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• QR Code (size=3)
• QR Code (size=4)
• QR Code (size=5)
• QR Code (size=6)

Female	210907	1418	56	79	30	21
Female	120982	869	56	58	28	23
Male	176508	1530	54	60	38	22
NA	179321	2172	89	108	30	NA
NA	123185	901	40	49	22	NA
NA	52746	463	25	0	26	NA
Female	385534	3201	92	121	25	22
NA	33170	371	18	1	18	NA
NA	101645	1192	63	20	11	NA
Female	149061	1583	44	43	26	21
Male	165446	1439	33	69	25	22
Male	237213	1764	84	78	38	21
NA	173326	1495	88	86	44	NA
Male	133131	1373	55	44	30	21
NA	258873	2187	60	104	40	NA
NA	180083	1491	66	63	34	NA
Male	324799	4041	154	158	47	21
Female	230964	1706	53	102	30	21
Male	236785	2152	119	77	31	23
Female	135473	1036	41	82	23	21
NA	202925	1882	61	115	36	NA
Female	215147	1929	58	101	36	21
Male	344297	2242	75	80	30	22
Female	153935	1220	33	50	25	22
NA	132943	1289	40	83	39	NA
Male	174724	2515	92	123	34	25+
Male	174415	2147	100	73	31	21
Female	225548	2352	112	81	31	22
Male	223632	1638	73	105	33	23
Male	124817	1222	40	47	25	22
NA	221698	1812	45	105	33	NA
Female	210767	1677	60	94	35	22
Female	170266	1579	62	44	42	21
NA	260561	1731	75	114	43	NA
NA	84853	807	31	38	30	NA
Female	294424	2452	77	107	33	21
NA	101011	829	34	30	13	NA
NA	215641	1940	46	71	32	NA
Male	325107	2662	99	84	36	21
Male	7176	186	17	0	0	20
NA	167542	1499	66	59	28	NA
Female	106408	865	30	33	14	21
Female	96560	1793	76	42	17	25
Male	265769	2527	146	96	32	21
NA	269651	2747	67	106	30	NA
Female	149112	1324	56	56	35	21
Male	175824	2702	107	57	20	20
Female	152871	1383	58	59	28	24
Male	111665	1179	34	39	28	23
NA	116408	2099	61	34	39	NA
Male	362301	4308	119	76	34	21
NA	78800	918	42	20	26	NA
Female	183167	1831	66	91	39	24
NA	277965	3373	89	115	39	NA
NA	150629	1713	44	85	33	NA
Male	168809	1438	66	76	28	21
Male	24188	496	24	8	4	23
Male	329267	2253	259	79	39	23
NA	65029	744	17	21	18	NA
NA	101097	1161	64	30	14	NA
Male	218946	2352	41	76	29	21
Male	244052	2144	68	101	44	22
Male	341570	4691	168	94	21	20
Female	103597	1112	43	27	16	18
NA	233328	2694	132	92	28	NA
Male	256462	1973	105	123	35	22
NA	206161	1769	71	75	28	NA
NA	311473	3148	112	128	38	NA
Female	235800	2474	94	105	23	21
NA	177939	2084	82	55	36	NA
NA	207176	1954	70	56	32	NA
Male	196553	1226	57	41	29	21
Male	174184	1389	53	72	25	23
Female	143246	1496	103	67	27	22
Male	187559	2269	121	75	36	21
Female	187681	1833	62	114	28	21
NA	119016	1268	52	118	23	NA
NA	182192	1943	52	77	40	NA
Male	73566	893	32	22	23	21
NA	194979	1762	62	66	40	NA
Female	167488	1403	45	69	28	22
Female	143756	1425	46	105	34	21
NA	275541	1857	63	116	33	NA
Female	243199	1840	75	88	28	21
Male	182999	1502	88	73	34	23
NA	135649	1441	46	99	30	NA
Male	152299	1420	53	62	33	21
NA	120221	1416	37	53	22	NA
Male	346485	2970	90	118	38	25+
NA	145790	1317	63	30	26	NA
Male	193339	1644	78	100	35	22
NA	80953	870	25	49	8	NA
Male	122774	1654	45	24	24	22
Female	130585	1054	46	67	29	20
Male	112611	937	41	46	20	21
Male	286468	3004	144	57	29	21
NA	241066	2008	82	75	45	NA
Male	148446	2547	91	135	37	21
