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(122.36
+ ,123.33
+ ,123.04
+ ,124.53
+ ,125.13
+ ,125.85
+ ,126.50
+ ,126.53
+ ,127.07
+ ,124.55
+ ,124.90
+ ,124.32
+ ,122.84
+ ,123.31
+ ,123.31
+ ,124.87
+ ,124.64
+ ,124.73
+ ,124.90
+ ,124.04
+ ,123.28
+ ,123.86
+ ,122.29
+ ,124.09
+ ,124.54
+ ,125.65
+ ,125.70
+ ,125.53
+ ,125.61
+ ,125.55
+ ,125.41
+ ,127.60
+ ,124.68
+ ,124.41
+ ,126.43
+ ,126.38
+ ,125.78
+ ,124.70
+ ,125.07
+ ,125.25
+ ,126.58
+ ,127.13
+ ,125.82
+ ,123.70
+ ,124.39
+ ,123.70
+ ,124.42
+ ,121.05
+ ,121.02
+ ,123.23
+ ,121.32
+ ,120.91
+ ,120.72
+ ,123.31
+ ,119.58
+ ,119.53
+ ,120.59
+ ,118.63
+ ,118.47
+ ,111.81
+ ,114.71
+ ,117.34
+ ,115.77
+ ,118.38
+ ,117.84
+ ,118.83
+ ,120.02
+ ,116.21
+ ,117.08
+ ,120.20
+ ,119.83
+ ,118.92
+ ,118.03
+ ,117.71
+ ,119.55
+ ,116.13
+ ,115.97
+ ,115.99
+ ,114.96
+ ,116.46
+ ,116.55
+ ,113.05
+ ,117.44
+ ,118.84
+ ,117.06
+ ,117.54
+ ,119.31
+ ,118.72
+ ,121.55
+ ,122.61
+ ,121.53
+ ,123.31
+ ,124.07
+ ,123.59
+ ,122.97
+ ,123.22
+ ,123.04
+ ,122.96
+ ,122.81
+ ,122.81
+ ,122.62
+ ,120.82
+ ,119.41
+ ,121.56
+ ,121.59
+ ,118.50
+ ,118.77
+ ,118.86
+ ,117.60
+ ,119.90
+ ,121.83
+ ,121.84
+ ,122.12
+ ,122.12
+ ,121.36
+ ,119.66
+ ,119.32
+ ,120.36
+ ,117.06
+ ,117.48
+ ,115.60
+ ,113.86
+ ,116.92
+ ,117.75
+ ,117.75
+ ,115.31
+ ,116.28
+ ,115.22
+ ,115.65
+ ,115.11
+ ,118.67
+ ,118.04
+ ,116.50
+ ,119.78
+ ,119.95
+ ,120.37
+ ,119.79
+ ,119.43
+ ,121.06
+ ,121.74
+ ,121.09
+ ,122.97
+ ,120.50
+ ,117.18
+ ,115.03
+ ,113.36
+ ,112.59
+ ,111.65
+ ,111.98
+ ,114.87
+ ,114.67
+ ,114.09
+ ,114.77
+ ,117.05
+ ,117.22
+ ,113.18
+ ,110.95
+ ,112.14
+ ,112.72
+ ,110.01
+ ,110.29
+ ,110.74
+ ,110.32
+ ,105.89
+ ,108.97
+ ,109.34
+ ,106.57
+ ,99.49
+ ,101.81
+ ,104.29
+ ,109.73
+ ,105.06
+ ,107.97
+ ,108.13
+ ,109.86
+ ,108.95
+ ,111.20
+ ,110.69
+ ,106.10
+ ,105.68
+ ,104.12
+ ,104.71
+ ,104.30
+ ,103.52
+ ,107.76
+ ,107.80
+ ,107.30
+ ,108.64
+ ,105.03
+ ,108.30
+ ,107.21
+ ,109.27
+ ,109.50
+ ,111.68
+ ,111.80
+ ,111.75
+ ,106.68
+ ,106.37
+ ,105.76
+ ,109.01
+ ,109.01
+ ,109.01
+ ,109.01
+ ,107.69
+ ,105.19
+ ,105.48
+ ,102.22
+ ,100.54
+ ,105.00
+ ,105.44
+ ,107.89
+ ,108.64
+ ,106.70
+ ,109.10
+ ,105.23
+ ,108.41
+ ,108.80
+ ,110.39
+ ,110.22
+ ,110.86
+ ,108.58
+ ,107.70
+ ,106.62
+ ,109.84
+ ,107.16
+ ,107.26
+ ,108.70
+ ,109.85
+ ,109.41
+ ,112.36
+ ,111.03
+ ,110.67
+ ,109.21
+ ,113.58
+ ,113.88
+ ,114.08
+ ,112.33
+ ,113.92
+ ,114.41
+ ,114.57
+ ,115.35
+ ,113.13
+ ,113.29
+ ,112.56
+ ,113.06
+ ,113.46
+ ,115.39
+ ,116.62
+ ,117.04
+ ,117.42
+ ,115.62
+ ,115.16
+ ,115.69
+ ,112.85
+ ,114.05
+ ,112.00
+ ,113.74
+ ,116.26
+ ,118.63
+ ,116.49
+ ,118.23
+ ,116.83
+ ,118.82
+ ,114.36
