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(358.59
+ ,362.96
+ ,362.42
+ ,364.97
+ ,364.04
+ ,361.06
+ ,358.48
+ ,352.96
+ ,359.59
+ ,360.39
+ ,357.40
+ ,362.93
+ ,364.55
+ ,365.73
+ ,364.70
+ ,364.65
+ ,359.43
+ ,362.14
+ ,356.97
+ ,354.82
+ ,353.17
+ ,357.06
+ ,356.18
+ ,355.01
+ ,355.65
+ ,357.31
+ ,357.07
+ ,357.91
+ ,358.48
+ ,358.97
+ ,351.77
+ ,352.16
+ ,359.08
+ ,360.35
+ ,359.53
+ ,359.30
+ ,358.41
+ ,359.68
+ ,355.31
+ ,357.08
+ ,349.71
+ ,354.13
+ ,345.49
+ ,341.69
+ ,344.25
+ ,340.17
+ ,342.47
+ ,344.43
+ ,333.23
+ ,339.72
+ ,342.61
+ ,346.36
+ ,339.09
+ ,339.73
+ ,341.12
+ ,335.94
+ ,333.46
+ ,335.66
+ ,341.12
+ ,342.21
+ ,342.62
+ ,346.06
+ ,344.43
+ ,346.65
+ ,343.74
+ ,335.67
+ ,342.75
+ ,341.77
+ ,345.84
+ ,346.52
+ ,350.79
+ ,345.44
+ ,345.87
+ ,338.48
+ ,337.21
+ ,340.81
+ ,339.86
+ ,342.86
+ ,343.33
+ ,341.73
+ ,351.38
+ ,351.13
+ ,345.99
+ ,347.55
+ ,346.02
+ ,345.29
+ ,347.03
+ ,348.01
+ ,345.48
+ ,349.40
+ ,351.05
+ ,349.70
+ ,350.86
+ ,354.45
+ ,355.30
+ ,357.48
+ ,355.24
+ ,351.79
+ ,355.22
+ ,351.02
+ ,350.28
+ ,350.17
+ ,348.16
+ ,340.30
+ ,343.75
+ ,344.71
+ ,344.13
+ ,342.14
+ ,345.04
+ ,346.02
+ ,346.43
+ ,347.07
+ ,339.33
+ ,339.10
+ ,337.19
+ ,339.58
+ ,327.85
+ ,326.81
+ ,321.73
+ ,320.45
+ ,327.69
+ ,323.95
+ ,320.47
+ ,322.13
+ ,316.34
+ ,314.78
+ ,308.90
+ ,308.62
+ ,314.41
+ ,306.88
+ ,310.60
+ ,321.60
+ ,321.50
+ ,325.68
+ ,324.35
+ ,320.01
+ ,326.88
+ ,332.39
+ ,331.48
+ ,332.62
+ ,324.79
+ ,327.12
+ ,328.91
+ ,328.37
+ ,324.83
+ ,325.90
+ ,326.18
+ ,328.94
+ ,333.78
+ ,328.06
+ ,325.87
+ ,325.41
+ ,318.86
+ ,319.13
+ ,310.16
+ ,311.73
+ ,306.54
+ ,311.16
+ ,311.98
+ ,306.72
+ ,308.05
+ ,300.76
+ ,301.90
+ ,293.09
+ ,292.76
+ ,294.58
+ ,289.90
+ ,296.69
+ ,297.21
+ ,293.31
+ ,296.25
+ ,298.60
+ ,296.87
+ ,301.02
+ ,304.73
+ ,301.92
+ ,295.72
+ ,293.18
+ ,298.35
+ ,297.99
+ ,299.85
+ ,299.85
+ ,304.45
+ ,299.45
+ ,298.14
+ ,298.78
+ ,297.02
+ ,301.33
+ ,294.96
+ ,296.69
+ ,300.73
+ ,301.96
+ ,297.38
+ ,293.87
+ ,285.96
+ ,285.41
+ ,283.70
+ ,284.76
+ ,277.11
+ ,274.73
+ ,274.73
+ ,274.73
+ ,274.73
+ ,274.69
+ ,275.42
+ ,264.15
+ ,276.24
+ ,268.88
+ ,277.97
+ ,280.49
+ ,281.09
+ ,276.16
+ ,272.58
+ ,270.94
+ ,284.31
+ ,283.94
+ ,284.18
+ ,282.83
+ ,283.84
+ ,282.71
+ ,279.29
+ ,280.70
+ ,274.47
+ ,273.44
+ ,275.49
+ ,279.46
+ ,280.19
+ ,288.21
+ ,284.80
+ ,281.41
+ ,283.39
+ ,287.97
+ ,290.77
+ ,290.60
+ ,289.67
+ ,289.84
+ ,298.55
+ ,296.07
+ ,297.14
+ ,295.34
+ ,296.25
+ ,294.30
+ ,296.15
+ ,296.49
+ ,298.05
+ ,301.03
+ ,300.52
+ ,301.50
+ ,296.93
+ ,289.84
+ ,291.44
+ ,286.88
+ ,286.74
+ ,288.93
+ ,292.19
+ ,295.39
+ ,295.86
+ ,293.36
+ ,292.86
+ ,292.73
+ ,296.73
+ ,285.02
+ ,285.24
+ ,288.62
+ ,283.36
+ ,285.84
+ ,291.48
+ ,291.41
+ ,287.77
+ ,284.97
+ ,286.05
+ ,278.19