NA	204713	1885	71	68	33	NA
Female	182079	1626	63	124	33	21
Male	140344	1468	53	33	25	22
Male	220516	2445	62	98	32	21
Male	243060	1964	63	58	29	24
Male	162765	1381	32	68	28	22
NA	182613	1369	39	81	28	NA
Male	232138	1659	62	131	31	24
Female	265318	2888	117	110	52	21
Male	85574	1290	34	37	21	22
Female	310839	2845	92	130	24	22
Female	225060	1982	93	93	41	21
Male	232317	1904	54	118	33	24
Female	144966	1391	144	39	32	21
NA	43287	602	14	13	19	NA
NA	155754	1743	61	74	20	NA
Male	164709	1559	109	81	31	22
NA	201940	2014	38	109	31	NA
NA	235454	2143	73	151	32	NA
Male	220801	2146	75	51	18	19
Female	99466	874	50	28	23	22
Male	92661	1590	61	40	17	23
Male	133328	1590	55	56	20	20
Male	61361	1210	77	27	12	20
NA	125930	2072	75	37	17	NA
Male	100750	1281	72	83	30	23
NA	224549	1401	50	54	31	NA
NA	82316	834	32	27	10	NA
Female	102010	1105	53	28	13	20
Male	101523	1272	42	59	22	20
Male	243511	1944	71	133	42	23
Male	22938	391	10	12	1	25
NA	41566	761	35	0	9	NA
Male	152474	1605	65	106	32	21
NA	61857	530	25	23	11	NA
Female	99923	1988	66	44	25	22
Male	132487	1386	41	71	36	21
Female	317394	2395	86	116	31	22
Male	21054	387	16	4	0	22
Male	209641	1742	42	62	24	23
Female	22648	620	19	12	13	21
Female	31414	449	19	18	8	21
Male	46698	800	45	14	13	20
Male	131698	1684	65	60	19	19
NA	91735	1050	35	7	18	NA
Male	244749	2699	95	98	33	22
NA	184510	1606	49	64	40	NA
NA	79863	1502	37	29	22	NA
Female	128423	1204	64	32	38	21
Female	97839	1138	38	25	24	21
NA	38214	568	34	16	8	NA
NA	151101	1459	32	48	35	NA
Male	272458	2158	65	100	43	21
NA	172494	1111	52	46	43	21
Male	108043	1421	62	45	14	21
Female	328107	2833	65	129	41	21
NA	250579	1955	83	130	38	NA
Male	351067	2922	95	136	45	22
Female	158015	1002	29	59	31	22
NA	98866	1060	18	25	13	NA
NA	85439	956	33	32	28	NA
Male	229242	2186	247	63	31	22
NA	351619	3604	139	95	40	NA
Male	84207	1035	29	14	30	22
Female	120445	1417	118	36	16	18
Female	324598	3261	110	113	37	21
Female	131069	1587	67	47	30	23
Female	204271	1424	42	92	35	21
NA	165543	1701	65	70	32	NA
NA	141722	1249	94	19	27	NA
Female	116048	946	64	50	20	19
Male	250047	1926	81	41	18	19
Male	299775	3352	95	91	31	23
Female	195838	1641	67	111	31	21
Male	173260	2035	63	41	21	21
Female	254488	2312	83	120	39	21
NA	104389	1369	45	135	41	NA
NA	136084	1577	30	27	13	NA
NA	199476	2201	70	87	32	NA
Male	92499	961	32	25	18	21
Female	224330	1900	83	131	39	20
Female	135781	1254	31	45	14	19
Male	74408	1335	67	29	7	21
Female	81240	1597	66	58	17	22
NA	14688	207	10	4	0	NA
Male	181633	1645	70	47	30	21
Male	271856	2429	103	109	37	25
NA	7199	151	5	7	0	NA
NA	46660	474	20	12	5	NA
NA	17547	141	5	0	1	NA
NA	133368	1639	36	37	16	NA
Male	95227	872	34	37	32	23
NA	152601	1318	48	46	24	NA
Female	98146	1018	40	15	17	19
NA	79619	1383	43	42	11	NA
Female	59194	1314	31	7	24	19
Male	139942	1335	42	54	22	19
Female	118612	1403	46	54	12	19
Male	72880	910	33	14	19	19
Male	65475	616	18	16	13	20
NA	99643	1407	55	33	17	NA
Male	71965	771	35	32	15	19
NA	77272	766	59	21	16	NA
NA	49289	473	19	15	24	NA
