R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(1021.3
+ ,1039.79
+ ,938.12
+ ,947.36
+ ,956.6
+ ,956.6
+ ,942.74
+ ,951.98
+ ,919.63
+ ,901.15
+ ,887.28
+ ,836.45
+ ,841.07
+ ,836.45
+ ,831.83
+ ,817.97
+ ,771.75
+ ,707.05
+ ,716.3
+ ,725.54
+ ,716.3
+ ,707.05
+ ,716.3
+ ,780.99
+ ,859.56
+ ,961.22
+ ,938.12
+ ,988.95
+ ,910.39
+ ,901.15
+ ,896.53
+ ,910.39
+ ,988.95
+ ,988.95
+ ,965.85
+ ,975.09
+ ,1002.82
+ ,1025.92
+ ,1081.38
+ ,1164.56
+ ,1201.53
+ ,1229.26
+ ,1275.47
+ ,1275.47
+ ,1307.82
+ ,1252.36
+ ,1261.61
+ ,1340.17
+ ,1414.11
+ ,1409.49
+ ,1432.59
+ ,1520.4
+ ,1529.64
+ ,1455.7
+ ,1427.97
+ ,1538.88
+ ,1612.82
+ ,1635.93
+ ,1603.58
+ ,1589.72
+ ,1557.37
+ ,1589.72
+ ,1668.28
+ ,1635.93
+ ,1615.68
+ ,1644.69
+ ,1622.71
+ ,1626.11
+ ,1705.55
+ ,1841.35
+ ,2029.03
+ ,2024.21
+ ,1952.87
+ ,2153.06
+ ,2339.29
+ ,2502.89
+ ,2515.37
+ ,2445.68
+ ,2491.11
+ ,2691.32
+ ,2651.8
+ ,2593.49
+ ,2697.23
+ ,2751.63
+ ,2713.9
+ ,2747.21
+ ,2982.32
+ ,3063.39
+ ,3058.7
+ ,3074.38
+ ,3341.06
+ ,3500.03
+ ,392.88
+ ,3071.52
+ ,2516.41
+ ,2350.7
+ ,2488.68
+ ,2872.65
+ ,3220.21
+ ,3078.04
+ ,3043.98
+ ,3134.34
+ ,3141.85
+ ,3128.01
+ ,3241.16
+ ,3389.48
+ ,3406.36
+ ,3449.84
+ ,3606.24
+ ,3653.99
+ ,3607.31
+ ,3712.52
+ ,3803.47
+ ,3806.33
+ ,3768.4
+ ,3952.06
+ ,4134.85
+ ,4060.9
+ ,3999.88
+ ,4004.03
+ ,3977.34
+ ,3650.08
+ ,3708.85
+ ,3764.78
+ ,3761.86
+ ,3802.55
+ ,3773.52
+ ,3428.7
+ ,3194.21
+ ,3095.56
+ ,3064.85
+ ,3022.98
+ ,2887.66
+ ,3178.86
+ ,3438.47
+ ,3493.87
+ ,3421.89
+ ,3390.28
+ ,3319.24
+ ,3287.84
+ ,3222.82
+ ,3182.69
+ ,3180.21
+ ,3116.34
+ ,3297.46
+ ,3357.48
+ ,3386.03
+ ,3319.45
+ ,3363.59
+ ,3303.47
+ ,3210.55
+ ,3050.27
+ ,3010.55
+ ,3011.65
+ ,3104.98
+ ,3087.85
+ ,3160.16
+ ,3319.22
+ ,3432.49
+ ,3475.68
+ ,3347.48
+ ,3388.81
+ ,3610.23
+ ,3691.45
+ ,3587.86
+ ,3704.62
+ ,3798.75
+ ,3956.54
+ ,4121.94
+ ,4148.56
+ ,4100.37
+ ,4060.71
+ ,4147.86
+ ,3926.61
+ ,3865.41
+ ,3978.57
+ ,3851.95
+ ,3701.22
+ ,3738.65
+ ,3766.9
+ ,3711.02
+ ,3675.22
+ ,3560.53
+ ,3723.8
+ ,3914.27
+ ,3870.77
+ ,3924.36
+ ,3968.89
+ ,3982.93
+ ,3917.09
+ ,3969.18
+ ,4149.81
+ ,4406.88
+ ,423.82
+ ,417.72
+ ,4527.16
+ ,4617.39
+ ,4656.23
+ ,4579.9
+ ,4652.4
+ ,4722.95
+ ,4845.81
+ ,4975.21
+ ,5083.64
+ ,5378.04
+ ,5684.44
+ ,5841.87
+ ,5857.23
+ ,6174.52
+ ,6413.17
+ ,6780.11
+ ,6524.94
+ ,6466.7
+ ,6495.61
+ ,6399.52
+ ,6729.98
+ ,7060.77
+ ,7423.27
+ ,8069.17
+ ,8650.68
+ ,8938.07
+ ,9482.08
+ ,10225.26
+ ,9390.27
+ ,8546.11
+ ,8073.77
+ ,8655.31
+ ,9150.1
+ ,9775.81
+ ,9785.14
+ ,9363.44
+ ,9304.18
+ ,9030.26
+ ,8920.8
+ ,8606.08
+ ,8353.75
+ ,8615.63
+ ,8128.64
+ ,8715.94
+ ,8500.8
+ ,8142.58
+ ,7614.66
+ ,7558.95
