Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
'Female' 12.9 'Female' 7.4 'Male' 12.2 'Female' 12.8 'Male' 7.4 'Male' 6.7 'Male' 12.6 'Female' 14.8 'Male' 13.3 'Male' 11.1 'Male' 8.2 'Male' 11.4 'Male' 6.4 'Male' 10.6 'Female' 12.0 'Female' 6.3 'Female' 11.3 'Male' 11.9 'Female' 9.3 'Male' 9.6 'Female' 10.0 'Male' 13.8 'Female' 10.8 'Male' 13.8 'Male' 11.7 'Male' 10.9 'Male' 16.1 'Female' 13.4 'Male' 9.9 'Female' 11.5 'Female' 8.3 'Female' 11.7 'Male' 9.0 'Male' 9.7 'Male' 10.8 'Male' 10.3 'Female' 10.4 'Male' 12.7 'Male' 9.3 'Female' 11.8 'Male' 5.9 'Male' 11.4 'Male' 13.0 'Male' 10.8 'Male' 12.3 'Female' 11.3 'Male' 11.8 'Male' 7.9 'Female' 12.7 'Male' 12.3 'Male' 11.6 'Male' 6.7 'Male' 10.9 'Male' 12.1 'Male' 13.3 'Male' 10.1 'Female' 5.7 'Male' 14.3 'Female' 8.0 'Male' 13.3 'Male' 9.3 'Female' 12.5 'Female' 7.6 'Male' 15.9 'Female' 9.2 'Male' 9.1 'Female' 11.1 'Male' 13.0 'Male' 14.5 'Female' 12.2 'Female' 12.3 'Female' 11.4 'Female' 8.8 'Male' 14.6 'Male' 7.3 'Female' 12.6 'Male' NA 'Female' 13.0 'Male' 12.6 'Female' 13.2 'Female' 9.9 'Male' 7.7 'Female' 10.5 'Female' 13.4 'Female' 10.9 'Male' 4.3 'Female' 10.3 'Male' 11.8 'Male' 11.2 'Female' 11.4 'Female' 8.6 'Female' 13.2 'Male' 12.6 'Male' 5.6 'Male' 9.9 'Female' 8.8 'Male' 7.7 'Female' 9.0 'Male' 7.3 'Male' 11.4 'Male' 13.6 'Male' 7.9 'Male' 10.7 'Female' 10.3 'Male' 8.3 'Male' 9.6 'Male' 14.2 'Female' 8.5 'Female' 13.5 'Female' 4.9 'Female' 6.4 'Female' 9.6 'Female' 11.6 'Male' 11.1 'Male' 4.35 'Male' 12.7 'Male' 18.1 'Male' 17.85 'Female' 16.6 'Male' 12.6 'Male' 17.1 'Female' 19.1 'Male' 16.1 'Female' 13.35 'Female' 18.4 'Male' 14.7 'Male' 10.6 'Male' 12.6 'Male' 16.2 'Male' 13.6 'Male' 18.9 'Male' 14.1 'Male' 14.5 'Female' 16.15 'Male' 14.75 'Male' 14.8 'Male' 12.45 'Male' 12.65 'Male' 17.35 'Male' 8.6 'Female' 18.4 'Male' 16.1 'Male' 11.6 'Male' 17.75 'Male' 15.25 'Male' 17.65 'Female' 15.6 'Female' 16.35 'Female' 17.65 'Male' 13.6 'Female' 11.7 'Female' 14.35 'Female' 14.75 'Male' 18.25 'Female' 9.9 'Male' 16 'Male' 18.25 'Female' 16.85 'Male' 14.6 'Male' 13.85 'Male' 18.95 'Female' 15.6 'Female' 14.85 'Female' 11.75 'Female' 18.45 'Male' 15.9 'Female' 17.1 'Male' 16.1 'Female' 19.9 'Male' 10.95 'Female' 18.45 'Male' 15.1 'Female' 15 'Female' 11.35 'Male' 15.95 'Female' 18.1 'Male' 14.6 'Male' 15.4 'Male' 15.4 'Male' 17.6 'Male' 13.35 'Female' 19.1 'Male' 15.35 'Female' 7.6 'Female' 13.4 'Female' 13.9 'Male' 19.1 'Female' 15.25 'Male' 12.9 'Female' 16.1 'Female' 17.35 'Female' 13.15 'Female' 12.15 'Male' 12.6 'Male' 10.35 'Male' 15.4 'Male' 9.6 'Female' 18.2 'Female' 13.6 'Male' 14.85 'Female' 14.75 'Female' 14.1 'Female' 14.9 'Female' 16.25 'Male' 19.25 'Male' 13.6 'Female' 13.6 'Female' 15.65 'Male' 12.75 'Female' 14.6 'Male' 9.85 'Male' 12.65 'Male' 11.9 'Female' 19.2 'Male' 16.6 'Male' 11.2 'Male' 15.25 'Female' 11.9 'Female' 13.2 'Female' 16.35 'Male' 12.4 'Male' 15.85 'Female' 14.35 'Male' 18.15 'Male' 11.15 'Female' 15.65 'Female' 17.75 'Female' 7.65 'Male' 12.35 'Male' 15.6 'Female' 19.3 'Female' 15.2 'Female' 17.1 'Male' 15.6 'Male' 18.4 'Female' 19.05 'Female' 18.55 'Female' 19.1 'Male' 13.1 'Male' 12.85 'Male' 9.5 'Male' 4.5 'Female' 11.85 'Male' 13.6 'Male' 11.7 'Male' 12.4 'Female' 13.35 'Female' 11.4 'Male' 14.9 'Female' 19.9 'Male' 17.75 'Male' 11.2 'Male' 14.6 'Female' 17.6 'Male' 14.05 'Female' 16.1 'Male' 13.35 'Male' 11.85 'Female' 11.95 'Male' 14.75 'Female' 15.15 'Male' 13.2 'Female' 16.85 'Male' 7.85 'Female' 7.7 'Female' 12.6 'Male' 7.85 'Male' 10.95 'Female' 12.35 'Male' 9.95 'Male' 14.9 'Female' 16.65 'Male' 13.4 'Female' 13.95 'Female' 15.7 'Male' 16.85 'Male' 10.95 'Female' 15.35 'Male' 12.2 'Female' 15.1 'Female' 17.75 'Male' 15.2 'Female' 14.6 'Female' 16.65 'Male' 8.1
Names of X columns:
gender TOT
Response Variable (column number)
Factor Variable (column number)
Include Intercept Term ?
TRUE
FALSE
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
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')
Compute
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
Click here to blog (archive) this computation