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Data X:
'Female' 26 'Female' 51 'Male' 57 'Female' 37 'Male' 67 'Male' 43 'Male' 52 'Female' 52 'Male' 43 'Male' 84 'Male' 67 'Male' 49 'Male' 70 'Male' 52 'Female' 58 'Female' 68 'Female' 62 'Male' 43 'Female' 56 'Male' 56 'Female' 74 'Male' 63 'Female' 58 'Male' 57 'Male' 63 'Male' 53 'Male' 57 'Female' 51 'Male' 64 'Female' 53 'Female' 29 'Female' 54 'Male' 58 'Male' 43 'Male' 51 'Male' 53 'Female' 54 'Male' 56 'Male' 61 'Female' 47 'Male' 39 'Male' 48 'Male' 50 'Male' 35 'Male' 30 'Female' 68 'Male' 49 'Male' 61 'Female' 67 'Male' 47 'Male' 56 'Male' 50 'Male' 43 'Male' 67 'Male' 62 'Male' 57 'Female' 41 'Male' 54 'Female' 45 'Male' 48 'Male' 61 'Female' 56 'Female' 41 'Male' 43 'Female' 53 'Male' 44 'Female' 66 'Male' 58 'Male' 46 'Female' 37 'Female' 51 'Female' 51 'Female' 56 'Male' 66 'Male' 45 'Female' 37 'Male' 59 'Female' 42 'Male' 38 'Female' 66 'Female' 34 'Male' 53 'Female' 49 'Female' 55 'Female' 49 'Male' 59 'Female' 40 'Male' 58 'Male' 60 'Female' 63 'Female' 56 'Female' 54 'Male' 52 'Male' 34 'Male' 69 'Female' 32 'Male' 48 'Female' 67 'Male' 58 'Male' 57 'Male' 42 'Male' 64 'Male' 58 'Female' 66 'Male' 26 'Male' 61 'Male' 52 'Female' 51 'Female' 55 'Female' 50 'Female' 60 'Female' 56 'Female' 63 'Male' 61 'Male' 52 'Male' 16 'Male' 46 'Male' 56 'Female' 52 'Male' 55 'Male' 50 'Female' 59 'Male' 60 'Female' 52 'Female' 44 'Male' 67 'Male' 52 'Male' 55 'Male' 37 'Male' 54 'Male' 72 'Male' 51 'Male' 48 'Female' 60 'Male' 50 'Male' 63 'Male' 33 'Male' 67 'Male' 46 'Male' 54 'Female' 59 'Male' 61 'Male' 33 'Male' 47 'Male' 69 'Male' 52 'Female' 55 'Female' 55 'Female' 41 'Male' 73 'Female' 51 'Female' 52 'Female' 50 'Male' 51 'Female' 60 'Male' 56 'Male' 56 'Female' 29 'Male' 66 'Male' 66 'Male' 73 'Female' 55 'Female' 64 'Female' 40 'Female' 46 'Male' 58 'Female' 43 'Male' 61 'Female' 51 'Male' 50 'Female' 52 'Male' 54 'Female' 66 'Female' 61 'Male' 80 'Female' 51 'Male' 56 'Male' 56 'Male' 56 'Male' 53 'Male' 47 'Female' 25 'Male' 47 'Female' 46 'Female' 50 'Female' 39 'Male' 51 'Female' 58 'Male' 35 'Female' 58 'Female' 60 'Female' 62 'Female' 63 'Male' 53 'Male' 46 'Male' 67 'Male' 59 'Female' 64 'Female' 38 'Male' 50 'Female' 48 'Female' 48 'Female' 47 'Female' 66 'Male' 47 'Male' 63 'Female' 58 'Female' 44 'Male' 51 'Female' 43 'Male' 55 'Male' 38 'Male' 56 'Female' 45 'Male' 50 'Male' 54 'Male' 57 'Female' 60 'Female' 55 'Female' 56 'Male' 49 'Male' 37 'Female' 43 'Male' 59 'Male' 46 'Female' 51 'Female' 58 'Female' 64 'Male' 53 'Male' 48 'Female' 51 'Female' 47 'Female' 59 'Male' 62 'Male' 62 'Female' 51 'Female' 64 'Female' 52 'Male' 67 'Male' 50 'Male' 54 'Male' 58 'Female' 56 'Male' 63 'Male' 31 'Male' 65 'Female' 71 'Female' 50 'Male' 57 'Female' 47 'Male' 54 'Male' 47 