R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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.

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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(179.257
+ ,179.947
+ ,179.094
+ ,181.624
+ ,184.954
+ ,187.928
+ ,187.151
+ ,189.959
+ ,192.492
+ ,191.103
+ ,191.737
+ ,192.31
+ ,192.013
+ ,192.106
+ ,192.141
+ ,194.58
+ ,196.421
+ ,199.021
+ ,198.136
+ ,199.426
+ ,200.997
+ ,201.277
+ ,201.663
+ ,202.874
+ ,204.256
+ ,205.597
+ ,205.471
+ ,211.064
+ ,212.856
+ ,217.036
+ ,219.302
+ ,219.759
+ ,221.388
+ ,220.834
+ ,221.788
+ ,222.358
+ ,222.972
+ ,224.164
+ ,224.915
+ ,226.294
+ ,224.69
+ ,227.021
+ ,229.284
+ ,229.189
+ ,230.032
+ ,229.389
+ ,231.053
+ ,232.56
+ ,232.681
+ ,231.555
+ ,231.428
+ ,232.141
+ ,234.939
+ ,235.424
+ ,235.471
+ ,236.355
+ ,238.693
+ ,236.958
+ ,237.06
+ ,239.282
+ ,238.252
+ ,241.552
+ ,236.23
+ ,238.909
+ ,240.723
+ ,242.12
+ ,242.1
+ ,243.276
+ ,244.677
+ ,243.494
+ ,244.902
+ ,245.247
+ ,245.578
+ ,243.052
+ ,238.121
+ ,241.863
+ ,241.203
+ ,243.634
+ ,242.351
+ ,245.18
+ ,246.126
+ ,244.424
+ ,245.166
+ ,247.258
+ ,245.094
+ ,246.02
+ ,243.082
+ ,245.555
+ ,243.685
+ ,247.277
+ ,245.029
+ ,246.169
+ ,246.778
+ ,244.577
+ ,246.048
+ ,245.775
+ ,245.328
+ ,245.477
+ ,241.903
+ ,243.219
+ ,248.088
+ ,248.521
+ ,247.389
+ ,249.057
+ ,248.916
+ ,249.193
+ ,250.768
+ ,253.106
+ ,249.829
+ ,249.447
+ ,246.755
+ ,250.785
+ ,250.14
+ ,255.755
+ ,254.671
+ ,253.919
+ ,253.741
+ ,252.729
+ ,253.81
+ ,256.653
+ ,255.231
+ ,258.405
+ ,251.061
+ ,254.811
+ ,254.895
+ ,258.325
+ ,257.608
+ ,258.759
+ ,258.621
+ ,257.852
+ ,260.56
+ ,262.358
+ ,260.812
+ ,261.165
+ ,257.164
+ ,260.72
+ ,259.581
+ ,264.743
+ ,261.845
+ ,262.262
+ ,261.631
+ ,258.953
+ ,259.966
+ ,262.85
+ ,262.204
+ ,263.418
+ ,262.752
+ ,266.433
+ ,267.722
+ ,266.003
+ ,262.971
+ ,265.521
+ ,264.676
+ ,270.223
+ ,269.508
+ ,268.457
+ ,265.814
+ ,266.68
+ ,263.018
+ ,269.285
+ ,269.829
+ ,270.911
+ ,266.844
+ ,271.244
+ ,269.907
+ ,271.296
+ ,270.157
+ ,271.322
+ ,267.179
+ ,264.101
+ ,265.518
+ ,269.419
+ ,268.714
+ ,272.482
+ ,268.351
+ ,268.175
+ ,270.674
+ ,272.764
+ ,272.599
+ ,270.333
+ ,270.846
+ ,270.491
+ ,269.16
+ ,274.027
+ ,273.784
+ ,276.663
+ ,274.525
+ ,271.344
+ ,271.115
+ ,270.798
+ ,273.911
+ ,273.985
+ ,271.917
+ ,273.338
+ ,270.601
+ ,273.547
+ ,275.363
+ ,281.229
+ ,277.793
+ ,279.913
+ ,282.5
+ ,280.041
+ ,282.166
+ ,290.304
+ ,283.519
+ ,287.816
+ ,285.226
+ ,287.595
+ ,289.741
+ ,289.148
+ ,288.301
+ ,290.155
+ ,289.648
+ ,288.225
+ ,289.351
+ ,294.735
+ ,305.333
+ ,309.03
+ ,310.215
+ ,321.935
+ ,325.734
+ ,320.846
+ ,323.023
+ ,319.753
+ ,321.753
+ ,320.757
+ ,324.479
+ ,324.641
+ ,322.767
+ ,324.181
+ ,321.389
+ ,327.897
+ ,334.287
+ ,332.653
+ ,334.819
+ ,335.264
+ ,339.622
+ ,342.44
+ ,346.585
+ ,335.378
+ ,337.01
+ ,339.13
+ ,341.193
+ ,343.507
+ ,348.915
+ ,346.431
+ ,348.322
+ ,348.288
+ ,346.597
+ ,351.076
+ ,355.215
+ ,350.562
+ ,355.266)
> par8 = 'FALSE'
> par7 = '1'
> par6 = ''
> par5 = '1'
> par4 = ''
> par3 = '1'
> par2 = 'periodic'
> par1 = '12'
> main = 'Seasonal Decomposition by Loess'
> #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> par1 <- as.numeric(par1) #seasonal period
> if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window
