STSM peer review

*The author of this computation has been verified*

R Software Module: /rwasp_structuraltimeseries.wasp (opens new window with default values)

Title produced by software: Structural Time Series Models

Date of computation: Thu, 16 Dec 2010 17:52:22 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6.htm/, Retrieved Thu, 16 Dec 2010 18:51:21 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

286602 283042 276687 277915 277128 277103 275037 270150 267140 264993 287259 291186 292300 288186 281477 282656 280190 280408 276836 275216 274352 271311 289802 290726 292300 278506 269826 265861 269034 264176 255198 253353 246057 235372 258556 260993 254663 250643 243422 247105 248541 245039 237080 237085 225554 226839 247934 248333 246969 245098 246263 255765 264319 268347 273046 273963 267430 271993 292710 295881 294563

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'George Udny Yule' @ 72.249.76.132 Structural Time Series Model t Observed Level Slope Seasonal Stand. Residuals 1 286602 286602 0 0 0 2 283042 283653.514678328 -194.918781431049 -611.514678327863 -0.513504172651212 3 276687 278738.112107994 -633.581801757586 -2051.11210799353 -1.10580595409501 4 277915 277701.909912795 -680.252271222706 213.090087204625 -0.0997065198244274 5 277128 277271.530240242 -645.890768061875 -143.530240241980 0.0602327365463566 6 277103 277213.666234932 -554.779819817128 -110.666234931874 0.139143721593626 7 275037 275809.967354789 -696.861674374174 -772.967354788608 -0.198553808025926 8 270150 271748.631096895 -1287.72185557428 -1598.63109689547 -0.78068783645142 9 267140 267979.819544199 -1736.67874160150 -839.819544198809 -0.572582134226973 10 264993 265323.003050931 -1906.33467108155 -330.003050931516 -0.211571373760679 11 287259 280417.130913402 1265.66684309408 6841.86908659751 3.89954991137942 12 291186 290454.505591438 2914.69562417399 731.494408562216 2.0088718445796 13 292300 291827.602669314 2635.81462614077 472.397330685744 -0.381768914671608 14 288186 289105.306168242 1630.43337354587 -919.30616824152 -1.17241415668180 15 281477 285177.447902346 601.232352478222 -3700.44790234588 -1.19525698085119 16 282656 283202.099905819 121.719278756493 -546.099905818812 -0.580525201913526 17 280190 281322.896910550 -253.224981554856 -1132.89691054965 -0.451681847842913 18 280408 280363.258527809 -385.766407594984 44.7414721908378 -0.158669731712760 19 276836 277419.915821799 -865.538410338143 -583.915821798573 -0.574248363255685 20 275216 275491.15769903 -1065.00033327702 -275.157699030219 -0.238969163696083 21 274352 274611.958751139 -1030.13291846558 -259.958751139443 0.0417967307084539 22 271311 276026.198911086 -571.326758062151 -4715.19891108608 0.55005764334406 23 289802 282777.257880196 802.026312437257 7024.74211980361 1.64583404226181 24 290726 288224.915851438 1670.53224517741 2501.08414856235 1.04459985922816 25 292300 289964.117576245 1683.34735444758 2335.88242375478 0.015828751956963 26 278506 281998.089648172 -124.045506568646 -3492.08964817203 -2.15946615518098 27 269826 274827.818024539 -1432.84780051521 -5001.81802453924 -1.54256078982195 28 265861 267747.948348433 -2481.0253160722 -1886.94834843271 -1.25987164650344 29 269034 267801.884705637 -2008.2796636789 1232.11529436317 0.569696858211719 30 264176 264104.828719529 -2323.88077131080 71.1712804712921 -0.37826232203378 31 255198 257203.49419422 -3179.21790478733 -2005.49419421973 -1.02322729611630 32 253353 253381.570478368 -3299.2713841071 -28.5704783683416 -0.143704090850896 33 246057 248384.476653014 -3616.38993146270 -2327.47665301379 -0.379863530546178 34 235372 243648.865106564 -3825.38359246229 -8276.86510656376 -0.250373649485884 35 258556 248979.766908410 -2117.6031672791 9576.23309159042 2.04668896015447 36 260993 255068.423107095 -589.408546914742 5924.57689290524 1.84078304131215 37 254663 251037.498183637 -1231.03726836792 