R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(52.61
+ ,65.04
+ ,67.54
+ ,63.58
+ ,57.35
+ ,54.93
+ ,54.30
+ ,58.89
+ ,65.95
+ ,82.65
+ ,100.08
+ ,100.68
+ ,97.53
+ ,92.29
+ ,85.08
+ ,91.61
+ ,93.61
+ ,90.40
+ ,99.31
+ ,107.71
+ ,106.18
+ ,98.80
+ ,99.58
+ ,98.85
+ ,92.69
+ ,91.82
+ ,92.63
+ ,98.41
+ ,94.56
+ ,85.78
+ ,84.59
+ ,83.49
+ ,84.68
+ ,80.12
+ ,84.37
+ ,85.94
+ ,87.07
+ ,84.52
+ ,83.13
+ ,75.95
+ ,70.12
+ ,78.10
+ ,83.06
+ ,87.92
+ ,90.21
+ ,89.95
+ ,97.08
+ ,102.08
+ ,100.64
+ ,97.73
+ ,97.61
+ ,100.32
+ ,102.04
+ ,107.80
+ ,111.51
+ ,110.18
+ ,110.08
+ ,117.40
+ ,119.82
+ ,118.79
+ ,113.18
+ ,122.76
+ ,120.43
+ ,129.16
+ ,132.48
+ ,135.68
+ ,141.49
+ ,122.40
+ ,137.06
+ ,144.84
+ ,154.64
+ ,148.04
+ ,152.76
+ ,172.00
+ ,169.03
+ ,179.68
+ ,190.38
+ ,233.23
+ ,231.45
+ ,244.87
+ ,299.12
+ ,385.01
+ ,381.48
+ ,321.56
+ ,317.27
+ ,323.09
+ ,392.72
+ ,372.37
+ ,386.52
+ ,412.83
+ ,404.91
+ ,406.73
+ ,392.41
+ ,363.31
+ ,357.95
+ ,375.10
+ ,369.74
+ ,386.14
+ ,353.40
+ ,346.87
+ ,362.53
+ ,349.87
+ ,347.03
+ ,332.94
+ ,327.48
+ ,327.92
+ ,308.91
+ ,285.71
+ ,318.81
+ ,284.76
+ ,301.04
+ ,315.16
+ ,388.34
+ ,383.37
+ ,416.77
+ ,423.24
+ ,429.90
+ ,486.07
+ ,394.41
+ ,410.93
+ ,430.88
+ ,447.29
+ ,431.65
+ ,456.53
+ ,452.93
+ ,440.90
+ ,416.46
+ ,451.49
+ ,432.00
+ ,436.19
+ ,428.55
+ ,421.40
+ ,425.18
+ ,437.24
+ ,431.92
+ ,412.65
+ ,419.37
+ ,436.40
+ ,421.37
+ ,423.66
+ ,402.45
+ ,402.82
+ ,400.46
+ ,425.73
+ ,417.93
+ ,403.43
+ ,404.96
+ ,393.64
+ ,399.98
+ ,375.93
+ ,366.57
+ ,353.90
+ ,347.51
+ ,364.10
+ ,328.64
+ ,348.01
+ ,329.63
+ ,350.96
+ ,336.16
+ ,332.15
+ ,349.46
+ ,383.64
+ ,369.82
+ ,345.50
+ ,337.80
+ ,334.76
+ ,338.02
+ ,346.74
+ ,371.84
+ ,375.90
+ ,373.31
+ ,391.91
+ ,374.28
+ ,384.69
+ ,372.16
+ ,371.97
+ ,351.76
+ ,352.89
+ ,330.48
+ ,347.70
+ ,345.58
+ ,360.76
+ ,364.40
+ ,374.62
+ ,369.07
+ ,341.80
+ ,337.87
+ ,336.58
+ ,332.66
+ ,335.74
+ ,321.64
+ ,329.38
+ ,321.84
+ ,324.56
+ ,330.90
+ ,310.91
+ ,318.07
+ ,312.36
+ ,315.19
+ ,332.89
+ ,310.67
+ ,321.26
+ ,316.15
+ ,283.87
+ ,280.65
+ ,280.21
+ ,265.93
+ ,267.80
+ ,278.03
+ ,291.86
+ ,262.61
+ ,264.80
+ ,265.67
+ ,251.05
+ ,256.11
+ ,279.75
+ ,282.52
+ ,288.89
+ ,308.46
+ ,292.89
+ ,280.79
+ ,273.61
+ ,276.67
+ ,277.92
+ ,250.28
+ ,264.70
+ ,268.95
+ ,261.69
+ ,257.99
+ ,251.28
+ ,243.14
+ ,246.81
+ ,224.50
+ ,241.25
+ ,254.97
+ ,261.39
+ ,266.67
+ ,264.28
+ ,270.45
+ ,274.97
+ ,281.13
+ ,300.65
+ ,321.12
+ ,354.79
+ ,318.97
+ ,298.71
+ ,318.85
+ ,327.89
+ ,348.19
+ ,335.18
+ ,332.98
+ ,331.04
+ ,317.52
+ ,325.31
+ ,317.59
+ ,313.37
+ ,313.00
+ ,314.77
+ ,298.37
+ ,311.10
+ ,308.79
+ ,297.30
+ ,293.58
+ ,291.35
+ ,291.51
+ ,289.94
+ ,287.07
+ ,280.74
+ ,294.95
+ ,288.98
+ ,285.63
+ ,294.55
+ ,290.67
+ ,314.78
+ ,306.50
+ ,304.48
+ ,308.65
+ ,307.01
+ ,298.59
