R version 2.6.1 (2007-11-26)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(13328,12873,14000,13477,14237,13674,13529,14058,12975,14326,14008,16193,14483,14011,15057,14884,15414,14440,14900,15074,14442,15307,14938,17193,15528,14765,15838,15723,16150,15486,15986,15983,15692,16490,15686,18897,16316,15636,17163,16534,16518,16375,16290,16352,15943,16362,16393,19051,16747,16320,17910,16961,17480,17049,16879,17473,16998,17307,17418,20169,17871,17226,19062,17804,19100,18522,18060,18869,18127,18871,18890,21263,19547,18450,20254,19240,20216,19420,19415,20018,18652,19978,19509,21971)
> par1 = '12'
> #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> par1 <- as.numeric(par1)
> nx <- length(x)
> x <- ts(x,frequency=par1)
> m <- StructTS(x,type='BSM')
> m$coef
      level       slope        seas     epsilon 
15111.73322    52.62117 56591.08950     0.00000 
> m$fitted
         level     slope         sea
Jan 1 13328.00  0.000000     0.00000
Feb 1 13200.68  3.825789  -327.68195
Mar 1 13460.24 22.003195   539.76082
Apr 1 13535.98 26.285106   -58.98042
May 1 13789.37 42.187168   447.63269
Jun 1 13834.09 42.330054  -160.08941
Jul 1 13756.22 36.776205  -227.22142
Aug 1 13824.13 38.052962   233.87369
Sep 1 13582.50 26.984106  -607.49878
Oct 1 13729.72 31.822590   596.27954
Nov 1 13869.97 36.349977   138.02576
Dec 1 14657.84 68.960045  1535.16350
Jan 2 14941.15 73.653081  -458.15481
Feb 2 14973.53 72.566100  -962.53375
Mar 2 14910.42 66.942435   146.57735
Apr 2 14966.96 66.396541   -82.95709
May 2 14983.56 63.559413   430.44363
Jun 2 14842.05 51.857643  -402.04709
Jul 2 14877.71 50.965892    22.28670
Aug 2 14855.42 47.104965   218.57844
Sep 2 14996.32 51.860535  -554.31577
Oct 2 15087.07 53.771601   219.93238
Nov 2 15228.06 57.940246  -290.05968
Dec 2 15465.38 66.299389  1727.62493
Jan 3 15699.54 74.018588  -171.54423
Feb 3 15769.06 73.806527 -1004.06334
Mar 3 15770.37 70.205950    67.63287
Apr 3 15767.92 66.407904   -44.91784
May 3 15725.66 60.536107   424.33715
Jun 3 15780.29 60.212499  -294.28620
Jul 3 15877.40 62.226208   108.60183
Aug 3 15928.75 61.639751    54.25312
Sep 3 16100.43 67.479736  -408.43172
Oct 3 16282.11 73.446724   207.89326
Nov 3 16325.34 71.887373  -639.33748
Dec 3 16661.11 85.409757  2235.88690
Jan 4 16723.02 84.205388  -407.02304
Feb 4 16713.72 79.375971 -1077.71723
Mar 4 16831.90 81.405388   331.10352
Apr 4 16796.14 75.196749  -262.13613
May 4 16595.82 60.451407   -77.81678
Jun 4 16599.73 57.410115  -224.73079
Jul 4 16513.59 49.684777  -223.58785
Aug 4 16484.45 45.456173  -132.45407
Sep 4 16491.11 43.384905  -548.10923
Oct 4 16429.60 37.815400   -67.59835
Nov 4 16668.13 48.425956  -275.13326
Dec 4 16787.42 52.162497  2263.58403
Jan 5 16927.73 56.811105  -180.73402
Feb 5 17113.09 63.604250  -793.09288
Mar 5 17270.64 68.586144   639.35818
Apr 5 17247.89 63.724633  -286.89245
May 5 17325.12 64.445436   154.88085
Jun 5 17312.09 60.302271  -263.08586
Jul 5 17242.37 53.347047  -363.36716
Aug 5 17351.52 56.330033   121.47900
Sep 5 17478.75 60.114324  -480.75277
Oct 5 17568.47 61.691899  -261.47411
Nov 5 17693.33 65.052478  -275.32698
Dec 5 17860.25 70.467949  2308.75056
Jan 6 18029.64 75.726145  -158.64055
Feb 6 18110.52 76.000459  -884.52451
Mar 6 18234.56 78.557540   827.44273
Apr 6 18242.18 74.777053  -438.17876
May 6 18466.95 82.777792   633.05427
Jun 6 18650.20 88.140360  -128.20153
Jul 6 18689.73 85.545467  -629.72878
Aug 6 18776.81 85.627321    92.19200
Sep 6 18804.71 82.548084  -677.71359
Oct 6 18981.11 87.551806  -110.10658
Nov 6 19161.42 92.495673  -271.42357
Dec 6 19208.51 90.076217  2054.49224
Jan 7 19420.19 96.554900   126.80665
Feb 7 19505.39 95.949572 -1055.38740
Mar 7 19548.93 93.156265   705.07349
Apr 7 19680.04 95.179910  -440.04009
May 7 19740.60 93.333498   475.40362
Jun 7 19722.66 87.398485  -302.65634