> par3 <- as.numeric(par3) #s.degree
> if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window
> par5 <- as.numeric(par5)#t.degree
> if (par6 != '') par6 <- as.numeric(par6)#l.window
> par7 <- as.numeric(par7)#l.degree
> if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust
> nx <- length(x)
> x <- ts(x,frequency=par1)
> if (par6 != '') {
+ m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8)
+ } else {
+ m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8)
+ }
> m$time.series
          seasonal    trend   remainder
Jan  1 -0.63705736 179.9745 -0.08039731
Feb  1 -0.69415709 181.1518 -0.51066981
Mar  1 -3.37666794 182.3292  0.14146881
Apr  1 -0.31355523 183.4805 -1.54297750
May  1  0.47722245 184.6319 -0.15508878
Jun  1  1.66341904 185.7559  0.50871164
Jul  1  0.24046948 186.8799  0.03065820
Aug  1  0.51293256 187.9944  1.45167743
Sep  1  0.78234684 189.1089  2.60074547
Oct  1 -0.01096529 190.1579  0.95602449
Nov  1  0.73238901 191.2070 -0.20236290
Dec  1  0.62362327 192.0936 -0.40723507
Jan  2 -0.63705736 192.9802 -0.33019235
Feb  2 -0.69415709 193.7938 -0.99360723
Mar  2 -3.37666794 194.6073  0.91038902
Apr  2 -0.31355523 195.4389 -0.54531895
May  2  0.47722245 196.2705 -0.32669189
Jun  2  1.66341904 197.1935  0.16408361
Jul  2  0.24046948 198.1165 -0.22099475
Aug  2  0.51293256 199.1789 -0.26580219
Sep  2  0.78234684 200.2412 -0.02656082
Oct  2 -0.01096529 201.5099 -0.22193165
Nov  2  0.73238901 202.7786 -1.84796890
Dec  2  0.62362327 204.3017 -2.05129526
Jan  3 -0.63705736 205.8248 -0.93170673
Feb  3 -0.69415709 207.5420 -1.25079535
Mar  3 -3.37666794 209.2591 -0.41147285
Apr  3 -0.31355523 211.0157  0.36181614
May  3  0.47722245 212.7723 -0.39355985
Jun  3  1.66341904 214.4318  0.94079932
Jul  3  0.24046948 216.0912  2.97030462
Aug  3  0.51293256 217.5992  1.64686592
Sep  3  0.78234684 219.1072  1.49847603
Oct  3 -0.01096529 220.3384  0.50660641
Nov  3  0.73238901 221.5695 -0.51392962
Dec  3  0.62362327 222.4947 -0.76028536
Jan  4 -0.63705736 223.4198  0.18927378
Feb  4 -0.69415709 224.1898  0.66830905
Mar  4 -3.37666794 224.9599  3.33175543
Apr  4 -0.31355523 225.7113  0.89627702
May  4  0.47722245 226.4626 -2.24986637
Jun  4  1.66341904 227.1877 -1.83014365
Jul  4  0.24046948 227.9128  1.13072521
Aug  4  0.51293256 228.5919  0.08420670
Sep  4  0.78234684 229.2709 -0.02126300
Oct  4 -0.01096529 229.9391 -0.53910332
Nov  4  0.73238901 230.6072 -0.28661007
Dec  4  0.62362327 231.2651  0.67128176
Jan  5 -0.63705736 231.9230  1.39508847
Feb  5 -0.69415709 232.5349 -0.28569513
Mar  5 -3.37666794 233.1467  1.65793240
Apr  5 -0.31355523 233.7290 -1.27443884
May  5  0.47722245 234.3113  0.15052495
Jun  5  1.66341904 234.8659 -1.10527312
Jul  5  0.24046948 235.4205 -0.18992506
Aug  5  0.51293256 235.9997 -0.15762927
Sep  5  0.78234684 236.5789  1.33171533
Oct  5 -0.01096529 237.1505 -0.18151508
Nov  5  0.73238901 237.7220 -1.39441191
Dec  5  0.62362327 238.2550  0.40334216
Jan  6 -0.63705736 238.7880  0.10101112
Feb  6 -0.69415709 239.3186  2.92751215
Mar  6 -3.37666794 239.8492 -0.24257570
Apr  6 -0.31355523 240.3893 -1.16677047
May  6  0.47722245 240.9294 -0.68363022
Jun  6  1.66341904 241.4421 -0.98552437
Jul  6  0.24046948 241.9548 -0.09527237
Aug  6  0.51293256 242.3279  0.43519381
Sep  6  0.78234684 242.7009  1.19370879