+ ,112.02
+ ,113.24
+ ,109.75
+ ,110.33
+ ,112.86
+ ,113.04
+ ,113.80
+ ,110.90
+ ,109.96
+ ,108.69
+ ,108.84
+ ,108.47
+ ,108.07
+ ,107.94
+ ,108.11
+ ,108.11
+ ,106.81
+ ,105.58
+ ,105.61
+ ,106.52
+ ,103.86
+ ,104.60
+ ,104.73
+ ,105.12
+ ,104.76
+ ,103.85
+ ,103.83
+ ,103.22
+ ,101.64
+ ,102.13
+ ,104.33
+ ,104.92
+ ,107.78
+ ,104.49
+ ,102.80
+ ,102.86
+ ,104.51
+ ,104.73
+ ,102.58
+ ,99.93
+ ,101.41
+ ,101.05
+ ,99.86
+ ,101.11
+ ,100.89
+ ,101.09
+ ,98.31
+ ,98.08
+ ,99.55
+ ,99.62
+ ,97.37
+ ,98.16
+ ,97.98
+ ,98.15
+ ,97.10
+ ,97.24
+ ,96.70
+ ,96.64
+ ,100.65
+ ,96.75
+ ,97.74
+ ,97.92
+ ,98.34
+ ,93.84
+ ,97.80
+ ,96.20
+ ,95.99
+ ,95.18
+ ,95.95
+ ,92.23
+ ,91.78
+ ,92.97
+ ,89.76
+ ,92.88
+ ,96.23
+ ,95.79
+ ,93.97
+ ,93.90
+ ,93.60
+ ,93.96
+ ,88.69
+ ,88.57
+ ,85.62
+ ,86.25
+ ,85.33
+ ,83.33
+ ,77.78
+ ,78.70
+ ,72.05
+ ,80.75
+ ,81.41
+ ,82.65
+ ,75.85
+ ,75.70
+ ,78.25
+ ,77.41
+ ,76.84
+ ,74.25
+ ,74.95
+ ,68.78
+ ,73.21
+ ,73.26
+ ,78.67
+ ,75.63
+ ,74.99
+ ,83.87
+ ,79.62
+ ,80.13
+ ,79.76
+ ,78.20
+ ,78.05
+ ,79.05
+ ,73.32
+ ,75.17
+ ,73.26
+ ,73.72
+ ,73.57
+ ,70.60
+ ,71.25
+ ,74.22
+ ,73.32
+ ,73.01
+ ,74.21
+ ,75.32
+ ,71.73
+ ,71.94
+ ,72.94
+ ,72.47
+ ,71.94
+ ,74.30
+ ,74.30)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '2'
> par7 = '0'
> par6 = '2'
> par5 = '1'
> par4 = '0'
> par3 = '1'
> par2 = '1'
> par1 = '24'
> #'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     ar2    sar1    sar2     sma1
      0.1902  0.0554  0.1902  0.0554  -0.6401
s.e.  0.6814  0.0820  0.6814  0.0820   0.1445

sigma^2 estimated as 4.165:  log likelihood = -789,  aic = 1590.01
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 372 
End = 395 
Frequency = 1 
 [1] 79.93892 79.55751 79.49858 79.45402 79.43973 79.43338 79.43101 79.43008
 [9] 79.42973 79.42959 79.42954 79.42952 79.42951 79.42951 79.42951 79.42951
[17] 79.42951 79.42951 79.42951 79.42951 79.42951 79.42951 79.42951 79.42951

$se
Time Series:
Start = 372 
End = 395 
Frequency = 1 
 [1] 2.040868 2.539219 2.929843 3.230235 3.490667 3.726251 3.945245 4.151684
 [9] 4.347944 4.535571 4.715683 4.889144 5.056651 5.218781 5.376023 5.528794
[17] 5.677455 5.822322 5.963671 6.101747 6.236766 6.368924 6.498394 6.625335

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 372 
End = 395 
Frequency = 1 
 [1] 75.93881 74.58064 73.75609 73.12276 72.59802 72.12993 71.69833 71.29278
 [9] 70.90776 70.53987 70.18680 69.84680 69.51848 69.20070 68.89251 68.59307
[17] 68.30170 68.01776 67.74071 67.47009 67.20545 66.94642 66.69266 66.44385
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 372 
End = 395 
Frequency = 1 
 [1] 83.93902 84.53438 85.24108 85.78528 86.28143 86.73683 87.16370 87.56738
