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Data X:
7.5 1.5 2.1 6 2.1 2 6.5 2.1 2 1 1.9 2.1 1 1.6 2 5.5 2.1 2.3 8.5 2.1 2.1 6.5 2.2 2.1 4.5 1.5 2.2 2 1.9 2.1 5 2.2 2.1 0.5 1.6 2.1 5 1.5 2 5 1.9 2.3 2.5 0.1 1.8 5 2.2 2 5.5 1.8 2.2 3.5 1.6 2 3 2.2 2.1 4 2.1 2 0.5 1.9 1.8 6.5 1.6 2.2 4.5 1.9 2.2 7.5 2.2 1.7 5.5 1.8 2.1 4 2.4 2.3 7.5 2.4 2.7 7 2.5 1.9 4 1.9 2 5.5 2.1 2 2.5 1.9 1.9 5.5 2.1 2 3.5 1.5 2 2.5 1.9 2.1 4.5 2.1 2 4.5 1.5 1.8 4.5 2.1 2 6 2.1 2.2 2.5 1.8 2.2 5 2.4 2.1 0 2.1 1.8 5 1.9 1.9 6.5 2.1 2.1 5 1.9 2 6 2.4 1.9 4.5 2.1 2.2 5.5 2.2 2 1 2.2 2 7.5 1.8 1.7 6 2.1 2 5 2.4 2.2 1 2.2 1.7 5 2.1 2 6.5 1.5 2.2 7 1.9 2 4.5 1.8 1.9 0 1.8 2 8.5 1.6 2 3.5 1.2 1.6 7.5 1.8 2.1 3.5 1.5 2.1 6 2.1 2 1.5 2.4 1.9 9 2.4 2.2 3.5 1.5 2.1 3.5 1.8 1.8 4 2.1 2.3 6.5 2.2 2.3 7.5 2.1 2.2 6 1.9 2.1 5 2.1 2.2 5.5 1.9 1.9 3.5 1.6 1.8 7.5 2.4 2.1 6.5 1.9 2 6.5 2.1 2.1 6.5 1.8 2.1 7 2.1 2.1 3.5 2.4 1.8 1.5 2.1 2 4 2.2 2.1 7.5 2.1 1.9 4.5 2.2 2.1 0 1.6 1 3.5 2.4 2.2 5.5 2.1 2.1 5 1.9 1.9 4.5 2.4 2 2.5 2.1 1.9 7.5 1.8 2 7 2.1 1.8 0 1.8 2 4.5 1.9 2 3 1.9 2 1.5 2.4 1.8 3.5 1.8 2 2.5 1.8 1.1 5.5 2.1 1.8 8 2.1 1.8 1 2.4 2 5 1.9 1.9 4.5 1.8 2.1 3 1.8 1.6 3 2.2 2.2 8 2.4 1.9 2.5 1.8 2 7 2.4 2.1 0 1.8 1.3 1 1.9 1.8 3.5 2.4 1.9 5.5 2.1 2.1 5.5 1.9 1.8 0.5 2.1 0.75 7.5 2.7 1.5 9 2.1 3 9.5 2.1 2.25 8.5 2.1 3 7 2.1 1.5 8 2.1 3 10 2.1 3 7 2.1 3 8.5 2.1 0.75 9 2.4 3 9.5 1.95 2.25 4 2.1 1.5 6 2.1 1.5 8 1.95 2.25 5.5 2.1 3 9.5 2.4 3 7.5 2.1 1.5 7 2.25 2.25 7.5 2.4 2.25 8 2.25 1.5 7 2.55 2.25 7 1.95 1.5 6 2.4 2.25 10 2.1 2.25 2.5 2.1 3 9 2.4 3 8 2.1 3 6 2.1 1.5 8.5 2.25 3 6 2.25 3 9 2.4 2.25 8 2.1 2.25 9 2.4 2.25 5.5 2.1 3 7 2.1 2.25 5.5 2.25 3 9 2.25 3 2 2.4 1.5 8.5 2.25 2.25 9 2.25 3 8.5 2.1 2.25 9 2.1 1.5 7.5 2.1 2.25 10 2.7 2.25 9 2.1 1.5 7.5 2.1 2.25 6 2.25 1.5 10.5 2.7 2.25 8.5 2.4 3 8 2.1 3 10 2.1 3 10.5 2.4 3 6.5 1.95 1.5 9.5 2.7 2.25 8.5 2.1 1.5 7.5 2.25 2.25 5 2.1 2.25 8 2.7 2.25 10 2.1 3 7 2.1 1.5 7.5 1.65 2.25 7.5 1.65 2.25 9.5 2.1 3 6 2.1 2.25 10 2.1 3 7 2.1 2.25 3 2.1 1.5 6 2.4 3 7 2.4 1.5 10 2.1 3 7 2.25 3 3.5 2.4 3 8 2.1 3 10 2.1 2.25 5.5 2.4 2.25 6 2.4 0.75 6.5 2.1 3 6.5 2.1 0.75 8.5 2.4 1.5 4 2.1 1.5 9.5 2.7 3 8 2.1 1.5 8.5 2.1 2.25 5.5 2.25 3 7 2.1 3 9 2.4 1.5 8 2.25 3 10 2.25 3 8 2.1 1.5 6 2.1 1.5 8 2.4 2.25 5 2.25 1.5 9 2.1 1.5 4.5 2.1 2.25 8.5 1.65 1.5 9.5 2.7 3 8.5 2.1 3 7.5 1.95 0.75 7.5 2.25 1.5 5 2.4 1.5 7 1.95 2.25 8 2.1 2.25 5.5 2.4 1.5 8.5 2.1 2.25 9.5 2.4 2.25 7 2.4 0.75 8 2.4 2.25 8.5 2.25 3 3.5 2.4 0.75 6.5 2.1 0.75 6.5 2.1 3 10.5 1.8 3 8.5 2.7 3 8 2.1 3 10 2.1 1.5 10 2.4 3 9.5 2.55 3 9 2.55 3 10 2.1 3 7.5 2.1 1.5 4.5 2.1 2.25 4.5 2.25 0.75 0.5 2.25 0.75 6.5 2.1 2.25 4.5 2.1 3 5.5 1.95 2.25 5 2.4 3 6 2.1 2.25 4 2.4 3 8 2.4 1.5 10.5 2.4 3 6.5 1.95 0.75 8 2.1 1.5 8.5 2.1 3 5.5 2.55 3 7 2.1 3 5 2.1 2.25 3.5 2.1 2.25 5 1.95 3 9 2.25 1.5 8.5 2.4 2.25 5 1.95 2.25 9.5 2.1 2.25 3 2.1 0.75 1.5 1.95 2.25 6 2.1 1.5 0.5 2.1 2.25 6.5 1.95 1.5 7.5 2.1 0.75 4.5 1.95 1.5 8 2.4 1.5 9 2.4 2.25 7.5 2.4 1.5 8.5 1.95 1.5 7 2.7 3 9.5 2.1 2.25 6.5 1.95 1.5 9.5 2.1 0.75 6 1.95 2.25 8 2.1 3 9.5 2.25 3 8 2.7 1.5 8 2.1 1.5 9 2.4 2.25 5 1.35 0.75
Names of X columns:
Ex PA PE
Response : Variable 1
Factor : Variable 2
Factor : Variable 3
Include Intercept Term ?
FALSE
TRUE
FALSE
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
par4 <- 'FALSE' par3 <- '' par2 <- '' par1 <- '' 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')
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