R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'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(1.3866
+ ,1.3582
+ ,1.3332
+ ,1.3595
+ ,1.3617
+ ,1.3684
+ ,1.3394
+ ,1.3262
+ ,1.3173
+ ,1.3085
+ ,1.327
+ ,1.3182
+ ,1.293
+ ,1.291
+ ,1.2984
+ ,1.2795
+ ,1.299
+ ,1.3174
+ ,1.326
+ ,1.3111
+ ,1.2816
+ ,1.276
+ ,1.2849
+ ,1.2818
+ ,1.2829
+ ,1.2796
+ ,1.3008
+ ,1.2967
+ ,1.2938
+ ,1.2833
+ ,1.2823
+ ,1.2765
+ ,1.2634
+ ,1.2596
+ ,1.2705
+ ,1.2591
+ ,1.2798
+ ,1.2763
+ ,1.2795
+ ,1.2782
+ ,1.2644
+ ,1.2596
+ ,1.2615
+ ,1.2555
+ ,1.2555
+ ,1.2658
+ ,1.2565
+ ,1.2783
+ ,1.2786
+ ,1.2782
+ ,1.2905
+ ,1.3042
+ ,1.2942
+ ,1.313
+ ,1.3671
+ ,1.3549
+ ,1.3558
+ ,1.3507
+ ,1.3494
+ ,1.3607
+ ,1.3295
+ ,1.3193
+ ,1.3308
+ ,1.3246
+ ,1.3392
+ ,1.3425
+ ,1.3496
+ ,1.3255
+ ,1.3231
+ ,1.3273
+ ,1.3276
+ ,1.3173
+ ,1.3196
+ ,1.3058
+ ,1.2966
+ ,1.2932
+ ,1.2947
+ ,1.305
+ ,1.3232
+ ,1.3125
+ ,1.2992
+ ,1.3266
+ ,1.3275
+ ,1.3223
+ ,1.3403
+ ,1.3322
+ ,1.3363
+ ,1.3425
+ ,1.3574
+ ,1.3683
+ ,1.3623
+ ,1.3563
+ ,1.3518
+ ,1.3494
+ ,1.3612
+ ,1.369
+ ,1.3771
+ ,1.3972
+ ,1.401
+ ,1.3908
+ ,1.3901
+ ,1.3856
+ ,1.4098
+ ,1.422
+ ,1.4238
+ ,1.4207
+ ,1.4095
+ ,1.4177
+ ,1.3866
+ ,1.3959
+ ,1.4102
+ ,1.3969
+ ,1.4004
+ ,1.385
+ ,1.389
+ ,1.384
+ ,1.392
+ ,1.3932
+ ,1.3858
+ ,1.3978
+ ,1.4029
+ ,1.394
+ ,1.4096
+ ,1.4058
+ ,1.4134
+ ,1.4096
+ ,1.4049
+ ,1.4009
+ ,1.3897
+ ,1.4019
+ ,1.3901
+ ,1.399
+ ,1.3901
+ ,1.3975
+ ,1.3991
+ ,1.4089
+ ,1.413
+ ,1.409
+ ,1.4217
+ ,1.4223
+ ,1.4191
+ ,1.4229
+ ,1.4227
+ ,1.4269
+ ,1.4229
+ ,1.4104
+ ,1.4053
+ ,1.4138
+ ,1.4303
+ ,1.4384
+ ,1.441
+ ,1.437
+ ,1.4357
+ ,1.4202
+ ,1.4166
+ ,1.417
+ ,1.4293
+ ,1.4294
+ ,1.4072
+ ,1.4101
+ ,1.4112
+ ,1.4243
+ ,1.433
+ ,1.4323
+ ,1.4324
+ ,1.427
+ ,1.4268
+ ,1.4364
+ ,1.4272
+ ,1.4314
+ ,1.422
+ ,1.4335
+ ,1.4262
+ ,1.433
+ ,1.4473
+ ,1.4522
+ ,1.4545
+ ,1.4594
+ ,1.4561
+ ,1.4611
+ ,1.4671
+ ,1.4712
+ ,1.4705
+ ,1.4658
+ ,1.478
+ ,1.4783
+ ,1.4768
+ ,1.467
+ ,1.465
+ ,1.4549
+ ,1.4643
+ ,1.4539
+ ,1.4537
+ ,1.4616
+ ,1.4722
+ ,1.4694
+ ,1.4763
+ ,1.475
+ ,1.4765
+ ,1.4864
+ ,1.4881
+ ,1.4864
+ ,1.4869
+ ,1.4918
+ ,1.4971
+ ,1.4921
+ ,1.5
+ ,1.502
+ ,1.5019
+ ,1.4874
+ ,1.4785
+ ,1.4788
+ ,1.48
+ ,1.4772
+ ,1.4658
+ ,1.4761
+ ,1.4867
+ ,1.4862
+ ,1.4984
+ ,1.4966
+ ,1.5037
+ ,1.4922
+ ,1.4868
+ ,1.4965
+ ,1.4875
+ ,1.4957
+ ,1.4863
+ ,1.4815
+ ,1.4968
+ ,1.4969
+ ,1.5083
+ ,1.5071
+ ,1.4918
+ ,1.5023
+ ,1.5074
+ ,1.509
+ ,1.512
+ ,1.5068
+ ,1.4787
+ ,1.4774
+ ,1.4768
+ ,1.473
+ ,1.4757
+ ,1.4647
+ ,1.4541
+ ,1.456
+ ,1.4343
+ ,1.4337
+ ,1.4368
+ ,1.4279
+ ,1.4276
+ ,1.4398
+ ,1.4405
