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Data X:
1122 1 0 632 1 0 795 1 0 889 1 0 833 1 0 1095 1 0 1099 1 0 1003 1 0 1025 1 0 1131 1 0 656 1 0 1446 1 0 1713 1 0 1073 1 0 1570 1 0 1960 1 0 960 1 0 1315 1 0 876 1 0 1102 1 0 1266 1 0 1331 1 0 1155 1 0 520 1 0 630 1 0 868 1 0 765 1 0 783 1 0 902 1 0 733 1 0 947 1 0 822 1 0 558 1 0 797 1 0 732 1 0 657 1 0 750 1 0 683 1 0 665 1 0 775 1 0 640 1 0 543 1 0 762 1 0 762 1 0 703 1 0 696 1 0 1091 1 1 1028 1 1 903 1 1 1267 1 1 1189 1 1 835 1 1 984 1 1 615 1 1 1328 1 1 823 1 1 716 1 1 1946 1 1 622 1 1 2647 1 1 1102 1 1 801 1 1 839 1 1 1706 1 1 666 1 1 581 1 1 1398 1 1 1267 1 1 1607 1 1 877 1 1 1232 1 1 604 1 1 1443 1 1 583 1 1 768 1 1 909 1 1 713 1 1 764 1 1 1297 1 1 571 1 1 596 1 1 908 1 1 549 1 1 672 1 1 934 1 1 432 1 1 2062 1 1 1335 1 1 1824 1 1 1184 1 1 768 1 1 1019 1 1 1314 1 1 1332 1 1 1045 1 1 767 1 1 1271 1 1 1789 1 1 536 1 1 540 1 1 510 1 1 581 1 1 470 1 1 330 1 1 504 1 1 595 1 1 1597 1 1 1371 1 1 1134 1 1 1055 1 1 1634 1 1 1271 1 1 1143 1 1 1058 1 1 1483 1 1 656 1 1 638 1 1 1084 1 1 1034 1 1 793 1 1 864 1 1 725 1 1 665 1 1 1129 1 1 989 1 1 605 1 1 1099 1 1 1045 1 1 711 1 1 589 1 1 786 1 1 702 1 1 740 1 1 643 1 1 539 1 1 871 1 1 1207 1 1 896 1 1 856 1 1 1418 1 1 865 1 1 677 1 1 1111 1 1 607 1 1 1586 1 1 1328 0 0 1243 0 0 1096 0 0 1369 0 0 1347 0 0 1145 0 0 1416 0 0 1772 0 0 1171 0 0 693 0 0 2594 0 0 1713 0 0 983 0 0 823 0 0 1034 0 0 2222 0 0 1749 0 0 1375 0 0 1933 0 0 1415 0 0 1536 0 0 1504 0 0 1010 0 0 1019 0 0 911 0 0 1301 0 0 1042 0 0 1380 0 0 1817 0 0 804 0 0 948 0 0 933 0 0 1057 0 0 878 0 0 1413 0 0 1204 0 0 1185 0 0 594 0 1 668 0 1 768 0 1 880 0 1 786 0 1 1408 0 1 806 0 1 763 0 1 1183 0 1 1090 0 1 897 0 1 2396 0 1 736 0 1 630 0 1 2779 0 1 778 0 1 761 0 1 706 0 1 603 0 1 1349 0 1 3470 0 1 1103 0 1 939 0 1 1121 0 1 1910 0 1 1170 0 1 658 0 1 712 0 1 578 0 1 736 0 1 768 0 1 865 0 1 1299 0 1 936 0 1 884 0 1 702 0 1 2499 0 1 841 0 1 859 0 1 2443 0 1 1780 0 1 1556 0 1 1522 0 1 2510 0 1 3551 0 1 2742 0 1 1710 0 1 1577 0 1 1780 0 1 1389 0 1 750 0 1 1679 0 1 1797 0 1 645 0 1 1826 0 1 1200 0 1 793 0 1 3758 0 1 839 0 1 1315 0 1 939 0 1 1288 0 1 1016 0 1 908 0 1 1048 0 1 1230 0 1 1056 0 1 920 0 1 2173 0 1 801 0 1 724 0 1 1075 0 1 1272 0 1 617 0 1 1062 0 1 877 0 1 967 0 1 608 0 1 794 0 1 940 0 1 774 0 1 588 0 1 742 0 1 799 0 1 703 0 1 782 0 1 477 0 1 686 0 1 98 0 1 670 0 1 1018 0 1 872 0 1 1763 0 1 814 0 1 870 0 1 671 0 1 795 0 1 1002 0 1 847 0 1 1397 0 1
Names of X columns:
reactiontime celebrity orientation
Response : Variable 1
Factor : Variable 2
Factor : Variable 3
Include Intercept Term ?
FALSE
TRUE
FALSE
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
par4 <- 'FALSE' par3 <- 'orientation' par2 <- 'celebirty ' par1 <- 'reactiontime' 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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