 [9] 87.95170 88.31931 88.67228 89.01224 89.34055 89.65832 89.96651 90.26595
[17] 90.55732 90.84126 91.11831 91.38893 91.65357 91.91260 92.16636 92.41517
> 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] 122.36000 123.33000 123.04000 124.53000 125.13000 125.85000 126.50000
  [8] 126.53000 127.07000 124.55000 124.90000 124.32000 122.84000 123.31000
 [15] 123.31000 124.87000 124.64000 124.73000 124.90000 124.04000 123.28000
 [22] 123.86000 122.29000 124.09000 124.54000 125.65000 125.70000 125.53000
 [29] 125.61000 125.55000 125.41000 127.60000 124.68000 124.41000 126.43000
 [36] 126.38000 125.78000 124.70000 125.07000 125.25000 126.58000 127.13000
 [43] 125.82000 123.70000 124.39000 123.70000 124.42000 121.05000 121.02000
 [50] 123.23000 121.32000 120.91000 120.72000 123.31000 119.58000 119.53000
 [57] 120.59000 118.63000 118.47000 111.81000 114.71000 117.34000 115.77000
 [64] 118.38000 117.84000 118.83000 120.02000 116.21000 117.08000 120.20000
 [71] 119.83000 118.92000 118.03000 117.71000 119.55000 116.13000 115.97000
 [78] 115.99000 114.96000 116.46000 116.55000 113.05000 117.44000 118.84000
 [85] 117.06000 117.54000 119.31000 118.72000 121.55000 122.61000 121.53000
 [92] 123.31000 124.07000 123.59000 122.97000 123.22000 123.04000 122.96000
 [99] 122.81000 122.81000 122.62000 120.82000 119.41000 121.56000 121.59000
[106] 118.50000 118.77000 118.86000 117.60000 119.90000 121.83000 121.84000
[113] 122.12000 122.12000 121.36000 119.66000 119.32000 120.36000 117.06000
[120] 117.48000 115.60000 113.86000 116.92000 117.75000 117.75000 115.31000
[127] 116.28000 115.22000 115.65000 115.11000 118.67000 118.04000 116.50000
[134] 119.78000 119.95000 120.37000 119.79000 119.43000 121.06000 121.74000
[141] 121.09000 122.97000 120.50000 117.18000 115.03000 113.36000 112.59000
[148] 111.65000 111.98000 114.87000 114.67000 114.09000 114.77000 117.05000
[155] 117.22000 113.18000 110.95000 112.14000 112.72000 110.01000 110.29000
[162] 110.74000 110.32000 105.89000 108.97000 109.34000 106.57000  99.49000
[169] 101.81000 104.29000 109.73000 105.06000 107.97000 108.13000 109.86000
[176] 108.95000 111.20000 110.69000 106.10000 105.68000 104.12000 104.71000
[183] 104.30000 103.52000 107.76000 107.80000 107.30000 108.64000 105.03000
[190] 108.30000 107.21000 109.27000 109.50000 111.68000 111.80000 111.75000
[197] 106.68000 106.37000 105.76000 109.01000 109.01000 109.01000 109.01000
[204] 107.69000 105.19000 105.48000 102.22000 100.54000 105.00000 105.44000
[211] 107.89000 108.64000 106.70000 109.10000 105.23000 108.41000 108.80000
[218] 110.39000 110.22000 110.86000 108.58000 107.70000 106.62000 109.84000
[225] 107.16000 107.26000 108.70000 109.85000 109.41000 112.36000 111.03000
