "'Male'"
70 "'S'" "'Female'"
75 "'S'" "'Female'"
60 "'S'" "'Female'"
66 "'S'" "'Male'"
59 "'S'" "'Female'"
78 "'S'" "'Female'"
67 "'S'" "'Female'"
59 "'B'" "'Male'"
66 "'S'" "'Female'"
71 "'S'" "'Male'"
66 "'B'" "'Female'"
72 "'B'" "'Female'"
71 "'B'" "'Male'"
59 "'B'" "'Female'"
78 "'B'" "'Female'"
65 "'B'" "'Male'"
65 "'B'" "'Female'"
71 "'B'" "'Female'"
72 "'B'" "'Male'"
66 "'B'" "'Female'"
69 "'B'" "'Female'"
51 "'S'" "'Male'"
56 "'S'" "'Male'"
67 "'S'" "'Male'"
69 "'S'" "'Male'"
57 "'B'" "'Female'"
56 "'B'" "'Male'"
55 "'S'" "'Male'"
63 "'S'" "'Female'"
67 "'S'" "'Male'"
65 "'S'" "'Female'"
47 "'S'" "'Female'"
76 "'S'" "'Male'"
64 "'S'" "'Male'"
68 "'S'" "'Male'"
64 "'S'" "'Male'"
65 "'S'" "'Male'"
71 "'B'" "'Male'"
63 "'S'" "'Male'"
60 "'S'" "'Male'"
68 "'S'" "'Female'"
72 "'S'" "'Male'"
70 "'S'" "'Male'"
61 "'S'" "'Male'"
61 "'S'" "'Male'"
62 "'S'" "'Male'"
71 "'S'" "'Male'"
71 "'S'" "'Female'"
51 "'S'" "'Male'"
56 "'B'" "'Male'"
70 "'S'" "'Male'"
73 "'S'" "'Male'"
76 "'S'" "'Male'"
68 "'S'" "'Female'"
48 "'S'" "'Female'"
52 "'S'" "'Male'"
60 "'S'" "'Female'"
59 "'S'" "'Female'"
57 "'S'" "'Male'"
79 "'S'" "'Female'"
60 "'S'" "'Male'"
60 "'S'" "'Male'"
59 "'S'" "'Female'"
62 "'B'" "'Male'"
59 "'B'" "'Male'"
61 "'S'" "'Male'"
71 "'S'" "'Female'"
57 "'B'" "'Female'"
66 "'B'" "'Female'"
63 "'B'" "'Female'"
69 "'B'" "'Male'"
58 "'S'" "'Female'"
59 "'S'" "'Male'"
48 "'B'" "'Female'"
66 "'B'" "'Male'"
73 "'B'" "'Female'"
67 "'B'" "'Male'"
61 "'B'" "'Female'"
68 "'B'" "'Female'"
75 "'B'" "'Male'"
62 "'B'" "'Female'"
69 "'B'" "'Male'"
58 "'S'" "'Male'"
60 "'S'" "'Male'"
74 "'B'" "'Male'"
55 "'S'" "'Male'"
62 "'S'" "'Female'"
63 "'B'" "'Male'"
69 "'S'" "'Female'"
58 "'B'" "'Female'"
58 "'B'" "'Female'"
68 "'S'" "'Male'"
72 "'B'" "'Female'"
62 "'B'" "'Male'"
62 "'B'" "'Female'"
65 "'B'" "'Female'"
69 "'B'" "'Female'"
66 "'B'" "'Female'"
72 "'B'" "'Male'"
62 "'B'" "'Male'"
75 "'B'" "'Male'"
58 "'B'" "'Male'"
66 "'B'" "'Female'"
55 "'B'" "'Female'"
47 "'B'" "'Male'"
72 "'S'" "'Female'"
62 "'B'" "'Female'"
64 "'B'" "'Female'"
64 "'B'" "'Female'"
19 "'S'" "'Male'"
50 "'B'" "'Male'"
68 "'S'" "'Female'"
70 "'B'" "'Female'"
79 "'S'" "'Male'"
69 "'B'" "'Female'"
71 "'S'" "'Male'"
48 "'B'" "'Male'"
73 "'B'" "'Female'"
74 "'B'" "'Male'"
66 "'B'" "'Male'"
71 "'S'" "'Male'"
74 "'S'" "'Female'"
78 "'B'" "'Female'"
75 "'S'" "'Female'"
53 "'S'" "'Male'"
60 "'B'" "'Male'"
70 "'S'" "'Male'"
69 "'B'" "'Male'"
65 "'B'" "'Female'"
78 "'S'" "'Female'"
78 "'B'" "'Female'"
59 "'S'" "'Male'"
72 "'S'" "'Male'"
70 "'S'" "'Female'"
63 "'B'" "'Female'"
63 "'S'" "'Female'"
71 "'B'" "'Male'"
74 "'S'" "'Male'"
67 "'S'" "'Female'"
66 "'S'" "'Female'"
62 "'S'" "'Female'"
80 "'B'" "'Male'"
73 "'S'" "'Male'"
67 "'S'" "'Male'"
61 "'S'" "'Male'"
73 "'B'" "'Female'"
74 "'S'" "'Male'"
32 "'S'" "'Male'"
69 "'B'" "'Male'"
69 "'S'" "'Female'"
84 "'B'" "'Female'"
64 "'B'" "'Male'"
58 "'B'" "'Female'"
59 "'B'" "'Male'"
78 "'B'" "'Male'"
57 "'S'" "'Female'"
60 "'S'" "'Male'"
68 "'S'" "'Female'"
68 "'S'" "'Male'"
73 "'S'" "'Male'"
69 "'S'" "'Female'"
67 "'B'" "'Male'"
60 "'B'" "'Female'"
65 "'S'" "'Male'"
66 "'B'" "'Female'"
74 "'B'" "'Male'"
81 "'S'" "'Female'"
72 "'B'" "'Female'"
55 "'B'" "'Male'"
49 "'B'" "'Male'"
74 "'B'" "'Female'"
53 "'B'" "'Male'"
64 "'B'" "'Male'"
65 "'B'" "'Female'"
57 "'B'" "'Male'"
51 "'B'" "'Female'"
80 "'B'" "'Female'"
67 "'B'" "'Male'"
70 "'B'" "'Male'"
74 "'B'" "'Female'"
75 "'B'" "'Male'"
70 "'B'" "'Female'"
69 "'B'" "'Female'"
65 "'B'" "'Male'"
55 "'S'" "'Female'"
71 "'B'" "'Female'"
65 "'B'" "'Male'"

