R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(8.9634
+ ,8.9522
+ ,8.8682
+ ,8.7331
+ ,8.3188
+ ,8.3462
+ ,8.3087
+ ,8.3836
+ ,8.8412
+ ,9.5001
+ ,10.1883
+ ,10.2931
+ ,10.1945
+ ,10.3014
+ ,10.0675
+ ,9.6715
+ ,9.5019
+ ,9.4597
+ ,9.4362
+ ,9.5919
+ ,10.0167
+ ,10.322
+ ,11.166
+ ,11.5454
+ ,11.3712
+ ,11.0723
+ ,10.813
+ ,10.3016
+ ,10.4227
+ ,10.3162
+ ,10.4519
+ ,10.8567
+ ,11.2716
+ ,11.4341
+ ,12.1273
+ ,11.9814
+ ,11.8352
+ ,11.9847
+ ,11.545
+ ,11.5285
+ ,11.5539
+ ,11.622
+ ,11.6578
+ ,11.6767
+ ,11.8752
+ ,13.2643
+ ,14.2297
+ ,14.308
+ ,13.7915
+ ,13.7633
+ ,13.9775
+ ,13.6478
+ ,13.2247
+ ,13.0971
+ ,13.1039
+ ,13.206
+ ,13.7901
+ ,14.6457
+ ,15.5764
+ ,15.6102
+ ,15.8855
+ ,16.0137
+ ,15.6186
+ ,15.384
+ ,15.2751
+ ,15.0912
+ ,14.9222
+ ,15.6231
+ ,16.6737
+ ,17.6805
+ ,19.1919
+ ,19.1711
+ ,18.5658
+ ,18.1285
+ ,16.791
+ ,16.9468
+ ,17.3164
+ ,17.1816
+ ,16.7627
+ ,17.239
+ ,17.8838
+ ,18.9038
+ ,20.0274
+ ,20.0087
+ ,19.6366
+ ,19.8163
+ ,18.8602
+ ,17.9206
+ ,17.6889
+ ,17.84
+ ,17.678
+ ,17.7258
+ ,18.5865
+ ,19.9804
+ ,21.1584
+ ,21.2921
+ ,20.9445
+ ,20.5731
+ ,19.3274
+ ,17.7866
+ ,17.7483
+ ,17.5648
+ ,17.4763
+ ,17.7264
+ ,18.5736
+ ,19.9236
+ ,21.3286
+ ,20.7249
+ ,20.3334
+ ,19.7658
+ ,18.7569
+ ,17.6963
+ ,17.7978
+ ,18.1771
+ ,18.3738
+ ,18.1996
+ ,18.8443
+ ,20.1001
+ ,21.2458
+ ,20.8381
+ ,20.1967
+ ,19.8159
+ ,18.5784
+ ,19.21
+ ,19.3419
+ ,19.12
+ ,19.1563
+ ,18.9783
+ ,20.2913
+ ,22.5439
+ ,23.2821
+ ,22.6191
+ ,22.1599
+ ,21.2766
+ ,19.0846
+ ,18.9096
+ ,18.8095
+ ,20.1164
+ ,20.7762
+ ,20.9044
+ ,22.0026
+ ,23.6401
+ ,25.04
+ ,24.7185
+ ,24.1752
+ ,24.1382
+ ,22.3949
+ ,21.3743
+ ,21.4911
+ ,21.2187
+ ,21.2137
+ ,21.6735
+ ,22.5096
+ ,24.3097
+ ,25.7989
+ ,25.4376
+ ,23.878
+ ,23.6966
+ ,23.3544
+ ,21.1993
+ ,22.0431
+ ,22.0203
+ ,21.886
+ ,21.9771
+ ,23.0759
+ ,24.9859
+ ,26.2614
+ ,26.1127
+ ,25.6296
+ ,25.2926
+ ,22.8146
+ ,22.2974
+ ,22.8868
+ ,22.4612
+ ,22.3165
+ ,22.7319
+ ,23.2692
+ ,24.9432
+ ,27.8272
+ ,27.4059
+ ,26.6232
+ ,26.8779
+ ,25.105
+ ,23.601
+ ,23.5374
+ ,23.5248
+ ,22.9465
+ ,23.6633
+ ,25.5932
+ ,27.7683
+ ,29.4691
+ ,28.3472
+ ,28.3879
+ ,27.9696
+ ,26.0075
+ ,24.2533
+ ,24.4999
+ ,23.8988
+ ,23.6683
+ ,23.9427
+ ,26.0155
+ ,28.9529
+ ,30.302
+ ,29.874
+ ,28.2257
+ ,28.0811
+ ,26.3398
+ ,25.4847
+ ,25.4823
+ ,24.9697
+ ,25.2282
+ ,25.9257
+ ,28.7818
+ ,27.9552
+ ,33.3475
+ ,32.7834
+ ,31.6586
+ ,31.6613
+ ,29.1839
+ ,28.8825
+ ,27.6334
+ ,27.7511
+ ,27.3792
+ ,27.7748
+ ,31.4329
+ ,33.2735
+ ,35.0962
+ ,34.9537
+ ,31.8307
+ ,30.9984
+ ,28.629
+ ,26.4379
+ ,25.4408
+ ,24.6681
+ ,24.0994
+ ,24.6043
+ ,27.2492
+ ,29.5511
+ ,29.8522
+ ,31.6989
+ ,29.6357
+ ,30.5197
+ ,32.7823
+ ,24.9942
+ ,23.5187
+ ,24.0249
+ ,24.5692
+ ,24.402
+ ,26.7089
+ ,31.6874
+ ,32.8801
+ ,32.7906
+ ,30.8785
+ ,30.3024
+ ,28.3679
+ ,25.6578
+ ,25.1598
+ ,24.6143
+ ,24.528
+ ,25.2905
+ ,30.0016
+ ,34.2728
+ ,34.4408
+ ,34.1907
