R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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(236.23
+ ,239.63
+ ,239.38
+ ,240.44
+ ,238.91
+ ,235.21
+ ,231.98
+ ,226.43
+ ,232.52
+ ,235.84
+ ,232.5
+ ,238.61
+ ,241.71
+ ,242.42
+ ,241.39
+ ,239.78
+ ,234.79
+ ,237.41
+ ,232.07
+ ,230.78
+ ,229.89
+ ,233.2
+ ,233.89
+ ,230.92
+ ,231.11
+ ,231.66
+ ,231.37
+ ,232.38
+ ,232.87
+ ,233.42
+ ,226.36
+ ,224.56
+ ,234.4
+ ,235.94
+ ,233.1
+ ,232.92
+ ,232.63
+ ,234.98
+ ,230.24
+ ,231.83
+ ,223.13
+ ,227
+ ,219.67
+ ,217.99
+ ,219.86
+ ,216.47
+ ,218.05
+ ,223.38
+ ,212.21
+ ,216.49
+ ,221.29
+ ,225.45
+ ,218.37
+ ,216.42
+ ,221.54
+ ,216.41
+ ,212.87
+ ,217.03
+ ,222.65
+ ,230.4
+ ,227.91
+ ,228.72
+ ,228.66
+ ,228.27
+ ,225.9
+ ,216.84
+ ,222.73
+ ,225.56
+ ,228.76
+ ,226.32
+ ,230.96
+ ,226.52
+ ,227.84
+ ,220.77
+ ,217.66
+ ,224.68
+ ,223.89
+ ,226.87
+ ,228.37
+ ,225.27
+ ,234.83
+ ,238.08
+ ,228.55
+ ,228.71
+ ,228.96
+ ,227.75
+ ,227.72
+ ,229.29
+ ,223.93
+ ,226.79
+ ,229.52
+ ,226.39
+ ,226.79
+ ,230.86
+ ,232.33
+ ,234.26
+ ,232.2
+ ,228.83
+ ,232.41
+ ,228.21
+ ,227.66
+ ,229.35
+ ,228.75
+ ,218.74
+ ,222.16
+ ,226.21
+ ,225.36
+ ,223.28
+ ,227.44
+ ,226.12
+ ,224.6
+ ,225.23
+ ,217.21
+ ,216.98
+ ,215.83
+ ,219.92
+ ,208.53
+ ,206.45
+ ,204.67
+ ,202.97
+ ,212.09
+ ,210.09
+ ,203.55
+ ,204.38
+ ,198.59
+ ,199.47
+ ,192.62
+ ,193.4
+ ,198.76
+ ,191.77
+ ,191.93
+ ,203.56
+ ,205
+ ,205.9
+ ,204.4
+ ,199.64
+ ,207.09
+ ,212.96
+ ,210.42
+ ,210.88
+ ,203.7
+ ,204.15
+ ,208.41
+ ,211.19
+ ,209.8
+ ,212.54
+ ,213.59
+ ,217.29
+ ,221.8
+ ,213.19
+ ,211.2
+ ,211.32
+ ,204.09
+ ,202.08
+ ,192.94
+ ,198.55
+ ,195.59
+ ,199.02
+ ,199.26
+ ,196.71
+ ,197.76
+ ,190.02
+ ,191.58
+ ,187.2
+ ,183.79
+ ,185.24
+ ,183.33
+ ,197.2
+ ,195.4
+ ,189.02
+ ,186.52
+ ,193.54
+ ,188.9
+ ,192.89
+ ,194.87
+ ,192.97
+ ,184.52
+ ,182.49
+ ,192.25
+ ,192.31
+ ,195.73
+ ,195.14
+ ,200.15
+ ,195.93
+ ,190.38
+ ,190.98
+ ,189.72
+ ,192.69
+ ,189.93
+ ,188.39
+ ,193.52
+ ,192.69
+ ,187.88
+ ,182.19
+ ,174.16
+ ,173.66
+ ,177.02
+ ,178.39
+ ,171.35
+ ,165.72
+ ,165.72
+ ,165.72
+ ,165.72
+ ,167
+ ,170.23
+ ,158.67
+ ,174.02
+ ,168.34
+ ,172.97
+ ,175.05
+ ,173.2
+ ,167.52
+ ,165.88
+ ,161.84
+ ,179.08
+ ,175.53
+ ,175.38
+ ,172.44
+ ,173.62
+ ,171.85
+ ,170.71
+ ,173
+ ,167.85
+ ,163.6
+ ,168.33
+ ,172.2
+ ,171.49
+ ,178.36
+ ,175.39
+ ,169.05
+ ,172.36
+ ,177.3
+ ,181.56
+ ,177.02
+ ,175.79
+ ,175.76
+ ,186.22
+ ,182.15
+ ,182.73
+ ,180.77
+ ,180.9
+ ,181.17
+ ,182.86
+ ,183.93
+ ,184.99
+ ,187.57
+ ,185.13
+ ,184.88
+ ,179.89
+ ,172.42
+ ,175.82
