R version 2.12.1 (2010-12-16)
Copyright (C) 2010 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.

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(420.677
+ ,417.428
+ ,423.245
+ ,423.113
+ ,418.873
+ ,405.733
+ ,397.812
+ ,389.918
+ ,391.116
+ ,443.814
+ ,460.373
+ ,455.422
+ ,456.288
+ ,452.233
+ ,459.256
+ ,461.146
+ ,451.391
+ ,443.101
+ ,438.81
+ ,430.457
+ ,435.721
+ ,488.28
+ ,505.814
+ ,502.338
+ ,500.91
+ ,501.434
+ ,515.476
+ ,520.862
+ ,519.517
+ ,511.805
+ ,508.607
+ ,505.327
+ ,511.435
+ ,570.158
+ ,591.665
+ ,593.572
+ ,586.346
+ ,586.063
+ ,591.504
+ ,594.033
+ ,585.597
+ ,572.45
+ ,562.917
+ ,554.675
+ ,553.997
+ ,601.31
+ ,622.255
+ ,616.735
+ ,606.48
+ ,595.079
+ ,598.588
+ ,599.917
+ ,591.573
+ ,575.489
+ ,567.223
+ ,555.338
+ ,555.252
+ ,608.249
+ ,630.859
+ ,628.632
+ ,624.435
+ ,609.67
+ ,615.83
+ ,621.17
+ ,604.212
+ ,584.348
+ ,573.717
+ ,555.234
+ ,544.897
+ ,598.866
+ ,620.081
+ ,607.699
+ ,589.96
+ ,578.665
+ ,580.166
+ ,579.457
+ ,571.56
+ ,560.46
+ ,551.397
+ ,536.763
+ ,540.562
+ ,588.184
+ ,607.049
+ ,598.968
+ ,577.644
+ ,562.64
+ ,565.867
+ ,561.274
+ ,554.144
+ ,539.9
+ ,526.271
+ ,511.841
+ ,505.282
+ ,554.083
+ ,584.225
+ ,568.858
+ ,539.516
+ ,521.612
+ ,525.562
+ ,526.519
+ ,515.713
+ ,503.454
+ ,489.301
+ ,479.02
+ ,475.102
+ ,523.682
+ ,551.528
+ ,531.626
+ ,511.037
+ ,492.417
+ ,492.188
+ ,492.865
+ ,480.961
+ ,461.935
+ ,456.608
+ ,441.977
+ ,439.148
+ ,488.18
+ ,520.564
+ ,501.492
+ ,485.025
+ ,464.196
+ ,460.17
+ ,467.037
+ ,460.07
+ ,447.988
+ ,442.867
+ ,436.087
+ ,431.328
+ ,484.015
+ ,509.673
+ ,512.927
+ ,502.831
+ ,470.984
+ ,471.067
+ ,476.049
+ ,474.605
+ ,470.439
+ ,461.251
+ ,454.724
+ ,455.626
+ ,516.847
+ ,525.192
+ ,522.975
+ ,518.585
+ ,509.239
+ ,512.238
+ ,519.164
+ ,517.009
+ ,509.933
+ ,509.127
+ ,500.857
+ ,506.971
+ ,569.323
+ ,579.714
+ ,577.992
+ ,565.464
+ ,547.344
+ ,554.788
+ ,562.325
+ ,560.854
+ ,555.332
+ ,543.599
+ ,536.662
+ ,542.722
+ ,593.53
+ ,610.763
+ ,612.613
+ ,611.324
+ ,594.167
+ ,595.454
+ ,590.865
+ ,589.379
+ ,584.428
+ ,573.1
+ ,567.456
+ ,569.028
+ ,620.735
+ ,628.884
+ ,628.232
+ ,612.117
+ ,595.404
+ ,597.141
+ ,593.408
+ ,590.072
+ ,579.799
+ ,574.205
+ ,572.775
+ ,572.942
+ ,619.567
+ ,625.809
+ ,619.916
+ ,587.625
+ ,565.742
+ ,557.274
+ ,560.576
+ ,548.854
+ ,531.673
+ ,525.919
+ ,511.038
+ ,498.662
+ ,555.362
+ ,564.591
+ ,541.657
+ ,527.07
+ ,509.846
+ ,514.258
+ ,516.922
+ ,507.561
+ ,492.622
+ ,490.243
+ ,469.357
+ ,477.58
+ ,528.379
+ ,533.59
+ ,517.945
+ ,506.174
+ ,501.866
+ ,516.141
+ ,528.222
+ ,532.638
+ ,536.322
+ ,536.535
+ ,523.597
+ ,536.214
+ ,586.57
+ ,596.594
+ ,580.523
+ ,564.478
+ ,557.56
+ ,575.093
+ ,580.112
+ ,574.761
+ ,563.25
+ ,551.531
+ ,537.034
+ ,544.686
+ ,600.991
+ ,604.378
+ ,586.111
+ ,563.668
+ ,548.604
+ ,551.174
+ ,555.654
+ ,547.97
+ ,540.324
+ ,530.577
+ ,520.579
+ ,518.654
+ ,572.273
+ ,581.302
+ ,563.28
+ ,547.612)
