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Data X:
94.8 93.7 95.4 94.8 96.1 96.2 88.7 87.1 91.2 91.6 91.0 88.7 96.6 96.6 91.9 92.1 94.5 92.8 91.8 91.9 96.3 95.1 90.0 89.9 97.0 96.3 97.4 95.5 93.4 94.0 95.0 95.6 95.2 95.3 95.5 94.5 94.3 93.3 94.7 92.4 95.6 95.8 92.4 92.7 91.6 92.0 92.7 92.8 94.6 94.6 94.0 94.2 97.0 96.9 96.6 96.3 93.4 92.3 94.3 92.9 93.7 94.1 91.9 92.6 92.6 92.9 102.8 100.9 92.6 90.6 94.7 94.3 92.3 89.7 95.5 95.9 94.5 93.7 91.2 89.4 96.8 95.9 96.7 96.9 98.0 97.4 92.5 90.7 92.4 92.6 95.0 94.6 94.9 95.0 93.6 93.7 95.7 95.9 94.2 94.1 93.9 92.8 99.9 97.3 94.7 95.0 92.6 90.1 98.3 97.6 93.8 93.6 93.7 93.7 102.3 100.4 99.2 99.0 93.9 93.8 94.7 95.0 90.9 88.5 92.4 92.6 96.9 97.0 95.3 93.6 84.2 82.5 97.4 97.4 94.7 92.6 92.2 92.5 90.5 88.6 97.0 96.9 93.7 93.9 93.8 94.1 93.7 93.2 95.6 96.2 95.9 94.2 96.3 97.4 94.3 94.3 95.8 94.4 99.0 97.1 94.2 91.7 91.5 91.9 93.2 93.4 94.5 94.8 95.3 94.1 93.3 93.9 92.5 90.7 97.1 96.8 95.1 95.9 95.0 95.1 90.2 88.2 90.0 87.7 95.4 94.8 96.1 95.8 92.6 93.2 93.4 93.6 97.4 94.8 96.2 96.0 93.6 93.5 94.1 93.5 96.8 96.5 90.1 87.6 94.6 95.6 93.9 94.0 91.7 89.2 95.7 95.4 98.1 97.9 94.9 94.7 91.4 88.8 94.5 92.0 95.6 95.9 92.4 93.0 99.3 99.4 93.3 93.5 95.0 95.1 99.7 99.5 97.0 97.0 95.7 95.9 94.6 94.9 95.7 95.9 92.3 90.2 91.4 90.3 89.5 87.5 94.7 91.8 95.8 94.5 98.9 98.4 95.1 95.5 93.1 93.2 90.2 90.2 97.3 97.5 89.8 87.8 94.6 93.0 94.7 94.4 94.0 94.4 88.7 89.3 94.0 92.6 96.4 96.4 95.7 95.9 93.4 93.7 97.3 97.0 92.0 91.5 95.5 96.1 93.9 93.9 96.6 96.2 98.0 96.1 94.5 94.8 95.5 95.3 93.3 93.4 94.8 95.0 92.2 90.1 96.7 95.0 94.4 94.9 94.3 93.6 94.5 94.9 90.4 88.1 96.1 95.0 97.5 97.7 93.1 93.3 95.0 94.4 91.6 89.3 93.8 93.9 95.2 95.3 94.8 94.9 93.7 93.1 93.6 93.5 93.6 93.4 93.9 94.2 95.5 94.9 93.8 93.5 94.4 93.9 94.0 93.8 95.7 93.8 93.1 93.1 93.6 95.0 96.0 96.0 94.8 94.4 94.9 95.2 94.6 95.1 89.2 86.3 91.6 91.9 95.3 95.7 95.5 95.6 95.1 95.1 95.9 96.0 97.8 97.8 97.2 94.3 100.6 98.5 93.3 92.2 89.9 90.4 97.5 96.1 92.9 91.3 94.1 94.1 97.6 97.2 101.6 100.8 94.2 94.5 92.7 90.4 93.6 94.0 92.2 92.8 94.2 92.8 92.0 89.5 94.1 94.2 96.0 95.6 94.5 94.5 93.4 93.0 94.5 95.1 91.9 90.0 93.5 93.6 95.8 96.5 93.8 94.2 96.0 96.5 90.8 89.8 88.9 87.3 95.0 95.7 92.4 92.5 94.9 94.4 94.8 92.7 89.6 90.7 94.9 95.2 95.5 96.2 96.1 96.4 95.6 94.3 97.7 98.4 93.9 94.3 95.5 95.4 92.7 92.6 92.0 90.4 95.7 94.0 94.8 93.9 95.8 96.8 92.4 90.7 94.3 92.8 95.6 93.9 93.1 93.5 97.3 96.6 97.7 98.2 98.6 96.7 91.4 89.0 84.5 82.1 94.2 94.6 92.8 92.8 91.9 89.6 93.0 91.1 96.5 95.3 93.5 93.9 96.6 96.9 94.7 95.3 92.9 93.3 94.0 94.7 94.9 94.1 96.7 96.5 90.1 87.9 90.9 91.5 90.8 90.6 93.0 92.3 96.8 95.1 97.4 96.5 89.7 90.2 93.9 94.4 92.0 89.9 95.9 95.4 93.6 93.2 90.8 88.8 94.1 92.4 94.3 94.5 97.5 97.7 98.5 96.6 96.0 95.0 91.4 89.4 96.7 95.1 94.3 94.8 96.6 96.2 85.1 83.1 91.7 89.9 91.0 88.6 91.8 91.8 89.9 90.3 91.6 91.7 89.2 89.3 88.7 88.7 88.2 89.4 94.0 92.3 95.9 96.1 91.0 90.9 100.8 99.0 95.5 95.3 94.9 94.4 96.5 94.4 95.3 95.5 98.4 96.4 95.3 95.8 94.2 94.4 94.3 94.6 99.1 99.3 94.1 93.7 96.4 