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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 19:49:09 +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/t1418845887sdigrbjw9bp8lqu.htm/, Retrieved Thu, 16 May 2024 22:10:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270620, Retrieved Thu, 16 May 2024 22:10:37 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [] [2014-12-17 19:49:09] [58179e1d3a5a39b9daf58e365d8a3352] [Current]
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Dataseries X:
0 "'S'" 149
0 "'S'" 148
1 "'S'" 158
1 "'S'" 128
1 "'S'" 224
0 "'S'" 159
1 "'S'" 105
1 "'S'" 159
1 "'S'" 167
1 "'S'" 165
1 "'S'" 159
0 "'S'" 176
0 "'S'" 54
0 "'B'" 91
1 "'S'" 163
0 "'S'" 124
0 "'S'" 121
1 "'S'" 148
0 "'S'" 221
1 "'S'" 149
1 "'S'" 244
1 "'B'" 148
1 "'S'" 150
0 "'S'" 153
0 "'S'" 94
0 "'S'" 156
1 "'S'" 132
1 "'S'" 105
0 "'S'" 151
1 "'B'" 131
0 "'S'" 157
1 "'S'" 162
1 "'S'" 163
1 "'B'" 59
0 "'S'" 187
1 "'B'" 116
1 "'S'" 148
1 "'B'" 155
1 "'S'" 125
1 "'S'" 116
1 "'S'" 138
1 "'S'" 164
0 "'S'" 162
0 "'S'" 99
0 "'S'" 186
1 "'S'" 188
0 "'S'" 177
0 "'S'" 139
0 "'S'" 162
1 "'B'" 108
0 "'S'" 159
1 "'S'" 110
0 "'B'" 96
0 "'B'" 87
1 "'B'" 97
0 "'B'" 127
0 "'B'" 74
1 "'B'" 114
0 "'B'" 95
0 "'B'" 121
1 "'B'" 130
0 "'B'" 52
0 "'B'" 118




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270620&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270620&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270620&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'Herman Ole Andreas Wold' @ wold.wessa.net







ANOVA Model
Response ~ Treatment_A * Treatment_B
means95.66721.88953.571-18.21

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 95.667 & 21.889 & 53.571 & -18.21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270620&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]95.667[/C][C]21.889[/C][C]53.571[/C][C]-18.21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270620&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270620&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
means95.66721.88953.571-18.21







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A11604.9391604.9391.5070.224
Treatment_B125200.29725200.29723.6680
Treatment_A:Treatment_B11064.551064.5510.321
Residuals5962819.8651064.743

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 1604.939 & 1604.939 & 1.507 & 0.224 \tabularnewline
Treatment_B & 1 & 25200.297 & 25200.297 & 23.668 & 0 \tabularnewline
Treatment_A:Treatment_B & 1 & 1064.55 & 1064.55 & 1 & 0.321 \tabularnewline
Residuals & 59 & 62819.865 & 1064.743 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270620&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]1604.939[/C][C]1604.939[/C][C]1.507[/C][C]0.224[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]25200.297[/C][C]25200.297[/C][C]23.668[/C][C]0[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]1064.55[/C][C]1064.55[/C][C]1[/C][C]0.321[/C][/ROW]
[ROW][C]Residuals[/C][C]59[/C][C]62819.865[/C][C]1064.743[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270620&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270620&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_A11604.9391604.9391.5070.224
Treatment_B125200.29725200.29723.6680
Treatment_A:Treatment_B11064.551064.5510.321
Residuals5962819.8651064.743







Tukey Honest Significant Difference Comparisons
difflwruprp adj
1-010.106-6.36526.5770.224
'S'-'B'44.25226.04362.4610
1:'B'-0:'B'21.889-18.77862.5560.49
0:'S'-0:'B'53.57119.20187.9410.001
1:'S'-0:'B'57.2523.53190.9690
0:'S'-1:'B'31.683-2.68766.0530.081
1:'S'-1:'B'35.3611.64269.0810.036
1:'S'-0:'S'3.679-22.09929.4560.982

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
1-0 & 10.106 & -6.365 & 26.577 & 0.224 \tabularnewline
'S'-'B' & 44.252 & 26.043 & 62.461 & 0 \tabularnewline
1:'B'-0:'B' & 21.889 & -18.778 & 62.556 & 0.49 \tabularnewline
0:'S'-0:'B' & 53.571 & 19.201 & 87.941 & 0.001 \tabularnewline
1:'S'-0:'B' & 57.25 & 23.531 & 90.969 & 0 \tabularnewline
0:'S'-1:'B' & 31.683 & -2.687 & 66.053 & 0.081 \tabularnewline
1:'S'-1:'B' & 35.361 & 1.642 & 69.081 & 0.036 \tabularnewline
1:'S'-0:'S' & 3.679 & -22.099 & 29.456 & 0.982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270620&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]10.106[/C][C]-6.365[/C][C]26.577[/C][C]0.224[/C][/ROW]
[ROW][C]'S'-'B'[/C][C]44.252[/C][C]26.043[/C][C]62.461[/C][C]0[/C][/ROW]
[ROW][C]1:'B'-0:'B'[/C][C]21.889[/C][C]-18.778[/C][C]62.556[/C][C]0.49[/C][/ROW]
[ROW][C]0:'S'-0:'B'[/C][C]53.571[/C][C]19.201[/C][C]87.941[/C][C]0.001[/C][/ROW]
[ROW][C]1:'S'-0:'B'[/C][C]57.25[/C][C]23.531[/C][C]90.969[/C][C]0[/C][/ROW]
[ROW][C]0:'S'-1:'B'[/C][C]31.683[/C][C]-2.687[/C][C]66.053[/C][C]0.081[/C][/ROW]
[ROW][C]1:'S'-1:'B'[/C][C]35.361[/C][C]1.642[/C][C]69.081[/C][C]0.036[/C][/ROW]
[ROW][C]1:'S'-0:'S'[/C][C]3.679[/C][C]-22.099[/C][C]29.456[/C][C]0.982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270620&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270620&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
1-010.106-6.36526.5770.224
'S'-'B'44.25226.04362.4610
1:'B'-0:'B'21.889-18.77862.5560.49
0:'S'-0:'B'53.57119.20187.9410.001
1:'S'-0:'B'57.2523.53190.9690
0:'S'-1:'B'31.683-2.68766.0530.081
1:'S'-1:'B'35.3611.64269.0810.036
1:'S'-0:'S'3.679-22.09929.4560.982







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group30.220.882
59

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 0.22 & 0.882 \tabularnewline
  & 59 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270620&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]0.22[/C][C]0.882[/C][/ROW]
[ROW][C] [/C][C]59[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270620&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270620&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)
Group30.220.882
59



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