Wessa.net

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Data X:
sexweightheightrepwt repht M 77 182 77 180 F 58 161 51 159 F 53 161 54 158 M 68 177 70 175 F 59 157 59 155 M 76 170 76 165 M 76 167 77 165 M 69 186 73 180 M 71 178 71 175 M 65 171 64 170 M 70 175 75 174 F 166 57 56 163 F 51 161 52 158 F 64 168 64 165 F 52 163 57 160 F 65 166 66 165 M 92 187 101 185 F 62 168 62 165 M 76 197 75 200 F 61 175 61 171 M 119 180 124 178 F 61 170 61 170 M 65 175 66 173 M 66 173 70 170 F 54 171 59 168 F 50 166 50 165 F 63 169 61 168 F 58 166 60 160 F 39 157 41 153 M 101 183 100 180 F 71 166 71 165 M 75 178 73 175 M 79 173 76 173 F 52 164 52 161 F 68 169 63 170 M 64 176 65 175 F 56 166 54 165 M 69 174 69 171 M 88 178 86 175 M 65 187 67 188 F 54 164 53 160 M 80 178 80 178 F 63 163 59 159 M 78 183 80 180 M 85 179 82 175 F 54 160 55 158 F 54 174 56 173 F 75 162 75 158 M 82 182 85 183 F 56 165 57 163 M 74 169 73 170 M 102 185 107 185 M 65 176 64 172 M 73 183 74 180 M 75 172 70 169 M 57 173 58 170 M 68 165 69 165 M 71 177 71 170 M 71 180 76 175 F 78 173 75 169 M 97 189 98 185 F 60 162 59 160 F 64 165 63 163 F 64 164 62 161 F 52 158 51 155 M 80 178 76 175 F 62 175 61 171 M 66 173 66 175 F 55 165 54 163 F 56 163 57 159 F 50 166 50 161 F 50 160 55 150 F 63 160 64 158 M 69 182 70 180 M 69 183 70 183 F 61 165 60 163 M 55 168 56 170 F 53 169 52 175 F 60 167 55 163 F 56 170 56 170 M 59 182 61 183 M 62 178 66 175 F 53 165 53 165 F 57 163 59 160 F 57 162 56 160 M 70 173 68 170 F 56 161 56 161 M 84 184 86 183 M 69 180 71 180 M 88 189 87 185 F 56 165 57 160 M 103 185 101 182 F 50 169 50 165 F 52 159 52 153 F 55 164 55 163 M 63 178 63 175 F 47 163 47 160 F 45 163 45 160 F 62 175 63 173 F 53 164 51 160 F 52 152 51 150 F 57 167 55 164 F 64 166 64 165 F 59 166 55 163 M 84 183 90 183 M 79 179 79 171 F 55 174 57 171 M 67 179 67 179 F 76 167 77 165 F 62 168 62 163 M 83 184 83 181 M 96 184 94 183 M 75 169 76 165 M 65 178 66 178 M 78 178 77 175 M 69 167 73 165 F 68 178 68 175 F 55 165 55 163 F 45 157 45 153 F 68 171 68 169 F 44 157 44 155 F 62 166 61 163 M 87 185 89 185 F 56 160 53 158 F 50 148 47 148 M 83 177 84 175 F 53 162 53 160 F 64 172 62 168 M 90 188 91 185 M 85 191 83 188 M 66 175 68 175 F 52 163 53 160 F 53 165 55 163 F 54 176 55 176 F 64 171 66 171 F 55 160 55 155 F 55 165 55 165 F 59 157 55 158 F 70 173 67 170 M 88 184 86 183 F 57 168 58 165 F 47 162 47 160 F 47 150 45 152 F 48 163 44 160 M 54 169 58 165 M 69 172 68 174 F 57 167 56 165 F 51 163 50 160 F 54 161 54 160 F 53 162 52 158 F 59 172 58 171 M 56 163 58 161 F 59 159 59 155 F 63 170 62 168 F 66 166 66 165 M 96 191 95 188 F 53 158 50 155 M 76 169 75 165 M 61 170 61 170 M 62 168 64 168 M 71 178 68 178 M 66 170 67 165 M 81 178 82 175 M 68 174 68 173 M 80 176 78 175 F 63 165 59 160 M 70 173 70 173 F 56 162 56 160 F 60 172 55 168 F 58 169 54 166 M 76 183 75 180 F 50 158 49 155 M 88 185 93 188 M 89 173 86 173 F 59 164 59 165 F 51 156 51 158 F 62 164 61 161 M 74 175 71 175 M 83 180 80 180 M 90 181 91 178 M 79 177 81 178
Names of X columns:
sex weight height rewt repht
Response : Variable 1


Within Ss Factor : Variable 2


Within Ss Factor : Variable 3


Between Ss Factor : Variable 4


Subject Identifier Column : Variable 5


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R Code
par5 <- '1' par4 <- '2' par3 <- '4' par2 <- '3' par1 <- '5' 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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.
R Engine error message	Error in array(list(sexweightheightrepwt, repht, M, 77, 182, 77, 180, : object 'sexweightheightrepwt' not found Execution halted