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Data X:
14 38 18 32 11 35 12 33 16 37 18 29 14 31 14 36 15 35 15 38 17 31 19 34 10 35 16 38 18 37 14 33 14 32 17 38 14 38 16 32 18 33 11 31 14 38 12 39 17 32 9 32 16 35 14 37 15 33 11 33 16 28 13 32 17 31 15 37 14 30 16 33 9 31 15 33 17 31 13 33 15 32 16 33 16 32 12 33 12 28 11 35 15 39 15 34 17 38 13 32 16 38 14 30 11 33 12 38 12 32 15 32 16 34 15 34 12 36 12 34 8 28 13 34 11 35 14 35 15 31 10 37 11 35 12 27 15 40 15 37 14 36 16 38 15 39 15 41 13 27 12 30 17 37 13 31 15 31 13 27 15 36 16 38 15 37 16 33 15 34 14 31 15 39 14 34 13 32 7 33 17 36 13 32 15 41 14 28 13 30 16 36 12 35 14 31 17 34 15 36 17 36 12 35 16 37 11 28 15 39 9 32 16 35 15 39 10 35 10 42 15 34 11 33 13 41 14 33 18 34 16 32 14 40 14 40 14 35 14 36 12 37 14 27 15 39 15 38 15 31 13 33 17 32 17 39 19 36 15 33 13 33 9 32 15 37 15 30 15 38 16 29 11 22 14 35 11 35 15 34 13 35 15 34 16 34 14 35 15 23 16 31 16 27 11 36 12 31 9 32 16 39 13 37 16 38 12 39 9 34 13 31 13 32 14 37 19 36 13 32 12 35 13 36
Names of X columns:
happiness seperate
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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