+ ,33.6636
+ ,33.9073
+ ,30.2175
+ ,28.5274
+ ,25.9505
+ ,26.2398
+ ,26.2819
+ ,26.7362
+ ,28.8395
+ ,31.0951
+ ,33.7015
+ ,33.8091
+ ,32.1126
+ ,32
+ ,29.122
+ ,26.8124
+ ,25.4654
+ ,23.8331
+ ,24.714
+ ,28.3288
+ ,29.6391
+ ,32.4542
+ ,33.5657
+ ,33.1856
+ ,33.297
+ ,33.51
+ ,31.3789
+ ,29.4555
+ ,27.2699
+ ,27.2586
+ ,27.8591
+ ,29.6362
+ ,30.9587
+ ,31.8633
+ ,33.8188
+ ,33.7531
+ ,33.6103
+ ,32.9052
+ ,29.5005
+ ,27.3634
+ ,27.2298
+ ,26.5211
+ ,26.5228
+ ,27.2991
+ ,29.1726
+ ,30.297
+ ,32.5287
+ ,32.487
+ ,32.4197
+ ,30.854
+ ,28.6995
+ ,27.7881
+ ,26.5609
+ ,25.9431
+ ,25.5578
+ ,27.1275
+ ,30.2556
+ ,34.0976
+ ,34.5614
+ ,34.2948
+ ,33.3418
+ ,31.8187
+ ,29.0818
+ ,27.3444
+ ,26.6233
+ ,26.1869
+ ,26.2953
+ ,28.7043
+ ,32.0653
+ ,34.5401
+ ,34.6636
+ ,34.2557
+ ,32.0526
+ ,30.6892
+ ,28.012
+ ,26.1528
+ ,23.2276
+ ,24.244
+ ,24.8141
+ ,27.8632
+ ,29.6233
+ ,32.4245
+ ,33.3417
+ ,33.0442
+ ,32.0526
+ ,30.2182
+ ,28.9292
+ ,26.8221
+ ,26.1032
+ ,25.9792
+ ,27.1443
+ ,29.4993
+ ,31.656
+ ,33.3665
+ ,35.0521
+ ,34.4076
+ ,33.069
+ ,31.5816
+ ,30.0695
+ ,29.0035
+ ,28.6813
+ ,28.359
+ ,30.0447
+ ,31.5073
+ ,34.16
+ ,35.57
+ ,36.42
+ ,35.12
+ ,33.14
+ ,30.29
+ ,28.2
+ ,26.5
+ ,25.47
+ ,24.96
+ ,25.6
+ ,27.76
+ ,30.13
+ ,32.35
+ ,32.8
+ ,32.54
+ ,29.78
+ ,28.79
+ ,26.8
+ ,25.41
+ ,24.34
+ ,24.39
+ ,25
+ ,26.27
+ ,27.88
+ ,29.35
+ ,29.83
+ ,29.46)
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = 'FALSE'
> par9 = '1'
> par8 = '0'
> par7 = '1'
> par6 = '1'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '-0.5'
> par1 = '12'
> par1 <- as.numeric(par1) #cut off periods
> par2 <- as.numeric(par2) #lambda
> par3 <- as.numeric(par3) #degree of non-seasonal differencing
> par4 <- as.numeric(par4) #degree of seasonal differencing
> par5 <- as.numeric(par5) #seasonal period
> par6 <- as.numeric(par6) #p
> par7 <- as.numeric(par7) #q
> par8 <- as.numeric(par8) #P
> par9 <- as.numeric(par9) #Q
> if (par10 == 'TRUE') par10 <- TRUE
> if (par10 == 'FALSE') par10 <- FALSE
> if (par2 == 0) x <- log(x)
> if (par2 != 0) x <- x^par2
> lx <- length(x)
> first <- lx - 2*par1
> nx <- lx - par1
> nx1 <- nx + 1
> fx <- lx - nx
> if (fx < 1) {
+ fx <- par5
+ nx1 <- lx + fx - 1
+ first <- lx - 2*fx
+ }
> first <- 1
> if (fx < 3) fx <- round(lx/10,0)
> (arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))

Call:
arima(x = x[1:nx], order = c(par6, par3, par7), seasonal = list(order = c(par8, 
    par4, par9), period = par5), include.mean = par10, method = "ML")

Coefficients:
         ar1      ma1     sma1
      0.5627  -0.7496  -0.7573
s.e.  0.1133   0.0887   0.0414

sigma^2 estimated as 9.664e-06:  log likelihood = 1610.38,  aic = -3212.76
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 385 
End = 396 
Frequency = 1 
 [1] 0.1786555 0.1831043 0.1893264 0.1951359 0.1990829 0.1999512 0.1974130
 [8] 0.1906089 0.1832236 0.1772270 0.1746919 0.1758731

$se
Time Series:
