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Data X:
NA 501 NA 488 NA 504 NA 578 NA 545 NA 632 NA 728 NA 725 NA 585 NA 542 NA 480 NA 530 NA 518 NA 489 NA 528 NA 599 NA 572 NA 659 NA 739 NA 758 NA 602 NA 587 NA 497 NA 558 NA 555 NA 523 NA 532 NA 623 NA 598 NA 683 NA 774 NA 780 NA 609 NA 604 NA 531 NA 592 76.83 578 77.74 543 80.47 565 79.56 648 82.28 615 100.92 697 113.2 785 90.92 830 86.83 645 82.74 643 83.65 551 80.92 606 83.19 585 83.65 553 83.65 576 83.65 665 86.83 656 100.47 720 91.38 826 101.38 838 95.92 652 88.19 661 88.19 584 80.47 644 80.92 623 79.56 553 80.92 599 88.19 657 91.83 680 96.38 759 97.29 878 102.29 881 99.1 705 92.74 684 87.29 577 85.47 656 91.38 645 92.74 593 89.56 617 88.65 686 93.2 679 99.56 773 109.11 906 124.56 934 115.47 713 96.38 710 92.29 600 86.83 676 87.29 645 85.92 602 85.92 601 88.65 709 91.83 706 112.29 817 101.83 930 125.02 983 102.74 745 95.01 735 91.83 620 86.38 698 87.29 665 88.19 626 89.1 649 89.1 740 103.65 729 127.75 824 125.47 937 125.47 994 109.11 781 100.01 759 95.01 643 85.01 728 86.83 691 86.83 649 86.83 656 86.83 735 100.47 748 111.38 837 105.47 995 102.74 1040 105.01 809 96.38 793 94.1 692 86.83 763 92.74 723 93.2 655 95.47 658 96.38 761 99.56 768 120.47 885 123.2 1067 114.11 1038 120.93 812 102.74 790 101.83 692 95.47 782 100.01 758 100.01 709 98.2 715 100.01 788 103.65 794 114.56 893 134.11 1046 131.84 1075 113.65 812 107.29 822 102.29 714 94.56 802 97.29 748 98.2 731 95.47 748 100.47 827 116.38 788 117.29 937 140.93 1076 120.02 1125 111.38 840 108.65 864 105.92 717 99.1 813 101.83 811 102.74 732 102.74 745 105.47 844 108.65 833 139.57 935 110.47 1110 118.65 1124 120.02 868 109.11 860 108.2 762 101.38 877 106.38 NA 108.65 NA 107.74 NA 105.92 NA 129.56 NA 139.11 NA 125.93 NA 123.65 NA 118.65 NA 110.47 NA 110.02 NA 100.47 NA 104.1 NA 106.6 NA 105.5 NA 107.5 NA 117.9 NA 136.3 NA 156.8 NA 135.8 NA 130 NA 117.5 NA 115.8 NA 105.5 NA 111.6 NA 113.2 NA 113.1 NA 112.5 NA 120 NA 147.6 NA 149.9 NA 131.2 NA 134.6 NA 122.2 NA 117.7 NA 106.8 NA 111.5 NA 111.3 NA 109.5 NA 112.1 NA 127 NA 135.9 NA 150.4 NA 135.6 NA 134.9 NA 124.1 NA 120.8 NA 112.8 NA 117.4 NA 118.6 NA 119.2 NA 119.7 NA 128.6 NA 142.8 NA 170 NA 145.9 NA 140.1 NA 128.7 NA 123.4 NA 114.6 NA 120.2 NA 122 NA 121.3 NA 123.2 NA 141.1 NA 129.7 NA 152.4 NA 141.9 NA 137 NA 129 NA 124.6 NA 117.3 NA 122.7 NA 121 NA 122 NA 122 NA 126.3 NA 158.1 NA 164.9 NA 143.3 NA 151.4 NA 136.8 NA 133.1 NA 124.8 NA 132.6 NA 130.2 NA 129.6 NA 129.7 NA 133.7 NA 148.3 NA 155.1 NA 157.2 NA 147.2 NA 142.7 NA 135.9 NA 123.8 NA 132.3 NA 132.7 NA 130.7 NA 129.9 NA 145.5 NA 156.6 NA 161.7 NA 156 NA 146.1 NA 136.8 NA 132.5 NA 129.5 NA 129.5 NA 134.7 NA 136.6 NA 138.4 NA 149.6 NA 159.5 NA 171.4 NA 162.1 NA 163.1 NA 152.4 NA 145.5 NA 133.9 NA 136.6 NA 139.4 NA 141.2 NA 144.9 NA 181.4 NA 187 NA 211.4 NA 178.1 NA 168 NA 154.4 NA 150.4 NA 139.4 NA 144.7 NA 143 NA 148.3 NA 152.7 NA 173.3 NA 226.3 NA 218.2 NA 184.6 NA 174.9 NA 161.4 NA 161.4 NA 145.8 NA
Names of X columns:
water_usage occupied_rooms
Response : Variable 1
Factor : Variable 2
Factor : Variable 3
Include Intercept Term ?
0
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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1 seconds
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Big Analytics Cloud Computing Center
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