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Author's title

Author*Unverified author*
R Software Modulerwasp_Two Factor ANOVA.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationFri, 27 May 2011 16:07:54 +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/2011/May/27/t13065122186bs3gnmthbqdscq.htm/, Retrieved Mon, 13 May 2024 12:04:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=122520, Retrieved Mon, 13 May 2024 12:04:56 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact237
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [] [] [1970-01-01 00:00:00] [780e8a3d261badf8e728dfcd19f74b3f]
- RMPD    [Two-Way ANOVA] [] [2011-05-27 16:07:54] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
"l"	-6	"f"
"l"	-7.33	"m"
"l"	-11	"m"
"l"	3	"f"
"l"	5	"f"
"l"	-13	"m"
"l"	1.33	"m"
"l"	-6.5	"m"
"l"	-5.67	"m"
"l"	-11.3	"f"
"l"	9	"m"
"l"	-2.67	"f"
"l"	15.33	"f"
"l"	-8.33	"m"
"l"	28.33	"f"
"l"	-12.3	"m"
"l"	2	"m"
"h"	7.5	"f"
"h"	-5	"f"
"h"	2.33	"m"
"h"	12	"f"
"h"	2.33	"f"
"h"	9.67	"f"
"h"	-4	"m"
"h"	-2	"m"
"h"	49.33	"m"
"h"	-4.67	"f"
"h"	30	"f"
"h"	37.67	"f"
"h"	5.67	"m"




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ www.yougetit.org

\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 & 'Herman Ole Andreas Wold' @ www.yougetit.org \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=122520&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]'Herman Ole Andreas Wold' @ www.yougetit.org[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=122520&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=122520&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 time1 seconds
R Server'Herman Ole Andreas Wold' @ www.yougetit.org







ANOVA Model
Response ~ Treatment_A * Treatment_B
means11.188-6.66-0.922-8.786

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 11.188 & -6.66 & -0.922 & -8.786 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=122520&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]11.188[/C][C]-6.66[/C][C]-0.922[/C][C]-8.786[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=122520&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=122520&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
means11.188-6.66-0.922-8.786







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A11063.6341063.6345.3350.029
Treatment_B1254.685254.6851.2770.269
Treatment_A:Treatment_B1135.928135.9280.6820.417
Residuals265184.02199.385

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 1063.634 & 1063.634 & 5.335 & 0.029 \tabularnewline
Treatment_B & 1 & 254.685 & 254.685 & 1.277 & 0.269 \tabularnewline
Treatment_A:Treatment_B & 1 & 135.928 & 135.928 & 0.682 & 0.417 \tabularnewline
Residuals & 26 & 5184.02 & 199.385 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=122520&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]1063.634[/C][C]1063.634[/C][C]5.335[/C][C]0.029[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]254.685[/C][C]254.685[/C][C]1.277[/C][C]0.269[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]135.928[/C][C]135.928[/C][C]0.682[/C][C]0.417[/C][/ROW]
[ROW][C]Residuals[/C][C]26[/C][C]5184.02[/C][C]199.385[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=122520&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=122520&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_A11063.6341063.6345.3350.029
Treatment_B1254.685254.6851.2770.269
Treatment_A:Treatment_B1135.928135.9280.6820.417
Residuals265184.02199.385







Tukey Honest Significant Difference Comparisons
difflwruprp adj
l-h-12.016-22.71-1.3220.029
m-f-5.707-16.3064.8910.278
l:f-h:f-6.66-26.70913.3880.799
h:m-h:f-0.922-23.00521.1620.999
l:m-h:f-16.368-34.7422.0070.094
h:m-l:f5.739-16.94328.4210.898
l:m-l:f-9.707-28.7979.3830.514
l:m-h:m-15.446-36.6635.7710.215

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
l-h & -12.016 & -22.71 & -1.322 & 0.029 \tabularnewline
m-f & -5.707 & -16.306 & 4.891 & 0.278 \tabularnewline
l:f-h:f & -6.66 & -26.709 & 13.388 & 0.799 \tabularnewline
h:m-h:f & -0.922 & -23.005 & 21.162 & 0.999 \tabularnewline
l:m-h:f & -16.368 & -34.742 & 2.007 & 0.094 \tabularnewline
h:m-l:f & 5.739 & -16.943 & 28.421 & 0.898 \tabularnewline
l:m-l:f & -9.707 & -28.797 & 9.383 & 0.514 \tabularnewline
l:m-h:m & -15.446 & -36.663 & 5.771 & 0.215 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=122520&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]l-h[/C][C]-12.016[/C][C]-22.71[/C][C]-1.322[/C][C]0.029[/C][/ROW]
[ROW][C]m-f[/C][C]-5.707[/C][C]-16.306[/C][C]4.891[/C][C]0.278[/C][/ROW]
[ROW][C]l:f-h:f[/C][C]-6.66[/C][C]-26.709[/C][C]13.388[/C][C]0.799[/C][/ROW]
[ROW][C]h:m-h:f[/C][C]-0.922[/C][C]-23.005[/C][C]21.162[/C][C]0.999[/C][/ROW]
[ROW][C]l:m-h:f[/C][C]-16.368[/C][C]-34.742[/C][C]2.007[/C][C]0.094[/C][/ROW]
[ROW][C]h:m-l:f[/C][C]5.739[/C][C]-16.943[/C][C]28.421[/C][C]0.898[/C][/ROW]
[ROW][C]l:m-l:f[/C][C]-9.707[/C][C]-28.797[/C][C]9.383[/C][C]0.514[/C][/ROW]
[ROW][C]l:m-h:m[/C][C]-15.446[/C][C]-36.663[/C][C]5.771[/C][C]0.215[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=122520&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=122520&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
l-h-12.016-22.71-1.3220.029
m-f-5.707-16.3064.8910.278
l:f-h:f-6.66-26.70913.3880.799
h:m-h:f-0.922-23.00521.1620.999
l:m-h:f-16.368-34.7422.0070.094
h:m-l:f5.739-16.94328.4210.898
l:m-l:f-9.707-28.7979.3830.514
l:m-h:m-15.446-36.6635.7710.215







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group30.7010.56
26

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 0.701 & 0.56 \tabularnewline
  & 26 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=122520&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]0.701[/C][C]0.56[/C][/ROW]
[ROW][C] [/C][C]26[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=122520&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=122520&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)
Group30.7010.56
26



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