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270450&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]3 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=270450&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270450&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






ANOVA Model
Response ~ Treatment_A * Treatment_B
means66.6080.354-1.431-1.41

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 66.608 & 0.354 & -1.431 & -1.41 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270450&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]66.608[/C][C]0.354[/C][C]-1.431[/C][C]-1.41[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270450&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270450&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
Response ~ Treatment_A * Treatment_B
means66.6080.354-1.431-1.41






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A120.95920.9590.2860.593
Treatment_B1272.494272.4943.7220.055
Treatment_A:Treatment_B127.70427.7040.3780.539
Residuals22516471.41273.206 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 20.959 & 20.959 & 0.286 & 0.593 \tabularnewline
Treatment_B & 1 & 272.494 & 272.494 & 3.722 & 0.055 \tabularnewline
Treatment_A:Treatment_B & 1 & 27.704 & 27.704 & 0.378 & 0.539 \tabularnewline
Residuals & 225 & 16471.412 & 73.206 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270450&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]20.959[/C][C]20.959[/C][C]0.286[/C][C]0.593[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]272.494[/C][C]272.494[/C][C]3.722[/C][C]0.055[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]27.704[/C][C]27.704[/C][C]0.378[/C][C]0.539[/C][/ROW]
[ROW][C]Residuals[/C][C]225[/C][C]16471.412[/C][C]73.206[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270450&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270450&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
Treatment_A120.95920.9590.2860.593
Treatment_B1272.494272.4943.7220.055
Treatment_A:Treatment_B127.70427.7040.3780.539
Residuals22516471.41273.206






