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Data X:
0 83.00 0 74.25 0 58.75 0 92.25 0 99.50 0 92.25 0 75.00 0 56.75 0 90.50 0 67.00 0 69.50 0 76.25 0 80.50 0 86.75 0 65.75 0 60.75 0 91.00 0 68.00 0 70.50 0 74.50 0 81.25 0 78.25 0 73.00 0 96.00 0 66.00 0 78.25 0 38.25 0 76.00 0 59.25 0 57.00 0 99.50 0 75.75 0 84.25 0 63.00 0 61.75 0 83.25 0 69.75 0 78.50 0 76.75 0 75.50 0 88.75 0 83.25 0 95.50 0 66.75 0 92.00 0 80.75 0 92.00 0 78.00 0 81.75 0 88.25 0 58.50 0 71.75 0 73.75 0 49.50 0 84.25 0 78.00 0 85.50 0 95.50 0 38.00 0 73.75 0 68.00 0 59.50 0 81.75 0 71.75 0 88.75 0 96.50 0 85.50 0 95.25 0 92.75 0 95.50 0 66.75 0 88.00 0 80.50 0 59.75 0 38.50 0 73.00 1 63.00 1 94.50 1 58.00 1 73.00 1 69.25 1 79.50 1 54.75 1 75.50 1 79.75 1 73.00 1 88.00 1 76.75 1 64.50 1 63.00 1 51.75 1 77.00 1 48.00 1 74.25 1 68.00 1 63.25 1 59.50 1 83.00 1 56.00 1 79.25 1 55.75 1 78.00 1 65.50 1 62.00 1 74.50 1 56.00 1 73.00 1 73.75 1 39.25 1 39.25 1 54.75 1 49.75 1 74.50 1 67.00 1 84.25 1 54.75 1 61.00 1 76.00 1 40.50 1 21.75 1 63.50 1 90.50 1 89.25 1 85.50 1 80.50 1 73.50 1 53.00 1 63.00 1 81.00 1 68.00 1 70.50 1 72.50 1 73.75 1 74.00 1 62.25 1 63.25 1 86.75 1 43.00 1 80.50 1 88.75 1 76.25 1 88.25 1 68.00 1 91.25 1 80.00 1 91.25 1 94.75 1 80.50 1 77.00 1 77.00 1 66.75 1 95.50 1 96.25 1 63.75 1 49.25 1 76.25 1 62.00 1 90.75 1 61.75 1 78.00 1 92.00 1 64.25 1 47.50 1 22.50 1 68.00 1 58.50 1 88.75 1 70.25 1 66.75 1 59.25 1 66.00
Names of X columns:
Gender Totaalscoreperc
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 <- '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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