+ ,171.72
+ ,171.05
+ ,176.08
+ ,178.14
+ ,183.39
+ ,182.12
+ ,177.1
+ ,174.23
+ ,176.24
+ ,178.5
+ ,168.19
+ ,166.42
+ ,174.26
+ ,171.34
+ ,172.6
+ ,181.73
+ ,181.08
+ ,174.91
+ ,171.93
+ ,172.25
+ ,167.29
+ ,171.25
+ ,169.23
+ ,171.24
+ ,160.77
+ ,160.41
+ ,160.89
+ ,161.43
+ ,154.26
+ ,158.31
+ ,159.76
+ ,157.71
+ ,160.66
+ ,156.89
+ ,157.18
+ ,152.54
+ ,150.51
+ ,146.63
+ ,155.64
+ ,157.35
+ ,158.43
+ ,160.37
+ ,163.1
+ ,163.77
+ ,157.35
+ ,155.81
+ ,153.36
+ ,162.89
+ ,168.29
+ ,162.18
+ ,161.04
+ ,159.74
+ ,170.55
+ ,171.62
+ ,168.08
+ ,180.79
+ ,181.64
+ ,180.55
+ ,180.9
+ ,184.55
+ ,189.13
+ ,183.56
+ ,181.04
+ ,185.02
+ ,182.67
+ ,186.73
+ ,181.84
+ ,186.4
+ ,187.64
+ ,191.9
+ ,188.16
+ ,186.55
+ ,189.47
+ ,188.8
+ ,181.66
+ ,184.74
+ ,195.04
+ ,182.38
+ ,187.96
+ ,194.58
+ ,191.64
+ ,177.05
+ ,186.46
+ ,172.76
+ ,178.95
+ ,193.84
+ ,176.37
+ ,167.35
+ ,168.37
+ ,174.88
+ ,175.77
+ ,155.81
+ ,175.03
+ ,179.96
+ ,171.59
+ ,170.93
+ ,165.22
+ ,149.6
+ ,149.63
+ ,137.71
+ ,134.98
+ ,164.02
+ ,154.66
+ ,133.36
+ ,143.2
+ ,148.79
+ ,162.56
+ ,154.19
+ ,145.09
+ ,148.44
+ ,146.24
+ ,141.91
+ ,165.92
+ ,156.85
+ ,165.01
+ ,159.89
+ ,167.07
+ ,174.15
+ ,151.02
+ ,148.16
+ ,153.91
+ ,150.94
+ ,151.97
+ ,140.18
+ ,153.15
+ ,147.44
+ ,140.43
+ ,144.43
+ ,130.63
+ ,117.87
+ ,133.24
+ ,140.61
+ ,136.35
+ ,146.25
+ ,146.97
+ ,151.8
+ ,131.72
+ ,140.79
+ ,145.98
+ ,137.51
+ ,146.06
+ ,151.38
+ ,144.46
+ ,143.33)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '2'
> par7 = '1'
> par6 = '0'
> 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:
          ma1    sar1     sar2
      -0.3129  0.0314  -0.1974
s.e.   0.2025  0.1976   0.0751

sigma^2 estimated as 31.32:  log likelihood = -1174.87,  aic = 2357.75
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 376 
End = 395 
Frequency = 1 
 [1] 143.3895 145.8176 145.2604 144.7636 144.8580 144.9590 144.9436 144.9231
 [9] 144.9255 144.9297 144.9293 144.9285 144.9285 144.9287 144.9287 144.9287
[17] 144.9287 144.9287 144.9287 144.9287

$se
Time Series:
Start = 376 
End = 395 
Frequency = 1 
 [1]  5.596642  6.891601  7.464252  8.098530  8.774909  9.386056  9.943772
 [8] 10.474250 10.982051 11.467038 11.931786 12.379147 12.810995 13.228749
[15] 13.633688 14.026943 14.409473 14.782108 15.145576 15.500524

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 376 
End = 395 
Frequency = 1 
 [1] 132.4201 132.3100 130.6305 128.8905 127.6592 126.5623 125.4538 124.3936
 [9] 123.4007 122.4543 121.5430 120.6654 119.8190 119.0003 118.2067 117.4359
[17] 116.6861 115.9557 115.2433 114.5476
