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Data X:
38 12 32 11 35 14 33 12 37 21 29 12 31 22 36 11 35 10 38 13 31 10 34 8 35 15 38 14 37 10 33 14 32 14 38 11 38 10 32 13 33 7 31 14 38 12 39 14 32 11 32 9 35 11 37 15 33 14 33 13 28 9 32 15 31 10 37 11 30 13 33 8 31 20 33 12 31 10 33 10 32 9 33 14 32 8 33 14 28 11 35 13 39 9 34 11 38 15 32 11 38 10 30 14 33 18 38 14 32 11 32 12 34 13 34 9 36 10 34 15 28 20 34 12 35 12 35 14 31 13 37 11 35 17 27 12 40 13 37 14 36 13 38 15 39 13 41 10 27 11 30 19 37 13 31 17 31 13 27 9 36 11 38 10 37 9 33 12 34 12 31 13 39 13 34 12 32 15 33 22 36 13 32 15 41 13 28 15 30 10 36 11 35 16 31 11 34 11 36 10 36 10 35 16 37 12 28 11 39 16 32 19 35 11 39 16 35 15 42 24 34 14 33 15 41 11 33 15 34 12 32 10 40 14 40 13 35 9 36 15 37 15 27 14 39 11 38 8 31 11 33 11 32 8 39 10 36 11 33 13 33 11 32 20 37 10 30 15 38 12 29 14 22 23 35 14 35 16 34 11 35 12 34 10 34 14 35 12 23 12 31 11 27 12 36 13 31 11 32 19 39 12 37 17 38 9 39 12 34 19 31 18 32 15 37 14 36 11 32 9 35 18 36 16
Names of X columns:
Seperateness Depression
Factor 1


Factor 2


Type of test to use (?)


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')

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center

Tabulation of Expected Results
Seperateness x Depression
	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24
22	0.01	0.03	0.06	0.11	0.17	0.13	0.12	0.12	0.1	0.04	0.02	0.02	0.02	0.02	0.01	0.01	0.01	0.01
23	0.01	0.03	0.06	0.11	0.17	0.13	0.12	0.12	0.1	0.04	0.02	0.02	0.02	0.02	0.01	0.01	0.01	0.01
27	0.03	0.15	0.31	0.56	0.86	0.65	0.59	0.62	0.49	0.19	0.09	0.09	0.12	0.09	0.03	0.06	0.03	0.03
28	0.03	0.15	0.31	0.56	0.86	0.65	0.59	0.62	0.49	0.19	0.09	0.09	0.12	0.09	0.03	0.06	0.03	0.03
29	0.01	0.06	0.12	0.22	0.35	0.26	0.23	0.25	0.2	0.07	0.04	0.04	0.05	0.04	0.01	0.02	0.01	0.01
30	0.03	0.15	0.31	0.56	0.86	0.65	0.59	0.62	0.49	0.19	0.09	0.09	0.12	0.09	0.03	0.06	0.03	0.03
31	0.09	0.46	0.93	1.67	2.59	1.94	1.76	1.85	1.48	0.56	0.28	0.28	0.37	0.28	0.09	0.19	0.09	0.09
32	0.12	0.62	1.23	2.22	3.46	2.59	2.35	2.47	1.98	0.74	0.37	0.37	0.49	0.37	0.12	0.25	0.12	0.12
33	0.11	0.56	1.11	2	3.11	2.33	2.11	2.22	1.78	0.67	0.33	0.33	0.44	0.33	0.11	0.22	0.11	0.11
34	0.09	0.46	0.93	1.67	2.59	1.94	1.76	1.85	1.48	0.56	0.28	0.28	0.37	0.28	0.09	0.19	0.09	0.09
35	0.11	0.56	1.11	2	3.11	2.33	2.11	2.22	1.78	0.67	0.33	0.33	0.44	0.33	0.11	0.22	0.11	0.11
36	0.08	0.4	0.8	1.44	2.25	1.69	1.52	1.6	1.28	0.48	0.24	0.24	0.32	0.24	0.08	0.16	0.08	0.08
37	0.08	0.4	0.8	1.44	2.25	1.69	1.52	1.6	1.28	0.48	0.24	0.24	0.32	0.24	0.08	0.16	0.08	0.08
38	0.09	0.43	0.86	1.56	2.42	1.81	1.64	1.73	1.38	0.52	0.26	0.26	0.35	0.26	0.09	0.17	0.09	0.09
39	0.06	0.31	0.62	1.11	1.73	1.3	1.17	1.23	0.99	0.37	0.19	0.19	0.25	0.19	0.06	0.12	0.06	0.06
40	0.02	0.09	0.19	0.33	0.52	0.39	0.35	0.37	0.3	0.11	0.06	0.06	0.07	0.06	0.02	0.04	0.02	0.02
41	0.02	0.09	0.19	0.33	0.52	0.39	0.35	0.37	0.3	0.11	0.06	0.06	0.07	0.06	0.02	0.04	0.02	0.02
42	0.01	0.03	0.06	0.11	0.17	0.13	0.12	0.12	0.1	0.04	0.02	0.02	0.02	0.02	0.01	0.01	0.01	0.01

Tabulation of Results
Seperateness x Depression
	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24
22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
23	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
27	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0
28	0	0	1	0	2	0	0	0	1	0	0	0	0	1	0	0	0	0
29	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0
30	0	0	0	1	0	0	1	1	1	0	0	0	1	0	0	0	0	0
31	0	0	0	3	4	0	3	1	0	0	1	1	0	1	0	1	0	0
32	0	2	3	1	4	1	1	1	4	0	0	0	2	1	0	0	0	0
33	1	1	0	1	2	3	2	4	2	0	0	1	0	0	0	1	0	0
34	0	1	1	1	3	4	1	2	1	0	0	0	1	0	0	0	0	0
35	0	0	1	1	2	3	1	3	2	3	1	1	0	0	0	0	0	0
36	0	0	0	3	5	0	3	0	1	1	0	0	0	0	0	0	0	0
37	0	0	1	2	2	1	1	2	2	0	1	0	0	0	1	0	0	0
38	0	1	1	3	1	3	1	2	2	0	0	0	0	0	0	0	0	0
39	0	0	1	1	1	2	2	1	0	2	0	0	0	0	0	0	0	0
40	0	0	0	0	0	0	2	1	0	0	0	0	0	0	0	0	0	0
41	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0
42	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1

Statistical Results
Pearson's Chi-squared test
Pearson Chi Square Statistic	506.77
Degrees of Freedom	289
P value	0