> par8 = 'FALSE'
> par7 = '1'
> par6 = ''
> par5 = '1'
> par4 = ''
> par3 = '0'
> 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  12.374432 411.3970  -3.094476071
Feb  1  -1.163211 413.2506   5.340606693
Mar  1   2.483510 415.1042   5.657325026
Apr  1   4.696994 417.1126   1.303360561
May  1  -2.212665 419.1211   1.964538659
Jun  1 -13.427829 421.2967  -2.135829303
Jul  1 -21.435327 423.4722  -4.224864025
Aug  1 -32.339649 425.7293  -3.471614231
Sep  1 -31.953022 427.9863  -4.917313899
Oct  1  20.217564 430.7372  -7.140740625
Nov  1  36.056345 433.4880  -9.171362641
Dec  1  26.702861 436.9477  -8.228590701
Jan  2  12.374432 440.4074   3.506125986
Feb  2  -1.163211 444.0799   9.316270679
Mar  2   2.483510 447.7524   9.020050940
Apr  2   4.696994 451.3988   5.050243347
May  2  -2.212665 455.0451  -1.441421682
Jun  2 -13.427829 458.5502  -2.021399417
Jul  2 -21.435327 462.0554  -1.810043912
Aug  2 -32.339649 466.0179  -3.221219130
Sep  2 -31.953022 469.9804  -2.306343811
Oct  2  20.217564 475.0482  -6.985791607
Nov  2  36.056345 480.1161 -10.358434694
Dec  2  26.702861 486.0689 -10.433774257
Jan  3  12.374432 492.0217  -3.486169072
Feb  3  -1.163211 498.4907   4.106507860
Mar  3   2.483510 504.9597   8.032820361
Apr  3   4.696994 511.8149   4.350073696
May  3  -2.212665 518.6702   3.059469596
Jun  3 -13.427829 525.6079  -0.375069099
Jul  3 -21.435327 532.5456  -2.503274552
Aug  3 -32.339649 539.2400  -1.573349206
Sep  3 -31.953022 545.9344  -2.546373323
Oct  3  20.217564 552.1768  -2.236400499
Nov  3  36.056345 558.4193  -2.810622965
Dec  3  26.702861 563.8210   3.048109918
Jan  4  12.374432 569.2228   4.748787548
Feb  4  -1.163211 573.3863  13.839902833
Mar  4   2.483510 577.5498  11.470653687
Apr  4   4.696994 580.3017   9.034323869
May  4  -2.212665 583.0535   4.756136615
Jun  4 -13.427829 584.6109   1.266911059
Jul  4 -21.435327 586.1683  -1.815981257
Aug  4 -32.339649 587.0167  -0.002038230
Sep  4 -31.953022 587.8651  -1.915044666
Oct  4  20.217564 588.5258  -7.433379592
Nov  4  36.056345 589.1866  -2.987909808
Dec  4  26.702861 589.7494   0.282779005
Jan  5  12.374432 590.3122   3.793412565
Feb  5  -1.163211 590.6737   5.568537045
Mar  5   2.483510 591.0352   5.069297094
Apr  5   4.696994 591.3424   3.877649028
May  5  -2.212665 591.6495   2.136143526
Jun  5 -13.427829 592.2878  -3.370952425
Jul  5 -21.435327 592.9260  -4.267715135
Aug  5 -32.339649 594.1545  -6.476874694
Sep  5 -31.953022 595.3830  -8.177983714
Oct  5  20.217564 597.0268  -8.995365216
Nov  5  36.056345 598.6706  -3.867942008
Dec  5  26.702861 599.9912   1.937972175
Jan  6  12.374432 601.3117  10.748831105
Feb  6  -1.163211 601.5400   9.293245961
Mar  6   2.483510 601.7682  11.578296386
Apr  6   4.696994 600.6462  15.826768743
May  6  -2.212665 599.5243   6.900383665
Jun  6 -13.427829 597.1860   0.589801336
Jul  6 -21.435327 594.8478   0.304552247
Aug  6 -32.339649 591.8660  -4.292327572
