Home » date » 2012 » Feb » 18 »

Example of week 15 compendium

*The author of this computation has been verified*

R Software Module: /rwasp_Mixed Model ANOVA.wasp (opens new window with default values)

Title produced by software: Mixed Within-Between Two-Way ANOVA

Date of computation: Sat, 18 Feb 2012 17:15:26 -0500

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2012/Feb/18/t1329603377b7hq1now40cu86s.htm/, Retrieved Sun, 26 May 2013 09:34:44 +0000

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

System-generated keywords (parent):

(pk = 0)

Estimated Impact

Dataseries X:

» Textfile « » CSV « » Correlation Matrix « » Notched Boxplots «

'1'	'Trt'	'NoCue'	'Neut'	433.5
'1'	'Trt'	'NoCue'	'Cong'	423.9
'1'	'Trt'	'NoCue'	'Inc'	490.9
'1'	'Trt'	'Cent'	'Neut'	385.2
'1'	'Trt'	'Cent'	'Cong'	368.6
'1'	'Trt'	'Cent'	'Inc'	473.4
'1'	'Trt'	'Double'	'Neut'	378.9
'1'	'Trt'	'Double'	'Cong'	378.9
'1'	'Trt'	'Double'	'Inc'	455.4
'1'	'Trt'	'Spatial'	'Neut'	328.1
'1'	'Trt'	'Spatial'	'Cong'	350.2
'1'	'Trt'	'Spatial'	'Inc'	407.2
'2'	'Trt'	'NoCue'	'Neut'	436.4
'2'	'Trt'	'NoCue'	'Cong'	441.9
'2'	'Trt'	'NoCue'	'Inc'	483.3
'2'	'Trt'	'Cent'	'Neut'	374.2
'2'	'Trt'	'Cent'	'Cong'	389.8
'2'	'Trt'	'Cent'	'Inc'	455.7
'2'	'Trt'	'Double'	'Neut'	357.0
'2'	'Trt'	'Double'	'Cong'	384.2
'2'	'Trt'	'Double'	'Inc'	433.0
'2'	'Trt'	'Spatial'	'Neut'	339.4
'2'	'Trt'	'Spatial'	'Cong'	337.6
'2'	'Trt'	'Spatial'	'Inc'	421.0
'3'	'Trt'	'NoCue'	'Neut'	428.7
'3'	'Trt'	'NoCue'	'Cong'	428.1
'3'	'Trt'	'NoCue'	'Inc'	503.3
'3'	'Trt'	'Cent'	'Neut'	371.2
'3'	'Trt'	'Cent'	'Cong'	368.0
'3'	'Trt'	'Cent'	'Inc'	436.5
'3'	'Trt'	'Double'	'Neut'	392.3
'3'	'Trt'	'Double'	'Cong'	356.3
'3'	'Trt'	'Double'	'Inc'	432.7
'3'	'Trt'	'Spatial'	'Neut'	331.3
'3'	'Trt'	'Spatial'	'Cong'	334.6
'3'	'Trt'	'Spatial'	'Inc'	431.4
'4'	'Trt'	'NoCue'	'Neut'	415.5
'4'	'Trt'	'NoCue'	'Cong'	433.3
'4'	'Trt'	'NoCue'	'Inc'	498.5
'4'	'Trt'	'Cent'	'Neut'	384.8
'4'	'Trt'	'Cent'	'Cong'	383.2
'4'	'Trt'	'Cent'	'Inc'	438.5
'4'	'Trt'	'Double'	'Neut'	370.3
'4'	'Trt'	'Double'	'Cong'	399.4
'4'	'Trt'	'Double'	'Inc'	445.9
'4'	'Trt'	'Spatial'	'Neut'	320.7
