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Author*Unverified author*
R Software ModuleIan.Hollidayrwasp_Mixed Model ANOVA for Clare.wasp
Title produced by softwareMixed Within-Between Two-Way ANOVA
Date of computationFri, 09 Mar 2012 13:41:51 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Mar/09/t1331318648qvqxri8zc40ra8h.htm/, Retrieved Thu, 02 May 2024 15:56:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=163910, Retrieved Thu, 02 May 2024 15:56:07 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Mixed Within-Between Two-Way ANOVA] [Mixed anova for C...] [2012-03-09 18:41:51] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
'1'	'cont'	'pre'	'rad'	6.92
'2'	'cont'	'pre'	'rad'	1.49
'3'	'cont'	'pre'	'rad'	4.25
'4'	'cont'	'pre'	'rad'	4.88
'5'	'cont'	'pre'	'rad'	3.04
'6'	'cont'	'pre'	'rad'	5.07
'1'	'cont'	'pre'	'rot'	4.54
'2'	'cont'	'pre'	'rot'	5.47
'3'	'cont'	'pre'	'rot'	4.01
'4'	'cont'	'pre'	'rot'	4.36
'5'	'cont'	'pre'	'rot'	4.17
'6'	'cont'	'pre'	'rot'	3.17
'1'	'cont'	'post'	'rad'	4.73
'2'	'cont'	'post'	'rad'	2.06
'3'	'cont'	'post'	'rad'	4.79
'4'	'cont'	'post'	'rad'	2.82
'5'	'cont'	'post'	'rad'	5.5
'6'	'cont'	'post'	'rad'	3.4
'1'	'cont'	'post'	'rot'	5.37
'2'	'cont'	'post'	'rot'	3.23
'3'	'cont'	'post'	'rot'	3.09
'4'	'cont'	'post'	'rot'	10.11
'5'	'cont'	'post'	'rot'	3.75
'6'	'cont'	'post'	'rot'	4.65
'7'	'train'	'pre'	'rad'	2.82
'8'	'train'	'pre'	'rad'	3.41
'9'	'train'	'pre'	'rad'	3.95
'10'	'train'	'pre'	'rad'	4.27
'11'	'train'	'pre'	'rad'	3.75
'12'	'train'	'pre'	'rad'	18.17
'7'	'train'	'pre'	'rot'	12.59
'8'	'train'	'pre'	'rot'	20.56
'9'	'train'	'pre'	'rot'	26.21
'10'	'train'	'pre'	'rot'	20.56
'11'	'train'	'pre'	'rot'	10
'12'	'train'	'pre'	'rot'	56.71
'7'	'train'	'post'	'rad'	3.46
'8'	'train'	'post'	'rad'	4.21
'9'	'train'	'post'	'rad'	4.49
'10'	'train'	'post'	'rad'	15.68
'11'	'train'	'post'	'rad'	3.14
'12'	'train'	'post'	'rad'	3.22
'7'	'train'	'post'	'rot'	10.2
'8'	'train'	'post'	'rot'	6.43
'9'	'train'	'post'	'rot'	9.8
'10'	'train'	'post'	'rot'	15.68
'11'	'train'	'post'	'rot'	7.22
'12'	'train'	'post'	'rot'	23.28




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Servervre.aston.ac.uk @ vre.aston.ac.uk

\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 & 7 seconds \tabularnewline
R Server & vre.aston.ac.uk @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163910&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]vre.aston.ac.uk @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163910&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163910&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 time7 seconds
R Servervre.aston.ac.uk @ vre.aston.ac.uk