+ ,7820.75
+ ,7828.9
+ ,7904.59
+ ,8140.97
+ ,8483.01
+ ,8322.68
+ ,8268.01
+ ,8402.05
+ ,8177.78
+ ,7950.54
+ ,8049.94
+ ,7674.13
+ ,7666.36
+ ,7570.18
+ ,7694.45
+ ,7810.64
+ ,7748.43
+ ,7040.64
+ ,7077.26
+ ,7245.51
+ ,7289.12
+ ,7486.92
+ ,7519.88
+ ,7554.84
+ ,7780.89
+ ,7748.09
+ ,7152.25
+ ,6484.66
+ ,6254.58
+ ,5867.32
+ ,5544.16
+ ,5822.74
+ ,5690.63
+ ,5564.78
+ ,5088.39
+ ,4784.22
+ ,5332.46
+ ,5541.48
+ ,5723.92
+ ,5736.99
+ ,5992.07
+ ,6091.43
+ ,6158.17
+ ,6303.79
+ ,6349.71
+ ,6802.96
+ ,7132.68
+ ,7073.29
+ ,7264.5
+ ,7105.33
+ ,7218.71
+ ,7225.72
+ ,7354.25
+ ,7745.46
+ ,8070.26
+ ,8366.33
+ ,8667.51
+ ,8854.34
+ ,9218.1
+ ,9332.9
+ ,9358.31
+ ,9248.66
+ ,9401.2
+ ,9652.04
+ ,9957.38
+ ,10110.63
+ ,10169.26
+ ,10343.78
+ ,10750.21
+ ,11337.5
+ ,11786.96
+ ,12083.04
+ ,12007.74
+ ,11745.93
+ ,11051.51
+ ,11445.9
+ ,11924.88
+ ,12247.63
+ ,12690.91
+ ,12910.7
+ ,13202.12
+ ,13654.67
+ ,13862.82
+ ,13523.93
+ ,14211.17
+ ,14510.35
+ ,14289.23
+ ,14111.82
+ ,13086.59
+ ,13351.54
+ ,13747.69
+ ,12855.61
+ ,12926.93
+ ,12121.95
+ ,11731.65
+ ,11639.51
+ ,12163.78
+ ,12029.53
+ ,11234.18
+ ,9852.13
+ ,9709.04
+ ,9332.75
+ ,7108.6
+ ,6691.49
+ ,6143.05
+ ,6379.15
+ ,5994.58
+ ,5607.94
+ ,6046.13
+ ,6624.96
+ ,6652.54
+ ,6696
+ ,7315.16
+ ,7907.79
+ ,8066.35
+ ,7939.64
+ ,8068.48
+ ,8186.33
+ ,7975.21
+ ,8357.51
+ ,8463.38
+ ,7937.68
+ ,8034.62
+ ,8056.61
+ ,8176.95
+ ,8441.04
+ ,8697.39
+ ,8665.57
+ ,8625.77
+ ,8718.42
+ ,8822.34
+ ,8597.67
+ ,8782.05
+ ,8661.06
+ ,8265.32
+ ,8072.58
+ ,721.85
+ ,7138.6
+ ,7351.11
+ ,7077
+ ,7272.37
+ ,7577.84)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '0'
> par7 = '1'
> par6 = '1'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '0.0'
> par1 = '11'
> par10 <- 'FALSE'
> par9 <- '0'
> par8 <- '0'
> par7 <- '1'
> par6 <- '1'
> par5 <- '12'
> par4 <- '0'
> par3 <- '1'
> par2 <- '0.0'
> par1 <- '11'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: Wessa P., (2009), ARIMA Forecasting (v1.0.5) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_arimaforecasting.wasp/
> #Source of accompanying publication: 
> #Technical description: 
> 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
      0.3297  -0.7774
s.e.  0.0721   0.0444

sigma^2 estimated as 0.04579:  log likelihood = 45.63,  aic = -85.26
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 375 
End = 385 
Frequency = 1 
 [1] 9.067113 9.061202 9.059253 9.058610 9.058398 9.058328 9.058305 9.058298
 [9] 9.058295 9.058294 9.058294

$se
Time Series:
Start = 375 
End = 385 
Frequency = 1 
 [1] 0.2139792 0.2444475 0.2593334 0.2702920 0.2799112 0.2889279 0.2975820
 [8] 0.3059628 0.3141108 0.3220499 0.3297969