+ ,1.4433
+ ,1.4338
+ ,1.4406
+ ,1.4389
+ ,1.4442
+ ,1.435
+ ,1.4304
+ ,1.4273
+ ,1.4528
+ ,1.4481
+ ,1.4563
+ ,1.4486
+ ,1.4374
+ ,1.4369
+ ,1.4279
+ ,1.4132
+ ,1.4064
+ ,1.4135
+ ,1.4151
+ ,1.4085
+ ,1.4072
+ ,1.3999
+ ,1.3966
+ ,1.3913
+ ,1.3937
+ ,1.3984
+ ,1.3847
+ ,1.3691
+ ,1.3675
+ ,1.376
+ ,1.374
+ ,1.3718
+ ,1.3572
+ ,1.3607
+ ,1.3649
+ ,1.3726
+ ,1.3567
+ ,1.3519
+ ,1.3626
+ ,1.3577
+ ,1.3547
+ ,1.3489
+ ,1.357
+ ,1.3525
+ ,1.3548
+ ,1.3641
+ ,1.3668
+ ,1.3582
+ ,1.3662
+ ,1.3557
+ ,1.361
+ ,1.3657
+ ,1.3765
+ ,1.3705
+ ,1.3723
+ ,1.3756
+ ,1.366
+ ,1.3548
+ ,1.3471
+ ,1.3519
+ ,1.3338
+ ,1.3356
+ ,1.3353
+ ,1.3471
+ ,1.3482
+ ,1.3479
+ ,1.3468
+ ,1.3396
+ ,1.334
+ ,1.3296
+ ,1.3384
+ ,1.3585
+ ,1.3583
+ ,1.3615
+ ,1.3544
+ ,1.3535
+ ,1.3432
+ ,1.3486
+ ,1.3373
+ ,1.3339
+ ,1.3311
+ ,1.3321
+ ,1.329
+ ,1.3245
+ ,1.3256
+ ,1.3315
+ ,1.3238
+ ,1.3089
+ ,1.2924
+ ,1.2727
+ ,1.2746
+ ,1.2969
+ ,1.2698
+ ,1.2686
+ ,1.2587
+ ,1.2492
+ ,1.2349
+ ,1.2428
+ ,1.227
+ ,1.2334
+ ,1.2497
+ ,1.236
+ ,1.2223
+ ,1.2309
+ ,1.2255
+ ,1.2384
+ ,1.2307
+ ,1.2155
+ ,1.2218
+ ,1.2268
+ ,1.206
+ ,1.1959
+ ,1.1942)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '1'
> par7 = '0'
> par6 = '0'
> par5 = '1'
> par4 = '0'
> par3 = '1'
> par2 = '1.9'
> par1 = '0'
> par1 <- 30 #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 <- 5 #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:
        sar1
      0.0797
s.e.  0.0558

sigma^2 estimated as 0.000689:  log likelihood = 744.08,  aic = -1484.17
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 337 
End = 366 
Frequency = 1 
 [1] 1.714481 1.713813 1.713264 1.713460 1.712853 1.712676 1.712622 1.712579
 [9] 1.712594 1.712546 1.712532 1.712528 1.712524 1.712525 1.712521 1.712520
[17] 1.712520 1.712520 1.712520 1.712519 1.712519 1.712519 1.712519 1.712519
[25] 1.712519 1.712519 1.712519 1.712519 1.712519 1.712519

$se
Time Series:
Start = 337 
End = 366 
Frequency = 1 
 [1] 0.02624882 0.03712144 0.04546429 0.05249764 0.05869415 0.06517812
 [7] 0.07107300 0.07651507 0.08159497 0.08637664 0.09095924 0.09532179
[13] 0.09949323 0.10349668 0.10735093 0.11107492 0.11467805 0.11817137
[19] 0.12156434 0.12486515 0.12808116 0.13121837 0.13428231 0.13727788
[25] 0.14020946 0.14308101 0.14589605 0.14865780 0.15136916 0.15403280

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 337 
End = 366 
Frequency = 1 
 [1] 1.663034 1.641055 1.624154 1.610565 1.597813 1.584927 1.573319 1.562609
 [9] 1.552668 1.543248 1.534252 1.525697 1.517517 1.509672 1.502114 1.494813
[17] 1.487751 1.480904 1.474254 1.467784 1.461480 1.455331 1.449326 1.443455
