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Data X:
0.00 83.00 0.00 63.00 0.00 94.50 0.00 58.00 0.00 73.00 0.00 69.25 0.00 74.25 0.00 58.75 0.00 92.25 0.00 79.50 0.00 99.50 0.00 54.75 0.00 92.25 0.00 75.50 0.00 75.00 0.00 56.75 0.00 79.75 0.00 90.50 0.00 73.00 0.00 88.00 0.00 76.75 0.00 67.00 0.00 69.50 0.00 76.25 0.00 64.50 0.00 80.50 0.00 86.75 0.00 65.75 0.00 60.75 0.00 63.00 0.00 51.75 0.00 77.00 0.00 48.00 0.00 91.00 0.00 68.00 0.00 74.25 0.00 70.50 0.00 74.50 0.00 81.25 0.00 68.00 0.00 78.25 0.00 73.00 0.00 63.25 0.00 59.50 0.00 96.00 0.00 83.00 0.00 56.00 0.00 66.00 0.00 79.25 0.00 55.75 0.00 78.25 0.00 38.25 0.00 76.00 0.00 78.00 0.00 65.50 0.00 59.25 0.00 62.00 0.00 57.00 0.00 74.50 0.00 99.50 0.00 56.00 0.00 73.00 0.00 73.75 0.00 75.75 0.00 84.25 0.00 39.25 0.00 63.00 0.00 39.25 0.00 54.75 0.00 61.75 0.00 49.75 0.00 74.50 0.00 83.25 0.00 67.00 0.00 69.75 0.00 78.50 1.00 84.25 1.00 54.75 1.00 76.75 1.00 61.00 1.00 75.50 1.00 88.75 1.00 76.00 1.00 83.25 1.00 40.50 1.00 21.75 1.00 63.50 1.00 90.50 1.00 89.25 1.00 85.50 1.00 95.50 1.00 80.50 1.00 66.75 1.00 92.00 1.00 73.50 1.00 53.00 1.00 63.00 1.00 81.00 1.00 68.00 1.00 70.50 1.00 72.50 1.00 80.75 1.00 73.75 1.00 74.00 1.00 62.25 1.00 63.25 1.00 86.75 1.00 43.00 1.00 92.00 1.00 80.50 1.00 88.75 1.00 76.25 1.00 88.25 1.00 78.00 1.00 81.75 1.00 88.25 1.00 68.00 1.00 58.50 1.00 71.75 1.00 73.75 1.00 91.25 1.00 49.50 1.00 80.00 1.00 91.25 1.00 84.25 1.00 94.75 1.00 78.00 1.00 85.50 1.00 80.50 1.00 77.00 1.00 77.00 1.00 66.75 1.00 95.50 1.00 38.00 1.00 95.50 1.00 73.75 1.00 96.25 1.00 68.00 1.00 63.75 1.00 49.25 1.00 76.25 1.00 59.50 1.00 81.75 1.00 62.00 1.00 71.75 1.00 90.75 1.00 88.75 1.00 61.75 1.00 78.00 1.00 96.50 1.00 85.50 1.00 92.00 1.00 95.25 1.00 92.75 1.00 95.50 1.00 64.25 1.00 47.50 1.00 22.50 1.00 68.00 1.00 58.50 1.00 66.75 1.00 88.75 1.00 88.00 1.00 70.25 1.00 80.50 1.00 66.75 1.00 59.25 1.00 59.75 1.00 66.00 1.00 38.50 1.00 73.00
Names of X columns:
Gender Totalscore
Response Variable (column number)
Factor Variable (column number)
Include Intercept Term ?
TRUE
TRUE
FALSE
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
par3 <- 'FALSE' par2 <- '1' par1 <- '2' 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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