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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 computationMon, 15 Nov 2010 16:57:49 +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/2010/Nov/15/t1289840163eianqitp5k2c4js.htm/, Retrieved Sat, 27 Apr 2024 22:01:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=94919, Retrieved Sat, 27 Apr 2024 22:01:32 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [workshop 2 Questi...] [2010-11-15 16:57:49] [a4848c79f7a98c5639a543e143e21e11] [Current]
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Dataseries X:
0	1	0
0	0	1
0	1	1
0	1	0
0	1	1
0	0	1
0	1	0
0	1	0
1	1	1
0	0	0
0	1	1
0	1	0
0	1	0
0	1	0
0	0	0
0	1	0
0	0	1
0	0	0
0	1	1
0	0	1
0	1	0
0	0	0
0	0	1
0	0	1
0	0	1
0	1	1
0	0	0
0	1	1
0	1	1
0	0	1
0	1	1
0	1	1
1	1	1
0	0	0
0	0	0
0	1	1
0	1	1
0	0	1
0	0	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94919&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94919&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94919&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 time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







ANOVA Model
Response ~ Treatment_A * Treatment_B
means0000.154

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 0 & 0 & 0 & 0.154 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94919&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]0[/C][C]0[/C][C]0[/C][C]0.154[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94919&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94919&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
means0000.154







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A10.0790.0791.6390.209
Treatment_B10.0710.0711.4670.234
Treatment_A:Treatment_B10.0550.0551.1360.294
Residuals351.6920.048

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 0.079 & 0.079 & 1.639 & 0.209 \tabularnewline
Treatment_B & 1 & 0.071 & 0.071 & 1.467 & 0.234 \tabularnewline
Treatment_A:Treatment_B & 1 & 0.055 & 0.055 & 1.136 & 0.294 \tabularnewline
Residuals & 35 & 1.692 & 0.048 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94919&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]0.079[/C][C]0.079[/C][C]1.639[/C][C]0.209[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]0.071[/C][C]0.071[/C][C]1.467[/C][C]0.234[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]0.055[/C][C]0.055[/C][C]1.136[/C][C]0.294[/C][/ROW]
[ROW][C]Residuals[/C][C]35[/C][C]1.692[/C][C]0.048[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94919&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94919&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_A10.0790.0791.6390.209
Treatment_B10.0710.0711.4670.234
Treatment_A:Treatment_B10.0550.0551.1360.294
Residuals351.6920.048







Tukey Honest Significant Difference Comparisons
difflwruprp adj
1-00.091-0.0530.2350.209
1-00.087-0.0590.2320.234
1:0-0:00-0.2990.2991
0:1-0:00-0.2920.2921
1:1-0:00.154-0.1240.4320.453
0:1-1:00-0.2720.2721
1:1-1:00.154-0.1030.4110.384
1:1-0:10.154-0.0960.4030.358

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
1-0 & 0.091 & -0.053 & 0.235 & 0.209 \tabularnewline
1-0 & 0.087 & -0.059 & 0.232 & 0.234 \tabularnewline
1:0-0:0 & 0 & -0.299 & 0.299 & 1 \tabularnewline
0:1-0:0 & 0 & -0.292 & 0.292 & 1 \tabularnewline
1:1-0:0 & 0.154 & -0.124 & 0.432 & 0.453 \tabularnewline
0:1-1:0 & 0 & -0.272 & 0.272 & 1 \tabularnewline
1:1-1:0 & 0.154 & -0.103 & 0.411 & 0.384 \tabularnewline
1:1-0:1 & 0.154 & -0.096 & 0.403 & 0.358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94919&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]1-0[/C][C]0.091[/C][C]-0.053[/C][C]0.235[/C][C]0.209[/C][/ROW]
[ROW][C]1-0[/C][C]0.087[/C][C]-0.059[/C][C]0.232[/C][C]0.234[/C][/ROW]
[ROW][C]1:0-0:0[/C][C]0[/C][C]-0.299[/C][C]0.299[/C][C]1[/C][/ROW]
[ROW][C]0:1-0:0[/C][C]0[/C][C]-0.292[/C][C]0.292[/C][C]1[/C][/ROW]
[ROW][C]1:1-0:0[/C][C]0.154[/C][C]-0.124[/C][C]0.432[/C][C]0.453[/C][/ROW]
[ROW][C]0:1-1:0[/C][C]0[/C][C]-0.272[/C][C]0.272[/C][C]1[/C][/ROW]
[ROW][C]1:1-1:0[/C][C]0.154[/C][C]-0.103[/C][C]0.411[/C][C]0.384[/C][/ROW]
[ROW][C]1:1-0:1[/C][C]0.154[/C][C]-0.096[/C][C]0.403[/C][C]0.358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94919&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94919&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
1-00.091-0.0530.2350.209
1-00.087-0.0590.2320.234
1:0-0:00-0.2990.2991
0:1-0:00-0.2920.2921
1:1-0:00.154-0.1240.4320.453
0:1-1:00-0.2720.2721
1:1-1:00.154-0.1030.4110.384
1:1-0:10.154-0.0960.4030.358







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group31.4140.255
35

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 1.414 & 0.255 \tabularnewline
  & 35 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94919&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]1.414[/C][C]0.255[/C][/ROW]
[ROW][C] [/C][C]35[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94919&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94919&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)
Group31.4140.255
35



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