Female	135131	1376	66	38	15	19
Female	108446	1232	60	22	17	19
NA	89746	1521	36	28	18	NA
NA	44296	572	25	10	20	NA
NA	77648	1059	47	31	16	NA
Male	181528	1544	54	32	16	19
Male	134019	1230	53	32	18	19
NA	124064	1206	40	43	22	NA
NA	92630	1205	40	27	8	NA
Female	121848	1255	39	37	17	20
NA	52915	613	14	20	18	NA
Female	81872	721	45	32	16	19
Female	58981	1109	36	0	23	19
Female	53515	740	28	5	22	18
NA	60812	1126	44	26	13	NA
Male	56375	728	30	10	13	19
Male	65490	689	22	27	16	19
NA	80949	592	17	11	16	NA
Male	76302	995	31	29	20	21
Male	104011	1613	55	25	22	18
Female	98104	2048	54	55	17	18
NA	67989	705	21	23	18	NA
Male	30989	301	14	5	17	21
Female	135458	1803	81	43	12	20
NA	73504	799	35	23	7	NA
Male	63123	861	43	34	17	19
NA	61254	1186	46	36	14	NA
Male	74914	1451	30	35	23	21
Female	31774	628	23	0	17	21
Male	81437	1161	38	37	14	20
NA	87186	1463	54	28	15	NA
NA	50090	742	20	16	17	NA
Male	65745	979	53	26	21	24
Male	56653	675	45	38	18	22
Male	158399	1241	39	23	18	21
NA	46455	676	20	22	17	NA
Male	73624	1049	24	30	17	21
NA	38395	620	31	16	16	NA
Male	91899	1081	35	18	15	19
Male	139526	1688	151	28	21	18
NA	52164	736	52	32	16	NA
Female	51567	617	30	21	14	19
NA	70551	812	31	23	15	NA
NA	84856	1051	29	29	17	NA
Female	102538	1656	57	50	15	19
Male	86678	705	40	12	15	19
NA	85709	945	44	21	10	NA
NA	34662	554	25	18	6	NA
Male	150580	1597	77	27	22	20
Male	99611	982	35	41	21	18
NA	19349	222	11	13	1	NA
Female	99373	1212	63	12	18	19
Female	86230	1143	44	21	17	19
Female	30837	435	19	8	4	20
Male	31706	532	13	26	10	20
Male	89806	882	42	27	16	21
NA	62088	608	38	13	16	NA
NA	40151	459	29	16	9	NA
NA	27634	578	20	2	16	NA
NA	76990	826	27	42	17	NA
NA	37460	509	20	5	7	NA
NA	54157	717	19	37	15	NA
NA	49862	637	37	17	14	NA
NA	84337	857	26	38	14	NA
Female	64175	830	42	37	18	20
Female	59382	652	49	29	12	21
Female	119308	707	30	32	16	18
Female	76702	954	49	35	21	19
NA	103425	1461	67	17	19	NA
NA	70344	672	28	20	16	NA
NA	43410	778	19	7	1	NA
NA	104838	1141	49	46	16	NA
NA	62215	680	27	24	10	NA
NA	69304	1090	30	40	19	NA
NA	53117	616	22	3	12	NA
Male	19764	285	12	10	2	19
NA	86680	1145	31	37	14	NA
Female	84105	733	20	17	17	19
NA	77945	888	20	28	19	NA
NA	89113	849	39	19	14	NA
NA	91005	1182	29	29	11	NA
NA	40248	528	16	8	4	NA
Male	64187	642	27	10	16	19
NA	50857	947	21	15	20	NA
NA	56613	819	19	15	12	NA
NA	62792	757	35	28	15	NA
Male	72535	894	14	17	16	19

panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method=par1)
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
cor.test(y[1,],y[2,],method=par1)
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
a<-table.element(a,dum,header=TRUE)
rp <- cor.test(y[i,],y[j,],method='pearson')
a<-table.element(a,round(rp$estimate,4))
rs <- cor.test(y[i,],y[j,],method='spearman')
a<-table.element(a,round(rs$estimate,4))
rk <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,round(rk$estimate,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=T)
a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')