'4'	'Trt'	'Spatial'	'Cong'	342.8
'4'	'Trt'	'Spatial'	'Inc'	407.0
'5'	'Trt'	'NoCue'	'Neut'	429.1
'5'	'Trt'	'NoCue'	'Cong'	436.9
'5'	'Trt'	'NoCue'	'Inc'	499.0
'5'	'Trt'	'Cent'	'Neut'	378.1
'5'	'Trt'	'Cent'	'Cong'	394.6
'5'	'Trt'	'Cent'	'Inc'	471.4
'5'	'Trt'	'Double'	'Neut'	370.6
'5'	'Trt'	'Double'	'Cong'	370.7
'5'	'Trt'	'Double'	'Inc'	447.1
'5'	'Trt'	'Spatial'	'Neut'	332.5
'5'	'Trt'	'Spatial'	'Cong'	329.9
'5'	'Trt'	'Spatial'	'Inc'	418.3
'6'	'Trt'	'NoCue'	'Neut'	435.3
'6'	'Trt'	'NoCue'	'Cong'	422.7
'6'	'Trt'	'NoCue'	'Inc'	480.0
'6'	'Trt'	'Cent'	'Neut'	390.7
'6'	'Trt'	'Cent'	'Cong'	366.9
'6'	'Trt'	'Cent'	'Inc'	461.5
'6'	'Trt'	'Double'	'Neut'	354.1
'6'	'Trt'	'Double'	'Cong'	385.6
'6'	'Trt'	'Double'	'Inc'	435.4
'6'	'Trt'	'Spatial'	'Neut'	336.3
'6'	'Trt'	'Spatial'	'Cong'	335.0
'6'	'Trt'	'Spatial'	'Inc'	399.6
'7'	'Trt'	'NoCue'	'Neut'	435.4
'7'	'Trt'	'NoCue'	'Cong'	412.3
'7'	'Trt'	'NoCue'	'Inc'	484.9
'7'	'Trt'	'Cent'	'Neut'	387.8
'7'	'Trt'	'Cent'	'Cong'	380.5
'7'	'Trt'	'Cent'	'Inc'	443.4
'7'	'Trt'	'Double'	'Neut'	361.9
'7'	'Trt'	'Double'	'Cong'	346.4
'7'	'Trt'	'Double'	'Inc'	452.1
'7'	'Trt'	'Spatial'	'Neut'	351.1
'7'	'Trt'	'Spatial'	'Cong'	345.6
'7'	'Trt'	'Spatial'	'Inc'	424.7
'8'	'Trt'	'NoCue'	'Neut'	402.9
'8'	'Trt'	'NoCue'	'Cong'	412.7
'8'	'Trt'	'NoCue'	'Inc'	502.4
'8'	'Trt'	'Cent'	'Neut'	387.8
'8'	'Trt'	'Cent'	'Cong'	358.2
'8'	'Trt'	'Cent'	'Inc'	437.8
'8'	'Trt'	'Double'	'Neut'	357.4
'8'	'Trt'	'Double'	'Cong'	401.8
'8'	'Trt'	'Double'	'Inc'	459.5
'8'	'Trt'	'Spatial'	'Neut'	360.1
'8'	'Trt'	'Spatial'	'Cong'	335.6
'8'	'Trt'	'Spatial'	'Inc'	405.1
'9'	'Trt'	'NoCue'	'Neut'	419.2
'9'	'Trt'	'NoCue'	'Cong'	420.1
'9'	'Trt'	'NoCue'	'Inc'	500.0
'9'	'Trt'	'Cent'	'Neut'	386.1
'9'	'Trt'	'Cent'	'Cong'	381.4
'9'	'Trt'	'Cent'	'Inc'	460.0
'9'	'Trt'	'Double'	'Neut'	370.2
'9'	'Trt'	'Double'	'Cong'	369.4
'9'	'Trt'	'Double'	'Inc'	445.4
'9'	'Trt'	'Spatial'	'Neut'	351.2
'9'	'Trt'	'Spatial'	'Cong'	336.6
'9'	'Trt'	'Spatial'	'Inc'	422.3
'10'	'Trt'	'NoCue'	'Neut'	441.6
'10'	'Trt'	'NoCue'	'Cong'	432.2
'10'	'Trt'	'NoCue'	'Inc'	516.9
'10'	'Trt'	'Cent'	'Neut'	382.5
'10'	'Trt'	'Cent'	'Cong'	376.9
