Free Statistics

of Irreproducible Research!

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 computationTue, 05 Dec 2017 15:35:47 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/05/t1512485715pcdolj8no41ur61.htm/, Retrieved Tue, 14 May 2024 00:00:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308555, Retrieved Tue, 14 May 2024 00:00:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [2-way ANOVA inten...] [2017-12-05 14:35:47] [431300f4593cfe73715ac2c22e82996b] [Current]
Feedback Forum

Post a new message
Dataseries X:
10	'desctat'	'hypetest'
8	'desctat'	'hypetest'
8	'desctat'	'hypetest'
9	'desctat'	'hypetest_unknown'
5	'desctat'	'hypetest_unknown'
10	'desctat'	'hypetest'
8	'desctat'	'hypetest'
9	'desctat'	'hypetest'
8	'desctat'	'hypetest'
7	'desctat'	'hypetest_unknown'
10	'desctat'	'hypetest_unknown'
10	'desctat'	'hypetest'
9	'desctat'	'hypetest_unknown'
4	'desctat'	'hypetest_unknown'
4	'descstat_unknown'	'hypetest_unknown'
8	'desctat'	'hypetest'
9	'descstat_unknown'	'hypetest_unknown'
10	'descstat_unknown'	'hypetest'
8	'desctat'	'hypetest'
5	'desctat'	'hypetest_unknown'
10	'descstat_unknown'	'hypetest_unknown'
8	'desctat'	'hypetest_unknown'
7	'descstat_unknown'	'hypetest_unknown'
8	'desctat'	'hypetest_unknown'
8	'descstat_unknown'	'hypetest_unknown'
9	'desctat'	'hypetest'
8	'desctat'	'hypetest'
6	'desctat'	'hypetest_unknown'
8	'desctat'	'hypetest'
8	'desctat'	'hypetest_unknown'
5	'desctat'	'hypetest'
9	'desctat'	'hypetest_unknown'
8	'desctat'	'hypetest'
8	'desctat'	'hypetest'
8	'descstat_unknown'	'hypetest_unknown'
6	'desctat'	'hypetest'
6	'desctat'	'hypetest'
9	'descstat_unknown'	'hypetest_unknown'
8	'descstat_unknown'	'hypetest_unknown'
9	'desctat'	'hypetest'
10	'descstat_unknown'	'hypetest_unknown'
8	'descstat_unknown'	'hypetest'
8	'descstat_unknown'	'hypetest_unknown'
7	'descstat_unknown'	'hypetest'
7	'descstat_unknown'	'hypetest_unknown'
10	'desctat'	'hypetest'
8	'descstat_unknown'	'hypetest_unknown'
7	'desctat'	'hypetest'
10	'descstat_unknown'	'hypetest_unknown'
7	'desctat'	'hypetest_unknown'
7	'desctat'	'hypetest_unknown'
9	'desctat'	'hypetest_unknown'
9	'desctat'	'hypetest_unknown'
8	'descstat_unknown'	'hypetest_unknown'
6	'desctat'	'hypetest_unknown'
8	'descstat_unknown'	'hypetest_unknown'
9	'descstat_unknown'	'hypetest_unknown'
2	'desctat'	'hypetest'
6	'desctat'	'hypetest'
8	'desctat'	'hypetest_unknown'
8	'desctat'	'hypetest'
7	'desctat'	'hypetest'
8	'desctat'	'hypetest'
6	'descstat_unknown'	'hypetest_unknown'
10	'desctat'	'hypetest_unknown'
10	'desctat'	'hypetest_unknown'
10	'descstat_unknown'	'hypetest_unknown'
8	'desctat'	'hypetest_unknown'
8	'desctat'	'hypetest'
7	'desctat'	'hypetest'
10	'desctat'	'hypetest_unknown'
5	'descstat_unknown'	'hypetest'
3	'descstat_unknown'	'hypetest_unknown'
2	'descstat_unknown'	'hypetest_unknown'
3	'desctat'	'hypetest'
4	'desctat'	'hypetest'
2	'desctat'	'hypetest_unknown'
6	'desctat'	'hypetest'
8	'desctat'	'hypetest_unknown'
8	'desctat'	'hypetest'
5	'desctat'	'hypetest'
10	'desctat'	'hypetest_unknown'
9	'desctat'	'hypetest'
8	'desctat'	'hypetest'
9	'descstat_unknown'	'hypetest_unknown'
8	'descstat_unknown'	'hypetest_unknown'
5	'desctat'	'hypetest_unknown'
7	'desctat'	'hypetest_unknown'
9	'desctat'	'hypetest'
8	'descstat_unknown'	'hypetest_unknown'
4	'descstat_unknown'	'hypetest_unknown'
7	'descstat_unknown'	'hypetest_unknown'
8	'desctat'	'hypetest_unknown'
7	'descstat_unknown'	'hypetest_unknown'
7	'desctat'	'hypetest'
9	'descstat_unknown'	'hypetest_unknown'
6	'desctat'	'hypetest_unknown'
7	'desctat'	'hypetest'
4	'desctat'	'hypetest'
6	'desctat'	'hypetest'
10	'desctat'	'hypetest_unknown'
9	'desctat'	'hypetest_unknown'
10	'desctat'	'hypetest'
8	'desctat'	'hypetest_unknown'
4	'desctat'	'hypetest'
8	'desctat'	'hypetest_unknown'
5	'desctat'	'hypetest_unknown'
8	'desctat'	'hypetest_unknown'
9	'desctat'	'hypetest'
8	'desctat'	'hypetest'
4	'desctat'	'hypetest'
8	'desctat'	'hypetest'
10	'desctat'	'hypetest_unknown'
6	'desctat'	'hypetest'
7	'descstat_unknown'	'hypetest_unknown'
10	'descstat_unknown'	'hypetest_unknown'
9	'desctat'	'hypetest'
8	'descstat_unknown'	'hypetest_unknown'
3	'desctat'	'hypetest'
8	'descstat_unknown'	'hypetest_unknown'
7	'descstat_unknown'	'hypetest_unknown'
7	'desctat'	'hypetest_unknown'
8	'descstat_unknown'	'hypetest_unknown'
8	'desctat'	'hypetest'
7	'descstat_unknown'	'hypetest_unknown'
7	'desctat'	'hypetest'
9	'desctat'	'hypetest_unknown'
9	'desctat'	'hypetest'
9	'desctat'	'hypetest'
4	'desctat'	'hypetest'
6	'descstat_unknown'	'hypetest_unknown'
6	'descstat_unknown'	'hypetest_unknown'
6	'desctat'	'hypetest'
8	'desctat'	'hypetest'
3	'desctat'	'hypetest_unknown'
8	'desctat'	'hypetest'
8	'desctat'	'hypetest'
6	'desctat'	'hypetest'
10	'desctat'	'hypetest_unknown'
2	'desctat'	'hypetest'
9	'desctat'	'hypetest'
6	'desctat'	'hypetest'
6	'descstat_unknown'	'hypetest'
5	'desctat'	'hypetest'
4	'desctat'	'hypetest_unknown'
7	'descstat_unknown'	'hypetest_unknown'
5	'desctat'	'hypetest'
8	'desctat'	'hypetest'
6	'desctat'	'hypetest'
9	'desctat'	'hypetest'
6	'descstat_unknown'	'hypetest_unknown'
4	'desctat'	'hypetest'
7	'desctat'	'hypetest'
2	'desctat'	'hypetest'
8	'descstat_unknown'	'hypetest_unknown'
9	'desctat'	'hypetest_unknown'
6	'descstat_unknown'	'hypetest_unknown'
5	'desctat'	'hypetest'
7	'desctat'	'hypetest'
8	'desctat'	'hypetest'
4	'desctat'	'hypetest_unknown'
9	'desctat'	'hypetest_unknown'
9	'desctat'	'hypetest'
9	'desctat'	'hypetest'
7	'desctat'	'hypetest'
5	'desctat'	'hypetest_unknown'
7	'desctat'	'hypetest'
9	'desctat'	'hypetest_unknown'
8	'descstat_unknown'	'hypetest_unknown'
6	'desctat'	'hypetest'
9	'desctat'	'hypetest'
8	'desctat'	'hypetest_unknown'
7	'desctat'	'hypetest'
7	'desctat'	'hypetest'
7	'desctat'	'hypetest_unknown'
8	'desctat'	'hypetest_unknown'
10	'desctat'	'hypetest_unknown'
6	'desctat'	'hypetest'
6	'desctat'	'hypetest'