Oct  6 -0.01096529 242.9090  0.59592340
Nov  6  0.73238901 243.1171  1.05247158
Dec  6  0.62362327 243.1715  1.45185086
Jan  7 -0.63705736 243.2259  2.98914503
Feb  7 -0.69415709 243.2389  0.50725889
Mar  7 -3.37666794 243.2519 -1.75421612
Apr  7 -0.31355523 243.2843 -1.10770383
May  7  0.47722245 243.3166 -2.59085651
Jun  7  1.66341904 243.4360 -1.46544518
Jul  7  0.24046948 243.5554 -1.44488771
Aug  7  0.51293256 243.8339  0.83318133
Sep  7  0.78234684 244.1124  1.23129917
Oct  7 -0.01096529 244.4562 -0.02118847
Nov  7  0.73238901 244.8000 -0.36634253
Dec  7  0.62362327 245.0316  1.60282077
Jan  8 -0.63705736 245.2632  0.46789895
Feb  8 -0.69415709 245.3601  1.35400752
Mar  8 -3.37666794 245.4571  1.00152722
Apr  8 -0.31355523 245.4637  0.40481303
May  8  0.47722245 245.4703 -2.26256613
Jun  8  1.66341904 245.4319  0.18168019
Jul  8  0.24046948 245.3935 -0.60492734
Aug  8  0.51293256 245.3544  0.30161797
Sep  8  0.78234684 245.3154  0.68021209
Oct  8 -0.01096529 245.3467 -0.75870331
Nov  8  0.73238901 245.3779 -0.06228513
Dec  8  0.62362327 245.5094 -0.35800686
Jan  9 -0.63705736 245.6409  0.32418631
Feb  9 -0.69415709 245.8570  0.31411154
Mar  9 -3.37666794 246.0732 -0.79355210
Apr  9 -0.31355523 246.4119 -2.87934821
May  9  0.47722245 246.7506  0.86019070
Jun  9  1.66341904 247.2030 -0.34542698
Jul  9  0.24046948 247.6554 -0.50689852
Aug  9  0.51293256 248.1268  0.41728780
Sep  9  0.78234684 248.5981 -0.46447707
Oct  9 -0.01096529 249.0422  0.16179974
Nov  9  0.73238901 249.4862  0.54941013
Dec  9  0.62362327 249.9323  2.55004696
Jan 10 -0.63705736 250.3785  0.08759868
Feb 10 -0.69415709 250.8044 -0.66320427
Mar 10 -3.37666794 251.2303 -1.09859610
Apr 10 -0.31355523 251.5729 -0.47434159
May 10  0.47722245 251.9155 -2.25275205
Jun 10  1.66341904 252.2949  1.79672133
Jul 10  0.24046948 252.6742  1.75634086
Aug 10  0.51293256 253.1557  0.25032539
Sep 10  0.78234684 253.6373 -0.67864126
Oct 10 -0.01096529 254.0396 -1.29964645
Nov 10  0.73238901 254.4419 -1.36431806
Dec 10  0.62362327 254.7507  1.27867013
Jan 11 -0.63705736 255.0595  0.80857322
Feb 11 -0.69415709 255.4042  3.69491524
Mar 11 -3.37666794 255.7490 -1.31133160
Apr 11 -0.31355523 256.1581 -1.03355174
May 11  0.47722245 256.5672 -2.14943684
Jun 11  1.66341904 256.9992 -0.33766438
Jul 11  0.24046948 257.4313 -0.06374578
Aug 11  0.51293256 257.9044  0.34167354
Sep 11  0.78234684 258.3775 -0.53885833
Oct 11 -0.01096529 258.8673 -1.00433439
Nov 11  0.73238901 259.3571  0.47052312
Dec 11  0.62362327 259.7800  1.95439948
Jan 12 -0.63705736 260.2029  1.24619073
Feb 12 -0.69415709 260.4932  1.36596722
Mar 12 -3.37666794 260.7835 -0.24284516
Apr 12 -0.31355523 260.8883  0.14526116
May 12  0.47722245 260.9931 -1.88929750
Jun 12  1.66341904 261.0264  2.05315184
Jul 12  0.24046948 261.0598  0.54474731
Aug 12  0.51293256 261.2664  0.48265716
Sep 12  0.78234684 261.4730 -0.62438418
Oct 12 -0.01096529 261.8780 -2.91407084
Nov 12  0.73238901 262.2830 -3.04942392
Dec 12  0.62362327 262.6700 -0.44359846
Jan 13 -0.63705736 263.0569 -0.21585812
Feb 13 -0.69415709 263.4497  0.66246856
Mar 13 -3.37666794 263.8425  2.28620637
Apr 13 -0.31355523 264.3789  2.36768887
May 13  0.47722245 264.9153  2.32950640
Jun 13  1.66341904 265.3812 -1.04165532
Jul 13  0.24046948 265.8472 -3.11667089