+ ,293.51
+ ,294.90
+ ,296.14
+ ,294.25
+ ,291.75
+ ,290.49
+ ,288.68
+ ,310.07
+ ,297.45
+ ,300.81
+ ,301.56
+ ,296.89
+ ,305.23
+ ,298.45
+ ,298.75
+ ,273.02
+ ,266.62
+ ,266.06
+ ,284.48
+ ,275.71
+ ,284.19
+ ,284.81
+ ,267.29
+ ,272.95
+ ,262.35
+ ,246.34
+ ,251.03
+ ,247.54
+ ,254.80
+ ,245.08
+ ,251.30
+ ,261.48
+ ,258.85
+ ,270.89
+ ,257.55
+ ,253.08
+ ,238.81
+ ,241.22
+ ,280.75
+ ,284.56
+ ,289.35
+ ,289.56
+ ,289.55
+ ,305.00
+ ,289.22
+ ,301.82
+ ,293.56
+ ,300.59
+ ,298.67
+ ,311.55
+ ,310.08
+ ,312.06
+ ,309.13
+ ,292.31
+ ,284.41
+ ,290.02
+ ,291.52
+ ,296.81
+ ,315.60
+ ,319.63
+ ,303.89
+ ,300.53
+ ,321.84
+ ,309.48
+ ,307.68
+ ,310.53
+ ,327.91
+ ,343.18
+ ,345.48
+ ,342.03
+ ,349.57
+ ,322.50
+ ,310.74
+ ,318.96
+ ,327.53
+ ,320.00
+ ,320.72
+ ,330.86
+ ,342.34
+ ,322.37
+ ,306.86
+ ,301.75
+ ,307.27
+ ,301.30
+ ,315.18
+ ,342.11
+ ,333.18
+ ,332.26
+ ,332.32
+ ,330.00
+ ,321.78
+ ,318.59
+ ,344.78
+ ,324.09
+ ,322.03
+ ,325.32
+ ,325.10
+ ,335.10
+ ,334.66
+ ,334.54
+ ,341.15
+ ,320.47
+ ,323.85
+ ,328.06
+ ,328.93
+ ,337.50
+ ,335.65
+ ,361.05
+ ,353.19
+ ,352.28
+ ,392.53
+ ,393.03
+ ,420.42
+ ,434.91
+ ,468.38
+ ,466.35
+ ,480.93
+ ,511.25
+ ,508.39
+ ,479.80
+ ,495.63
+ ,487.09
+ ,473.06
+ ,473.03
+ ,487.87
+ ,479.28
+ ,500.60
+ ,502.82
+ ,497.13
+ ,496.06
+ ,489.80
+ ,481.66
+ ,486.17
+ ,492.94
+ ,522.45
+ ,545.71
+ ,533.77
+ ,570.26
+ ,623.56
+ ,639.94
+ ,589.13
+ ,559.45
+ ,569.96
+ ,590.43
+ ,588.37
+ ,565.80
+ ,629.69
+ ,576.28
+ ,641.89
+ ,625.70
+ ,717.52
+ ,749.58
+ ,690.29
+ ,666.55
+ ,689.18
+ ,666.24
+ ,662.32
+ ,665.83
+ ,681.23
+ ,704.87
+ ,783.13
+ ,757.97
+ ,775.93
+ ,812.08
+ ,824.40
+ ,886.89
+ ,984.07
+ ,1015.59
+ ,897.30
+ ,980.37
+ ,957.37
+ ,968.96
+ ,1062.80
+ ,1047.67
+ ,967.91
+ ,1021.58
+ ,1014.02
+ ,1034.98
+ ,1068.80
+ ,1038.38
+ ,1133.26
+ ,1259.55
+ ,1207.42
+ ,1234.59
+ ,1297.03)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '0'
> par7 = '0'
> par6 = '0'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '0.3'
> par1 = '12'
> #'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:
         sma1
      -0.9815
s.e.   0.1024

sigma^2 estimated as 0.008383:  log likelihood = 408.48,  aic = -812.96
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 453 
End = 464 
Frequency = 1 
 [1] 8.084341 8.136720 8.139230 8.129719 8.135480 8.159448 8.158537 8.149006
 [9] 8.163394 8.195347 8.186617 8.221196

$se
Time Series:
Start = 453 
End = 464 
Frequency = 1 
 [1] 0.0921219 0.1302800 0.1595598 0.1842438 0.2059908 0.2256406 0.2437112
 [8] 0.2605314 0.2763296 0.2912723 0.3054848 0.3190650

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 453 
End = 464 
Frequency = 1 
 [1] 7.903782 7.881371 7.826493 7.768601 7.731738 7.717192 7.680863 7.638364
 [9] 7.621788 7.624453 7.587867 7.595828