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 375 
End = 385 
Frequency = 1 
 [1] 8.647714 8.582085 8.550959 8.528838 8.509772 8.492030 8.475045 8.458611
 [9] 8.442638 8.427076 8.411892
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 375 
End = 385 
Frequency = 1 
 [1] 9.486513 9.540319 9.567546 9.588382 9.607024 9.624627 9.641566 9.657985
 [9] 9.673952 9.689512 9.704696
> 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]  1021.300  1039.790   938.120   947.360   956.600   956.600   942.740
  [8]   951.980   919.630   901.150   887.280   836.450   841.070   836.450
 [15]   831.830   817.970   771.750   707.050   716.300   725.540   716.300
 [22]   707.050   716.300   780.990   859.560   961.220   938.120   988.950
 [29]   910.390   901.150   896.530   910.390   988.950   988.950   965.850
 [36]   975.090  1002.820  1025.920  1081.380  1164.560  1201.530  1229.260
 [43]  1275.470  1275.470  1307.820  1252.360  1261.610  1340.170  1414.110
 [50]  1409.490  1432.590  1520.400  1529.640  1455.700  1427.970  1538.880
 [57]  1612.820  1635.930  1603.580  1589.720  1557.370  1589.720  1668.280
 [64]  1635.930  1615.680  1644.690  1622.710  1626.110  1705.550  1841.350
 [71]  2029.030  2024.210  1952.870  2153.060  2339.290  2502.890  2515.370
 [78]  2445.680  2491.110  2691.320  2651.800  2593.490  2697.230  2751.630
 [85]  2713.900  2747.210  2982.320  3063.390  3058.700  3074.380  3341.060
 [92]  3500.030   392.880  3071.520  2516.410  2350.700  2488.680  2872.650
 [99]  3220.210  3078.040  3043.980  3134.340  3141.850  3128.010  3241.160
[106]  3389.480  3406.360  3449.840  3606.240  3653.990  3607.310  3712.520
[113]  3803.470  3806.330  3768.400  3952.060  4134.850  4060.900  3999.880
[120]  4004.030  3977.340  3650.080  3708.850  3764.780  3761.860  3802.550
[127]  3773.520  3428.700  3194.210  3095.560  3064.850  3022.980  2887.660
[134]  3178.860  3438.470  3493.870  3421.890  3390.280  3319.240  3287.840
[141]  3222.820  3182.690  3180.210  3116.340  3297.460  3357.480  3386.030
[148]  3319.450  3363.590  3303.470  3210.550  3050.270  3010.550  3011.650
[155]  3104.980  3087.850  3160.160  3319.220  3432.490  3475.680  3347.480
[162]  3388.810  3610.230  3691.450  3587.860  3704.620  3798.750  3956.540
[169]  4121.940  4148.560  4100.370  4060.710  4147.860  3926.610  3865.410
[176]  3978.570  3851.950  3701.220  3738.650  3766.900  3711.020  3675.220
[183]  3560.530  3723.800  3914.270  3870.770  3924.360  3968.890  3982.930
[190]  3917.090  3969.180  4149.810  4406.880   423.820   417.720  4527.160
[197]  4617.390  4656.230  4579.900  4652.400  4722.950  4845.810  4975.210
