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Data X:
'F' 12.9 'F' 7.4 'M' 12.2 'F' 12.8 'M' 7.4 'M' 6.7 'M' 12.6 'F' 14.8 'M' 13.3 'M' 11.1 'M' 8.2 'M' 11.4 'M' 6.4 'M' 10.6 'F' 12.0 'F' 6.3 'F' 11.3 'M' 11.9 'F' 9.3 'M' 9.6 'F' 10.0 'M' 6.4 'M' 13.8 'F' 10.8 'M' 13.8 'M' 11.7 'M' 10.9 'M' 16.1 'F' 13.4 'M' 9.9 'F' 11.5 'F' 8.3 'F' 11.7 'M' 6.1 'M' 9.0 'M' 9.7 'M' 10.8 'M' 10.3 'F' 10.4 'M' 12.7 'M' 9.3 'F' 11.8 'M' 5.9 'M' 11.4 'M' 13.0 'M' 10.8 'M' 12.3 'F' 11.3 'M' 11.8 'M' 7.9 'F' 12.7 'M' 12.3 'M' 11.6 'M' 6.7 'M' 10.9 'M' 12.1 'M' 13.3 'M' 10.1 'F' 5.7 'M' 14.3 'F' 8.0 'M' 13.3 'M' 9.3 'F' 12.5 'F' 7.6 'M' 15.9 'F' 9.2 'M' 9.1 'F' 11.1 'M' 13.0 'M' 14.5 'F' 12.2 'F' 12.3 'F' 11.4 'F' 8.8 'M' 14.6 'M' 7.3 'F' 12.6 'M' NA 'F' 13.0 'M' 12.6 'F' 13.2 'F' 9.9 'M' 7.7 'F' 10.5 'F' 13.4 'F' 10.9 'M' 4.3 'F' 10.3 'M' 11.8 'M' 11.2 'F' 11.4 'F' 8.6 'F' 13.2 'M' 12.6 'M' 5.6 'M' 9.9 'F' 8.8 'M' 7.7 'F' 9.0 'M' 7.3 'M' 11.4 'M' 13.6 'M' 7.9 'M' 10.7 'F' 10.3 'M' 8.3 'M' 9.6 'M' 14.2 'F' 8.5 'F' 13.5 'F' 4.9 'F' 6.4 'F' 9.6 'F' 11.6 'M' 11.1 'M' 4.35 'M' 12.7 'M' 18.1 'M' 17.85 'F' 16.6 'M' 12.6 'M' 17.1 'F' 19.1 'M' 16.1 'F' 13.35 'F' 18.4 'M' 14.7 'M' 10.6 'M' 12.6 'M' 16.2 'M' 13.6 'M' 18.9 'M' 14.1 'M' 14.5 'F' 16.15 'M' 14.75 'M' 14.8 'M' 12.45 'M' 12.65 'M' 17.35 'M' 8.6 'F' 18.4 'M' 16.1 'M' 11.6 'M' 17.75 'M' 15.25 'M' 17.65 'F' 15.6 'F' 16.35 'F' 17.65 'M' 13.6 'F' 11.7 'F' 14.35 'F' 14.75 'M' 18.25 'F' 9.9 'M' 16 'M' 18.25 'F' 16.85 'M' 14.6 'M' 13.85 'M' 18.95 'F' 15.6 'F' 14.85 'F' 11.75 'F' 18.45 'M' 15.9 'F' 17.1 'M' 16.1 'F' 19.9 'M' 10.95 'F' 18.45 'M' 15.1 'F' 15 'F' 11.35 'M' 15.95 'F' 18.1 'M' 14.6 'M' 15.4 'M' 15.4 'M' 17.6 'M' 13.35 'F' 19.1 'M' 15.35 'F' 7.6 'F' 13.4 'F' 13.9 'M' 19.1 'F' 15.25 'M' 12.9 'F' 16.1 'F' 17.35 'F' 13.15 'F' 12.15 'M' 12.6 'M' 10.35 'M' 15.4 'M' 9.6 'F' 18.2 'F' 13.6 'M' 14.85 'F' 14.75 'F' 14.1 'F' 14.9 'F' 16.25 'M' 19.25 'M' 13.6 'F' 13.6 'F' 15.65 'M' 12.75 'F' 14.6 'M' 9.85 'M' 12.65 'M' 11.9 'F' 19.2 'M' 16.6 'M' 11.2 'M' 15.25 'F' 11.9 'F' 13.2 'F' 16.35 'M' 12.4 'M' 15.85 'F' 14.35 'M' 18.15 'M' 11.15 'F' 15.65 'F' 17.75 'F' 7.65 'M' 12.35 'M' 15.6 'F' 19.3 'F' 15.2 'F' 17.1 'M' 15.6 'M' 18.4 'F' 19.05 'F' 18.55 'F' 19.1 'M' 13.1 'M' 12.85 'M' 9.5 'M' 4.5 'F' 11.85 'M' 13.6 'M' 11.7 'M' 12.4 'F' 13.35 'F' 11.4 'M' 14.9 'F' 19.9 'M' 17.75 'M' 11.2 'M' 14.6 'F' 17.6 'M' 14.05 'F' 16.1 'M' 13.35 'M' 11.85 'F' 11.95 'M' 14.75 'F' 15.15 'M' 13.2 'F' 16.85 'M' 7.85 'F' 7.7 'F' 12.6 'M' 7.85 'M' 10.95 'F' 12.35 'M' 9.95 'M' 14.9 'F' 16.65 'M' 13.4 'F' 13.95 'F' 15.7 'M' 16.85 'M' 10.95 'F' 15.35 'M' 12.2 'F' 15.1 'F' 17.75 'M' 15.2 'F' 14.6 'F' 16.65 'M' 8.1
Names of X columns:
Gender TOT
Factor 1
Factor 2
Type of test to use
(?)