Jul 7 19854.74 89.781836  -439.73695
Aug 7 19957.24 90.460129    60.76428
Sep 7 19856.49 80.262607 -1204.48609
Oct 7 19952.94 81.125926    25.06110
Nov 7 19964.55 77.419511  -455.54675
Dec 7 20006.56 75.531890  1964.44180
> m$resid
          Jan         Feb         Mar         Apr         May         Jun
1  0.00000000 -1.30937963  1.31765525  0.31197723  1.58162611  0.01923769
2  1.70324730 -0.33083647 -1.01555962 -0.07381375 -0.35362284 -1.49507756
3  1.26208555 -0.03377945 -0.53768902 -0.53104466 -0.79017734 -0.04322003
4 -0.17400525 -0.69206611  0.28620976 -0.86037098 -2.01850104 -0.41463775
5  0.64949998  0.94713157  0.69150670 -0.67131153  0.09914722 -0.56903782
6  0.72766651  0.03794424  0.35326463 -0.52145735  1.10218502  0.73835604
7  0.89409845 -0.08353314 -0.38533598  0.27902660 -0.25448421 -0.81784717
          Jul         Aug         Sep         Oct         Nov         Dec
1 -0.93705701  0.24382642 -2.18590324  0.93576693  0.84003525  5.79802749
2 -0.12063996 -0.55126673  0.70696024  0.29253887  0.65426179  1.34474029
3  0.27198342 -0.08061010  0.81632027  0.84579988 -0.22347260  1.95194819
4 -1.05576588 -0.58095166 -0.28612215 -0.77292538  1.47794698  0.52177296
5 -0.95581497  0.41058995  0.52175410  0.21780564  0.46449523  0.74916679
6 -0.35733757  0.01127754 -0.42445992  0.69001365  0.68197836 -0.33384407
7  0.32843973  0.09348737 -1.40571677  0.11902064 -0.51102528 -0.26027992
> mylevel <- as.numeric(m$fitted[,'level'])
> myslope <- as.numeric(m$fitted[,'slope'])
> myseas <- as.numeric(m$fitted[,'sea'])
> myresid <- as.numeric(m$resid)
> myfit <- mylevel+myseas
> postscript(file="/var/www/html/rcomp/tmp/1jfhy1204976011.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time')
> grid()
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/21p4x1204976011.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
> acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
> acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/3wo361204976012.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> spectrum(as.numeric(x),main='Observed')
> spectrum(mylevel,main='Level')
> spectrum(myseas,main='Seasonal')
> spectrum(myresid,main='Standardized Residals')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/4se3t1204976012.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> cpgram(as.numeric(x),main='Observed')
> cpgram(mylevel,main='Level')
> cpgram(myseas,main='Seasonal')
> cpgram(myresid,main='Standardized Residals')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/56bnl1204976012.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> hist(m$resid,main='Residual Histogram')
> plot(density(m$resid),main='Residual Kernel Density')
> qqnorm(m$resid,main='Residual Normal QQ Plot')
> qqline(m$resid)
> plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')
> par(op)
> dev.off()
null device 
          1 
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Structural Time Series Model',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,'Level',header=TRUE)
> a<-table.element(a,'Slope',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Stand. Residuals',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,mylevel[i])
+ a<-table.element(a,myslope[i])
+ a<-table.element(a,myseas[i])
+ a<-table.element(a,myresid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/6h5t31204976012.tab") 
> 
> system("convert tmp/1jfhy1204976011.ps tmp/1jfhy1204976011.png")
> system("convert tmp/21p4x1204976011.ps tmp/21p4x1204976011.png")
> system("convert tmp/3wo361204976012.ps tmp/3wo361204976012.png")
> system("convert tmp/4se3t1204976012.ps tmp/4se3t1204976012.png")
> system("convert tmp/56bnl1204976012.ps tmp/56bnl1204976012.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.039   0.811   2.299