[204]  5083.640  5378.040  5684.440  5841.870  5857.230  6174.520  6413.170
[211]  6780.110  6524.940  6466.700  6495.610  6399.520  6729.980  7060.770
[218]  7423.270  8069.170  8650.680  8938.070  9482.080 10225.260  9390.270
[225]  8546.110  8073.770  8655.310  9150.100  9775.810  9785.140  9363.440
[232]  9304.180  9030.260  8920.800  8606.080  8353.750  8615.630  8128.640
[239]  8715.940  8500.800  8142.580  7614.660  7558.950  7820.750  7828.900
[246]  7904.590  8140.970  8483.010  8322.680  8268.010  8402.050  8177.780
[253]  7950.540  8049.940  7674.130  7666.360  7570.180  7694.450  7810.640
[260]  7748.430  7040.640  7077.260  7245.510  7289.120  7486.920  7519.880
[267]  7554.840  7780.890  7748.090  7152.250  6484.660  6254.580  5867.320
[274]  5544.160  5822.740  5690.630  5564.780  5088.390  4784.220  5332.460
[281]  5541.480  5723.920  5736.990  5992.070  6091.430  6158.170  6303.790
[288]  6349.710  6802.960  7132.680  7073.290  7264.500  7105.330  7218.710
[295]  7225.720  7354.250  7745.460  8070.260  8366.330  8667.510  8854.340
[302]  9218.100  9332.900  9358.310  9248.660  9401.200  9652.040  9957.380
[309] 10110.630 10169.260 10343.780 10750.210 11337.500 11786.960 12083.040
[316] 12007.740 11745.930 11051.510 11445.900 11924.880 12247.630 12690.910
[323] 12910.700 13202.120 13654.670 13862.820 13523.930 14211.170 14510.350
[330] 14289.230 14111.820 13086.590 13351.540 13747.690 12855.610 12926.930
[337] 12121.950 11731.650 11639.510 12163.780 12029.530 11234.180  9852.130
[344]  9709.040  9332.750  7108.600  6691.490  6143.050  6379.150  5994.580
[351]  5607.940  6046.130  6624.960  6652.540  6696.000  7315.160  7907.790
[358]  8066.350  7939.640  8068.480  8186.330  7975.210  8357.510  8463.380
[365]  7937.680  8034.620  8056.610  8176.950  8441.040  8697.390  8665.570
[372]  8625.770  8718.420  8822.340  8665.574  8614.498  8597.723  8592.199
[379]  8590.379  8589.778  8589.580  8589.515  8589.494  8589.487  8589.484
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 375 
End = 385 
Frequency = 1 
 [1] 0.2658405 0.3135960 0.3379856 0.3564008 0.3728944 0.3886399 0.4040161
 [8] 0.4191574 0.4341187 0.4489278 0.4636026
> postscript(file="/var/wessaorg/rcomp/tmp/1qjbp1355695400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2zx2m1355695400.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/399891355695400.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/4u2d81355695400.tab") 
> 
> try(system("convert tmp/1qjbp1355695400.ps tmp/1qjbp1355695400.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zx2m1355695400.ps tmp/2zx2m1355695400.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.616   0.366   1.957