'10'	'Trt'	'Cent'	'Inc'	442.9
'10'	'Trt'	'Double'	'Neut'	385.6
'10'	'Trt'	'Double'	'Cong'	385.9
'10'	'Trt'	'Double'	'Inc'	457.8
'10'	'Trt'	'Spatial'	'Neut'	342.2
'10'	'Trt'	'Spatial'	'Cong'	331.5
'10'	'Trt'	'Spatial'	'Inc'	408.1
'11'	'Ctrl'	'NoCue'	'Neut'	446.7
'11'	'Ctrl'	'NoCue'	'Cong'	433.8
'11'	'Ctrl'	'NoCue'	'Inc'	517.3
'11'	'Ctrl'	'Cent'	'Neut'	380.3
'11'	'Ctrl'	'Cent'	'Cong'	371.6
'11'	'Ctrl'	'Cent'	'Inc'	493.7
'11'	'Ctrl'	'Double'	'Neut'	390.9
'11'	'Ctrl'	'Double'	'Cong'	394.2
'11'	'Ctrl'	'Double'	'Inc'	482.1
'11'	'Ctrl'	'Spatial'	'Neut'	345.2
'11'	'Ctrl'	'Spatial'	'Cong'	330.7
'11'	'Ctrl'	'Spatial'	'Inc'	391.5
'12'	'Ctrl'	'NoCue'	'Neut'	420.7
'12'	'Ctrl'	'NoCue'	'Cong'	442.7
'12'	'Ctrl'	'NoCue'	'Inc'	513.3
'12'	'Ctrl'	'Cent'	'Neut'	374.7
'12'	'Ctrl'	'Cent'	'Cong'	373.0
'12'	'Ctrl'	'Cent'	'Inc'	486.7
'12'	'Ctrl'	'Double'	'Neut'	380.0
'12'	'Ctrl'	'Double'	'Cong'	374.9
'12'	'Ctrl'	'Double'	'Inc'	495.7
'12'	'Ctrl'	'Spatial'	'Neut'	352.6
'12'	'Ctrl'	'Spatial'	'Cong'	347.6
'12'	'Ctrl'	'Spatial'	'Inc'	424.4
'13'	'Ctrl'	'NoCue'	'Neut'	422.1
'13'	'Ctrl'	'NoCue'	'Cong'	424.2
'13'	'Ctrl'	'NoCue'	'Inc'	503.6
'13'	'Ctrl'	'Cent'	'Neut'	364.8
'13'	'Ctrl'	'Cent'	'Cong'	364.3
'13'	'Ctrl'	'Cent'	'Inc'	474.6
'13'	'Ctrl'	'Double'	'Neut'	383.8
'13'	'Ctrl'	'Double'	'Cong'	363.6
'13'	'Ctrl'	'Double'	'Inc'	477.1
'13'	'Ctrl'	'Spatial'	'Neut'	338.0
'13'	'Ctrl'	'Spatial'	'Cong'	332.8
'13'	'Ctrl'	'Spatial'	'Inc'	420.3
'14'	'Ctrl'	'NoCue'	'Neut'	431.7
'14'	'Ctrl'	'NoCue'	'Cong'	436.4
'14'	'Ctrl'	'NoCue'	'Inc'	481.9
'14'	'Ctrl'	'Cent'	'Neut'	376.7
'14'	'Ctrl'	'Cent'	'Cong'	388.7
'14'	'Ctrl'	'Cent'	'Inc'	484.4
'14'	'Ctrl'	'Double'	'Neut'	383.4
'14'	'Ctrl'	'Double'	'Cong'	363.5
'14'	'Ctrl'	'Double'	'Inc'	467.4
'14'	'Ctrl'	'Spatial'	'Neut'	315.9
'14'	'Ctrl'	'Spatial'	'Cong'	354.7
'14'	'Ctrl'	'Spatial'	'Inc'	408.6
'15'	'Ctrl'	'NoCue'	'Neut'	430.9
'15'	'Ctrl'	'NoCue'	'Cong'	446.8
'15'	'Ctrl'	'NoCue'	'Inc'	505.1
'15'	'Ctrl'	'Cent'	'Neut'	372.7
'15'	'Ctrl'	'Cent'	'Cong'	382.8
'15'	'Ctrl'	'Cent'	'Inc'	483.5
'15'	'Ctrl'	'Double'	'Neut'	369.3
'15'	'Ctrl'	'Double'	'Cong'	378.1
'15'	'Ctrl'	'Double'	'Inc'	461.1
