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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 computationSat, 04 Dec 2010 12:25:19 +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/Dec/04/t1291465397i8of9lwzmlgc9k1.htm/, Retrieved Sun, 05 May 2024 04:10:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105117, Retrieved Sun, 05 May 2024 04:10:01 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Two-Way ANOVA] [Golfballs] [2010-10-25 12:43:22] [b98453cac15ba1066b407e146608df68]
-   PD  [Two-Way ANOVA] [WS5: Vraag 8] [2010-10-29 09:31:50] [1fd136673b2a4fecb5c545b9b4a05d64]
- R  D    [Two-Way ANOVA] [WS5- Task 8] [2010-11-02 12:31:34] [19f9551d4d95750ef21e9f3cf8fe2131]
-    D      [Two-Way ANOVA] [p_Stress_Anova1] [2010-12-04 12:16:48] [19f9551d4d95750ef21e9f3cf8fe2131]
-               [Two-Way ANOVA] [p_Stress_Anova1] [2010-12-04 12:25:19] [fca744d17b21beb005bf086e7071b2bb] [Current]
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Dataseries X:
10	'Y'	'F'
6	'N'	'F'
13	'Y'	'F'
12	'N'	'M'
8	'Y'	'F'
6	'N'	'F'
10	'N'	'F'
10	'N'	'F'
9	'N'	'F'
9	'N'	'F'
7	'Y'	'M'
5	'N'	'F'
14	'Y'	'M'
8	'N'	'F'
6	'N'	'F'
10	'N'	'M'
10	'N'	'M'
7	'N'	'F'
10	'N'	'M'
8	'Y'	'F'
6	'Y'	'M'
13	'N'	'F'
10	'N'	'F'
12	'Y'	'F'
7	'N'	'M'
15	'Y'	'F'
8	'Y'	'M'
10	'N'	'F'
9	'N'	'F'
13	'N'	'M'
8	'N'	'F'
11	'N'	'M'
7	'N'	'F'
9	'Y'	'F'
10	'N'	'M'
8	'N'	'M'
15	'N'	'M'
9	'N'	'M'
7	'N'	'F'
11	'N'	'M'
9	'N'	'M'
8	'Y'	'F'
8	'N'	'M'
12	'Y'	'M'
12	'N'	'F'
13	'N'	'F'
9	'N'	'F'
9	'Y'	'F'
7	'N'	'F'
11	'N'	'M'
8	'N'	'F'
10	'N'	'M'
13	'N'	'M'
12	'N'	'F'
12	'Y'	'M'
9	'Y'	'F'
8	'N'	'F'
9	'N'	'F'
12	'Y'	'M'
12	'Y'	'F'
16	'N'	'M'
11	'N'	'M'
13	'N'	'F'
10	'N'	'F'
9	'N'	'F'
14	'N'	'M'
13	'Y'	'F'
12	'N'	'M'
9	'N'	'F'
9	'Y'	'M'
10	'N'	'M'
8	'Y'	'F'
9	'Y'	'F'
9	'Y'	'M'
11	'N'	'M'
12	'N'	'F'
7	'N'	'F'
11	'Y'	'F'
9	'N'	'M'
11	'N'	'M'
9	'N'	'M'
8	'N'	'M'
9	'N'	'F'
8	'N'	'M'
9	'N'	'F'
10	'N'	'F'
9	'Y'	'M'
10	'Y'	'F'
11	'N'	'F'
17	'N'	'F'
7	'N'	'F'
11	'Y'	'F'
9	'N'	'F'
10	'N'	'F'
11	'N'	'F'
8	'N'	'F'
12	'N'	'F'
10	'N'	'F'
7	'N'	'M'
9	'N'	'M'
7	'N'	'F'
12	'N'	'M'
8	'N'	'F'
13	'N'	'M'
9	'Y'	'F'
15	'N'	'M'
8	'N'	'F'
9	'Y'	'M'
14	'N'	'M'
14	'Y'	'F'
9	'N'	'F'
13	'N'	'F'
11	'N'	'F'
10	'N'	'M'
6	'N'	'F'
8	'N'	'M'
10	'N'	'F'
10	'Y'	'F'
10	'Y'	'F'
10	'Y'	'F'
12	'N'	'F'
10	'N'	'F'
9	'N'	'F'
9	'Y'	'F'
11	'Y'	'F'
7	'N'	'M'
7	'N'	'F'
5	'N'	'F'
9	'N'	'F'
11	'N'	'M'
15	'N'	'M'
9	'Y'	'F'
9	'N'	'M'
9	'Y'	'M'
8	'Y'	'F'
13	'N'	'M'
10	'N'	'M'
13	'N'	'F'
9	'Y'	'F'
11	'N'	'M'
9	'N'	'F'
8	'N'	'M'
10	'Y'	'F'
9	'Y'	'M'
8	'Y'	'F'
8	'N'	'F'
13	'N'	'M'
12	'Y'	'M'
8	'N'	'F'
11	'Y'	'F'
8	'Y'	'M'
12	'N'	'F'
15	'N'	'F'
11	'N'	'F'
11	'Y'	'F'
10	'Y'	'F'
5	'N'	'F'
11	'N'	'F'
12	'N'	'F'




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=105117&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=105117&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105117&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







ANOVA Model
Response ~ Treatment_A * Treatment_B
means9.3880.7371.301-1.759

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 9.388 & 0.737 & 1.301 & -1.759 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105117&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]9.388[/C][C]0.737[/C][C]1.301[/C][C]-1.759[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105117&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105117&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
means9.3880.7371.301-1.759







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A10.1530.1530.030.864
Treatment_B124.78524.7854.7760.03
Treatment_A:Treatment_B122.91222.9124.4150.037
Residuals155804.3885.19

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 0.153 & 0.153 & 0.03 & 0.864 \tabularnewline
Treatment_B & 1 & 24.785 & 24.785 & 4.776 & 0.03 \tabularnewline
Treatment_A:Treatment_B & 1 & 22.912 & 22.912 & 4.415 & 0.037 \tabularnewline
Residuals & 155 & 804.388 & 5.19 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105117&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]0.153[/C][C]0.153[/C][C]0.03[/C][C]0.864[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]24.785[/C][C]24.785[/C][C]4.776[/C][C]0.03[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]22.912[/C][C]22.912[/C][C]4.415[/C][C]0.037[/C][/ROW]
[ROW][C]Residuals[/C][C]155[/C][C]804.388[/C][C]5.19[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105117&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105117&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_A10.1530.1530.030.864
Treatment_B124.78524.7854.7760.03
Treatment_A:Treatment_B122.91222.9124.4150.037
Residuals155804.3885.19







Tukey Honest Significant Difference Comparisons
difflwruprp adj
Y-N0.068-0.7140.850.864
M-F0.8120.0761.5480.031
Y:F-N:F0.737-0.5342.0080.437
N:M-N:F1.3010.1612.4410.018
Y:M-N:F0.279-1.4111.9690.974
N:M-Y:F0.564-0.8041.9320.708
Y:M-Y:F-0.458-2.311.3930.918
Y:M-N:M-1.022-2.7860.7420.437

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
Y-N & 0.068 & -0.714 & 0.85 & 0.864 \tabularnewline
M-F & 0.812 & 0.076 & 1.548 & 0.031 \tabularnewline
Y:F-N:F & 0.737 & -0.534 & 2.008 & 0.437 \tabularnewline
N:M-N:F & 1.301 & 0.161 & 2.441 & 0.018 \tabularnewline
Y:M-N:F & 0.279 & -1.411 & 1.969 & 0.974 \tabularnewline
N:M-Y:F & 0.564 & -0.804 & 1.932 & 0.708 \tabularnewline
Y:M-Y:F & -0.458 & -2.31 & 1.393 & 0.918 \tabularnewline
Y:M-N:M & -1.022 & -2.786 & 0.742 & 0.437 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105117&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]Y-N[/C][C]0.068[/C][C]-0.714[/C][C]0.85[/C][C]0.864[/C][/ROW]
[ROW][C]M-F[/C][C]0.812[/C][C]0.076[/C][C]1.548[/C][C]0.031[/C][/ROW]
[ROW][C]Y:F-N:F[/C][C]0.737[/C][C]-0.534[/C][C]2.008[/C][C]0.437[/C][/ROW]
[ROW][C]N:M-N:F[/C][C]1.301[/C][C]0.161[/C][C]2.441[/C][C]0.018[/C][/ROW]
[ROW][C]Y:M-N:F[/C][C]0.279[/C][C]-1.411[/C][C]1.969[/C][C]0.974[/C][/ROW]
[ROW][C]N:M-Y:F[/C][C]0.564[/C][C]-0.804[/C][C]1.932[/C][C]0.708[/C][/ROW]
[ROW][C]Y:M-Y:F[/C][C]-0.458[/C][C]-2.31[/C][C]1.393[/C][C]0.918[/C][/ROW]
[ROW][C]Y:M-N:M[/C][C]-1.022[/C][C]-2.786[/C][C]0.742[/C][C]0.437[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105117&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105117&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
Y-N0.068-0.7140.850.864
M-F0.8120.0761.5480.031
Y:F-N:F0.737-0.5342.0080.437
N:M-N:F1.3010.1612.4410.018
Y:M-N:F0.279-1.4111.9690.974
N:M-Y:F0.564-0.8041.9320.708
Y:M-Y:F-0.458-2.311.3930.918
Y:M-N:M-1.022-2.7860.7420.437







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group30.930.428
155

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 0.93 & 0.428 \tabularnewline
  & 155 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105117&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]0.93[/C][C]0.428[/C][/ROW]
[ROW][C] [/C][C]155[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105117&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105117&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)
Group30.930.428
155



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')