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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 computationThu, 03 Sep 2015 08:30:48 +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/2015/Sep/03/t1441265513qow1nbrd96qnn2z.htm/, Retrieved Thu, 16 May 2024 11:10:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280499, Retrieved Thu, 16 May 2024 11:10:38 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [vraag2] [2015-09-03 07:30:48] [71b0783e9a96780cfc39e1fa76ab729e] [Current]
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Dataseries X:
0.5 "'VC'" 4.2
0.5 "'VC'" 11.5
0.5 "'VC'" 7.3
0.5 "'VC'" 5.8
0.5 "'VC'" 6.4
0.5 "'VC'" 10
0.5 "'VC'" 11.2
0.5 "'VC'" 11.2
0.5 "'VC'" 5.2
0.5 "'VC'" 7
1 "'VC'" 16.5
1 "'VC'" 16.5
1 "'VC'" 15.2
1 "'VC'" 17.3
1 "'VC'" 22.5
1 "'VC'" 17.3
1 "'VC'" 13.6
1 "'VC'" 14.5
1 "'VC'" 18.8
1 "'VC'" 15.5
2 "'VC'" 23.6
2 "'VC'" 18.5
2 "'VC'" 33.9
2 "'VC'" 25.5
2 "'VC'" 26.4
2 "'VC'" 32.5
2 "'VC'" 26.7
2 "'VC'" 21.5
2 "'VC'" 23.3
2 "'VC'" 29.5
0.5 "'OJ'" 15.2
0.5 "'OJ'" 21.5
0.5 "'OJ'" 17.6
0.5 "'OJ'" 9.7
0.5 "'OJ'" 14.5
0.5 "'OJ'" 10
0.5 "'OJ'" 8.2
0.5 "'OJ'" 9.4
0.5 "'OJ'" 16.5
0.5 "'OJ'" 9.7
1 "'OJ'" 19.7
1 "'OJ'" 23.3
1 "'OJ'" 23.6
1 "'OJ'" 26.4
1 "'OJ'" 20
1 "'OJ'" 25.2
1 "'OJ'" 25.8
1 "'OJ'" 21.2
1 "'OJ'" 14.5
1 "'OJ'" 27.3
2 "'OJ'" 25.5
2 "'OJ'" 26.4
2 "'OJ'" 22.4
2 "'OJ'" 24.5
2 "'OJ'" 24.8
2 "'OJ'" 30.9
2 "'OJ'" 26.4
2 "'OJ'" 27.3
2 "'OJ'" 29.4
2 "'OJ'" 23




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time120 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 & 120 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280499&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]120 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=280499&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280499&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 time120 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.50000.50.2500.500.501.50.50.50.50.501.50.51.50.50.51.51.50.51.50.51.1671.511.51.51.51.51.5000000000NANANA0.250.5NA0.5NANANANANANANA1.5NANANA11NANANA0NA0.333NANANANANANANANANANANANANANANANA

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 0.5 & 0 & 0 & 0 & 0.5 & 0.25 & 0 & 0.5 & 0 & 0.5 & 0 & 1.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0 & 1.5 & 0.5 & 1.5 & 0.5 & 0.5 & 1.5 & 1.5 & 0.5 & 1.5 & 0.5 & 1.167 & 1.5 & 1 & 1.5 & 1.5 & 1.5 & 1.5 & 1.5 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & NA & NA & NA & 0.25 & 0.5 & NA & 0.5 & NA & NA & NA & NA & NA & NA & NA & 1.5 & NA & NA & NA & 1 & 1 & NA & NA & NA & 0 & NA & 0.333 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280499&T=1

[TABLE]
[ROW]ANOVA Model[/C][/ROW]
[ROW]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]0.5[/C][C]0[/C][C]0[/C][C]0[/C][C]0.5[/C][C]0.25[/C][C]0[/C][C]0.5[/C][C]0[/C][C]0.5[/C][C]0[/C][C]1.5[/C][C]0.5[/C][C]0.5[/C][C]0.5[/C][C]0.5[/C][C]0[/C][C]1.5[/C][C]0.5[/C][C]1.5[/C][C]0.5[/C][C]0.5[/C][C]1.5[/C][C]1.5[/C][C]0.5[/C][C]1.5[/C][C]0.5[/C][C]1.167[/C][C]1.5[/C][C]1[/C][C]1.5[/C][C]1.5[/C][C]1.5[/C][C]1.5[/C][C]1.5[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.25[/C][C]0.5[/C][C]NA[/C][C]0.5[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]1.5[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]1[/C][C]1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0[/C][C]NA[/C][C]0.333[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280499&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280499&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
means0.50000.50.2500.500.501.50.50.50.50.501.50.51.50.50.51.51.50.51.50.51.1671.511.51.51.51.51.5000000000NANANA0.250.5NA0.5NANANANANANANA1.5NANANA11NANANA0NA0.333NANANANANANANANANANANANANANANANA







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A10001
Treatment_B120.9880.53.0950.047
Treatment_A:Treatment_B11.0540.1320.8160.61
Residuals81.2920.161

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 0 & 0 & 0 & 1 \tabularnewline
Treatment_B & 1 & 20.988 & 0.5 & 3.095 & 0.047 \tabularnewline
Treatment_A:Treatment_B & 1 & 1.054 & 0.132 & 0.816 & 0.61 \tabularnewline
Residuals & 8 & 1.292 & 0.161 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280499&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]0[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]20.988[/C][C]0.5[/C][C]3.095[/C][C]0.047[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]1.054[/C][C]0.132[/C][C]0.816[/C][C]0.61[/C][/ROW]
[ROW][C]Residuals[/C][C]8[/C][C]1.292[/C][C]0.161[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280499&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280499&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_A10001
Treatment_B120.9880.53.0950.047
Treatment_A:Treatment_B11.0540.1320.8160.61
Residuals81.2920.161



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
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')