Start = 385 
End = 396 
Frequency = 1 
 [1] 0.003108631 0.004006385 0.004570824 0.004995777 0.005349422 0.005661747
 [7] 0.005947598 0.006214951 0.006468384 0.006710697 0.006943715 0.007168705

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 385 
End = 396 
Frequency = 1 
 [1] 0.1725626 0.1752518 0.1803676 0.1853441 0.1885980 0.1888542 0.1857557
 [8] 0.1784276 0.1705456 0.1640741 0.1610822 0.1618224
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 385 
End = 396 
Frequency = 1 
 [1] 0.1847484 0.1909568 0.1982853 0.2049276 0.2095677 0.2110482 0.2090703
 [8] 0.2027902 0.1959017 0.1903800 0.1883015 0.1899237
> if (par2 == 0) {
+ x <- exp(x)
+ forecast$pred <- exp(forecast$pred)
+ lb <- exp(lb)
+ ub <- exp(ub)
+ }
> if (par2 != 0) {
+ x <- x^(1/par2)
+ forecast$pred <- forecast$pred^(1/par2)
+ lb <- lb^(1/par2)
+ ub <- ub^(1/par2)
+ }
> if (par2 < 0) {
+ olb <- lb
+ lb <- ub
+ ub <- olb
+ }
> (actandfor <- c(x[1:nx], forecast$pred))
  [1]  8.96340  8.95220  8.86820  8.73310  8.31880  8.34620  8.30870  8.38360
  [9]  8.84120  9.50010 10.18830 10.29310 10.19450 10.30140 10.06750  9.67150
 [17]  9.50190  9.45970  9.43620  9.59190 10.01670 10.32200 11.16600 11.54540
 [25] 11.37120 11.07230 10.81300 10.30160 10.42270 10.31620 10.45190 10.85670
 [33] 11.27160 11.43410 12.12730 11.98140 11.83520 11.98470 11.54500 11.52850
 [41] 11.55390 11.62200 11.65780 11.67670 11.87520 13.26430 14.22970 14.30800
 [49] 13.79150 13.76330 13.97750 13.64780 13.22470 13.09710 13.10390 13.20600
 [57] 13.79010 14.64570 15.57640 15.61020 15.88550 16.01370 15.61860 15.38400
 [65] 15.27510 15.09120 14.92220 15.62310 16.67370 17.68050 19.19190 19.17110
 [73] 18.56580 18.12850 16.79100 16.94680 17.31640 17.18160 16.76270 17.23900
 [81] 17.88380 18.90380 20.02740 20.00870 19.63660 19.81630 18.86020 17.92060
 [89] 17.68890 17.84000 17.67800 17.72580 18.58650 19.98040 21.15840 21.29210
 [97] 20.94450 20.57310 19.32740 17.78660 17.74830 17.56480 17.47630 17.72640
[105] 18.57360 19.92360 21.32860 20.72490 20.33340 19.76580 18.75690 17.69630
[113] 17.79780 18.17710 18.37380 18.19960 18.84430 20.10010 21.24580 20.83810
[121] 20.19670 19.81590 18.57840 19.21000 19.34190 19.12000 19.15630 18.97830
[129] 20.29130 22.54390 23.28210 22.61910 22.15990 21.27660 19.08460 18.90960
[137] 18.80950 20.11640 20.77620 20.90440 22.00260 23.64010 25.04000 24.71850
[145] 24.17520 24.13820 22.39490 21.37430 21.49110 21.21870 21.21370 21.67350
[153] 22.50960 24.30970 25.79890 25.43760 23.87800 23.69660 23.35440 21.19930
[161] 22.04310 22.02030 21.88600 21.97710 23.07590 24.98590 26.26140 26.11270
[169] 25.62960 25.29260 22.81460 22.29740 22.88680 22.46120 22.31650 22.73190
[177] 23.26920 24.94320 27.82720 27.40590 26.62320 26.87790 25.10500 23.60100
[185] 23.53740 23.52480 22.94650 23.66330 25.59320 27.76830 29.46910 28.34720
[193] 28.38790 27.96960 26.00750 24.25330 24.49990 23.89880 23.66830 23.94270
