Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
3 36 3 1 36 6 3 56 8 3 48 8 3 32 7 1 44 5 2 39 7 3 34 8 3 41 9 2 50 9 1 39 3 1 62 9 3 52 7 3 37 9 3 50 8 1 41 6 3 55 7 3 41 8 3 56 9 2 39 7 2 52 6 1 46 8 1 44 7 3 48 7 3 41 8 1 50 9 1 50 9 3 44 7 1 52 4 2 54 7 3 44 7 2 52 9 2 37 7 3 52 9 1 50 10 3 36 5 3 50 6 1 52 9 2 55 9 1 31 8 1 36 6 2 49 6 1 42 5 1 37 8 3 41 8 2 30 5 2 52 6 1 30 9 1 41 8 1 44 4 1 66 8 2 48 9 3 43 7 3 57 7 3 46 6 3 54 9 2 48 9 2 48 8 2 52 4 3 62 6 3 58 10 2 58 8 2 62 7 2 48 7 3 46 8 2 34 3 3 66 8 3 52 10 2 55 7 2 55 5 3 57 10 3 56 5 2 55 8 1 56 9 2 54 6 2 55 9 2 46 8 2 52 5 3 32 8 2 44 3 3 46 7 1 59 8 3 46 10 3 46 9 1 54 10 1 66 9 2 56 8 1 59 8 3 57 8 1 52 9 3 48 4 2 44 6 3 41 7 1 50 4 1 48 9 2 48 7 3 59 8 1 34 3 46 8 1 54 7 2 55 7 2 54 9 3 59 8 3 44 8 1 54 9 3 52 9 3 66 10 3 44 7 2 57 8 3 39 5 3 60 9 3 45 8 3 41 7 1 50 8 3 39 8 1 43 7 3 48 6 3 37 7 3 58 7 2 46 6 3 43 6 2 44 7 3 34 9 2 30 6 2 50 10 2 39 4 3 37 8 3 55 7 1 48 10 1 41 3 39 5 3 36 9 2 43 8 1 50 9 3 55 8 3 43 8 3 60 9 2 48 8 1 30 9 3 43 7 3 39 6 3 52 8 2 39 6 3 39 5 2 56 3 3 59 6 3 46 8 2 57 7 3 50 8 3 54 6 3 50 9 3 60 9 1 59 10 3 41 7 2 48 5 3 59 8 3 60 9 1 56 8 1 56 8 3 51 4
Names of X columns:
MOMAGE MC30VRB MWARM30
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
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){ 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<-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')
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