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Data X:
21 1 0 22 1 1 22 1 0 18 1 1 23 1 1 12 1 1 20 1 0 22 1 1 21 1 1 19 1 1 22 1 1 15 1 1 20 1 1 19 1 0 18 1 0 15 0 0 20 1 1 21 1 0 21 0 1 15 1 0 16 1 1 23 1 1 21 1 0 18 1 1 25 1 1 9 1 1 30 0 1 20 0 0 23 1 1 16 1 0 16 1 0 19 1 0 25 1 1 18 1 1 23 1 1 21 1 1 10 1 0 14 0 1 22 1 1 26 1 0 23 1 1 23 1 1 24 1 1 24 1 1 18 0 1 23 1 0 15 1 1 19 0 1 16 1 0 25 0 1 23 0 1 17 0 1 19 1 1 21 0 1 18 1 1 27 1 1 21 0 0 13 1 1 8 0 0 29 0 1 28 1 1 23 1 0 21 1 0 19 1 1 19 1 0 20 0 1 18 1 0 19 1 1 17 1 1 19 0 0 25 1 0 19 1 0 22 0 0 23 0 1 14 1 0 16 1 0 24 0 1 20 1 0 12 0 0 24 1 1 22 0 0 12 0 0 22 0 0 20 0 1 10 0 0 23 0 1 17 0 1 22 0 0 24 0 0 18 0 0 21 0 1 20 0 1 20 0 1 22 0 0 19 0 1 20 0 0 26 0 1 23 0 1 24 0 1 21 0 1 21 0 1 19 0 0 8 0 1 17 0 1 20 0 1 11 0 0 8 0 0 15 0 0 18 0 0 18 0 0 19 0 0 19 0 1 23 1 1 22 1 1 21 1 1 25 1 1 30 0 0 17 0 1 27 1 1 23 1 0 23 1 1 18 1 0 18 1 0 23 1 1 19 1 1 15 1 1 20 1 1 16 1 1 24 0 1 25 1 1 25 1 1 19 1 0 19 1 1 16 1 1 19 1 1 19 1 1 23 1 1 21 1 1 22 1 0 19 1 1 20 0 1 20 1 1 3 1 1 23 1 1 23 1 0 20 1 0 15 1 1 16 1 0 7 1 0 24 1 1 17 1 0 24 1 1 24 1 1 19 1 0 25 0 1 20 0 1 28 1 1 23 1 0 27 0 0 18 0 0 28 0 0 21 0 1 19 1 0 23 1 1 27 0 0 22 0 1 28 0 0 25 0 1 21 0 0 22 0 0 28 0 1 20 0 0 29 0 1 25 1 1 25 1 1 20 0 1 20 1 1 16 1 0 20 0 1 20 1 0 23 0 0 18 0 0 25 1 1 18 0 0 19 0 1 25 0 0 25 0 0 25 0 0 24 0 0 19 0 1 26 0 1 10 0 1 17 0 1 13 0 0 17 0 0 30 0 1 25 1 0 4 0 0 16 0 0 21 0 0 23 1 1 22 0 1 17 1 0 20 0 0 20 1 1 22 0 0 16 1 1 23 0 1 0 0 0 18 0 1 25 0 1 23 1 1 12 1 0 18 0 0 24 1 0 11 1 1 18 0 1 23 1 1 24 0 1 29 0 0 18 1 0 15 0 0 29 1 1 16 1 1 19 1 0 22 0 0 16 1 0 23 0 1 23 1 1 19 1 0 4 1 0 20 1 0 24 0 1 20 1 1 4 1 1 24 1 1 22 0 0 16 1 1 3 1 1 15 0 1 24 1 0 17 0 0 20 0 1 27 0 0 26 0 1 23 0 1 17 1 0 20 1 1 22 1 0 19 1 1 24 1 1 19 1 0 23 0 1 15 0 0 27 1 1 26 0 0 22 0 1 22 1 0 18 0 0 15 0 1 22 0 1 27 0 0 10 0 1 20 0 1 17 0 0 23 0 1 19 0 0 13 0 0 27 0 1 23 0 1 16 0 0 25 0 1 2 0 0 26 0 0 20 0 1 23 1 0 22 0 0 24 0 1
Names of X columns:
NUMERACYTOT group gender
Factor 1
Factor 2
Type of test to use
(?)
Exact Pearson Chi-Squared by Simulation
Pearson Chi-Squared
Exact Pearson Chi-Squared by Simulation
McNemar Chi-Squared
Fisher Exact Test
Chart options
Title:
R Code
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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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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