[201] 26.01550 28.95290 30.30200 29.87400 28.22570 28.08110 26.33980 25.48470
[209] 25.48230 24.96970 25.22820 25.92570 28.78180 27.95520 33.34750 32.78340
[217] 31.65860 31.66130 29.18390 28.88250 27.63340 27.75110 27.37920 27.77480
[225] 31.43290 33.27350 35.09620 34.95370 31.83070 30.99840 28.62900 26.43790
[233] 25.44080 24.66810 24.09940 24.60430 27.24920 29.55110 29.85220 31.69890
[241] 29.63570 30.51970 32.78230 24.99420 23.51870 24.02490 24.56920 24.40200
[249] 26.70890 31.68740 32.88010 32.79060 30.87850 30.30240 28.36790 25.65780
[257] 25.15980 24.61430 24.52800 25.29050 30.00160 34.27280 34.44080 34.19070
[265] 33.66360 33.90730 30.21750 28.52740 25.95050 26.23980 26.28190 26.73620
[273] 28.83950 31.09510 33.70150 33.80910 32.11260 32.00000 29.12200 26.81240
[281] 25.46540 23.83310 24.71400 28.32880 29.63910 32.45420 33.56570 33.18560
[289] 33.29700 33.51000 31.37890 29.45550 27.26990 27.25860 27.85910 29.63620
[297] 30.95870 31.86330 33.81880 33.75310 33.61030 32.90520 29.50050 27.36340
[305] 27.22980 26.52110 26.52280 27.29910 29.17260 30.29700 32.52870 32.48700
[313] 32.41970 30.85400 28.69950 27.78810 26.56090 25.94310 25.55780 27.12750
[321] 30.25560 34.09760 34.56140 34.29480 33.34180 31.81870 29.08180 27.34440
[329] 26.62330 26.18690 26.29530 28.70430 32.06530 34.54010 34.66360 34.25570
[337] 32.05260 30.68920 28.01200 26.15280 23.22760 24.24400 24.81410 27.86320
[345] 29.62330 32.42450 33.34170 33.04420 32.05260 30.21820 28.92920 26.82210
[353] 26.10320 25.97920 27.14430 29.49930 31.65600 33.36650 35.05210 34.40760
[361] 33.06900 31.58160 30.06950 29.00350 28.68130 28.35900 30.04470 31.50730
[369] 34.16000 35.57000 36.42000 35.12000 33.14000 30.29000 28.20000 26.50000
[377] 25.47000 24.96000 25.60000 27.76000 30.13000 32.35000 32.80000 32.54000
[385] 31.33049 29.82655 27.89828 26.26188 25.23087 25.01221 25.65951 27.52414
[393] 29.78770 31.83758 32.76836 32.32968
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 385 
End = 396 
Frequency = 1 
 [1] 0.03666509 0.04674580 0.05194213 0.05533210 0.05830517 0.06172052
 [7] 0.06604611 0.07204155 0.07867463 0.08507959 0.08985537 0.09244611
> postscript(file="/var/wessaorg/rcomp/tmp/1a0yo1386212461.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$pred , lwd=2 , col ='white')
> box()
> par(opar)
> dev.off()
null device 
          1 
> prob.dec <- array(NA, dim=fx)
> prob.sdec <- array(NA, dim=fx)
> prob.ldec <- array(NA, dim=fx)
> prob.pval <- array(NA, dim=fx)
> perf.pe <- array(0, dim=fx)
> perf.mape <- array(0, dim=fx)
> perf.mape1 <- array(0, dim=fx)
> perf.se <- array(0, dim=fx)
> perf.mse <- array(0, dim=fx)
> perf.mse1 <- array(0, dim=fx)
> perf.rmse <- array(0, dim=fx)
> for (i in 1:fx) {
+ locSD <- (ub[i] - forecast$pred[i]) / 1.96
+ perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
+ perf.se[i] = (x[nx+i] - forecast$pred[i])^2
+ prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD)
+ prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD)
+ prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD)
+ prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD)
+ }
> perf.mape[1] = abs(perf.pe[1])
> perf.mse[1] = abs(perf.se[1])
> for (i in 2:fx) {
+ perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
+ perf.mape1[i] = perf.mape[i] / i
+ perf.mse[i] = perf.mse[i-1] + perf.se[i]
+ perf.mse1[i] = perf.mse[i] / i
+ }
> perf.rmse = sqrt(perf.mse1)
> postscript(file="/var/wessaorg/rcomp/tmp/2sao71386212461.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
> dum <- forecast$pred
> dum[1:par1] <- x[(nx+1):lx]
> lines(dum, lty=1)
> lines(ub,lty=3)
> lines(lb,lty=3)
> dev.off()
null device 
          1 
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'time',1,header=TRUE)
> a<-table.element(a,'Y[t]',1,header=TRUE)
> a<-table.element(a,'F[t]',1,header=TRUE)
> a<-table.element(a,'95% LB',1,header=TRUE)
> a<-table.element(a,'95% UB',1,header=TRUE)
> a<-table.element(a,'p-value<br />(H0: Y[t] = F[t])',1,header=TRUE)
> a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE)
> a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE)
> mylab <- paste('P(F[t]>Y[',nx,sep='')
> mylab <- paste(mylab,'])',sep='')
> a<-table.element(a,mylab,1,header=TRUE)
> a<-table.row.end(a)
> for (i in (nx-par5):nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.row.end(a)
+ }
> for (i in 1:fx) {
+ a<-table.row.start(a)
+ a<-table.element(a,nx+i,header=TRUE)
+ a<-table.element(a,round(x[nx+i],4))
+ a<-table.element(a,round(forecast$pred[i],4))
+ a<-table.element(a,round(lb[i],4))
+ a<-table.element(a,round(ub[i],4))
+ a<-table.element(a,round((1-prob.pval[i]),4))
+ a<-table.element(a,round((1-prob.dec[i]),4))
+ a<-table.element(a,round((1-prob.sdec[i]),4))
+ a<-table.element(a,round((1-prob.ldec[i]),4))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/3whl01386212461.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'time',1,header=TRUE)
> a<-table.element(a,'% S.E.',1,header=TRUE)
> a<-table.element(a,'PE',1,header=TRUE)
> a<-table.element(a,'MAPE',1,header=TRUE)
> a<-table.element(a,'Sq.E',1,header=TRUE)
> a<-table.element(a,'MSE',1,header=TRUE)
> a<-table.element(a,'RMSE',1,header=TRUE)
> a<-table.row.end(a)
> for (i in 1:fx) {
+ a<-table.row.start(a)
+ a<-table.element(a,nx+i,header=TRUE)
+ a<-table.element(a,round(perc.se[i],4))
+ a<-table.element(a,round(perf.pe[i],4))
+ a<-table.element(a,round(perf.mape1[i],4))
+ a<-table.element(a,round(perf.se[i],4))
+ a<-table.element(a,round(perf.mse1[i],4))
+ a<-table.element(a,round(perf.rmse[i],4))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4kl3q1386212461.tab") 
> 
> try(system("convert tmp/1a0yo1386212461.ps tmp/1a0yo1386212461.png",intern=TRUE))
character(0)
> try(system("convert tmp/2sao71386212461.ps tmp/2sao71386212461.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.514   0.736   5.220