'15'	'Ctrl'	'Spatial'	'Neut'	342.6
'15'	'Ctrl'	'Spatial'	'Cong'	336.2
'15'	'Ctrl'	'Spatial'	'Inc'	421.7
'16'	'Ctrl'	'NoCue'	'Neut'	425.6
'16'	'Ctrl'	'NoCue'	'Cong'	417.5
'16'	'Ctrl'	'NoCue'	'Inc'	495.2
'16'	'Ctrl'	'Cent'	'Neut'	373.9
'16'	'Ctrl'	'Cent'	'Cong'	378.6
'16'	'Ctrl'	'Cent'	'Inc'	490.9
'16'	'Ctrl'	'Double'	'Neut'	381.9
'16'	'Ctrl'	'Double'	'Cong'	358.5
'16'	'Ctrl'	'Double'	'Inc'	464.4
'16'	'Ctrl'	'Spatial'	'Neut'	340.3
'16'	'Ctrl'	'Spatial'	'Cong'	351.1
'16'	'Ctrl'	'Spatial'	'Inc'	408.4
'17'	'Ctrl'	'NoCue'	'Neut'	421.6
'17'	'Ctrl'	'NoCue'	'Cong'	432.6
'17'	'Ctrl'	'NoCue'	'Inc'	502.8
'17'	'Ctrl'	'Cent'	'Neut'	386.0
'17'	'Ctrl'	'Cent'	'Cong'	389.3
'17'	'Ctrl'	'Cent'	'Inc'	487.0
'17'	'Ctrl'	'Double'	'Neut'	369.5
'17'	'Ctrl'	'Double'	'Cong'	368.7
'17'	'Ctrl'	'Double'	'Inc'	482.0
'17'	'Ctrl'	'Spatial'	'Neut'	350.8
'17'	'Ctrl'	'Spatial'	'Cong'	333.9
'17'	'Ctrl'	'Spatial'	'Inc'	421.7
'18'	'Ctrl'	'NoCue'	'Neut'	432.5
'18'	'Ctrl'	'NoCue'	'Cong'	413.6
'18'	'Ctrl'	'NoCue'	'Inc'	484.4
'18'	'Ctrl'	'Cent'	'Neut'	388.4
'18'	'Ctrl'	'Cent'	'Cong'	374.6
'18'	'Ctrl'	'Cent'	'Inc'	475.4
'18'	'Ctrl'	'Double'	'Neut'	380.8
'18'	'Ctrl'	'Double'	'Cong'	372.6
'18'	'Ctrl'	'Double'	'Inc'	464.2
'18'	'Ctrl'	'Spatial'	'Neut'	337.4
'18'	'Ctrl'	'Spatial'	'Cong'	338.3
'18'	'Ctrl'	'Spatial'	'Inc'	407.7
'19'	'Ctrl'	'NoCue'	'Neut'	436.6
'19'	'Ctrl'	'NoCue'	'Cong'	421.7
'19'	'Ctrl'	'NoCue'	'Inc'	494.7
'19'	'Ctrl'	'Cent'	'Neut'	393.5
'19'	'Ctrl'	'Cent'	'Cong'	393.9
'19'	'Ctrl'	'Cent'	'Inc'	482.2
'19'	'Ctrl'	'Double'	'Neut'	368.6
'19'	'Ctrl'	'Double'	'Cong'	384.2
'19'	'Ctrl'	'Double'	'Inc'	477.8
'19'	'Ctrl'	'Spatial'	'Neut'	344.0
'19'	'Ctrl'	'Spatial'	'Cong'	339.6
'19'	'Ctrl'	'Spatial'	'Inc'	392.7
'20'	'Ctrl'	'NoCue'	'Neut'	412.5
'20'	'Ctrl'	'NoCue'	'Cong'	424.3
'20'	'Ctrl'	'NoCue'	'Inc'	488.2
'20'	'Ctrl'	'Cent'	'Neut'	372.9
'20'	'Ctrl'	'Cent'	'Cong'	393.0
'20'	'Ctrl'	'Cent'	'Inc'	475.3
'20'	'Ctrl'	'Double'	'Neut'	384.2
'20'	'Ctrl'	'Double'	'Cong'	366.5
'20'	'Ctrl'	'Double'	'Inc'	460.0
'20'	'Ctrl'	'Spatial'	'Neut'	338.1
'20'	'Ctrl'	'Spatial'	'Cong'	372.3
'20'	'Ctrl'	'Spatial'	'Inc'	418.3

