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Data X:
16198.9 16896.2 16554.2 16698 19554.2 19691.6 15903.8 15930.7 18003.8 17444.6 18329.6 17699.4 16260.7 15189.8 14851.9 15672.7 18174.1 17180.8 18406.6 17664.9 18466.5 17862.9 16016.5 16162.3 17428.5 17463.6 17167.2 16772.1 19630 19106.9 17183.6 16721.3 18344.7 18161.3 19301.4 18509.9 18147.5 17802.7 16192.9 16409.9 18374.4 17967.7 20515.2 20286.6 18957.2 19537.3 16471.5 18021.9 18746.8 20194.3 19009.5 19049.6 19211.2 20244.7 20547.7 21473.3 19325.8 19673.6 20605.5 21053.2 20056.9 20159.5 16141.4 18203.6 20359.8 21289.5 19711.6 20432.3 15638.6 17180.4 14384.5 15816.8 13855.6 15071.8 14308.3 14521.1 15290.6 15668.8 14423.8 14346.9 13779.7 13881 15686.3 15465.9 14733.8 14238.2 12522.5 13557.7 16189.4 16127.6 16059.1 16793.9 16007.1 16014 15806.8 16867.9 15160 16014.6 15692.1 15878.6 18908.9 18664.9 16969.9 17962.5 16997.5 17332.7 19858.9 19542.1 17681.2 17203.6
Names of X columns:
Uitvoer Invoer
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
1 seconds
R Server
Big Analytics Cloud Computing Center
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