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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, 01 Nov 2010 16:03:48 +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/01/t1288627442gbc1mcbh7sqg3x5.htm/, Retrieved Mon, 29 Apr 2024 16:35:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=90956, Retrieved Mon, 29 Apr 2024 16:35:32 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171
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]
F   PD    [Two-Way ANOVA] [Experiment 3: com...] [2010-11-01 16:03:48] [93ab421e12cd1017d2b38fdbcbdb62e0] [Current]
- R P       [Two-Way ANOVA] [] [2010-11-02 20:50:53] [1908ef7bb1a3d37a854f5aaad1a1c348]
- R P       [Two-Way ANOVA] [] [2011-11-08 19:34:59] [334a414766cb4c999f384eea803945a8]
Feedback Forum
2010-11-06 13:42:31 [7d66e2e510b144c68ca0882fd178e17c] [reply
Er is iets misgelopen met het invoeren van de data. Bij de means staan er enkele andere getallen en bij kolom diff ook. De p-values komen ook niet overeen met mijn resultaat. Waarschijnlijk is er iets verkeerd gelopen bij het overzetten van de data of het omvormen van 0 en 1 naar mannelijk en vrouwelijk.

Dit is de juiste berekening: http://www.freestatistics.org/blog/index.php?v=date/2010/Nov/05/t1288960107dnfvtk8eywn81x6.htm/

Je manier van analyseren klopt wel. Dus pas dit toe op deze berekening en je analyse is correct. Bij het geslacht moet je oppassen. Globaal kan er geen verschil zijn tussen mannen en vrouwen maar als ze gecombineerd worden met treatments treden er wel verschillen op.

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Dataseries X:
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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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90956&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90956&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90956&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







ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.30.178-0.2620.171-0.119-0.209

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 0.3 & 0.178 & -0.262 & 0.171 & -0.119 & -0.209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90956&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]0.3[/C][C]0.178[/C][C]-0.262[/C][C]0.171[/C][C]-0.119[/C][C]-0.209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90956&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90956&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
means0.30.178-0.2620.171-0.119-0.209







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A24.8522.42612.6010
Treatment_B20.1050.1050.5460.462
Treatment_A:Treatment_B20.2010.1010.5230.594
Residuals11121.3710.193

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 2 &  &  &  &  \tabularnewline
Treatment_A & 2 & 4.852 & 2.426 & 12.601 & 0 \tabularnewline
Treatment_B & 2 & 0.105 & 0.105 & 0.546 & 0.462 \tabularnewline
Treatment_A:Treatment_B & 2 & 0.201 & 0.101 & 0.523 & 0.594 \tabularnewline
Residuals & 111 & 21.371 & 0.193 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90956&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]2[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]2[/C][C]4.852[/C][C]2.426[/C][C]12.601[/C][C]0[/C][/ROW]
[ROW][C]Treatment_B[/C][C]2[/C][C]0.105[/C][C]0.105[/C][C]0.546[/C][C]0.462[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]2[/C][C]0.201[/C][C]0.101[/C][C]0.523[/C][C]0.594[/C][/ROW]
[ROW][C]Residuals[/C][C]111[/C][C]21.371[/C][C]0.193[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90956&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90956&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)
2
Treatment_A24.8522.42612.6010
Treatment_B20.1050.1050.5460.462
Treatment_A:Treatment_B20.2010.1010.5230.594
Residuals11121.3710.193







Tukey Honest Significant Difference Comparisons
difflwruprp adj
F-E0.122-0.1160.3590.447
H-E-0.353-0.591-0.1160.002
H-F-0.475-0.708-0.2420
V-M0.061-0.1030.2240.463
F:M-E:M0.178-0.2110.5670.768
H:M-E:M-0.262-0.640.1170.347
E:V-E:M0.171-0.2490.590.846
F:V-E:M0.229-0.190.6490.61
H:V-E:M-0.3-0.7430.1430.371
H:M-F:M-0.44-0.804-0.0760.009
E:V-F:M-0.008-0.4150.3991
F:V-F:M0.051-0.3560.4580.999
H:V-F:M-0.478-0.91-0.0470.021
E:V-H:M0.4320.0350.8290.024
F:V-H:M0.4910.0940.8880.006
H:V-H:M-0.038-0.460.3831
F:V-E:V0.059-0.3780.4950.999
H:V-E:V-0.471-0.93-0.0110.041
H:V-F:V-0.529-0.989-0.070.014