Tukey Honest Significant Difference Comparisons
difflwruprp adj
'S'-'B'-0.609-2.851.6330.593
'Male'-'Female'-2.184-4.4230.0560.056
'S':'Female'-'B':'Female'0.354-4.0114.7180.997
'B':'Male'-'B':'Female'-1.431-5.8172.9540.833
'S':'Male'-'B':'Female'-2.488-6.5071.5320.38
'B':'Male'-'S':'Female'-1.785-6.1492.5790.715
'S':'Male'-'S':'Female'-2.842-6.8381.1550.257
'S':'Male'-'B':'Male'-1.056-5.0762.9630.904

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
'S'-'B' & -0.609 & -2.85 & 1.633 & 0.593 \tabularnewline
'Male'-'Female' & -2.184 & -4.423 & 0.056 & 0.056 \tabularnewline
'S':'Female'-'B':'Female' & 0.354 & -4.011 & 4.718 & 0.997 \tabularnewline
'B':'Male'-'B':'Female' & -1.431 & -5.817 & 2.954 & 0.833 \tabularnewline
'S':'Male'-'B':'Female' & -2.488 & -6.507 & 1.532 & 0.38 \tabularnewline
'B':'Male'-'S':'Female' & -1.785 & -6.149 & 2.579 & 0.715 \tabularnewline
'S':'Male'-'S':'Female' & -2.842 & -6.838 & 1.155 & 0.257 \tabularnewline
'S':'Male'-'B':'Male' & -1.056 & -5.076 & 2.963 & 0.904 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270450&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]'S'-'B'[/C][C]-0.609[/C][C]-2.85[/C][C]1.633[/C][C]0.593[/C][/ROW]
[ROW][C]'Male'-'Female'[/C][C]-2.184[/C][C]-4.423[/C][C]0.056[/C][C]0.056[/C][/ROW]
[ROW][C]'S':'Female'-'B':'Female'[/C][C]0.354[/C][C]-4.011[/C][C]4.718[/C][C]0.997[/C][/ROW]
[ROW][C]'B':'Male'-'B':'Female'[/C][C]-1.431[/C][C]-5.817[/C][C]2.954[/C][C]0.833[/C][/ROW]
[ROW][C]'S':'Male'-'B':'Female'[/C][C]-2.488[/C][C]-6.507[/C][C]1.532[/C][C]0.38[/C][/ROW]
[ROW][C]'B':'Male'-'S':'Female'[/C][C]-1.785[/C][C]-6.149[/C][C]2.579[/C][C]0.715[/C][/ROW]
[ROW][C]'S':'Male'-'S':'Female'[/C][C]-2.842[/C][C]-6.838[/C][C]1.155[/C][C]0.257[/C][/ROW]
[ROW][C]'S':'Male'-'B':'Male'[/C][C]-1.056[/C][C]-5.076[/C][C]2.963[/C][C]0.904[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270450&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270450&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
'S'-'B'-0.609-2.851.6330.593
'Male'-'Female'-2.184-4.4230.0560.056
'S':'Female'-'B':'Female'0.354-4.0114.7180.997
'B':'Male'-'B':'Female'-1.431-5.8172.9540.833
'S':'Male'-'B':'Female'-2.488-6.5071.5320.38
'B':'Male'-'S':'Female'-1.785-6.1492.5790.715
'S':'Male'-'S':'Female'-2.842-6.8381.1550.257
'S':'Male'-'B':'Male'-1.056-5.0762.9630.904






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group30.3910.759
225 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 0.391 & 0.759 \tabularnewline
  & 225 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270450&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]0.391[/C][C]0.759[/C][/ROW]
[ROW][C] [/C][C]225[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270450&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270450&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)
Group30.3910.759
225


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])
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')