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308555&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]4 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308555&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308555&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center







ANOVA Model
Response ~ Treatment_A * Treatment_B
means7.2-0.1760.2630.272

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 7.2 & -0.176 & 0.263 & 0.272 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308555&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]7.2[/C][C]-0.176[/C][C]0.263[/C][C]0.272[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308555&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308555&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
means7.2-0.1760.2630.272







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A11.4961.4960.3880.534
Treatment_B18.9818.9812.3270.129
Treatment_A:Treatment_B10.290.290.0750.784
Residuals175675.2673.859

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 1.496 & 1.496 & 0.388 & 0.534 \tabularnewline
Treatment_B & 1 & 8.981 & 8.981 & 2.327 & 0.129 \tabularnewline
Treatment_A:Treatment_B & 1 & 0.29 & 0.29 & 0.075 & 0.784 \tabularnewline
Residuals & 175 & 675.267 & 3.859 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308555&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]1.496[/C][C]1.496[/C][C]0.388[/C][C]0.534[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]8.981[/C][C]8.981[/C][C]2.327[/C][C]0.129[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]0.29[/C][C]0.29[/C][C]0.075[/C][C]0.784[/C][/ROW]
[ROW][C]Residuals[/C][C]175[/C][C]675.267[/C][C]3.859[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308555&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308555&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_A11.4961.4960.3880.534
Treatment_B18.9818.9812.3270.129
Treatment_A:Treatment_B10.290.290.0750.784
Residuals175675.2673.859