Pearson Chi-Squared
Pearson Chi-Squared
Exact Pearson Chi-Squared by Simulation
McNemar Chi-Squared
Fisher Exact Test
Chart options
Title:
R Code
par3 <- 'Pearson Chi-Squared' par2 <- '' par1 <- '1' library(vcd) cat1 <- as.numeric(par1) # cat2<- as.numeric(par2) # simulate.p.value=FALSE if (par3 == 'Exact Pearson Chi-Squared by Simulation') simulate.p.value=TRUE x <- t(x) (z <- array(unlist(x),dim=c(length(x[,1]),length(x[1,])))) (table1 <- table(z[,cat1],z[,cat2])) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) bitmap(file='pic1.png') assoc(ftable(z[,cat1],z[,cat2],row.vars=1,dnn=c(V1,V2)),shade=T) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tabulation of Results',ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste(V1,' x ', V2),ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ', 1,TRUE) for(nc in 1:ncol(table1)){ a<-table.element(a, colnames(table1)[nc], 1, TRUE) } a<-table.row.end(a) for(nr in 1:nrow(table1) ){ a<-table.element(a, rownames(table1)[nr], 1, TRUE) for(nc in 1:ncol(table1) ){ a<-table.element(a, table1[nr, nc], 1, FALSE) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') (cst<-chisq.test(table1, simulate.p.value=simulate.p.value) ) if (par3 == 'McNemar Chi-Squared') { (cst <- mcnemar.test(table1)) } if (par3=='Fisher Exact Test') { (cst <- fisher.test(table1)) } if ((par3 != 'McNemar Chi-Squared') & (par3 != 'Fisher Exact Test')) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tabulation of Expected Results',ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste(V1,' x ', V2),ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ', 1,TRUE) for(nc in 1:ncol(table1)){ a<-table.element(a, colnames(table1)[nc], 1, TRUE) } a<-table.row.end(a) for(nr in 1:nrow(table1) ){ a<-table.element(a, rownames(table1)[nr], 1, TRUE) for(nc in 1:ncol(table1) ){ a<-table.element(a, round(cst$expected[nr, nc], digits=2), 1, FALSE) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab') } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Statistical Results',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, cst$method, 2,TRUE) a<-table.row.end(a) a<-table.row.start(a) if (par3=='Pearson Chi-Squared') a<-table.element(a, 'Pearson Chi Square Statistic', 1, TRUE) if (par3=='Exact Pearson Chi-Squared by Simulation') a<-table.element(a, 'Exact Pearson Chi Square Statistic', 1, TRUE) if (par3=='McNemar Chi-Squared') a<-table.element(a, 'McNemar Chi Square Statistic', 1, TRUE) if (par3=='Fisher Exact Test') a<-table.element(a, 'Odds Ratio', 1, TRUE) if (par3=='Fisher Exact Test') { if ((ncol(table1) == 2) & (nrow(table1) == 2)) { a<-table.element(a, round(cst$estimate, digits=2), 1,FALSE) } else { a<-table.element(a, '--', 1,FALSE) } } else { a<-table.element(a, round(cst$statistic, digits=2), 1,FALSE) } a<-table.row.end(a) if(!simulate.p.value){ if(par3!='Fisher Exact Test') { a<-table.row.start(a) a<-table.element(a, 'Degrees of Freedom', 1, TRUE) a<-table.element(a, cst$parameter, 1,FALSE) a<-table.row.end(a) } } a<-table.row.start(a) a<-table.element(a, 'P value', 1, TRUE) a<-table.element(a, round(cst$p.value, digits=2), 1,FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable2.tab')
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