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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 15:47:18 +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/t1418831254gl30expqlxx1piq.htm/, Retrieved Thu, 31 Oct 2024 22:52:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270430, Retrieved Thu, 31 Oct 2024 22:52:46 +0000
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Original text written by user:
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Estimated Impact95
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-       [Two-Way ANOVA] [] [2014-12-17 15:47:18] [61a57b1a717662ce9f6e819e563a5fa9] [Current]
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Dataseries X:
26 "'S'" "'Female'"
37 "'S'" "'Female'"
67 "'S'" "'Male'"
43 "'S'" "'Male'"
52 "'S'" "'Male'"
52 "'S'" "'Female'"
43 "'S'" "'Male'"
84 "'S'" "'Male'"
67 "'S'" "'Male'"
49 "'S'" "'Male'"
70 "'S'" "'Male'"
58 "'S'" "'Female'"
68 "'S'" "'Female'"
62 "'B'" "'Female'"
43 "'S'" "'Male'"
56 "'S'" "'Female'"
74 "'S'" "'Female'"
63 "'S'" "'Male'"
58 "'S'" "'Female'"
63 "'S'" "'Male'"
53 "'S'" "'Male'"
57 "'B'" "'Male'"
64 "'S'" "'Male'"
53 "'S'" "'Female'"
29 "'S'" "'Female'"
54 "'S'" "'Female'"
58 "'S'" "'Male'"
51 "'S'" "'Male'"
54 "'S'" "'Female'"
56 "'B'" "'Male'"
47 "'S'" "'Female'"
50 "'S'" "'Male'"
35 "'S'" "'Male'"
30 "'B'" "'Male'"
68 "'S'" "'Female'"
56 "'B'" "'Male'"
43 "'S'" "'Male'"
67 "'B'" "'Male'"
62 "'S'" "'Male'"
57 "'S'" "'Male'"
54 "'S'" "'Male'"
61 "'S'" "'Male'"
56 "'S'" "'Female'"
41 "'S'" "'Female'"
53 "'S'" "'Female'"
46 "'S'" "'Male'"
51 "'S'" "'Female'"
37 "'S'" "'Female'"
42 "'S'" "'Female'"
38 "'B'" "'Male'"
66 "'S'" "'Female'"
53 "'S'" "'Male'"
49 "'B'" "'Female'"
49 "'B'" "'Female'"
59 "'B'" "'Male'"
40 "'B'" "'Female'"
63 "'B'" "'Female'"
34 "'B'" "'Male'"
32 "'B'" "'Female'"
67 "'B'" "'Female'"
61 "'B'" "'Male'"
60 "'B'" "'Female'"
63 "'B'" "'Female'"
52 "'S'" "'Male'"
16 "'S'" "'Male'"
46 "'S'" "'Male'"
56 "'S'" "'Male'"
52 "'B'" "'Female'"
55 "'B'" "'Male'"
50 "'S'" "'Male'"
59 "'S'" "'Female'"
60 "'S'" "'Male'"
52 "'S'" "'Female'"
44 "'S'" "'Female'"
67 "'S'" "'Male'"
52 "'S'" "'Male'"
55 "'S'" "'Male'"
37 "'S'" "'Male'"
54 "'S'" "'Male'"
72 "'B'" "'Male'"
51 "'S'" "'Male'"
48 "'S'" "'Male'"
60 "'S'" "'Female'"
50 "'S'" "'Male'"
63 "'S'" "'Male'"
33 "'S'" "'Male'"
67 "'S'" "'Male'"
46 "'S'" "'Male'"
54 "'S'" "'Male'"
59 "'S'" "'Female'"
61 "'S'" "'Male'"
33 "'B'" "'Male'"
47 "'S'" "'Male'"
69 "'S'" "'Male'"
52 "'S'" "'Male'"
55 "'S'" "'Female'"
41 "'S'" "'Female'"
73 "'S'" "'Male'"
52 "'S'" "'Female'"
50 "'S'" "'Female'"
51 "'S'" "'Male'"
60 "'S'" "'Female'"
56 "'S'" "'Male'"
56 "'S'" "'Male'"
29 "'S'" "'Female'"
66 "'B'" "'Male'"
66 "'B'" "'Male'"
73 "'S'" "'Male'"
55 "'S'" "'Female'"
64 "'B'" "'Female'"
40 "'B'" "'Female'"
46 "'B'" "'Female'"
58 "'B'" "'Male'"
43 "'S'" "'Female'"
61 "'S'" "'Male'"
51 "'B'" "'Female'"
50 "'B'" "'Male'"
52 "'B'" "'Female'"
54 "'B'" "'Male'"
66 "'B'" "'Female'"
61 "'B'" "'Female'"
80 "'B'" "'Male'"
51 "'B'" "'Female'"
56 "'B'" "'Male'"
56 "'S'" "'Male'"
56 "'S'" "'Male'"
53 "'B'" "'Male'"
47 "'S'" "'Male'"
25 "'S'" "'Female'"
47 "'B'" "'Male'"
46 "'S'" "'Female'"
50 "'B'" "'Female'"
39 "'B'" "'Female'"
51 "'S'" "'Male'"
58 "'B'" "'Female'"
35 "'B'" "'Male'"
58 "'B'" "'Female'"
60 "'B'" "'Female'"
62 "'B'" "'Female'"
63 "'B'" "'Female'"
53 "'B'" "'Male'"
46 "'B'" "'Male'"
67 "'B'" "'Male'"
59 "'B'" "'Male'"
64 "'B'" "'Female'"
38 "'B'" "'Female'"
50 "'B'" "'Male'"
48 "'S'" "'Female'"
48 "'B'" "'Female'"
47 "'B'" "'Female'"
66 "'B'" "'Female'"
47 "'S'" "'Male'"
63 "'B'" "'Male'"
58 "'S'" "'Female'"
44 "'B'" "'Female'"
51 "'S'" "'Male'"
43 "'B'" "'Female'"
55 "'S'" "'Male'"
38 "'B'" "'Male'"
45 "'B'" "'Female'"
50 "'B'" "'Male'"
54 "'B'" "'Male'"
57 "'S'" "'Male'"
60 "'S'" "'Female'"
55 "'B'" "'Female'"
56 "'S'" "'Female'"
49 "'S'" "'Male'"
37 "'B'" "'Male'"
59 "'S'" "'Male'"
46 "'B'" "'Male'"
51 "'B'" "'Female'"
58 "'S'" "'Female'"
64 "'B'" "'Female'"
53 "'S'" "'Male'"
48 "'S'" "'Male'"
51 "'S'" "'Female'"
47 "'B'" "'Female'"
59 "'S'" "'Female'"
62 "'B'" "'Male'"
62 "'S'" "'Male'"
51 "'S'" "'Female'"
64 "'S'" "'Female'"
52 "'S'" "'Female'"
67 "'B'" "'Male'"
50 "'S'" "'Male'"
54 "'S'" "'Male'"
58 "'S'" "'Male'"
56 "'B'" "'Female'"
63 "'S'" "'Male'"
31 "'S'" "'Male'"
65 "'B'" "'Male'"
71 "'S'" "'Female'"
50 "'B'" "'Female'"
57 "'B'" "'Male'"
47 "'B'" "'Female'"
47 "'B'" "'Male'"
57 "'B'" "'Male'"
43 "'S'" "'Female'"
41 "'S'" "'Male'"
63 "'S'" "'Female'"
63 "'S'" "'Male'"
56 "'S'" "'Male'"
51 "'S'" "'Female'"
50 "'B'" "'Male'"
22 "'B'" "'Female'"
41 "'S'" "'Male'"
59 "'B'" "'Female'"
56 "'B'" "'Male'"
66 "'S'" "'Female'"
53 "'B'" "'Female'"
42 "'B'" "'Male'"
52 "'B'" "'Male'"
54 "'B'" "'Female'"
44 "'B'" "'Male'"
62 "'B'" "'Male'"
53 "'B'" "'Female'"
50 "'B'" "'Male'"
36 "'B'" "'Female'"
76 "'B'" "'Female'"
66 "'B'" "'Male'"
62 "'B'" "'Male'"
59 "'B'" "'Female'"
47 "'B'" "'Male'"
55 "'B'" "'Female'"
58 "'B'" "'Female'"
60 "'B'" "'Male'"
44 "'S'" "'Female'"
57 "'B'" "'Female'"
45 "'B'" "'Male'"