Sep  6 -31.953022 588.8842 -12.034156853
Oct  6  20.217564 586.0746  -7.426190196
Nov  6  36.056345 583.2651   0.759581171
Dec  6  26.702861 581.2575  -0.261374420
Jan  7  12.374432 579.2500  -1.664385263
Feb  7  -1.163211 577.9834   1.844849844
Mar  7   2.483510 576.7168   0.965720519
Apr  7   4.696994 575.7687  -1.008693820
May  7  -2.212665 574.8206  -1.047965594
Jun  7 -13.427829 573.8359   0.051944502
Jul  7 -21.435327 572.8511  -0.018812160
Aug  7 -32.339649 571.7580  -2.655367420
Sep  7 -31.953022 570.6649   1.850127857
Oct  7  20.217564 569.3174  -1.350928981
Nov  7  36.056345 567.9698   3.022818891
Dec  7  26.702861 566.2422   6.022986343
Jan  8  12.374432 564.5145   0.755098542
Feb  8  -1.163211 562.1997   1.603496108
Mar  8   2.483510 559.8850   3.498529243
Apr  8   4.696994 557.2295  -0.652474597
May  8  -2.212665 554.5740   1.782664126
Jun  8 -13.427829 551.7643   1.563566543
Jul  8 -21.435327 548.9545  -1.248197800
Aug  8 -32.339649 545.9142  -1.733551577
Sep  8 -31.953022 542.8739  -5.638854817
Oct  8  20.217564 539.7360  -5.870607162
Nov  8  36.056345 536.5982  11.570445202
Dec  8  26.702861 533.5994   8.555706655
Jan  9  12.374432 530.6007  -3.459087145
Feb  9  -1.163211 527.7066  -4.931370747
Mar  9   2.483510 524.8125  -1.734018780
Apr  9   4.696994 522.0296  -0.207607623
May  9  -2.212665 519.2467  -1.321053902
Jun  9 -13.427829 516.6288   0.253045008
Jul  9 -21.435327 514.0108  -3.274522840
Aug  9 -32.339649 511.5349  -0.175211314
Sep  9 -31.953022 509.0589  -2.003849251
Oct  9  20.217564 506.3043  -2.839814681
Nov  9  36.056345 503.5496  11.922024598
Dec  9  26.702861 500.5378   4.385359169
Jan 10  12.374432 497.5259   1.136638487
Feb 10  -1.163211 494.3455  -0.765240077
Mar 10   2.483510 491.1650  -1.460483071
Apr 10   4.696994 488.1336   0.034429219
May 10  -2.212665 485.1022  -1.928515927
Jun 10 -13.427829 482.5576  -7.194740229
Jul 10 -21.435327 480.0130  -1.969631290
Aug 10 -32.339649 477.8606  -3.543909118
Sep 10 -31.953022 475.7082  -4.607136408
Oct 10  20.217564 473.8015  -5.839021382
Nov 10  36.056345 471.8948  12.612898353
Dec 10  26.702861 470.4847   4.304453050
Jan 11  12.374432 469.0746   3.575952494
Feb 11  -1.163211 468.0898  -2.730602394
Mar 11   2.483510 467.1050  -9.418521713
Apr 11   4.696994 466.5472  -4.207180524
May 11  -2.212665 465.9894  -3.706696771
Jun 11 -13.427829 466.4227  -5.006836870
Jul 11 -21.435327 466.8560  -2.553643727
Aug 11 -32.339649 468.0371   0.389576058
Sep 11 -31.953022 469.2182  -5.937153620
Oct 11  20.217564 470.4974  -6.699998637
Nov 11  36.056345 471.7767   1.839961055
Dec 11  26.702861 473.1721  13.051989958
Jan 12  12.374432 474.5676  15.888963609
Feb 12  -1.163211 476.2078  -4.060575711
Mar 12   2.483510 477.8480  -9.264479461
Apr 12   4.696994 479.4431  -8.091133869
May 12  -2.212665 481.0383  -4.220645712
Jun 12 -13.427829 482.7679   1.098915754