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 376 
End = 395 
Frequency = 1 
 [1] 154.3589 159.3251 159.8903 160.6368 162.0568 163.3557 164.4334 165.4527
 [9] 166.4504 167.4051 168.3156 169.1916 170.0381 170.8570 171.6507 172.4215
[17] 173.1712 173.9016 174.6140 175.3097
> 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] 236.2300 239.6300 239.3800 240.4400 238.9100 235.2100 231.9800 226.4300
  [9] 232.5200 235.8400 232.5000 238.6100 241.7100 242.4200 241.3900 239.7800
 [17] 234.7900 237.4100 232.0700 230.7800 229.8900 233.2000 233.8900 230.9200
 [25] 231.1100 231.6600 231.3700 232.3800 232.8700 233.4200 226.3600 224.5600
 [33] 234.4000 235.9400 233.1000 232.9200 232.6300 234.9800 230.2400 231.8300
 [41] 223.1300 227.0000 219.6700 217.9900 219.8600 216.4700 218.0500 223.3800
 [49] 212.2100 216.4900 221.2900 225.4500 218.3700 216.4200 221.5400 216.4100
 [57] 212.8700 217.0300 222.6500 230.4000 227.9100 228.7200 228.6600 228.2700
 [65] 225.9000 216.8400 222.7300 225.5600 228.7600 226.3200 230.9600 226.5200
 [73] 227.8400 220.7700 217.6600 224.6800 223.8900 226.8700 228.3700 225.2700
 [81] 234.8300 238.0800 228.5500 228.7100 228.9600 227.7500 227.7200 229.2900
 [89] 223.9300 226.7900 229.5200 226.3900 226.7900 230.8600 232.3300 234.2600
 [97] 232.2000 228.8300 232.4100 228.2100 227.6600 229.3500 228.7500 218.7400
[105] 222.1600 226.2100 225.3600 223.2800 227.4400 226.1200 224.6000 225.2300
[113] 217.2100 216.9800 215.8300 219.9200 208.5300 206.4500 204.6700 202.9700
[121] 212.0900 210.0900 203.5500 204.3800 198.5900 199.4700 192.6200 193.4000
[129] 198.7600 191.7700 191.9300 203.5600 205.0000 205.9000 204.4000 199.6400
[137] 207.0900 212.9600 210.4200 210.8800 203.7000 204.1500 208.4100 211.1900
[145] 209.8000 212.5400 213.5900 217.2900 221.8000 213.1900 211.2000 211.3200
[153] 204.0900 202.0800 192.9400 198.5500 195.5900 199.0200 199.2600 196.7100
[161] 197.7600 190.0200 191.5800 187.2000 183.7900 185.2400 183.3300 197.2000
[169] 195.4000 189.0200 186.5200 193.5400 188.9000 192.8900 194.8700 192.9700
[177] 184.5200 182.4900 192.2500 192.3100 195.7300 195.1400 200.1500 195.9300
[185] 190.3800 190.9800 189.7200 192.6900 189.9300 188.3900 193.5200 192.6900
[193] 187.8800 182.1900 174.1600 173.6600 177.0200 178.3900 171.3500 165.7200
[201] 165.7200 165.7200 165.7200 167.0000 170.2300 158.6700 174.0200 168.3400
[209] 172.9700 175.0500 173.2000 167.5200 165.8800 161.8400 179.0800 175.5300
[217] 175.3800 172.4400 173.6200 171.8500 170.7100 173.0000 167.8500 163.6000
[225] 168.3300 172.2000 171.4900 178.3600 175.3900 169.0500 172.3600 177.3000
