Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
36 3 NA 36 1 88 56 3 94 48 3 90 32 3 73 44 1 68 39 2 80 34 3 86 41 3 86 50 2 91 39 1 79 62 1 96 52 3 92 37 3 72 50 3 96 41 1 70 55 3 86 41 3 87 56 3 88 39 2 79 52 2 90 46 1 95 44 1 85 48 3 NA 41 3 90 50 1 115 50 1 84 44 3 79 52 1 94 54 2 97 44 3 86 52 2 111 37 2 87 52 3 98 50 1 87 36 3 68 50 3 88 52 1 82 55 2 111 31 1 75 36 1 94 49 2 95 42 1 80 37 1 95 41 3 68 30 2 94 52 2 88 30 1 84 41 1 NA 44 1 101 66 1 98 48 2 78 43 3 109 57 3 102 46 3 81 54 3 97 48 2 75 48 2 97 52 2 NA 62 3 101 58 3 101 58 2 95 62 2 95 48 2 NA 46 3 95 34 2 90 66 3 107 52 3 92 55 2 86 55 2 70 57 3 95 56 3 96 55 2 91 56 1 87 54 2 92 55 2 97 46 2 102 52 2 91 32 3 68 44 2 88 46 3 97 59 1 90 46 3 101 46 3 94 54 1 101 66 1 109 56 2 100 59 1 103 57 3 94 52 1 97 48 3 85 44 2 75 41 3 77 50 1 87 48 1 78 48 2 108 59 3 97 34 1 105 46 3 106 54 1 107 55 2 95 54 2 107 59 3 115 44 3 101 54 1 85 52 3 90 66 3 115 44 3 95 57 2 97 39 3 112 60 3 97 45 3 77 41 3 90 50 1 94 39 3 103 43 1 77 48 3 98 37 3 90 58 3 111 46 2 77 43 3 88 44 2 75 34 3 92 30 2 78 50 2 106 39 2 80 37 3 87 55 3 92 48 1 NA 41 1 111 39 3 86 36 3 85 43 2 90 50 1 101 55 3 94 43 3 86 60 3 86 48 2 90 30 1 75 43 3 86 39 3 91 52 3 97 39 2 91 39 3 70 56 2 98 59 3 96 46 3 95 57 2 100 50 3 95 54 3 97 50 3 97 60 3 92 59 1 115 41 3 88 48 2 87 59 3 100 60 3 98 56 1 102 56 1 NA 51 3 96
Names of X columns:
MC30VRB MOMAGE WISCRY7V
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){ '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
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation