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Data X:
26 0 2011 51 0 2011 57 0 2011 37 0 2011 67 0 2011 43 0 2011 52 0 2011 52 0 2011 43 0 2011 84 0 2011 67 0 2011 49 0 2011 70 0 2011 52 0 2011 58 0 2011 68 0 2011 62 1 2011 43 0 2011 56 0 2011 56 1 2011 74 0 2011 65 0 2011 63 0 2011 58 0 2011 57 0 2011 63 0 2011 53 0 2011 57 1 2011 51 1 2011 64 0 2011 53 0 2011 29 0 2011 54 0 2011 51 0 2011 58 0 2011 43 0 2011 51 0 2011 53 0 2011 54 0 2011 56 1 2011 61 0 2011 47 0 2011 39 0 2011 48 0 2011 50 0 2011 35 0 2011 30 1 2011 68 0 2011 49 0 2011 61 1 2011 67 0 2011 47 1 2011 56 1 2011 50 1 2011 43 0 2011 67 1 2011 62 0 2011 57 0 2011 41 1 2011 54 0 2011 45 1 2011 48 1 2011 61 0 2011 56 0 2011 41 0 2011 43 0 2011 53 0 2011 44 1 2011 66 0 2011 58 0 2011 46 0 2011 37 1 2011 51 0 2011 51 0 2011 56 1 2011 66 1 2011 45 0 2011 37 0 2011 59 0 2011 42 0 2011 38 1 2011 66 0 2011 34 1 2011 53 0 2011 49 1 2011 55 1 2011 49 1 2011 59 1 2011 40 1 2011 58 1 2011 60 1 2011 63 1 2011 56 1 2011 54 1 2011 52 1 2011 34 1 2011 69 1 2011 32 1 2011 48 1 2011 67 1 2011 58 1 2011 57 1 2011 42 1 2011 64 1 2011 58 1 2011 66 1 2011 26 1 2011 61 1 2011 52 1 2011 51 1 2011 55 1 2011 50 1 2011 60 1 2011 56 1 2011 63 1 2011 61 1 2011 52 0 2012 16 0 2012 46 0 2012 56 0 2012 52 1 2012 55 1 2012 50 0 2012 59 0 2012 60 0 2012 52 0 2012 44 0 2012 67 0 2012 52 0 2012 55 0 2012 37 0 2012 54 0 2012 72 1 2012 51 0 2012 48 0 2012 60 0 2012 50 0 2012 63 0 2012 33 0 2012 67 0 2012 46 0 2012 54 0 2012 59 0 2012 61 0 2012 33 1 2012 47 0 2012 69 0 2012 52 0 2012 55 0 2012 55 0 2012 41 0 2012 73 0 2012 51 0 2012 52 0 2012 50 0 2012 51 0 2012 60 0 2012 56 0 2012 56 0 2012 29 0 2012 66 1 2012 66 1 2012 73 0 2012 55 0 2012 64 1 2012 40 1 2012 46 1 2012 58 1 2012 43 0 2012 61 0 2012 51 1 2012 50 1 2012 52 1 2012 54 1 2012 66 1 2012 61 1 2012 80 1 2012 51 1 2012 56 1 2012 56 0 2012 56 0 2012 53 1 2012 47 0 2012 25 0 2012 47 1 2012 46 0 2012 50 1 2012 39 1 2012 51 0 2012 58 1 2012 35 1 2012 58 1 2012 60 1 2012 62 1 2012 63 1 2012 53 1 2012 46 1 2012 67 1 2012 59 1 2012 64 1 2012 38 1 2012 50 1 2012 48 0 2012 48 1 2012 47 1 2012 66 1 2012 47 0 2012 63 1 2012 58 0 2012 44 1 2012 51 0 2012 43 1 2012 55 0 2012 38 1 2012 56 1 2012 45 1 2012 50 1 2012 54 1 2012 57 0 2012 60 0 2012 55 1 2012 56 0 2012 49 0 2012 37 1 2012 43 0 2012 59 0 2012 46 1 2012 51 1 2012 58 0 2012 64 1 2012 53 0 2012 48 0 2012 51 0 2012 47 1 2012 59 0 2012 62 1 2012 62 0 2012 51 0 2012 64 0 2012 52 0 2012 67 1 2012 50 0 2012 54 0 2012 58 0 2012 56 1 2012 63 0 2012 31 0 2012 65 1 2012 71 0 2012 50 1 2012 57 1 2012 47 1 2012 54 0 2012 47 1 2012 57 1 2012 43 0 2012 41 0 2012 63 0 2012 63 0 2012 56 0 2012 51 0 2012 50 1 2012 22 1 2012 41 0 2012 59 1 2012 56 1 2012 66 0 2012 53 1 2012 42 1 2012 52 1 2012 54 1 2012 44 1 2012 62 1 2012 53 1 2012 50 1 2012 36 1 2012 76 1 2012 66 1 2012 62 1 2012 59 1 2012 47 1 2012 55 1 2012 58 1 2012 60 1 2012 44 0 2012 57 1 2012 45 1 2012 58 0 2014 51 0 2014 57 0 2014 30 0 2014 46 0 2014 51 0 2014 56 0 2014 58 0 2014 44 0 2014 14 0 2014 53 0 2014 42 0 2014 49 1 2014 44 0 2014 62 1 2014 30 0 2014 46 0 2014 56 1 2014 50 0 2014 54 0 2014 48 0 2014 55 0 2014 35 0 2014 55 0 2014 41 0 2014 59 0 2014 54 0 2014 66 0 2014 55 0 2014 45 0 2014 51 0 2014 47 0 2014 42 0 2014 53 0 2014 53 0 2014 41 0 2014 55 0 2014 55 0 2014 46 0 2014 63 0 2014 43 0 2014 65 0 2014 59 0 2014 39 0 2014 44 0 2014 60 1 2014 57 0 2014 67 1 2014 52 1 2014 52 1 2014 69 0 2014 46 0 2014 46 0 2014 53 1 2014 40 0 2014 70 0 2014 54 0 2014 77 0 2014 45 1 2014 60 0 2014 47 1 2014 50 0 2014 66 0 2014 60 0 2014 41 1 2014 53 1 2014 34 1 2014 51 0 2014 69 0 2014 60 0 2014 45 1 2014 58 0 2014 39 0 2014 51 0 2014 52 0 2014 49 0 2014 63 0 2014 44 1 2014 51 0 2014 52 0 2014 60 1 2014 53 1 2014 53 1 2014 52 0 2014 31 0 2014 51 1 2014 65 1 2014 51 1 2014 49 1 2014 61 0 2014 58 1 2014 62 1 2014 54 0 2014 52 1 2014 72 0 2014 50 1 2014 65 0 2014 53 1 2014 56 0 2014 63 0 2014 62 1 2014 66 1 2014 50 1 2014 45 0 2014 58 1 2014 52 0 2014 53 1 2014 68 0 2014 59 1 2014 58 1 2014 52 1 2014 45 0 2014 58 1 2014 70 0 2014 69 0 2014 71 1 2014 46 0 2014 58 1 2014 39 0 2014 46 1 2014 64 1 2014 67 1 2014 44 1 2014 54 0 2014 41 0 2014 68 0 2014 63 0 2014 57 0 2014 61 0 2014 39 0 2014 69 1 2014 64 1 2014 38 1 2014 59 0 2014 51 0 2014 59 1 2014 51 0 2014 65 0 2014 47 1 2014 50 0 2014 57 1 2014 21 0 2014 47 0 2014 51 1 2014 37 0 2014 67 1 2014 43 1 2014 58 0 2014 51 0 2014 40 0 2014 41 1 2014 58 1 2014 64 1 2014 64 0 2014 58 0 2014 50 1 2014 59 1 2014 55 1 2014 59 1 2014 58 1 2014 41 1 2014 56 0 2014 63 0 2014 77 1 2014 60 0 2014 58 1 2014 64 0 2014 47 0 2014 46 0 2014 62 1 2014 60 1 2014 50 0 2014 46 0 2014 44 0 2014 58 0 2014 56 1 2014 43 1 2014 54 1 2014 54 1 2014 56 1 2014 65 1 2014 66 1 2014 62 1 2014 58 0 2014 67 1 2014 25 0 2014 56 0 2014 53 1 2014 56 0 2014 59 0 2014 46 0 2014 49 1 2014 56 1 2014 76 1 2014 33 1 2014 49 0 2014 53 0 2014 58 0 2014 72 1 2014 51 1 2014 42 1 2014 69 1 2014 51 1 2014 54 0 2014 52 0 2014 59 0 2014 51 1 2014 67 1 2014 64 1 2014 58 1 2014
Names of X columns:
AMS.I Opleiding_binair Jaar
Response : Variable 1
Factor : Variable 2
Factor : Variable 3