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
F-E & 0.122 & -0.116 & 0.359 & 0.447 \tabularnewline
H-E & -0.353 & -0.591 & -0.116 & 0.002 \tabularnewline
H-F & -0.475 & -0.708 & -0.242 & 0 \tabularnewline
V-M & 0.061 & -0.103 & 0.224 & 0.463 \tabularnewline
F:M-E:M & 0.178 & -0.211 & 0.567 & 0.768 \tabularnewline
H:M-E:M & -0.262 & -0.64 & 0.117 & 0.347 \tabularnewline
E:V-E:M & 0.171 & -0.249 & 0.59 & 0.846 \tabularnewline
F:V-E:M & 0.229 & -0.19 & 0.649 & 0.61 \tabularnewline
H:V-E:M & -0.3 & -0.743 & 0.143 & 0.371 \tabularnewline
H:M-F:M & -0.44 & -0.804 & -0.076 & 0.009 \tabularnewline
E:V-F:M & -0.008 & -0.415 & 0.399 & 1 \tabularnewline
F:V-F:M & 0.051 & -0.356 & 0.458 & 0.999 \tabularnewline
H:V-F:M & -0.478 & -0.91 & -0.047 & 0.021 \tabularnewline
E:V-H:M & 0.432 & 0.035 & 0.829 & 0.024 \tabularnewline
F:V-H:M & 0.491 & 0.094 & 0.888 & 0.006 \tabularnewline
H:V-H:M & -0.038 & -0.46 & 0.383 & 1 \tabularnewline
F:V-E:V & 0.059 & -0.378 & 0.495 & 0.999 \tabularnewline
H:V-E:V & -0.471 & -0.93 & -0.011 & 0.041 \tabularnewline
H:V-F:V & -0.529 & -0.989 & -0.07 & 0.014 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90956&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]F-E[/C][C]0.122[/C][C]-0.116[/C][C]0.359[/C][C]0.447[/C][/ROW]
[ROW][C]H-E[/C][C]-0.353[/C][C]-0.591[/C][C]-0.116[/C][C]0.002[/C][/ROW]
[ROW][C]H-F[/C][C]-0.475[/C][C]-0.708[/C][C]-0.242[/C][C]0[/C][/ROW]
[ROW][C]V-M[/C][C]0.061[/C][C]-0.103[/C][C]0.224[/C][C]0.463[/C][/ROW]
[ROW][C]F:M-E:M[/C][C]0.178[/C][C]-0.211[/C][C]0.567[/C][C]0.768[/C][/ROW]
[ROW][C]H:M-E:M[/C][C]-0.262[/C][C]-0.64[/C][C]0.117[/C][C]0.347[/C][/ROW]
[ROW][C]E:V-E:M[/C][C]0.171[/C][C]-0.249[/C][C]0.59[/C][C]0.846[/C][/ROW]
[ROW][C]F:V-E:M[/C][C]0.229[/C][C]-0.19[/C][C]0.649[/C][C]0.61[/C][/ROW]
[ROW][C]H:V-E:M[/C][C]-0.3[/C][C]-0.743[/C][C]0.143[/C][C]0.371[/C][/ROW]
[ROW][C]H:M-F:M[/C][C]-0.44[/C][C]-0.804[/C][C]-0.076[/C][C]0.009[/C][/ROW]
[ROW][C]E:V-F:M[/C][C]-0.008[/C][C]-0.415[/C][C]0.399[/C][C]1[/C][/ROW]
[ROW][C]F:V-F:M[/C][C]0.051[/C][C]-0.356[/C][C]0.458[/C][C]0.999[/C][/ROW]
[ROW][C]H:V-F:M[/C][C]-0.478[/C][C]-0.91[/C][C]-0.047[/C][C]0.021[/C][/ROW]
[ROW][C]E:V-H:M[/C][C]0.432[/C][C]0.035[/C][C]0.829[/C][C]0.024[/C][/ROW]
[ROW][C]F:V-H:M[/C][C]0.491[/C][C]0.094[/C][C]0.888[/C][C]0.006[/C][/ROW]
[ROW][C]H:V-H:M[/C][C]-0.038[/C][C]-0.46[/C][C]0.383[/C][C]1[/C][/ROW]
[ROW][C]F:V-E:V[/C][C]0.059[/C][C]-0.378[/C][C]0.495[/C][C]0.999[/C][/ROW]
[ROW][C]H:V-E:V[/C][C]-0.471[/C][C]-0.93[/C][C]-0.011[/C][C]0.041[/C][/ROW]
[ROW][C]H:V-F:V[/C][C]-0.529[/C][C]-0.989[/C][C]-0.07[/C][C]0.014[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90956&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90956&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
F-E0.122-0.1160.3590.447
H-E-0.353-0.591-0.1160.002
H-F-0.475-0.708-0.2420
V-M0.061-0.1030.2240.463
F:M-E:M0.178-0.2110.5670.768
H:M-E:M-0.262-0.640.1170.347
E:V-E:M0.171-0.2490.590.846
F:V-E:M0.229-0.190.6490.61
H:V-E:M-0.3-0.7430.1430.371
H:M-F:M-0.44-0.804-0.0760.009
E:V-F:M-0.008-0.4150.3991
F:V-F:M0.051-0.3560.4580.999
H:V-F:M-0.478-0.91-0.0470.021
E:V-H:M0.4320.0350.8290.024
F:V-H:M0.4910.0940.8880.006
H:V-H:M-0.038-0.460.3831
F:V-E:V0.059-0.3780.4950.999
H:V-E:V-0.471-0.93-0.0110.041
H:V-F:V-0.529-0.989-0.070.014







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group55.5040
111

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 5.504 & 0 \tabularnewline
  & 111 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90956&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]5[/C][C]5.504[/C][C]0[/C][/ROW]
[ROW][C] [/C][C]111[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90956&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90956&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)
Group55.5040
111



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