Repeated Measures ANOVA
effectDfnDFdFpp<0.05ges
group1106.940.025*0.264
cue1102.4290.150.054
group:cue1102.7160.130.06
flanker11012.6010.005*0.202
group:flanker11010.4450.009*0.174
cue:flanker1109.7550.011*0.042
group:cue:flanker11014.2840.004*0.061

\begin{tabular}{lllllllll}
\hline
Repeated Measures ANOVA \tabularnewline
effect & Dfn & DFd & F & p & p<0.05 & ges \tabularnewline
group & 1 & 10 & 6.94 & 0.025 & * & 0.264 \tabularnewline
cue & 1 & 10 & 2.429 & 0.15 &  & 0.054 \tabularnewline
group:cue & 1 & 10 & 2.716 & 0.13 &  & 0.06 \tabularnewline
flanker & 1 & 10 & 12.601 & 0.005 & * & 0.202 \tabularnewline
group:flanker & 1 & 10 & 10.445 & 0.009 & * & 0.174 \tabularnewline
cue:flanker & 1 & 10 & 9.755 & 0.011 & * & 0.042 \tabularnewline
group:cue:flanker & 1 & 10 & 14.284 & 0.004 & * & 0.061 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163910&T=1

[TABLE]
[ROW][C]Repeated Measures ANOVA[/C][/ROW]
[ROW][C]effect[/C][C]Dfn[/C][C]DFd[/C][C]F[/C][C]p[/C][C]p<0.05[/C][C]ges[/C][/ROW]
[ROW][C]group[/C][C]1[/C][C]10[/C][C]6.94[/C][C]0.025[/C][C]*[/C][C]0.264[/C][/ROW]
[ROW][C]cue[/C][C]1[/C][C]10[/C][C]2.429[/C][C]0.15[/C][C][/C][C]0.054[/C][/ROW]
[ROW][C]group:cue[/C][C]1[/C][C]10[/C][C]2.716[/C][C]0.13[/C][C][/C][C]0.06[/C][/ROW]
[ROW][C]flanker[/C][C]1[/C][C]10[/C][C]12.601[/C][C]0.005[/C][C]*[/C][C]0.202[/C][/ROW]
[ROW][C]group:flanker[/C][C]1[/C][C]10[/C][C]10.445[/C][C]0.009[/C][C]*[/C][C]0.174[/C][/ROW]
[ROW][C]cue:flanker[/C][C]1[/C][C]10[/C][C]9.755[/C][C]0.011[/C][C]*[/C][C]0.042[/C][/ROW]
[ROW][C]group:cue:flanker[/C][C]1[/C][C]10[/C][C]14.284[/C][C]0.004[/C][C]*[/C][C]0.061[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163910&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163910&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Repeated Measures ANOVA
effectDfnDFdFpp<0.05ges
group1106.940.025*0.264
cue1102.4290.150.054
group:cue1102.7160.130.06
flanker11012.6010.005*0.202
group:flanker11010.4450.009*0.174
cue:flanker1109.7550.011*0.042
group:cue:flanker11014.2840.004*0.061







Between Effects Comparisons
groupNMeanSDFLSD
cont64.369583333333330.937066455309696.51768190483453
train612.07541666666677.103631187052626.51768190483453

\begin{tabular}{lllllllll}
\hline
Between Effects Comparisons \tabularnewline
group & N & Mean & SD & FLSD \tabularnewline
cont & 6 & 4.36958333333333 & 0.93706645530969 & 6.51768190483453 \tabularnewline
train & 6 & 12.0754166666667 & 7.10363118705262 & 6.51768190483453 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163910&T=2

[TABLE]
[ROW][C]Between Effects Comparisons[/C][/ROW]
[ROW][C]group[/C][C]N[/C][C]Mean[/C][C]SD[/C][C]FLSD[/C][/ROW]
[ROW][C]cont[/C][C]6[/C][C]4.36958333333333[/C][C]0.93706645530969[/C][C]6.51768190483453[/C][/ROW]
[ROW][C]train[/C][C]6[/C][C]12.0754166666667[/C][C]7.10363118705262[/C][C]6.51768190483453[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163910&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163910&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Between Effects Comparisons
groupNMeanSDFLSD
cont64.369583333333330.937066455309696.51768190483453
train612.07541666666677.103631187052626.51768190483453