Jul 12 -21.435327 484.4975  -1.811189539
Aug 12 -32.339649 487.1504  -0.086746466
Sep 12 -31.953022 489.8033  -2.224252856
Oct 12  20.217564 493.2176   3.411833891
Nov 12  36.056345 496.6319  -7.496274653
Dec 12  26.702861 500.2530  -3.980904728
Jan 13  12.374432 503.8742   2.336409944
Feb 13  -1.163211 507.8840   2.518188815
Mar 13   2.483510 511.8939  -2.139396745
Apr 13   4.696994 516.2454  -1.778377840
May 13  -2.212665 520.5969  -1.375216371
Jun 13 -13.427829 524.8903  -1.529465091
Jul 13 -21.435327 529.1837   1.378619431
Aug 13 -32.339649 533.0083   0.188311748
Sep 13 -31.953022 536.8330   2.091054603
Oct 13  20.217564 540.3843   8.721092251
Nov 13  36.056345 543.9357  -0.278065392
Dec 13  26.702861 547.1977   4.091430735
Jan 14  12.374432 550.4597   2.629871610
Feb 14  -1.163211 553.3354  -4.828211315
Mar 14   2.483510 556.2111  -3.906658672
Apr 14   4.696994 558.8387  -1.210696301
May 14  -2.212665 561.4663   1.600408634
Jun 14 -13.427829 564.5130   4.246844902
Jul 14 -21.435327 567.5597  -2.525385589
Aug 14 -32.339649 570.9442  -1.942516414
Sep 14 -31.953022 574.3286   0.346403298
Oct 14  20.217564 577.3421  -4.029660546
Nov 14  36.056345 580.3556  -5.648919680
Dec 14  26.702861 582.9119   2.998275811
Jan 15  12.374432 585.4682  13.481416049
Feb 15  -1.163211 587.8437   7.486488054
Mar 15   2.483510 590.2193   2.751195627
Apr 15   4.696994 592.0249  -5.856920176
May 15  -2.212665 593.8306  -2.238893416
Jun 15 -13.427829 594.8360   3.019788780
Jul 15 -21.435327 595.8415  -1.306195784
Aug 15 -32.339649 596.2505   3.545149067
Sep 15 -31.953022 596.6595   4.321544455
Oct 15  20.217564 596.7693   3.748141354
Nov 15  36.056345 596.8791  -4.051457037
Dec 15  26.702861 596.7096   4.819554019
Jan 16  12.374432 596.5401   3.202509822
Feb 16  -1.163211 596.5204   0.046785664
Mar 16   2.483510 596.5008  -1.843302925
Apr 16   4.696994 596.4647  -7.753683019
May 16  -2.212665 596.4286  -4.143920549
Jun 16 -13.427829 595.7260  -2.499212564
Jul 16 -21.435327 595.0235   0.616828661
Aug 16 -32.339649 593.0585  12.056104008
Sep 16 -31.953022 591.0936  13.801429894
Oct 16  20.217564 587.8887  11.460752547
Nov 16  36.056345 584.6838   5.068879910
Dec 16  26.702861 580.2845  12.928598300
Jan 17  12.374432 575.8853  -0.634738562
Feb 17  -1.163211 570.5573  -3.652099931
Mar 17   2.483510 565.2293 -10.438825732
Apr 17   4.696994 559.6739  -3.794895153
May 17  -2.212665 554.1185  -3.051822010
Jun 17 -13.427829 548.9066  -3.805752735
Jul 17 -21.435327 543.6947   3.659649780
Aug 17 -32.339649 539.1787   4.198970981
Sep 17 -31.953022 534.6627  -4.047657281
Oct 17  20.217564 530.7959   4.348500088
Nov 17  36.056345 526.9292   1.605462167
Dec 17  26.702861 523.5336  -8.579495210
Jan 18  12.374432 520.1381  -5.442507839
Feb 18  -1.163211 517.3493  -6.340071978
Mar 18   2.483510 514.5605  -2.786000548
Apr 18   4.696994 512.4264  -0.201359293
May 18  -2.212665 510.2922  -0.518575473