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) # cat3 <- as.numeric(par3) intercept<-as.logical(par4) x <- t(x) x1<-as.numeric(x[,cat1]) f1<-as.character(x[,cat2]) f2 <- as.character(x[,cat3]) xdf<-data.frame(x1,f1, f2) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) (V3 <-dimnames(y)[[1]][cat3]) names(xdf)<-c('Response', 'Treatment_A', 'Treatment_B') if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment_A * Treatment_B- 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment_A * Treatment_B, 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$coefficients)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, lmxdf$call['formula'],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$coefficients[i], digits=3),,FALSE) } a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'ANOVA Statistics', 5+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',,TRUE) a<-table.element(a, 'Df',,FALSE) a<-table.element(a, 'Sum Sq',,FALSE) a<-table.element(a, 'Mean Sq',,FALSE) a<-table.element(a, 'F value',,FALSE) a<-table.element(a, 'Pr(>F)',,FALSE) a<-table.row.end(a) for(i in 1 : length(rownames(anova.xdf))-1){ a<-table.row.start(a) a<-table.element(a,rownames(anova.xdf)[i] ,,TRUE) a<-table.element(a, anova.xdf$Df[1],,FALSE) a<-table.element(a, round(anova.xdf$'Sum Sq'[i], digits=3),,FALSE) a<-table.element(a, round(anova.xdf$'Mean Sq'[i], digits=3),,FALSE) a<-table.element(a, round(anova.xdf$'F value'[i], digits=3),,FALSE) a<-table.element(a, round(anova.xdf$'Pr(>F)'[i], 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$'Df'[i+1],,FALSE) a<-table.element(a, round(anova.xdf$'Sum Sq'[i+1], digits=3),,FALSE) a<-table.element(a, round(anova.xdf$'Mean Sq'[i+1], digits=3),,FALSE) a<-table.element(a, ' ',,FALSE) a<-table.element(a, ' ',,FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') bitmap(file='anovaplot.png') boxplot(Response ~ Treatment_A + Treatment_B, data=xdf, xlab=V2, ylab=V1, main='Boxplots of ANOVA Groups') dev.off() bitmap(file='designplot.png') xdf2 <- xdf # to preserve xdf make copy for function names(xdf2) <- c(V1, V2, V3) plot.design(xdf2, main='Design Plot of Group Means') dev.off() bitmap(file='interactionplot.png') interaction.plot(xdf$Treatment_A, xdf$Treatment_B, xdf$Response, xlab=V2, ylab=V1, trace.label=V3, main='Possible Interactions Between Anova Groups') dev.off() if(intercept==TRUE){ thsd<-TukeyHSD(aov.xdf) names(thsd) <- c(V2, V3, paste(V2, ':', V3, sep='')) bitmap(file='TukeyHSDPlot.png') layout(matrix(c(1,2,3,3), 2,2)) plot(thsd, las=1) dev.off() } if(intercept==TRUE){ ntables<-length(names(thsd)) 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(nt in 1:ntables){ for(i in 1:length(rownames(thsd[[nt]]))){ a<-table.row.start(a) a<-table.element(a,rownames(thsd[[nt]])[i], 1, TRUE) for(j in 1:4){ a<-table.element(a,round(thsd[[nt]][i,j], digits=3), 1, FALSE) } a<-table.row.end(a) } } # end nt a<-table.end(a) table.save(a,file='hsdtable.tab') }#end if hsd tables 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')
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Computing time
1 seconds
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