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270430&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270430&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270430&T=0

As an alternative you can also use a 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'Sir Maurice George Kendall' @ kendall.wessa.net







ANOVA Model
Response ~ Treatment_A * Treatment_B
means53.039-1.020.6271.153

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 53.039 & -1.02 & 0.627 & 1.153 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270430&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]53.039[/C][C]-1.02[/C][C]0.627[/C][C]1.153[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270430&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270430&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ANOVA Model
Response ~ Treatment_A * Treatment_B
means53.039-1.020.6271.153







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A14.5014.5010.040.842
Treatment_B188.8988.890.790.375
Treatment_A:Treatment_B118.53118.5310.1650.685
Residuals22525330.236112.579

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 4.501 & 4.501 & 0.04 & 0.842 \tabularnewline
Treatment_B & 1 & 88.89 & 88.89 & 0.79 & 0.375 \tabularnewline
Treatment_A:Treatment_B & 1 & 18.531 & 18.531 & 0.165 & 0.685 \tabularnewline
Residuals & 225 & 25330.236 & 112.579 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270430&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]4.501[/C][C]4.501[/C][C]0.04[/C][C]0.842[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]88.89[/C][C]88.89[/C][C]0.79[/C][C]0.375[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]18.531[/C][C]18.531[/C][C]0.165[/C][C]0.685[/C][/ROW]
[ROW][C]Residuals[/C][C]225[/C][C]25330.236[/C][C]112.579[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270430&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270430&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A14.5014.5010.040.842
Treatment_B188.8988.890.790.375
Treatment_A:Treatment_B118.53118.5310.1650.685
Residuals22525330.236112.579







Tukey Honest Significant Difference Comparisons
difflwruprp adj
'S'-'B'-0.282-3.0622.4980.842
'Male'-'Female'1.247-1.534.0250.377
'S':'Female'-'B':'Female'-1.02-6.4324.3920.962
'B':'Male'-'B':'Female'0.627-4.8116.0660.991
'S':'Male'-'B':'Female'0.761-4.2245.7450.979
'B':'Male'-'S':'Female'1.647-3.7657.060.86
'S':'Male'-'S':'Female'1.781-3.1756.7370.789
'S':'Male'-'B':'Male'0.133-4.8515.1181

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
'S'-'B' & -0.282 & -3.062 & 2.498 & 0.842 \tabularnewline
'Male'-'Female' & 1.247 & -1.53 & 4.025 & 0.377 \tabularnewline
'S':'Female'-'B':'Female' & -1.02 & -6.432 & 4.392 & 0.962 \tabularnewline
'B':'Male'-'B':'Female' & 0.627 & -4.811 & 6.066 & 0.991 \tabularnewline
'S':'Male'-'B':'Female' & 0.761 & -4.224 & 5.745 & 0.979 \tabularnewline
'B':'Male'-'S':'Female' & 1.647 & -3.765 & 7.06 & 0.86 \tabularnewline
'S':'Male'-'S':'Female' & 1.781 & -3.175 & 6.737 & 0.789 \tabularnewline
'S':'Male'-'B':'Male' & 0.133 & -4.851 & 5.118 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270430&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]-0.282[/C][C]-3.062[/C][C]2.498[/C][C]0.842[/C][/ROW]
[ROW][C]'Male'-'Female'[/C][C]1.247[/C][C]-1.53[/C][C]4.025[/C][C]0.377[/C][/ROW]
[ROW][C]'S':'Female'-'B':'Female'[/C][C]-1.02[/C][C]-6.432[/C][C]4.392[/C][C]0.962[/C][/ROW]
[ROW][C]'B':'Male'-'B':'Female'[/C][C]0.627[/C][C]-4.811[/C][C]6.066[/C][C]0.991[/C][/ROW]
[ROW][C]'S':'Male'-'B':'Female'[/C][C]0.761[/C][C]-4.224[/C][C]5.745[/C][C]0.979[/C][/ROW]
[ROW][C]'B':'Male'-'S':'Female'[/C][C]1.647[/C][C]-3.765[/C][C]7.06[/C][C]0.86[/C][/ROW]
[ROW][C]'S':'Male'-'S':'Female'[/C][C]1.781[/C][C]-3.175[/C][C]6.737[/C][C]0.789[/C][/ROW]
[ROW][C]'S':'Male'-'B':'Male'[/C][C]0.133[/C][C]-4.851[/C][C]5.118[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270430&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270430&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Tukey Honest Significant Difference Comparisons
difflwruprp adj
'S'-'B'-0.282-3.0622.4980.842
'Male'-'Female'1.247-1.534.0250.377
'S':'Female'-'B':'Female'-1.02-6.4324.3920.962
'B':'Male'-'B':'Female'0.627-4.8116.0660.991
'S':'Male'-'B':'Female'0.761-4.2245.7450.979
'B':'Male'-'S':'Female'1.647-3.7657.060.86
'S':'Male'-'S':'Female'1.781-3.1756.7370.789
'S':'Male'-'B':'Male'0.133-4.8515.1181







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group30.1430.934
225

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 0.143 & 0.934 \tabularnewline
  & 225 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270430&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]0.143[/C][C]0.934[/C][/ROW]
[ROW][C] [/C][C]225[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270430&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270430&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group30.1430.934
225



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')