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 453 
End = 464 
Frequency = 1 
 [1] 8.264900 8.392068 8.451967 8.490836 8.539222 8.601703 8.636211 8.659647
 [9] 8.705000 8.766240 8.785368 8.846563
> 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]   52.610   65.040   67.540   63.580   57.350   54.930   54.300   58.890
  [9]   65.950   82.650  100.080  100.680   97.530   92.290   85.080   91.610
 [17]   93.610   90.400   99.310  107.710  106.180   98.800   99.580   98.850
 [25]   92.690   91.820   92.630   98.410   94.560   85.780   84.590   83.490
 [33]   84.680   80.120   84.370   85.940   87.070   84.520   83.130   75.950
 [41]   70.120   78.100   83.060   87.920   90.210   89.950   97.080  102.080
 [49]  100.640   97.730   97.610  100.320  102.040  107.800  111.510  110.180
 [57]  110.080  117.400  119.820  118.790  113.180  122.760  120.430  129.160
 [65]  132.480  135.680  141.490  122.400  137.060  144.840  154.640  148.040
 [73]  152.760  172.000  169.030  179.680  190.380  233.230  231.450  244.870
 [81]  299.120  385.010  381.480  321.560  317.270  323.090  392.720  372.370
 [89]  386.520  412.830  404.910  406.730  392.410  363.310  357.950  375.100
 [97]  369.740  386.140  353.400  346.870  362.530  349.870  347.030  332.940
[105]  327.480  327.920  308.910  285.710  318.810  284.760  301.040  315.160
[113]  388.340  383.370  416.770  423.240  429.900  486.070  394.410  410.930
[121]  430.880  447.290  431.650  456.530  452.930  440.900  416.460  451.490
[129]  432.000  436.190  428.550  421.400  425.180  437.240  431.920  412.650
[137]  419.370  436.400  421.370  423.660  402.450  402.820  400.460  425.730
[145]  417.930  403.430  404.960  393.640  399.980  375.930  366.570  353.900
[153]  347.510  364.100  328.640  348.010  329.630  350.960  336.160  332.150
[161]  349.460  383.640  369.820  345.500  337.800  334.760  338.020  346.740
[169]  371.840  375.900  373.310  391.910  374.280  384.690  372.160  371.970
[177]  351.760  352.890  330.480  347.700  345.580  360.760  364.400  374.620
[185]  369.070  341.800  337.870  336.580  332.660  335.740  321.640  329.380
[193]  321.840  324.560  330.900  310.910  318.070  312.360  315.190  332.890
[201]  310.670  321.260  316.150  283.870  280.650  280.210  265.930  267.800
[209]  278.030  291.860  262.610  264.800  265.670  251.050  256.110  279.750
[217]  282.520  288.890  308.460  292.890  280.790  273.610  276.670  277.920
[225]  250.280  264.700  268.950  261.690  257.990  251.280  243.140  246.810
[233]  224.500  241.250  254.970  261.390  266.670  264.280  270.450  274.970
[241]  281.130  300.650  321.120  354.790  318.970  298.710  318.850  327.890
[249]  348.190  335.180  332.980  331.040  317.520  325.310  317.590  313.370
[257]  313.000  314.770  298.370  311.100  308.790  297.300  293.580  291.350