+ ,281.21
+ ,277.92
+ ,280.08
+ ,269.24
+ ,268.48
+ ,268.83
+ ,269.54
+ ,262.37
+ ,265.12
+ ,265.34
+ ,263.32
+ ,267.18
+ ,260.75
+ ,261.78
+ ,257.27
+ ,255.63
+ ,251.39
+ ,259.49
+ ,261.18
+ ,261.65
+ ,262.01
+ ,265.23
+ ,268.10
+ ,262.27
+ ,263.59
+ ,257.85
+ ,265.69
+ ,271.15
+ ,266.69
+ ,265.77
+ ,262.32
+ ,270.48
+ ,273.03
+ ,269.13
+ ,280.65
+ ,282.75
+ ,281.44
+ ,281.99
+ ,282.86
+ ,287.21
+ ,283.11
+ ,280.66
+ ,282.39
+ ,280.83
+ ,284.71
+ ,279.99
+ ,283.50
+ ,284.88
+ ,288.60
+ ,284.80
+ ,287.20
+ ,286.22
+ ,286.54
+ ,279.58
+ ,283.08
+ ,288.88
+ ,280.18
+ ,284.16
+ ,290.57
+ ,286.82
+ ,273.00
+ ,278.69
+ ,264.54
+ ,271.92
+ ,283.60
+ ,269.25
+ ,263.58
+ ,264.16
+ ,268.85
+ ,269.67
+ ,249.41
+ ,268.99
+ ,268.65
+ ,260.16
+ ,256.55
+ ,251.47
+ ,234.93
+ ,232.96
+ ,215.49
+ ,213.68
+ ,236.07
+ ,235.41
+ ,214.77
+ ,225.85
+ ,224.64
+ ,238.26
+ ,232.44
+ ,222.50
+ ,225.28
+ ,220.49
+ ,216.86
+ ,234.70
+ ,230.06
+ ,238.27
+ ,238.56
+ ,242.70
+ ,249.14
+ ,234.89
+ ,227.78
+ ,234.04
+ ,230.70
+ ,230.17
+ ,218.23
+ ,232.20
+ ,220.76
+ ,215.60
+ ,217.69
+ ,204.35
+ ,191.44
+ ,203.84
+ ,211.86
+ ,210.57
+ ,219.57
+ ,219.98
+ ,226.01
+ ,207.04
+ ,212.52
+ ,217.92
+ ,210.45
+ ,218.53
+ ,223.32
+ ,218.76
+ ,217.63)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '2'
> par7 = '0'
> par6 = '1'
> par5 = '1'
> par4 = '0'
> par3 = '1'
> par2 = '1'
> par1 = '20'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> 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     sar1     sar2
      0.1312  -0.3680  -0.1717
s.e.  0.2285   0.2253   0.1068

sigma^2 estimated as 26.34:  log likelihood = -1142.44,  aic = 2292.88
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 376 
End = 395 
Frequency = 1 
 [1] 221.0481 221.8425 221.0376 221.1936 221.2739 221.2175 221.2245 221.2316
 [9] 221.2278 221.2280 221.2286 221.2283 221.2283 221.2284 221.2283 221.2283
[17] 221.2283 221.2283 221.2283 221.2283

$se
Time Series:
Start = 376 
End = 395 
Frequency = 1 
 [1]  5.132543  6.456439  7.378346  8.354463  9.199643  9.960066 10.675148
 [8] 11.344240 11.975021 12.574701 13.147034 13.695399 14.222665 14.731072
[15] 15.222504 15.698561 16.160600 16.609792 17.047152 17.473568

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 376 
End = 395 
Frequency = 1 
 [1] 210.9883 209.1879 206.5761 204.8189 203.2426 201.6958 200.3012 198.9969
 [9] 197.7568 196.5816 195.4604 194.3853 193.3519 192.3555 191.3922 190.4592
[17] 189.5536 188.6732 187.8159 186.9802
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 376 
End = 395 
Frequency = 1 
 [1] 231.1078 234.4971 235.4992 237.5684 239.3052 240.7393 242.1478 243.4663
 [9] 244.6988 245.8744 246.9968 248.0713 249.1047 250.1013 251.0645 251.9975
[17] 252.9031 253.7835 254.6408 255.4765