[233] 181.5600 177.0200 175.7900 175.7600 186.2200 182.1500 182.7300 180.7700
[241] 180.9000 181.1700 182.8600 183.9300 184.9900 187.5700 185.1300 184.8800
[249] 179.8900 172.4200 175.8200 171.7200 171.0500 176.0800 178.1400 183.3900
[257] 182.1200 177.1000 174.2300 176.2400 178.5000 168.1900 166.4200 174.2600
[265] 171.3400 172.6000 181.7300 181.0800 174.9100 171.9300 172.2500 167.2900
[273] 171.2500 169.2300 171.2400 160.7700 160.4100 160.8900 161.4300 154.2600
[281] 158.3100 159.7600 157.7100 160.6600 156.8900 157.1800 152.5400 150.5100
[289] 146.6300 155.6400 157.3500 158.4300 160.3700 163.1000 163.7700 157.3500
[297] 155.8100 153.3600 162.8900 168.2900 162.1800 161.0400 159.7400 170.5500
[305] 171.6200 168.0800 180.7900 181.6400 180.5500 180.9000 184.5500 189.1300
[313] 183.5600 181.0400 185.0200 182.6700 186.7300 181.8400 186.4000 187.6400
[321] 191.9000 188.1600 186.5500 189.4700 188.8000 181.6600 184.7400 195.0400
[329] 182.3800 187.9600 194.5800 191.6400 177.0500 186.4600 172.7600 178.9500
[337] 193.8400 176.3700 167.3500 168.3700 174.8800 175.7700 155.8100 175.0300
[345] 179.9600 171.5900 170.9300 165.2200 149.6000 149.6300 137.7100 134.9800
[353] 164.0200 154.6600 133.3600 143.2000 148.7900 162.5600 154.1900 145.0900
[361] 148.4400 146.2400 141.9100 165.9200 156.8500 165.0100 159.8900 167.0700
[369] 174.1500 151.0200 148.1600 153.9100 150.9400 151.9700 140.1800 143.3895
[377] 145.8176 145.2604 144.7636 144.8580 144.9590 144.9436 144.9231 144.9255
[385] 144.9297 144.9293 144.9285 144.9285 144.9287 144.9287 144.9287 144.9287
[393] 144.9287 144.9287 144.9287
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 376 
End = 395 
Frequency = 1 
 [1] 0.03903104 0.04726181 0.05138531 0.05594312 0.06057594 0.06474972
 [7] 0.06860444 0.07227451 0.07577719 0.07912140 0.08232832 0.08541555
[13] 0.08839526 0.09127764 0.09407170 0.09678516 0.09942460 0.10199575
[19] 0.10450366 0.10695278
> postscript(file="/var/www/html/freestat/rcomp/tmp/10aok1229552441.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/freestat/rcomp/tmp/2mw6a1229552441.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/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/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/freestat/rcomp/tmp/3jce61229552441.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/freestat/rcomp/tmp/4k9sf1229552441.tab") 
> 
> system("convert tmp/10aok1229552441.ps tmp/10aok1229552441.png")
> system("convert tmp/2mw6a1229552441.ps tmp/2mw6a1229552441.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.916   0.460   1.011