Tukey Honest Significant Difference Comparisons
difflwruprp adj
desctat-descstat_unknown-0.209-0.8720.4540.534
hypetest_unknown-hypetest0.4-0.180.980.175
desctat:hypetest-descstat_unknown:hypetest-0.176-2.5222.170.997
descstat_unknown:hypetest_unknown-descstat_unknown:hypetest0.263-2.152.6770.992
desctat:hypetest_unknown-descstat_unknown:hypetest0.36-2.032.750.98
descstat_unknown:hypetest_unknown-desctat:hypetest0.439-0.5331.4120.646
desctat:hypetest_unknown-desctat:hypetest0.536-0.3761.4480.425
desctat:hypetest_unknown-descstat_unknown:hypetest_unknown0.097-0.9771.170.996

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
desctat-descstat_unknown & -0.209 & -0.872 & 0.454 & 0.534 \tabularnewline
hypetest_unknown-hypetest & 0.4 & -0.18 & 0.98 & 0.175 \tabularnewline
desctat:hypetest-descstat_unknown:hypetest & -0.176 & -2.522 & 2.17 & 0.997 \tabularnewline
descstat_unknown:hypetest_unknown-descstat_unknown:hypetest & 0.263 & -2.15 & 2.677 & 0.992 \tabularnewline
desctat:hypetest_unknown-descstat_unknown:hypetest & 0.36 & -2.03 & 2.75 & 0.98 \tabularnewline
descstat_unknown:hypetest_unknown-desctat:hypetest & 0.439 & -0.533 & 1.412 & 0.646 \tabularnewline
desctat:hypetest_unknown-desctat:hypetest & 0.536 & -0.376 & 1.448 & 0.425 \tabularnewline
desctat:hypetest_unknown-descstat_unknown:hypetest_unknown & 0.097 & -0.977 & 1.17 & 0.996 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308555&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]desctat-descstat_unknown[/C][C]-0.209[/C][C]-0.872[/C][C]0.454[/C][C]0.534[/C][/ROW]
[ROW][C]hypetest_unknown-hypetest[/C][C]0.4[/C][C]-0.18[/C][C]0.98[/C][C]0.175[/C][/ROW]
[ROW][C]desctat:hypetest-descstat_unknown:hypetest[/C][C]-0.176[/C][C]-2.522[/C][C]2.17[/C][C]0.997[/C][/ROW]
[ROW][C]descstat_unknown:hypetest_unknown-descstat_unknown:hypetest[/C][C]0.263[/C][C]-2.15[/C][C]2.677[/C][C]0.992[/C][/ROW]
[ROW][C]desctat:hypetest_unknown-descstat_unknown:hypetest[/C][C]0.36[/C][C]-2.03[/C][C]2.75[/C][C]0.98[/C][/ROW]
[ROW][C]descstat_unknown:hypetest_unknown-desctat:hypetest[/C][C]0.439[/C][C]-0.533[/C][C]1.412[/C][C]0.646[/C][/ROW]
[ROW][C]desctat:hypetest_unknown-desctat:hypetest[/C][C]0.536[/C][C]-0.376[/C][C]1.448[/C][C]0.425[/C][/ROW]
[ROW][C]desctat:hypetest_unknown-descstat_unknown:hypetest_unknown[/C][C]0.097[/C][C]-0.977[/C][C]1.17[/C][C]0.996[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308555&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308555&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
desctat-descstat_unknown-0.209-0.8720.4540.534
hypetest_unknown-hypetest0.4-0.180.980.175
desctat:hypetest-descstat_unknown:hypetest-0.176-2.5222.170.997
descstat_unknown:hypetest_unknown-descstat_unknown:hypetest0.263-2.152.6770.992
desctat:hypetest_unknown-descstat_unknown:hypetest0.36-2.032.750.98
descstat_unknown:hypetest_unknown-desctat:hypetest0.439-0.5331.4120.646
desctat:hypetest_unknown-desctat:hypetest0.536-0.3761.4480.425
desctat:hypetest_unknown-descstat_unknown:hypetest_unknown0.097-0.9771.170.996







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group30.4070.748
175

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 0.407 & 0.748 \tabularnewline
  & 175 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308555&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]0.407[/C][C]0.748[/C][/ROW]
[ROW][C] [/C][C]175[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308555&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308555&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)
Group30.4070.748
175



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 1 ; par5 = 1 ; par6 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
R code (references can be found in the software module):
par4 <- 'FALSE'
par3 <- '3'
par2 <- '2'
par1 <- '1'
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')