Jun 18 -13.427829 508.5722  -2.522366954
Jul 18 -21.435327 506.8522   4.826174805
Aug 18 -32.339649 505.9479  -4.251243130
Sep 18 -31.953022 505.0436   4.489389473
Oct 18  20.217564 505.7397   2.421780514
Nov 18  36.056345 506.4357  -8.902023735
Dec 18  26.702861 509.2523 -18.010130343
Jan 19  12.374432 512.0689 -18.269292203
Feb 19  -1.163211 516.6969 -13.667651273
Mar 19   2.483510 521.3249  -7.667374773
Apr 19   4.696994 527.0156  -3.490623558
May 19  -2.212665 532.7064   2.144270221
Jun 19 -13.427829 538.2959  11.453937347
Jul 19 -21.435327 543.8854  14.084937714
Aug 19 -32.339649 548.5127   7.423976042
Sep 19 -31.953022 553.1400  15.027064907
Oct 19  20.217564 556.5941   9.758309636
Nov 19  36.056345 560.0483   0.489359074
Dec 19  26.702861 562.2589  -8.438739739
Jan 20  12.374432 564.4695 -12.365893805
Feb 20  -1.163211 565.8513  -7.128054330
Mar 20   2.483510 567.2331   5.376420715
Apr 20   4.696994 568.4065   7.008547623
May 20  -2.212665 569.5798   7.393817096
Jun 20 -13.427829 570.0739   6.603958173
Jul 20 -21.435327 570.5679   2.398432490
Aug 20 -32.339649 569.5710  -0.197348083
Sep 20 -31.953022 568.5741   8.064921883
Oct 20  20.217564 566.4428  14.330614757
Nov 20  36.056345 564.3115   4.010112341
Dec 20  26.702861 562.0796  -2.671434785
Jan 21  12.374432 559.8476  -8.554037164
Feb 21  -1.163211 557.8209  -8.053653687
Mar 21   2.483510 555.7941  -7.103634641
Apr 21   4.696994 554.2076  -3.250568355
May 21  -2.212665 552.6210  -2.438359504
Jun 21 -13.427829 551.0838   2.668066940
Jul 21 -21.435327 549.5465   2.465826625
Aug 21 -32.339649 548.0848   4.833897621
Sep 21 -31.953022 546.6230   3.984019155
Oct 21  20.217564 545.2237   6.831726655
Nov 21  36.056345 543.8244   1.421238864
Dec 21  26.702861 542.4024  -5.825260814
Jan 22  12.374432 540.9804  -5.742815744
> 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$outer
[1] 0
> postscript(file="/var/www/rcomp/tmp/1rbbl1322552871.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/rcomp/tmp/2ovh31322552871.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/www/rcomp/tmp/3tgdb1322552871.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/www/rcomp/tmp/462ad1322552871.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/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,'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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/52vdg1322552871.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/www/rcomp/tmp/65fw71322552871.tab") 
> 
> try(system("convert tmp/1rbbl1322552871.ps tmp/1rbbl1322552871.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ovh31322552871.ps tmp/2ovh31322552871.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tgdb1322552871.ps tmp/3tgdb1322552871.png",intern=TRUE))
character(0)
> try(system("convert tmp/462ad1322552871.ps tmp/462ad1322552871.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.948   0.300   4.230