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24300	362	1.439	24.90
24375	361	1.444	25.06
24375	361	1.435	25.10
24550	356	1.430	24.92
24725	364	1.427	25.46
24825	375	1.453	25.89
25100	391	1.448	25.39
24950	379	1.456	25.38
25325	389	1.449	25.25
25325	387	1.437	24.88
24800	379	1.437	25.00
24975	385	1.428	25.00
25125	399	1.413	24.07
25125	388	1.406	23.60
25125	392	1.414	23.18
25400	395	1.415	23.25
25175	395	1.409	23.04
24650	370	1.407	22.77
24775	376	1.400	22.25
24675	367	1.397	22.41
24825	356	1.391	22.50
24775	362	1.394	22.91
24675	346	1.398	22.88
24750	347	1.385	21.69
24875	350	1.369	21.19
25400	361	1.368	21.56
25400	358	1.376	22.00
24750	334	1.374	22.13
24900	331	1.372	22.27
24825	337	1.357	22.30
24875	337	1.361	21.94
24975	341	1.365	22.40
25375	344	1.373	22.77
25600	342	1.357	22.90
26000	353	1.352	23.03
25900	355	1.363	23.05
25850	354	1.358	22.41
26075	353	1.355	22.26
26275	365	1.349	21.90
26050	366	1.357	22.01
26000	354	1.353	22.62
25825	353	1.355	22.76
26075	363	1.364	23.40
26150	368	1.367	23.63
26275	366	1.358	24.05
26475	376	1.366	23.82
26500	383	1.356	23.71
26575	382	1.361	23.95
26425	386	1.366	23.61
26275	389	1.377	23.98
26375	388	1.371	23.56
25900	382	1.372	23.99
25850	379	1.376	24.33
25625	374	1.366	24.48
25900	380	1.355	24.31
26050	383	1.347	24.38
26150	384	1.352	24.63
26275	385	1.334	25.54
26100	378	1.336	25.75
25975	378	1.335	25.73
25975	378	1.347	25.85
26125	383	1.348	25.78
26175	381	1.348	25.86
26225	382	1.347	26.86
26225	382	1.340	27.36
26200	390	1.334	27.38
26275	401	1.330	26.58
26275	392	1.338	27.65
26275	401	1.359	27.73
26750	406	1.358	27.18
27075	408	1.362	27.32
27475	421	1.354	27.30
27525	412	1.354	26.90
27125	407	1.343	26.70
27000	411	1.349	26.75
26950	405	1.337	26.41
27075	416	1.334	26.29
27150	410	1.331	27.51
26875	397	1.332	27.91
26925	409	1.329	27.70
27150	406	1.325	27.28
27150	406	1.326	28.25
27425	412	1.332	27.62
27625	426	1.324	27.30
27475	423	1.309	25.94
28075	425	1.292	24.99
28075	425	1.273	25.50
28175	423	1.275	24.42
28350	434	1.297	26.58
28350	434	1.270	25.84
28500	440	1.267	26.76
29350	422	1.259	26.74
30225	431	1.249	26.68
29575	442	1.235	25.55
30125	448	1.243	26.40
30125	448	1.227	25.19
31150	475	1.233	23.94
31350	481	1.250	24.20
32175	488	1.236	24.20
31725	476	1.222	23.07
31600	474	1.231	24.07
30800	455	1.226	25.02
30800	455	1.238	24.65
29700	434	1.231	24.68
30875	447	1.216	24.63
31275	455	1.222	24.49
31500	459	1.227	25.05
31375	465	1.206	24.31
31400	464	1.196	23.90
31650	468	1.194	23.68
31975	467	1.201	24.50
31650	468	1.205	25.22
31975	467	1.213	25.48
32575	454	1.225	26.00
32025	470	1.226	26.07
33050	477	1.228	26.06
32300	462	1.236	26.22
32100	467	1.237	26.70
32250	469	1.239	27.20
32050	467	1.226	26.77
31975	469	1.227	26.11
32100	466	1.226	25.43
32025	469	1.229	24.99
32275	482	1.234	25.51
32100	474	1.220	24.00
32275	477	1.227	23.86
31975	468	1.233	22.96
32175	469	1.255	23.41
32375	480	1.253	23.17