[232] 110.67000 109.21000 113.58000 113.88000 114.08000 112.33000 113.92000
[239] 114.41000 114.57000 115.35000 113.13000 113.29000 112.56000 113.06000
[246] 113.46000 115.39000 116.62000 117.04000 117.42000 115.62000 115.16000
[253] 115.69000 112.85000 114.05000 112.00000 113.74000 116.26000 118.63000
[260] 116.49000 118.23000 116.83000 118.82000 114.36000 112.02000 113.24000
[267] 109.75000 110.33000 112.86000 113.04000 113.80000 110.90000 109.96000
[274] 108.69000 108.84000 108.47000 108.07000 107.94000 108.11000 108.11000
[281] 106.81000 105.58000 105.61000 106.52000 103.86000 104.60000 104.73000
[288] 105.12000 104.76000 103.85000 103.83000 103.22000 101.64000 102.13000
[295] 104.33000 104.92000 107.78000 104.49000 102.80000 102.86000 104.51000
[302] 104.73000 102.58000  99.93000 101.41000 101.05000  99.86000 101.11000
[309] 100.89000 101.09000  98.31000  98.08000  99.55000  99.62000  97.37000
[316]  98.16000  97.98000  98.15000  97.10000  97.24000  96.70000  96.64000
[323] 100.65000  96.75000  97.74000  97.92000  98.34000  93.84000  97.80000
[330]  96.20000  95.99000  95.18000  95.95000  92.23000  91.78000  92.97000
[337]  89.76000  92.88000  96.23000  95.79000  93.97000  93.90000  93.60000
[344]  93.96000  88.69000  88.57000  85.62000  86.25000  85.33000  83.33000
[351]  77.78000  78.70000  72.05000  80.75000  81.41000  82.65000  75.85000
[358]  75.70000  78.25000  77.41000  76.84000  74.25000  74.95000  68.78000
[365]  73.21000  73.26000  78.67000  75.63000  74.99000  83.87000  79.62000
[372]  79.93892  79.55751  79.49858  79.45402  79.43973  79.43338  79.43101
[379]  79.43008  79.42973  79.42959  79.42954  79.42952  79.42951  79.42951
[386]  79.42951  79.42951  79.42951  79.42951  79.42951  79.42951  79.42951
[393]  79.42951  79.42951  79.42951
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 372 
End = 395 
Frequency = 1 
 [1] 0.02553035 0.03191677 0.03685403 0.04065540 0.04394108 0.04691039
 [7] 0.04966882 0.05226841 0.05473950 0.05710178 0.05936939 0.06155324
[13] 0.06366211 0.06570330 0.06768294 0.06960629 0.07147791 0.07330175
[19] 0.07508131 0.07681965 0.07851951 0.08018335 0.08181335 0.08341151
> postscript(file="/var/www/html/rcomp/tmp/1ix7g1261077262.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2ahg91261077263.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/358ob1261077263.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/4eotl1261077263.tab") 
> 
> try(system("convert tmp/1ix7g1261077262.ps tmp/1ix7g1261077262.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ahg91261077263.ps tmp/2ahg91261077263.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.749   0.334   1.445