Aug 13  0.51293256 266.1029 -1.09479084
Sep 13  0.78234684 266.3585 -2.46486199
Oct 13 -0.01096529 266.5966  3.63741253
Nov 13  0.73238901 266.8346  1.94102063
Dec 13  0.62362327 267.1651  0.66825794
Jan 14 -0.63705736 267.4956 -1.04458985
Feb 14 -0.69415709 267.8149 -0.44072623
Mar 14 -3.37666794 268.1341 -1.73945148
Apr 14 -0.31355523 268.3557  1.24287678
May 14  0.47722245 268.5772  0.77454008
Jun 14  1.66341904 268.7362  0.51136394
Jul 14  0.24046948 268.8952 -2.29166606
Aug 14  0.51293256 268.9538  1.77728816
Sep 14  0.78234684 269.0124  0.11229120
Oct 14 -0.01096529 269.0043  2.30263668
Nov 14  0.73238901 268.9963  0.42831574
Dec 14  0.62362327 268.9791  1.71928993
Jan 15 -0.63705736 268.9619 -1.14582100
Feb 15 -0.69415709 268.9366 -4.14140189
Mar 15 -3.37666794 268.9112 -0.01657165
Apr 15 -0.31355523 268.9758  0.75676316
May 15  0.47722245 269.0403 -0.80356699
Jun 15  1.66341904 269.2593  1.55932234
Jul 15  0.24046948 269.4782 -1.36764218
Aug 15  0.51293256 269.8104 -2.14834054
Sep 15  0.78234684 270.1426 -0.25099009
Oct 15 -0.01096529 270.5222  2.25279021
Nov 15  0.73238901 270.9017  0.96490409
Dec 15  0.62362327 271.3136 -1.60424186
Jan 16 -0.63705736 271.7255 -0.24247292
Feb 16 -0.69415709 271.9902 -0.80502680
Mar 16 -3.37666794 272.2548  0.28183044
Apr 16 -0.31355523 272.3475  1.99301721
May 16  0.47722245 272.4402  0.86653900
Jun 16  1.66341904 272.5284  2.47118141
Jul 16  0.24046948 272.6166  1.66796997
Aug 16  0.51293256 272.7029 -1.87185768
Sep 16  0.78234684 272.7893 -2.45663652
Oct 16 -0.01096529 272.8812 -2.07218587
Nov 16  0.73238901 272.9730  0.20559835
Dec 16  0.62362327 273.2889  0.07247394
Jan 17 -0.63705736 273.6048 -1.05073557
Feb 17 -0.69415709 274.2393 -0.20716254
Mar 17 -3.37666794 274.8738 -0.89617839
Apr 17 -0.31355523 275.7199 -1.85937178
May 17  0.47722245 276.5660 -1.68023015
Jun 17  1.66341904 277.5920  1.97354590
Jul 17  0.24046948 278.6181 -1.06553191
Aug 17  0.51293256 279.8037 -0.40362295
Sep 17  0.78234684 280.9893  0.72833482
Oct 17 -0.01096529 282.1507 -2.09872524
Nov 17  0.73238901 283.3121 -1.87845172
Dec 17  0.62362327 284.2851  5.39526023
Jan 18 -0.63705736 285.2582 -1.10211292
Feb 18 -0.69415709 286.0362  2.47396085
Mar 18 -3.37666794 286.8142  1.78844576
Apr 18 -0.31355523 287.4078  0.50072291
May 18  0.47722245 288.0014  1.26233508
Jun 18  1.66341904 288.8151 -1.33053512
Jul 18  0.24046948 289.6288 -1.56825918
Aug 18  0.51293256 291.2370 -1.59497070
Sep 18  0.78234684 292.8453 -3.97963342
Oct 18 -0.01096529 295.3991 -7.16310634
Nov 18  0.73238901 297.9529 -9.33424568
Dec 18  0.62362327 300.9964 -6.88506121
Jan 19 -0.63705736 304.0400  1.93003815
Feb 19 -0.69415709 307.0947  2.62942187
Mar 19 -3.37666794 310.1495  3.44221671
Apr 19 -0.31355523 312.8970  9.35154485
May 19  0.47722245 315.6446  9.61220800
Jun 19  1.66341904 317.6699  1.51269168
Jul 19  0.24046948 319.6952  3.08732149
Aug 19  0.51293256 320.8027 -1.56260147
Sep 19  0.78234684 321.9101 -0.93947562
Oct 19 -0.01096529 322.6439 -1.87597408
Nov 19  0.73238901 323.3777  0.36886104
Dec 19  0.62362327 324.2959 -0.27855290
Jan 20 -0.63705736 325.2141 -1.81005194
Feb 20 -0.69415709 326.5182 -1.64299516
Mar 20 -3.37666794 327.8222 -3.05652725
Apr 20 -0.31355523 329.3946 -1.18403407
May 20  0.47722245 330.9670  2.84279414
