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Data X:
21 1 0 22 1 1 22 1 0 18 1 1 23 1 1 12 1 1 20 1 0 22 1 1 21 1 1 19 1 1 22 1 1 15 1 1 20 1 1 19 1 0 18 1 0 15 0 0 20 1 1 21 1 0 21 0 1 15 1 0 16 1 1 23 1 1 21 1 0 18 1 1 25 1 1 9 1 1 30 0 1 20 0 0 23 1 1 16 1 0 16 1 0 19 1 0 25 1 1 18 1 1 23 1 1 21 1 1 10 1 0 14 0 1 22 1 1 26 1 0 23 1 1 23 1 1 24 1 1 24 1 1 18 0 1 23 1 0 15 1 1 19 0 1 16 1 0 25 0 1 23 0 1 17 0 1 19 1 1 21 0 1 18 1 1 27 1 1 21 0 0 13 1 1 8 0 0 29 0 1 28 1 1 23 1 0 21 1 0 19 1 1 19 1 0 20 0 1 18 1 0 19 1 1 17 1 1 19 0 0 25 1 0 19 1 0 22 0 0 23 0 1 14 1 0 16 1 0 24 0 1 20 1 0 12 0 0 24 1 1 22 0 0 12 0 0 22 0 0 20 0 1 10 0 0 23 0 1 17 0 1 22 0 0 24 0 0 18 0 0 21 0 1 20 0 1 20 0 1 22 0 0 19 0 1 20 0 0 26 0 1 23 0 1 24 0 1 21 0 1 21 0 1 19 0 0 8 0 1 17 0 1 20 0 1 11 0 0 8 0 0 15 0 0 18 0 0 18 0 0 19 0 0 19 0 1 23 1 1 22 1 1 21 1 1 25 1 1 30 0 0 17 0 1 27 1 1 23 1 0 23 1 1 18 1 0 18 1 0 23 1 1 19 1 1 15 1 1 20 1 1 16 1 1 24 0 1 25 1 1 25 1 1 19 1 0 19 1 1 16 1 1 19 1 1 19 1 1 23 1 1 21 1 1 22 1 0 19 1 1 20 0 1 20 1 1 3 1 1 23 1 1 23 1 0 20 1 0 15 1 1 16 1 0 7 1 0 24 1 1 17 1 0 24 1 1 24 1 1 19 1 0 25 0 1 20 0 1 28 1 1 23 1 0 27 0 0 18 0 0 28 0 0 21 0 1 19 1 0 23 1 1 27 0 0 22 0 1 28 0 0 25 0 1 21 0 0 22 0 0 28 0 1 20 0 0 29 0 1 25 1 1 25 1 1 20 0 1 20 1 1 16 1 0 20 0 1 20 1 0 23 0 0 18 0 0 25 1 1 18 0 0 19 0 1 25 0 0 25 0 0 25 0 0 24 0 0 19 0 1 26 0 1 10 0 1 17 0 1 13 0 0 17 0 0 30 0 1 25 1 0 4 0 0 16 0 0 21 0 0 23 1 1 22 0 1 17 1 0 20 0 0 20 1 1 22 0 0 16 1 1 23 0 1 0 0 0 18 0 1 25 0 1 23 1 1 12 1 0 18 0 0 24 1 0 11 1 1 18 0 1 23 1 1 24 0 1 29 0 0 18 1 0 15 0 0 29 1 1 16 1 1 19 1 0 22 0 0 16 1 0 23 0 1 23 1 1 19 1 0 4 1 0 20 1 0 24 0 1 20 1 1 4 1 1 24 1 1 22 0 0 16 1 1 3 1 1 15 0 1 24 1 0 17 0 0 20 0 1 27 0 0 26 0 1 23 0 1 17 1 0 20 1 1 22 1 0 19 1 1 24 1 1 19 1 0 23 0 1 15 0 0 27 1 1 26 0 0 22 0 1 22 1 0 18 0 0 15 0 1 22 0 1 27 0 0 10 0 1 20 0 1 17 0 0 23 0 1 19 0 0 13 0 0 27 0 1 23 0 1 16 0 0 25 0 1 2 0 0 26 0 0 20 0 1 23 1 0 22 0 0 24 0 1
Names of X columns:
NUMERACYTOT group gender
Response : Variable 1


Factor : Variable 2


Factor : Variable 3


Include Intercept Term ?


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Title:
Label y-axis:
Label x-axis:

R Code
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')

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