> 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] 358.5900 362.9600 362.4200 364.9700 364.0400 361.0600 358.4800 352.9600
  [9] 359.5900 360.3900 357.4000 362.9300 364.5500 365.7300 364.7000 364.6500
 [17] 359.4300 362.1400 356.9700 354.8200 353.1700 357.0600 356.1800 355.0100
 [25] 355.6500 357.3100 357.0700 357.9100 358.4800 358.9700 351.7700 352.1600
 [33] 359.0800 360.3500 359.5300 359.3000 358.4100 359.6800 355.3100 357.0800
 [41] 349.7100 354.1300 345.4900 341.6900 344.2500 340.1700 342.4700 344.4300
 [49] 333.2300 339.7200 342.6100 346.3600 339.0900 339.7300 341.1200 335.9400
 [57] 333.4600 335.6600 341.1200 342.2100 342.6200 346.0600 344.4300 346.6500
 [65] 343.7400 335.6700 342.7500 341.7700 345.8400 346.5200 350.7900 345.4400
 [73] 345.8700 338.4800 337.2100 340.8100 339.8600 342.8600 343.3300 341.7300
 [81] 351.3800 351.1300 345.9900 347.5500 346.0200 345.2900 347.0300 348.0100
 [89] 345.4800 349.4000 351.0500 349.7000 350.8600 354.4500 355.3000 357.4800
 [97] 355.2400 351.7900 355.2200 351.0200 350.2800 350.1700 348.1600 340.3000
[105] 343.7500 344.7100 344.1300 342.1400 345.0400 346.0200 346.4300 347.0700
[113] 339.3300 339.1000 337.1900 339.5800 327.8500 326.8100 321.7300 320.4500
[121] 327.6900 323.9500 320.4700 322.1300 316.3400 314.7800 308.9000 308.6200
[129] 314.4100 306.8800 310.6000 321.6000 321.5000 325.6800 324.3500 320.0100
[137] 326.8800 332.3900 331.4800 332.6200 324.7900 327.1200 328.9100 328.3700
[145] 324.8300 325.9000 326.1800 328.9400 333.7800 328.0600 325.8700 325.4100
[153] 318.8600 319.1300 310.1600 311.7300 306.5400 311.1600 311.9800 306.7200
[161] 308.0500 300.7600 301.9000 293.0900 292.7600 294.5800 289.9000 296.6900
[169] 297.2100 293.3100 296.2500 298.6000 296.8700 301.0200 304.7300 301.9200
[177] 295.7200 293.1800 298.3500 297.9900 299.8500 299.8500 304.4500 299.4500
[185] 298.1400 298.7800 297.0200 301.3300 294.9600 296.6900 300.7300 301.9600
[193] 297.3800 293.8700 285.9600 285.4100 283.7000 284.7600 277.1100 274.7300
[201] 274.7300 274.7300 274.7300 274.6900 275.4200 264.1500 276.2400 268.8800
[209] 277.9700 280.4900 281.0900 276.1600 272.5800 270.9400 284.3100 283.9400
[217] 284.1800 282.8300 283.8400 282.7100 279.2900 280.7000 274.4700 273.4400
[225] 275.4900 279.4600 280.1900 288.2100 284.8000 281.4100 283.3900 287.9700
[233] 290.7700 290.6000 289.6700 289.8400 298.5500 296.0700 297.1400 295.3400
[241] 296.2500 294.3000 296.1500 296.4900 298.0500 301.0300 300.5200 301.5000
[249] 296.9300 289.8400 291.4400 286.8800 286.7400 288.9300 292.1900 295.3900
[257] 295.8600 293.3600 292.8600 292.7300 296.7300 285.0200 285.2400 288.6200
[265] 283.3600 285.8400 291.4800 291.4100 287.7700 284.9700 286.0500 278.1900
[273] 281.2100 277.9200 280.0800 269.2400 268.4800 268.8300 269.5400 262.3700