'Male' 57 'Female' 43 'Male' 41 'Female' 63 'Male' 63 'Male' 56 'Female' 51 'Male' 50 'Female' 22 'Male' 41 'Female' 59 'Male' 56 'Female' 66 'Female' 53 'Male' 42 'Male' 52 'Female' 54 'Male' 44 'Male' 62 'Female' 53 'Male' 50 'Female' 36 'Female' 76 'Male' 66 'Male' 62 'Female' 59 'Male' 47 'Female' 55 'Female' 58 'Male' 60 'Female' 44 'Female' 57 'Male' 45 'Male' 58 'Male' 51 'Female' 57 'Male' 30 'Male' 46 'Male' 51 'Male' 56 'Female' 58 'Male' 44 'Female' 14 'Female' 53 'Male' 42 'Female' 49 'Male' 44 'Female' 62 'Female' 30 'Male' 46 'Female' 56 'Male' 50 'Male' 54 'Female' 48 'Female' 55 'Female' 35 'Male' 55 'Female' 41 'Male' 59 'Male' 54 'Male' 66 'Male' 55 'Female' 45 'Male' 51 'Female' 47 'Male' 42 'Female' 53 'Female' 53 'Female' 41 'Female' 55 'Female' 55 'Female' 46 'Male' 63 'Male' 43 'Male' 65 'Female' 59 'Female' 39 'Female' 44 'Female' 60 'Female' 57 'Male' 67 'Male' 52 'Male' 52 'Male' 69 'Female' 46 'Male' 46 'Female' 53 'Male' 40 'Male' 70 'Female' 54 'Male' 77 'Female' 45 'Female' 60 'Female' 47 'Female' 50 'Female' 66 'Female' 60 'Male' 41 'Female' 53 'Female' 34 'Male' 51 'Male' 69 'Male' 60 'Male' 45 'Female' 58 'Male' 39 'Male' 51 'Female' 52 'Female' 49 'Female' 63 'Female' 44 'Male' 51 'Female' 52 'Female' 60 'Female' 53 'Female' 53 'Female' 52 'Female' 31 'Male' 51 'Male' 65 'Male' 51 'Female' 49 'Female' 61 'Male' 58 'Female' 62 'Male' 54 'Male' 52 'Male' 72 'Male' 50 'Male' 65 'Female' 53 'Female' 56 'Female' 63 'Female' 62 'Female' 66 'Male' 50 'Female' 45 'Female' 58 'Male' 52 'Female' 53 'Female' 68 'Male' 59 'Female' 58 'Male' 52 'Male' 45 'Female' 58 'Male' 70 'Female' 69 'Male' 71 'Female' 46 'Male' 58 'Male' 39 'Male' 46 'Female' 64 'Male' 67 'Male' 44 'Female' 54 'Male' 41 'Male' 68 'Male' 63 'Male' 57 'Female' 61 'Male' 39 'Female' 69 'Female' 64 'Male' 38 'Female' 59 'Male' 51 'Male' 59 'Female' 51 'Female' 65 'Male' 47 'Male' 50 'Male' 57 'Male' 21 'Female' 47 'Male' 51 'Male' 37 'Male' 67 'Male' 43 'Female' 58 'Female' 51 'Male' 40 'Female' 41 'Male' 58 'Male' 64 'Female' 64 'Male' 58 'Female' 50 'Male' 59 'Female' 55 'Female' 59 'Male' 58 'Male' 41 'Male' 56 'Male' 63 'Female' 77 'Female' 60 'Male' 58 'Female' 64 'Male' 47 'Female' 46 'Male' 62 'Male' 60 'Male' 50 'Male' 46 'Male' 44 'Male' 58 'Male' 56 'Male' 43 'Female' 54 'Male' 54 'Female' 56 'Female' 65 'Male' 66 'Male' 62 'Male' 58 'Male' 67 'Male' 25 'Male' 56 'Male' 53 'Female' 56 'Male' 59 'Male' 46 'Male' 49 'Female' 56 'Male' 76 'Male' 33 'Male' 49 'Male' 53 'Female' 58 'Male' 72 'Male' 51 'Female' 42 'Female' 69 'Male' 51 'Male' 54 'Male' 52 'Male' 59 'Female' 51 'Male' 67 'Female' 64 'Male' 58 'Male' 53
Names of X columns:
gender AMS.I
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')
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