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	17 seconds
R Server	vre.aston.ac.uk @ vre.aston.ac.uk

Repeated Measures ANOVA
effect	Dfn	DFd	F	p	p<0.05	ges
group	1	18	18.452	0	*	0.076
cue	3	54	516.584	0	*	0.897
group:cue	3	54	2.56	0.064		0.041
flanker	2	36	1349.671	0	*	0.927
group:flanker	2	36	8.779	0.001	*	0.076
cue:flanker	6	108	5.199	0	*	0.114
group:cue:flanker	6	108	6.372	0	*	0.137

Mauchlys Test for Sphericity
effect	W	p	p<0.05
cue	0.782	0.535
group:cue	0.782	0.535
flanker	0.881	0.341
group:flanker	0.881	0.341
cue:flanker	0.174	0.125
group:cue:flanker	0.174	0.125

Sphericity Corrections
effect	GGe	p[GG]	p[GG]<0.05	HFe	p[HF]	p[HF]<0.05
cue	0.865	0	*	1.023	0	*
group:cue	0.865	0.074		1.023	0.064
flanker	0.894	0	*	0.986	0	*
group:flanker	0.894	0.001	*	0.986	0.001	*
cue:flanker	0.602	0.002	*	0.772	0	*
group:cue:flanker	0.602	0	*	0.772	0	*

Between Effects Comparisons
group	N	Mean	SD	FLSD
Ctrl	10	409.956666666667	3.48954302077323	2.92682865735614
Trt	10	403.9725	2.68901816149306	2.92682865735614

Within Effects Comparisons
cue	flanker	N	Mean	SD	FLSD
Cent	Cong	20	378.895	10.6549951221308	7.3644182056978
Cent	Inc	20	467.74	19.2827547715296	7.3644182056978
Cent	Neut	20	380.615	7.73462992004143	7.3644182056978
Double	Cong	20	375.17	14.4670260283606	7.3644182056978
Double	Inc	20	459.805	17.3126809319825	7.3644182056978
Double	Neut	20	374.535	11.2964957305119	7.3644182056978
NoCue	Cong	20	427.885	10.2240827667352	7.3644182056978
NoCue	Inc	20	497.285	11.3140004093671	7.3644182056978
NoCue	Neut	20	427.925	10.5135939570984	7.3644182056978
Spatial	Cong	20	340.83	10.3312046477497	7.3644182056978
Spatial	Inc	20	413	10.9911926463634	7.3644182056978
Spatial	Neut	20	339.89	10.8526445964494	7.3644182056978

Charts produced by software:

http://www.freestatistics.org/blog/date/2012/Feb/18/t1329603377b7hq1now40cu86s/4hwsl1329603305.png (opens in new window)

http://www.freestatistics.org/blog/date/2012/Feb/18/t1329603377b7hq1now40cu86s/4hwsl1329603305.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2012/Feb/18/t1329603377b7hq1now40cu86s/6slek1329603305.png (opens in new window)

http://www.freestatistics.org/blog/date/2012/Feb/18/t1329603377b7hq1now40cu86s/6slek1329603305.ps (opens in new window)

Click here to open pdf file.

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')
a<-table.start()
nr <- nrow(ezout$Mauchly)
nc <- ncol(ezout$Mauchly)
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