Within Effects Comparisons
cueflankerNMeanSDFLSD
postrad124.791666666666673.564322792578834.01165023082291
postrot128.56755.933452352398384.01165023082291
prerad125.168333333333334.30662456588864.01165023082291
prerot1214.362515.50544369216534.01165023082291

\begin{tabular}{lllllllll}
\hline
Within Effects Comparisons \tabularnewline
cue & flanker & N & Mean & SD & FLSD \tabularnewline
post & rad & 12 & 4.79166666666667 & 3.56432279257883 & 4.01165023082291 \tabularnewline
post & rot & 12 & 8.5675 & 5.93345235239838 & 4.01165023082291 \tabularnewline
pre & rad & 12 & 5.16833333333333 & 4.3066245658886 & 4.01165023082291 \tabularnewline
pre & rot & 12 & 14.3625 & 15.5054436921653 & 4.01165023082291 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163910&T=3

[TABLE]
[ROW][C]Within Effects Comparisons[/C][/ROW]
[ROW][C]cue[/C][C]flanker[/C][C]N[/C][C]Mean[/C][C]SD[/C][C]FLSD[/C][/ROW]
[ROW][C]post[/C][C]rad[/C][C]12[/C][C]4.79166666666667[/C][C]3.56432279257883[/C][C]4.01165023082291[/C][/ROW]
[ROW][C]post[/C][C]rot[/C][C]12[/C][C]8.5675[/C][C]5.93345235239838[/C][C]4.01165023082291[/C][/ROW]
[ROW][C]pre[/C][C]rad[/C][C]12[/C][C]5.16833333333333[/C][C]4.3066245658886[/C][C]4.01165023082291[/C][/ROW]
[ROW][C]pre[/C][C]rot[/C][C]12[/C][C]14.3625[/C][C]15.5054436921653[/C][C]4.01165023082291[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163910&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163910&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Within Effects Comparisons
cueflankerNMeanSDFLSD
postrad124.791666666666673.564322792578834.01165023082291
postrot128.56755.933452352398384.01165023082291
prerad125.168333333333334.30662456588864.01165023082291
prerot1214.362515.50544369216534.01165023082291