[25] 1.437709 1.432081 1.426563 1.421150 1.415836 1.410615
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 337 
End = 366 
Frequency = 1 
 [1] 1.765929 1.786571 1.802374 1.816356 1.827894 1.840425 1.851925 1.862548
 [9] 1.872520 1.881844 1.890812 1.899358 1.907531 1.915379 1.922929 1.930227
[17] 1.937289 1.944136 1.950786 1.957255 1.963558 1.969707 1.975713 1.981584
[25] 1.987330 1.992958 1.998476 2.003889 2.009203 2.014424
> 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] 1.386600 1.358200 1.333200 1.359500 1.361700 1.368400 1.339400 1.326200
  [9] 1.317300 1.308500 1.327000 1.318200 1.293000 1.291000 1.298400 1.279500
 [17] 1.299000 1.317400 1.326000 1.311100 1.281600 1.276000 1.284900 1.281800
 [25] 1.282900 1.279600 1.300800 1.296700 1.293800 1.283300 1.282300 1.276500
 [33] 1.263400 1.259600 1.270500 1.259100 1.279800 1.276300 1.279500 1.278200
 [41] 1.264400 1.259600 1.261500 1.255500 1.255500 1.265800 1.256500 1.278300
 [49] 1.278600 1.278200 1.290500 1.304200 1.294200 1.313000 1.367100 1.354900
 [57] 1.355800 1.350700 1.349400 1.360700 1.329500 1.319300 1.330800 1.324600
 [65] 1.339200 1.342500 1.349600 1.325500 1.323100 1.327300 1.327600 1.317300
 [73] 1.319600 1.305800 1.296600 1.293200 1.294700 1.305000 1.323200 1.312500
 [81] 1.299200 1.326600 1.327500 1.322300 1.340300 1.332200 1.336300 1.342500
 [89] 1.357400 1.368300 1.362300 1.356300 1.351800 1.349400 1.361200 1.369000
 [97] 1.377100 1.397200 1.401000 1.390800 1.390100 1.385600 1.409800 1.422000
[105] 1.423800 1.420700 1.409500 1.417700 1.386600 1.395900 1.410200 1.396900
[113] 1.400400 1.385000 1.389000 1.384000 1.392000 1.393200 1.385800 1.397800
[121] 1.402900 1.394000 1.409600 1.405800 1.413400 1.409600 1.404900 1.400900
[129] 1.389700 1.401900 1.390100 1.399000 1.390100 1.397500 1.399100 1.408900
[137] 1.413000 1.409000 1.421700 1.422300 1.419100 1.422900 1.422700 1.426900
[145] 1.422900 1.410400 1.405300 1.413800 1.430300 1.438400 1.441000 1.437000
[153] 1.435700 1.420200 1.416600 1.417000 1.429300 1.429400 1.407200 1.410100
[161] 1.411200 1.424300 1.433000 1.432300 1.432400 1.427000 1.426800 1.436400
[169] 1.427200 1.431400 1.422000 1.433500 1.426200 1.433000 1.447300 1.452200
[177] 1.454500 1.459400 1.456100 1.461100 1.467100 1.471200 1.470500 1.465800
[185] 1.478000 1.478300 1.476800 1.467000 1.465000 1.454900 1.464300 1.453900
[193] 1.453700 1.461600 1.472200 1.469400 1.476300 1.475000 1.476500 1.486400
[201] 1.488100 1.486400 1.486900 1.491800 1.497100 1.492100 1.500000 1.502000
[209] 1.501900 1.487400 1.478500 1.478800 1.480000 1.477200 1.465800 1.476100
