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Data X:
12 41 11 39 14 30 12 31 21 34 12 35 22 39 11 34 10 36 13 37 10 38 8 36 15 38 14 39 10 33 14 32 14 36 11 38 10 39 13 32 7 32 14 31 12 39 14 37 11 39 9 41 11 36 15 33 14 33 13 34 9 31 15 27 10 37 11 34 13 34 8 32 20 29 12 36 10 29 10 35 9 37 14 34 8 38 14 35 11 38 13 37 9 38 11 33 15 36 11 38 10 32 14 32 18 32 14 34 11 32 12 37 13 39 9 29 10 37 15 35 20 30 12 38 12 34 14 31 13 34 11 35 17 36 12 30 13 39 14 35 13 38 15 31 13 34 10 38 11 34 19 39 13 37 17 34 13 28 9 37 11 33 10 37 9 35 12 37 12 32 13 33 13 38 12 33 15 29 22 33 13 31 15 36 13 35 15 32 10 29 11 39 16 37 11 35 11 37 10 32 10 38 16 37 12 36 11 32 16 33 19 40 11 38 16 41 15 36 24 43 14 30 15 31 11 32 15 32 12 37 10 37 14 33 13 34 9 33 15 38 15 33 14 31 11 38 8 37 11 33 11 31 8 39 10 44 11 33 13 35 11 32 20 28 10 40 15 27 12 37 14 32 23 28 14 34 16 30 11 35 12 31 10 32 14 30 12 30 12 31 11 40 12 32 13 36 11 32 19 35 12 38 17 42 9 34 12 35 19 35 18 33 15 36 14 32 11 33 9 34 18 32 16 34
Names of X columns:
depression connected
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
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 != 'McNemar Chi-Squared') { 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) a<-table.element(a, 'Chi Square Statistic', 1, TRUE) a<-table.element(a, round(cst$statistic, digits=2), 1,FALSE) a<-table.row.end(a) if(!simulate.p.value){ a<-table.row.start(a) a<-table.element(a, 'Degrees of Freedom', 1, TRUE) a<-table.element(a, cst$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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