3625.5018163629 -0.778868184343224 38 250643 250527.036653019 -1096.54167628592 115.963346980907 0.161087811175728 39 243422 247730.699337801 -1412.43090814126 -4308.69933780114 -0.375139009225978 40 247105 248642.866147849 -981.209418361088 -1537.86614784900 0.516878030877219 41 248541 247027.331471033 -1099.16017245965 1513.66852896716 -0.142032433025296 42 245039 244050.07569262 -1449.09026577852 988.924307379717 -0.420166870291771 43 237080 239692.625931415 -1991.14701737445 -2612.6259314146 -0.64916543320341 44 237085 236430.49346715 -2227.95474970988 654.506532849932 -0.283464343975813 45 225554 229940.029080625 -3021.8753196366 -4386.02908062471 -0.950570012871678 46 226839 234615.251741445 -1589.01479578791 -7776.25174144547 1.71599627009908 47 247934 238931.841920532 -490.690561779295 9002.15807946826 1.31711125399326 48 248333 240720.334200147 -66.9903134920694 7612.66579985306 0.510186904837453 49 246969 242754.735810031 324.151354397860 4214.26418996939 0.472008393862723 50 245098 244328.766295623 556.904166872985 769.233704377497 0.278817456230623 51 246263 249469.093653788 1408.2801782797 -3206.09365378785 1.01476386798951 52 255765 255350.099938604 2237.72036605097 414.900061396093 0.993370973336029 53 264319 260783.557578870 2831.00444204526 3535.44242113031 0.713581227538113 54 268347 265445.582349429 3171.47295153149 2901.41765057069 0.409102347320942 55 273046 272785.381465806 3946.95829275335 260.618534193956 0.929579390203477 56 273963 273978.819154089 3434.81370984221 -15.8191540891989 -0.613160401588959 57 267430 275520.919933663 3082.95113374645 -8090.91993366284 -0.421175038917083 58 271993 280665.544586771 3465.97178352948 -8672.54458677142 0.458685689751345 59 292710 284488.032369835 3532.16319488117 8221.96763016541 0.0794080017422295 60 295881 288639.682049450 3647.18967279553 7241.31795054964 0.138399093199395 61 294563 291496.436359777 3500.26912641378 3066.56364022347 -0.176832484645321

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6/1efgx1292521936.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6/1efgx1292521936.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6/2efgx1292521936.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6/2efgx1292521936.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6/36og01292521936.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6/36og01292521936.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6/46og01292521936.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6/46og01292521936.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6/5hyf31292521936.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t129252187584mrtnu6bizjuq6/5hyf31292521936.ps (open in new window)

Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 12 ;

R code (references can be found in the software module):

par1 <- as.numeric(par1) nx <- length(x) x <- ts(x,frequency=par1) m <- StructTS(x,type='BSM') m$coef m$fitted m$resid mylevel <- as.numeric(m$fitted[,'level']) myslope <- as.numeric(m$fitted[,'slope']) myseas <- as.numeric(m$fitted[,'sea']) myresid <- as.numeric(m$resid) myfit <- mylevel+myseas mylagmax <- nx/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level') acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(mylevel,main='Level') spectrum(myseas,main='Seasonal') spectrum(myresid,main='Standardized Residals') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(mylevel,main='Level') cpgram(myseas,main='Seasonal') cpgram(myresid,main='Standardized Residals') par(op) dev.off() bitmap(file='test1.png') plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b') grid() dev.off() bitmap(file='test5.png') 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() load(file='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='mytable.tab')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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