Parameters (Session):
par1 = 5 ; par2 = 3 ; par3 = 4 ; par4 = 2 ; par5 = 1 ;
Parameters (R input):
par1 = 5 ; par2 = 3 ; par3 = 4 ; par4 = 2 ; par5 = 1 ;
R code (references can be found in the software module):
cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
cat3 <- as.numeric(par3)
cat4 <-as.numeric(par4)
cat5 <-as.numeric(par5)
x <- t(x)
x1<-as.numeric(x[,cat1])
wf1<-as.character(x[,cat2])
wf2 <- as.character(x[,cat3])
bf1 <- as.character(x[,cat4])
sid<- as.character(x[,cat5]) # author of ez changed within subjects variable name from sid to wid
xdf<-data.frame(x1,wf1, wf2, bf1, sid)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
(V3 <-dimnames(y)[[1]][cat3])
(V4 <-dimnames(y)[[1]][cat4])
(V5 <-dimnames(y)[[1]][cat5])
names(xdf)<-c(V1, V2, V3, V4, V5)
library(ez)
library(Cairo)
(ezout <- ezANOVA(data=xdf, dv=.(mean_rt), wid=.(sid), within=.(cue, flanker), between=.(group) ) )
load(file='createtable')
a<-table.start()
nr <- nrow(ezout$ANOVA)
nc <- ncol(ezout$ANOVA)
a<-table.row.start(a)
a<-table.element(a,'Repeated Measures ANOVA', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'effect', 1,TRUE)
a<-table.element(a,'Dfn',1,TRUE)
a<-table.element(a,'DFd', 1,TRUE)
a<-table.element(a, 'F', 1,TRUE)
a<-table.element(a,'p', 1,TRUE)
a<-table.element(a,'p<0.05', 1,TRUE)
a<-table.element(a, 'ges', 1,TRUE) # generalized eta-sq - was partial eta-sq in earlier version
a<-table.row.end(a)
for ( i in 1:nr){
a<-table.row.start(a)
a<-table.element(a,ezout$ANOVA$Effect[i], 1, TRUE)
for(j in 2:nc){
if ( j != 6) # author of ez reduced number of columns in output from 8
a<-table.element(a,round(ezout$ANOVA[[j]][i], digits=3), 1, FALSE)
else a<-table.element(a, ezout$ANOVA[[j]][i], 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
nr <- nrow(ezout$Mauchly)
nc <- ncol(ezout$Mauchly)
if (!is.null(nr) & !is.null(nc) ){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Mauchlys Test for Sphericity', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'effect', 1,TRUE)
a<-table.element(a,'W',1,TRUE)
a<-table.element(a,'p', 1,TRUE)
a<-table.element(a,'p<0.05', 1,TRUE)
a<-table.row.end(a)
for ( i in 1:nr){
a<-table.row.start(a)
a<-table.element(a,ezout$Mauchly$Effect[i], 1, TRUE)
for(j in 2:nc){
if (j != 4)
a<-table.element(a,round(ezout$Mauchly[[j]][i], digits = 3), 1, FALSE)
else
a<-table.element(a,ezout$Mauchly[[j]][i], 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
nr <- nrow(ezout$Spher)
nc <- ncol(ezout$Sphe)
a<-table.row.start(a)
a<-table.element(a,'Sphericity Corrections', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'effect', 1,TRUE)
a<-table.element(a,'GGe',1,TRUE)
a<-table.element(a,'p[GG]', 1,TRUE)
a<-table.element(a,'p[GG]<0.05', 1,TRUE)
a<-table.element(a,'HFe', 1,TRUE)
a<-table.element(a,'p[HF]', 1,TRUE)
a<-table.element(a,'p[HF]<0.05', 1,TRUE)
a<-table.row.end(a)
for ( i in 1:nr){
a<-table.row.start(a)
a<-table.element(a,ezout$Spher$Effect[i], 1, TRUE)
for(j in 2:nc){
if ( ! ((j == 4) | (j == 7)) )
a<-table.element(a,round(ezout$Spher[[j]][i], digits=3), 1, FALSE)
else
a<-table.element(a,ezout$Spher[[j]][i], 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
ezP.between<-ezPlot(data = xdf, dv = .(mean_rt), between = .(group), wid = .(sid), do_lines=FALSE, x_lab='group', y_lab='RT' , x=.(group))
bitmap(file = 'between.cairo')
print(ezP.between)
dev.off()
ezstats_between<-ezStats(data = xdf, dv = .(mean_rt), between =.(group), wid = .(sid))
a<-table.start()
nr <- nrow(ezstats_between)
nc <- ncol(ezstats_between)
a<-table.row.start(a)
a<-table.element(a,'Between Effects Comparisons', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
for(i in 1:nc){
a<-table.element(a, names(ezstats_between)[i], 1,TRUE)
}
a<-table.row.end(a)
for ( i in 1:nr){
a<-table.row.start(a)
a<-table.element(a,ezstats_between[[1]][i], 1, TRUE)
for(j in 2:nc){
a<-table.element(a,ezstats_between[[j]][i], 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable3.tab')
ezP.within<-ezPlot(data = xdf, dv = .(mean_rt), within = .(cue, flanker), wid = .(sid), do_lines=TRUE, x_lab='flanker', y_lab='RT' , x=.(flanker), split=.(cue), split_lab = 'cue')
bitmap(file = 'within.cairo')
print(ezP.within)
dev.off()
ezstats_within <- ezStats(data = xdf, dv = .(mean_rt), within = .(cue, flanker), wid = .(sid))
a<-table.start()
nr <- nrow(ezstats_within)
nc <- ncol(ezstats_within)
a<-table.row.start(a)
a<-table.element(a,'Within Effects Comparisons', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
for(i in 1:nc){
a<-table.element(a, names(ezstats_within)[i], 1,TRUE)
}
a<-table.row.end(a)
for ( i in 1:nr){
a<-table.row.start(a)
a<-table.element(a,ezstats_within[[1]][i], 1, TRUE)
for(j in 2:nc){
a<-table.element(a, ezstats_within[[j]][i], 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
-SERVER-vre.aston.ac.uk