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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_Two Factor ANOVA.wasp

Title produced by software

Two-Way ANOVA

Date of computation

Tue, 01 Jun 2010 23:03:14 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Jun/02/t1275433696pnylxu7tc9ys68u.htm/, Retrieved Tue, 13 May 2025 06:49:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=76856, Retrieved Tue, 13 May 2025 06:49:40 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

263

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Two-Way ANOVA] [two-way anova wit...] [2010-05-26 17:02:24] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R PD  [Two-Way ANOVA] [ANOVA with good l...] [2010-05-28 23:09:47] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R       [Variability] [ANOVA with better...] [2010-05-29 09:47:12] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R         [Variability] [ANOVA with better...] [2010-05-29 09:54:40] [98fd0e87c3eb04e0cc2efde01dbafab6]
- RMP           [Two-Way ANOVA] [] [2010-06-01 23:03:14] [c8d667a84ceca7ca96013b6c84433b37] [Current]

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Dataseries X:

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Histogram

Boxplots

'GOOD'	'HIGH'	25
'GOOD'	'HIGH'	0
'GOOD'	'HIGH'	-16
'GOOD'	'HIGH'	5
'GOOD'	'HIGH'	11
'GOOD'	'HIGH'	-6
'GOOD'	'HIGH'	42
'GOOD'	'HIGH'	-2
'GOOD'	'HIGH'	-13
'GOOD'	'HIGH'	14
'GOOD'	'HIGH'	4
'GOOD'	'HIGH'	-22
'GOOD'	'HIGH'	19
'GOOD'	'HIGH'	6
'GOOD'	'HIGH'	-6
'GOOD'	'LOW'	-25
'GOOD'	'LOW'	-23
'GOOD'	'LOW'	-28
'GOOD'	'LOW'	-22
'GOOD'	'LOW'	-22
'GOOD'	'LOW'	-10
'GOOD'	'LOW'	-20
'GOOD'	'LOW'	-24
'GOOD'	'LOW'	-24
'GOOD'	'LOW'	-22
'GOOD'	'LOW'	-23
'GOOD'	'LOW'	-19
'GOOD'	'LOW'	-2
'GOOD'	'LOW'	12
'GOOD'	'LOW'	-8
'GOOD'	'LOW'	-17
'GOOD'	'LOW'	-30
'SCIENTIFIC'	'HIGH'	-19
'SCIENTIFIC'	'HIGH'	-24
'SCIENTIFIC'	'HIGH'	-4
'SCIENTIFIC'	'HIGH'	-24
'SCIENTIFIC'	'HIGH'	0
'SCIENTIFIC'	'HIGH'	-4
'SCIENTIFIC'	'HIGH'	5
'SCIENTIFIC'	'HIGH'	-1
'SCIENTIFIC'	'HIGH'	-9
'SCIENTIFIC'	'HIGH'	-5
'SCIENTIFIC'	'HIGH'	-6
'SCIENTIFIC'	'HIGH'	4
'SCIENTIFIC'	'HIGH'	-13
'SCIENTIFIC'	'HIGH'	-1
'SCIENTIFIC'	'HIGH'	-3
'SCIENTIFIC'	'HIGH'	-11
'SCIENTIFIC'	'HIGH'	-6
'SCIENTIFIC'	'HIGH'	-4
'SCIENTIFIC'	'LOW'	6
'SCIENTIFIC'	'LOW'	-5
'SCIENTIFIC'	'LOW'	14
'SCIENTIFIC'	'LOW'	-11
'SCIENTIFIC'	'LOW'	14
'SCIENTIFIC'	'LOW'	-5
'SCIENTIFIC'	'LOW'	-22
'SCIENTIFIC'	'LOW'	7
'SCIENTIFIC'	'LOW'	14
'SCIENTIFIC'	'LOW'	15
'SCIENTIFIC'	'LOW'	-6
'SCIENTIFIC'	'LOW'	9
'SCIENTIFIC'	'LOW'	-5
'NONE'	'HIGH'	-26
'NONE'	'HIGH'	-1
'NONE'	'HIGH'	22
'NONE'	'HIGH'	3
'NONE'	'HIGH'	-26
'NONE'	'HIGH'	4
'NONE'	'HIGH'	-21
'NONE'	'HIGH'	-19
'NONE'	'HIGH'	-12
'NONE'	'HIGH'	9
'NONE'	'HIGH'	-9
'NONE'	'HIGH'	-27
'NONE'	'HIGH'	-10
'NONE'	'HIGH'	-37
'NONE'	'HIGH'	0
'NONE'	'HIGH'	-10
'NONE'	'LOW'	-12
'NONE'	'LOW'	-4
'NONE'	'LOW'	13
'NONE'	'LOW'	-27
'NONE'	'LOW'	-7
'NONE'	'LOW'	-20
'NONE'	'LOW'	-4
'NONE'	'LOW'	-10
'NONE'	'LOW'	-3
'NONE'	'LOW'	-11
'NONE'	'LOW'	2
'NONE'	'LOW'	-9
'NONE'	'LOW'	20
'NONE'	'LOW'	9
'NONE'	'LOW'	-8
'NONE'	'LOW'	8
'NONE'	'LOW'	-6
'NONE'	'LOW'	6

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'George Udny Yule' @ 72.249.76.132
R Engine error message	Error in `contrasts<-`(`tmp`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels Calls: lm ... model.matrix -> model.matrix.default -> contrasts<- Execution halted

\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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Engine error message & Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : 
  contrasts can be applied only to factors with 2 or more levels
Calls: lm ... model.matrix -> model.matrix.default -> contrasts<-
Execution halted
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76856&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Engine error message[/C][C]Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : 
  contrasts can be applied only to factors with 2 or more levels
Calls: lm ... model.matrix -> model.matrix.default -> contrasts<-
Execution halted
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76856&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76856&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 Output	view raw output of R engine
Computing time	1 seconds
R Server	'George Udny Yule' @ 72.249.76.132
R Engine error message	Error in `contrasts<-`(`tmp`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels Calls: lm ... model.matrix -> model.matrix.default -> contrasts<- Execution halted

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code