95.3 95.0 95.1 90.1 90.3 91.6 91.3 93.8 92.4 94.2 92.9 92.8 93.1 94.5 94.6 94.4 93.1 94.6 94.7 95.1 95.4 95.8 95.2 91.9 91.1 96.0 95.8 94.9 95.1 94.6 94.6 94.1 92.1 96.5 95.6 93.3 91.5 95.9 95.1 92.7 92.3 92.0 89.8 90.9 88.8 91.7 92.5 95.4 96.3 94.6 92.8 96.7 96.9 94.7 93.8 95.5 93.6 95.4 93.6 96.9 95.9 93.6 93.4 94.7 93.8 96.0 96.0 91.1 89.0 93.7 93.6 94.8 95.6 92.2 90.5 95.3 95.7 95.0 95.2 93.1 93.1 93.7 93.9 96.8 96.0 98.7 98.6 93.9 93.5 95.3 92.4 92.1 92.3 98.5 96.5 91.1 89.2 94.9 93.0 93.6 94.0 95.9 95.9 97.3 96.9 91.9 90.2 91.7 89.6 97.2 95.6 93.4 93.7 95.9 95.9 96.7 96.9 95.6 96.1 94.6 93.4 96.0 95.8 95.2 95.2 92.4 93.2 97.9 96.0 95.4 95.8 96.2 96.2 97.2 95.8 93.3 93.8 96.1 94.5 95.6 94.7 93.9 93.6 94.2 94.9 98.7 95.7 98.0 98.0
Names of X columns:
Response Treatment
Response Variable (column number)
Factor Variable (column number)
Include Intercept Term ?
FALSE
TRUE
FALSE
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
par3 <- 'FALSE' par2 <- '2' par1 <- '1' cat1 <- as.numeric(par1) # cat2<- as.numeric(par2) # intercept<-as.logical(par3) x <- t(x) x1<-as.numeric(x[,cat1]) f1<-as.character(x[,cat2]) xdf<-data.frame(x1,f1) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) names(xdf)<-c('Response', 'Treatment') if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment - 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment, 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$coefficients)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, paste(V1, ' ~ ', V2), length(lmxdf$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) a<-table.row.start(a) a<-table.element(a, V2,,TRUE) a<-table.element(a, anova.xdf$Df[1],,FALSE) a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3),,FALSE) a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3),,FALSE) a<-table.element(a, round(anova.xdf$'F value'[1], digits=3),,FALSE) a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3),,FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residuals',,TRUE) a<-table.element(a, anova.xdf$Df[2],,FALSE) a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3),,FALSE) a<-table.element(a, round(anova.xdf$'Mean Sq'[2], 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, data=xdf, xlab=V2, ylab=V1) dev.off() if(intercept==TRUE){ 'Tukey Plot' thsd<-TukeyHSD(aov.xdf) bitmap(file='TukeyHSDPlot.png') plot(thsd) dev.off() } if(intercept==TRUE){ 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(i in 1:length(rownames(thsd[[1]]))){ a<-table.row.start(a) a<-table.element(a,rownames(thsd[[1]])[i], 1, TRUE) for(j in 1:4){ a<-table.element(a,round(thsd[[1]][i,j], digits=3), 1, FALSE) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') } 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<-leveneTest(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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Raw Output
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Big Analytics Cloud Computing Center
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