[217] 1.486700 1.486200 1.498400 1.496600 1.503700 1.492200 1.486800 1.496500
[225] 1.487500 1.495700 1.486300 1.481500 1.496800 1.496900 1.508300 1.507100
[233] 1.491800 1.502300 1.507400 1.509000 1.512000 1.506800 1.478700 1.477400
[241] 1.476800 1.473000 1.475700 1.464700 1.454100 1.456000 1.434300 1.433700
[249] 1.436800 1.427900 1.427600 1.439800 1.440500 1.443300 1.433800 1.440600
[257] 1.438900 1.444200 1.435000 1.430400 1.427300 1.452800 1.448100 1.456300
[265] 1.448600 1.437400 1.436900 1.427900 1.413200 1.406400 1.413500 1.415100
[273] 1.408500 1.407200 1.399900 1.396600 1.391300 1.393700 1.398400 1.384700
[281] 1.369100 1.367500 1.376000 1.374000 1.371800 1.357200 1.360700 1.364900
[289] 1.372600 1.356700 1.351900 1.362600 1.357700 1.354700 1.348900 1.357000
[297] 1.352500 1.354800 1.364100 1.366800 1.358200 1.366200 1.355700 1.361000
[305] 1.365700 1.376500 1.370500 1.372300 1.375600 1.366000 1.354800 1.347100
[313] 1.351900 1.333800 1.335600 1.335300 1.347100 1.348200 1.347900 1.346800
[321] 1.339600 1.334000 1.329600 1.338400 1.358500 1.358300 1.361500 1.354400
[329] 1.353500 1.343200 1.348600 1.337300 1.333900 1.331100 1.332100 1.329000
[337] 1.328091 1.327818 1.327595 1.327674 1.327427 1.327354 1.327333 1.327315
[345] 1.327321 1.327302 1.327296 1.327294 1.327293 1.327293 1.327292 1.327291
[353] 1.327291 1.327291 1.327291 1.327291 1.327291 1.327291 1.327291 1.327291
[361] 1.327291 1.327291 1.327291 1.327291 1.327291 1.327291
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 337 
End = 366 
Frequency = 1 
 [1] 0.00800149 0.01128778 0.01379887 0.01590264 0.01775740 0.01968819
 [7] 0.02143709 0.02304709 0.02454529 0.02595300 0.02729856 0.02857657
[13] 0.02979605 0.03096401 0.03208639 0.03316882 0.03421426 0.03522612
[19] 0.03620728 0.03716029 0.03808737 0.03899038 0.03987101 0.04073077
[25] 0.04157099 0.04239289 0.04319755 0.04398596 0.04475900 0.04551749
> postscript(file="/var/www/html/rcomp/tmp/1qups1292887600.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/www/html/rcomp/tmp/2ev431292887600.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/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/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/www/html/rcomp/tmp/33e1x1292887600.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/www/html/rcomp/tmp/4pxhl1292887600.tab") 
> 
> try(system("convert tmp/1qups1292887600.ps tmp/1qups1292887600.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ev431292887600.ps tmp/2ev431292887600.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.729   0.366   1.610