[265]  291.510  289.940  287.070  280.740  294.950  288.980  285.630  294.550
[273]  290.670  314.780  306.500  304.480  308.650  307.010  298.590  293.510
[281]  294.900  296.140  294.250  291.750  290.490  288.680  310.070  297.450
[289]  300.810  301.560  296.890  305.230  298.450  298.750  273.020  266.620
[297]  266.060  284.480  275.710  284.190  284.810  267.290  272.950  262.350
[305]  246.340  251.030  247.540  254.800  245.080  251.300  261.480  258.850
[313]  270.890  257.550  253.080  238.810  241.220  280.750  284.560  289.350
[321]  289.560  289.550  305.000  289.220  301.820  293.560  300.590  298.670
[329]  311.550  310.080  312.060  309.130  292.310  284.410  290.020  291.520
[337]  296.810  315.600  319.630  303.890  300.530  321.840  309.480  307.680
[345]  310.530  327.910  343.180  345.480  342.030  349.570  322.500  310.740
[353]  318.960  327.530  320.000  320.720  330.860  342.340  322.370  306.860
[361]  301.750  307.270  301.300  315.180  342.110  333.180  332.260  332.320
[369]  330.000  321.780  318.590  344.780  324.090  322.030  325.320  325.100
[377]  335.100  334.660  334.540  341.150  320.470  323.850  328.060  328.930
[385]  337.500  335.650  361.050  353.190  352.280  392.530  393.030  420.420
[393]  434.910  468.380  466.350  480.930  511.250  508.390  479.800  495.630
[401]  487.090  473.060  473.030  487.870  479.280  500.600  502.820  497.130
[409]  496.060  489.800  481.660  486.170  492.940  522.450  545.710  533.770
[417]  570.260  623.560  639.940  589.130  559.450  569.960  590.430  588.370
[425]  565.800  629.690  576.280  641.890  625.700  717.520  749.580  690.290
[433]  666.550  689.180  666.240  662.320  665.830  681.230  704.870  783.130
[441]  757.970  775.930  812.080  824.400  886.890  984.070 1015.590  897.300
[449]  980.370  957.370  968.960 1062.800 1060.430 1083.506 1084.620 1080.401
[457] 1082.956 1093.627 1093.220 1088.969 1095.391 1109.748 1105.813 1121.459
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 453 
End = 464 
Frequency = 1 
 [1] 0.03898326 0.05535263 0.06832546 0.07953577 0.08939507 0.09814975
 [7] 0.10655247 0.11457890 0.12182540 0.12839859 0.13534424 0.14123813
> postscript(file="/var/www/rcomp/tmp/1cmcl1323388874.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/rcomp/tmp/2laya1323388874.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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/rcomp/tmp/3s4e71323388874.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/rcomp/tmp/4g0bn1323388874.tab") 
> 
> try(system("convert tmp/1cmcl1323388874.ps tmp/1cmcl1323388874.png",intern=TRUE))
character(0)
> try(system("convert tmp/2laya1323388874.ps tmp/2laya1323388874.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.170   0.110   1.261