32300	474	1.258	24.12
32450	474	1.257	23.87
32425	471	1.266	24.27
30800	443	1.264	24.40
30850	441	1.257	24.16
30750	440	1.257	25.15
30175	436	1.270	25.09
30350	442	1.283	24.60
30125	437	1.300	24.33
30625	444	1.296	24.14
30375	440	1.284	24.36
30425	444	1.282	25.40
30325	440	1.285	26.15
29825	438	1.290	26.77
29450	427	1.293	26.94
29100	421	1.303	26.33
29450	424	1.299	26.24
29550	422	1.307	26.23
29575	436	1.303	25.88
29425	435	1.307	27.00
29050	433	1.322	26.91
28525	420	1.321	27.15
28575	417	1.318	27.78
28500	411	1.318	28.73
28875	430	1.325	28.83
28625	428	1.313	28.68
28625	428	1.302	27.56
28925	426	1.279	27.15
28925	432	1.280	27.41
28950	426	1.282	27.47
28950	426	1.286	28.76
29100	424	1.288	28.47
29700	424	1.284	27.94
30000	431	1.271	27.23
30400	441	1.270	27.01
30375	444	1.261	26.15
30425	447	1.261	26.11
30625	440	1.269	27.20
30700	442	1.271	27.36
30825	439	1.270	27.33
30800	439	1.268	27.43
31100	453	1.280	28.92
31175	459	1.282	29.45
31025	462	1.283	29.01
30975	466	1.287	29.25
31025	464	1.274	29.14
31350	473	1.270	29.64
31075	469	1.272	30.40
31125	474	1.273	30.62
30900	480	1.280	31.25
31150	482	1.285	31.75
31575	486	1.299	31.30
31575	486	1.308	30.70
31375	484	1.306	31.03
31100	484	1.307	31.46
30975	487	1.312	31.28
31200	490	1.336	31.03
31125	491	1.332	30.95
31075	495	1.341	31.17
31275	497	1.348	31.29
31175	496	1.346	31.91
30950	489	1.361	32.10
30725	485	1.365	31.71
30900	490	1.373	31.90
30700	493	1.371	32.02
30625	487	1.378	32.65
30700	495	1.386	33.77
30650	506	1.397	33.51
30525	492	1.387	34.26
30850	500	1.394	34.21
30725	499	1.383	34.13
31025	518	1.396	34.73
30975	517	1.410	34.73
30550	505	1.409	34.57
30900	522	1.390	34.80
31000	519	1.386	33.98
31000	519	1.386	34.40
31000	522	1.402	34.21
31000	519	1.393	34.61
31325	543	1.403	35.25
31000	537	1.391	35.23
31000	537	1.380	35.00
30300	520	1.386	34.52
30575	523	1.386	33.82
30575	526	1.393	34.35
30775	537	1.402	34.81
30550	534	1.401	34.96
30750	536	1.424	36.69
31025	557	1.408	36.42
31000	554	1.392	36.44
30850	550	1.395	37.41
30600	549	1.377	36.40
31150	575	1.370	36.15
31800	595	1.371	35.78
32500	636	1.363	36.95
32325	628	1.361	36.14
31800	594	1.348	36.36
31850	590	1.365	37.31
31625	590	1.367	37.58
31750	605	1.365	38.00
31650	618	1.350	37.23
31525	630	1.334	37.00
32075	638	1.332	37.87
32725	645	1.323	37.70
32900	640	1.315	36.17
32775	640	1.300	36.56
32825	634	1.312	37.70
33200	647	1.316	38.77
34100	682	1.325	39.02
33800	679	1.328	39.88
33525	677	1.336	39.56
33775	697	1.320	38.52
34000	702	1.321	37.20
33425	679	1.324	38.58
33550	685	1.327	39.41
33400	695	1.344	39.08
33300	685	1.336	38.81
33400	688	1.324	38.73
33000	685	1.326	38.70
33500	694	1.315	39.23
33550	694	1.316	39.82
33725	697	1.311	39.97
33700	700	1.306	40.37
33600	695	1.310	39.54
33550	693	1.314	39.21
33500	693	1.320	39.07
34200	722	1.314	39.78
34000	725	1.328	39.40
33600	715	1.336	38.90

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')