Jun 20  1.66341904 332.4547 -1.46507269
Jul 20  0.24046948 333.9423  0.63620663
Aug 20  0.51293256 335.2832 -0.53213605
Sep 20  0.78234684 336.6241  2.21557007
Oct 20 -0.01096529 337.8694  4.58154872
Nov 20  0.73238901 339.1148  6.73786096
Dec 20  0.62362327 340.2254 -5.47103364
Jan 21 -0.63705736 341.3361 -3.68901334
Feb 21 -0.69415709 342.2418 -2.41759375
Mar 21 -3.37666794 343.1474  1.42223697
Apr 21 -0.31355523 344.0931 -0.27253077
May 21  0.47722245 345.0387  3.39903651
Jun 21  1.66341904 346.1349 -1.36734937
Jul 21  0.24046948 347.2311  0.85041089
Aug 21  0.51293256 348.3424 -0.56729088
Sep 21  0.78234684 349.4536 -3.63894383
Oct 21 -0.01096529 350.5659  0.52105640
Nov 21  0.73238901 351.6782  2.80439022
Dec 21  0.62362327 352.7974 -2.85903858
Jan 22 -0.63705736 353.9166  1.98644751
> m$win
   s    t    l 
2531   19   13 
> m$deg
s t l 
0 1 1 
> m$jump
  s   t   l 
254   2   2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/wessaorg/rcomp/tmp/1xepo1324140222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/wessaorg/rcomp/tmp/2e3tu1324140222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend')
> acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
> acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/3pnnt1324140222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> spectrum(as.numeric(x),main='Observed')
> spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
> spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
> spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/4kc8e1324140222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> cpgram(as.numeric(x),main='Observed')
> cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
> cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
> cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
> par(op)
> 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,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Component',header=TRUE)
> a<-table.element(a,'Window',header=TRUE)
> a<-table.element(a,'Degree',header=TRUE)
> a<-table.element(a,'Jump',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,m$win['s'])
> a<-table.element(a,m$deg['s'])
> a<-table.element(a,m$jump['s'])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,m$win['t'])
> a<-table.element(a,m$deg['t'])
> a<-table.element(a,m$jump['t'])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Low-pass',header=TRUE)
> a<-table.element(a,m$win['l'])
> a<-table.element(a,m$deg['l'])
> a<-table.element(a,m$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/53fpq1324140222.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$time.series[i,'remainder'])
+ a<-table.element(a,m$time.series[i,'seasonal'])
+ a<-table.element(a,m$time.series[i,'trend'])
+ a<-table.element(a,m$time.series[i,'remainder'])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/63r4d1324140222.tab") 
> 
> try(system("convert tmp/1xepo1324140222.ps tmp/1xepo1324140222.png",intern=TRUE))
character(0)
> try(system("convert tmp/2e3tu1324140222.ps tmp/2e3tu1324140222.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pnnt1324140222.ps tmp/3pnnt1324140222.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kc8e1324140222.ps tmp/4kc8e1324140222.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.926   0.275   3.199