[281] 265.1200 265.3400 263.3200 267.1800 260.7500 261.7800 257.2700 255.6300
[289] 251.3900 259.4900 261.1800 261.6500 262.0100 265.2300 268.1000 262.2700
[297] 263.5900 257.8500 265.6900 271.1500 266.6900 265.7700 262.3200 270.4800
[305] 273.0300 269.1300 280.6500 282.7500 281.4400 281.9900 282.8600 287.2100
[313] 283.1100 280.6600 282.3900 280.8300 284.7100 279.9900 283.5000 284.8800
[321] 288.6000 284.8000 287.2000 286.2200 286.5400 279.5800 283.0800 288.8800
[329] 280.1800 284.1600 290.5700 286.8200 273.0000 278.6900 264.5400 271.9200
[337] 283.6000 269.2500 263.5800 264.1600 268.8500 269.6700 249.4100 268.9900
[345] 268.6500 260.1600 256.5500 251.4700 234.9300 232.9600 215.4900 213.6800
[353] 236.0700 235.4100 214.7700 225.8500 224.6400 238.2600 232.4400 222.5000
[361] 225.2800 220.4900 216.8600 234.7000 230.0600 238.2700 238.5600 242.7000
[369] 249.1400 234.8900 227.7800 234.0400 230.7000 230.1700 218.2300 221.0481
[377] 221.8425 221.0376 221.1936 221.2739 221.2175 221.2245 221.2316 221.2278
[385] 221.2280 221.2286 221.2283 221.2283 221.2284 221.2283 221.2283 221.2283
[393] 221.2283 221.2283 221.2283
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 376 
End = 395 
Frequency = 1 
 [1] 0.02321913 0.02910370 0.03338050 0.03776991 0.04157581 0.04502385
 [7] 0.04825482 0.05127766 0.05412982 0.05684046 0.05942738 0.06190617
[13] 0.06428953 0.06658763 0.06880901 0.07096089 0.07304941 0.07507985
[19] 0.07705681 0.07898431
> postscript(file="/var/www/html/rcomp/tmp/17h001261061513.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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.se <- array(0, dim=fx)
> perf.mse <- 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$pred[i]
+ perf.mape[i] = perf.mape[i] + abs(perf.pe[i])
+ perf.se[i] = (x[nx+i] - forecast$pred[i])^2
+ perf.mse[i] = perf.mse[i] + perf.se[i]
+ 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 = perf.mape / fx
> perf.mse = perf.mse / fx
> perf.rmse = sqrt(perf.mse)
> postscript(file="/var/www/html/rcomp/tmp/2m6ap1261061513.ps",horizontal=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[1:12] <- x[(nx+1):lx]
Warning message:
In NextMethod("[<-") :
  number of items to replace is not a multiple of replacement length
> 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/3gwo11261061513.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.mape[i],4))
+ a<-table.element(a,round(perf.se[i],4))
+ a<-table.element(a,round(perf.mse[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/4gx4u1261061513.tab") 
> 
> try(system("convert tmp/17h001261061513.ps tmp/17h001261061513.png",intern=TRUE))
character(0)
> try(system("convert tmp/2m6ap1261061513.ps tmp/2m6ap1261061513.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.682   0.346   0.792