prijsindex van de grondstoffen

*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: Sat, 05 Dec 2009 02:18:34 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u.htm/, Retrieved Sat, 05 Dec 2009 10:20:03 +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/2009/Dec/05/t1260004796n2u6idevqrpr72u.htm/}, year = {2009}, } @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 = {2009}, 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 «

226.9 235.9 216.2 226.2 198.3 176.7 166.2 157.6 163.4 159.7 191.0 239.4 321.9 362.7 413.6 407.1 383.2 347.7 333.8 312.3 295.4 283.3 287.6 265.7 250.2 234.7 244.0 231.2 223.8 223.5 210.5 201.6 190.7 207.5 198.8 196.6 204.2 227.4 229.7 217.9 221.4 216.3 197.0 193.8 196.8 180.5 174.8 181.6 190.0 190.6 179.0 174.1 161.1 168.6 169.4 152.2 148.3 137.7 145.0 153.4

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Structural Time Series Model t Observed Level Slope Seasonal Stand. Residuals 1 226.9 226.9 0 0 0 2 235.9 234.977571353527 7.99075453253924 0.0434084093216129 0.594929822638374 3 216.2 218.061229343349 -13.3610226105330 0.095609426326639 -1.53209263112949 4 226.2 224.582765187559 3.84801361013468 0.0768124997632141 1.21454688585603 5 198.3 200.382580252163 -20.4522261622188 0.0796729048664979 -1.70944985815841 6 176.7 176.857352278825 -23.1148358739779 0.079548580008023 -0.187302741310175 7 166.2 165.234350874981 -13.1579001509335 0.0797566745141656 0.700421788802957 8 157.6 157.130620446742 -8.77885762807506 0.0797664909587289 0.308043882232721 9 163.4 162.248948639991 3.26193331164645 0.0797556917219492 0.84700980605779 10 159.7 160.041836221989 -1.47655587168261 0.0797575873561435 -0.333329165429231 11 191 188.604594094335 24.5501066142232 0.0797564763551527 1.83084636176085 12 239.4 237.44757138447 45.5979369375229 0.0797568389544972 1.48061026230833 13 321.9 316.225908911363 74.0293211060381 3.14448808499036 2.12051058399833 14 362.7 364.77809954137 54.403633435663 -0.612418506262757 -1.33721830318151 15 413.6 414.546091571916 50.3657391950223 -0.596714793593977 -0.278702153542578 16 407.1 411.675139932608 4.32373016815022 -0.500722469679577 -3.22948447999051 17 383.2 386.004893101622 -21.6562516035199 -0.493514437453643 -1.82755058175271 18 347.7 349.350708232111 -34.6516493032746 -0.494490899316155 -0.914155286411903 19 333.8 332.891416091497 -18.8888811700869 -0.493861490625559 1.10883123212785 20 312.3 312.88037156769 -19.8611504295694 -0.493866633926793 -0.0683943103820708 21 295.4 295.688131170628 -17.5487561809360 -0.493869974243659 0.162665443119619 22 283.3 283.38918040804 -13.0002225676284 -0.493873451714364 0.319966734825559 23 287.6 286.826731454384 1.24179267764638 -0.493874888243411 1.00185499427581 24 265.7 267.759447264724 -16.3543989595443 -0.493875376889243 -1.23780463427042 25 250.2 245.662989617662 -21.2935027733085 4.97645338314252 -0.361247129724354 26 234.7 234.427193076188 -13.2936863599598 -0.357116866921838 0.551260925117778 27 244 242.771157779374 5.52803588723932 -0.411524115840896 1.30551040209414 28 231.2 232.780646166577 -7.89950344738987 -0.390740663385054 -0.942538568306839 29 223.8 224.240035215729 -8.45485439533761 -0.390626284115644 -0.0390659884846729 30 223.5 223.310157458594 -1.93473318315231 -0.390262596306262 0.458655756001019 31 210.5 211.640936161864 -10.3691053738551 -0.390512607893125 -0.593315702695328 32 201.6 201.93907417451 -9.79099037148415 -0.390510337627274 0.04066751730194 33 190.7 191.166200114861 -10.6417132872673 -0.390509425362144 -0.0598441291925417 34 207.5 205.931931627453 11.3717952553939 -0.390521918986778 1.54854092548502 35 198.8 200.486885591319 -3.19865212204089 -0.390520827991362 -1.02495856257155 36 196.6 197.011828170781 -3.4381352163987 -0.390520832928295 -0.016846445534218 37 204.2 199.147568499039 1.36524203119936 4.62506440994329 0.34791027983187 38 227.4 225.959402618204 21.9791778628982 -0.22925213537541 1.42832119137737 39 229.7 231.171259286662 7.40577790914908 -0.195730249159575 -1.01376894182994 40 217.9 219.536971814108 -9.07331031150869 -0.175448168684948 -1.15724897384732 41 221.4 220.781681241306 -0.135074167471849 -0.176911921592962 0.6287580510624 42 216.3 216.775485241292 -3.48922695159914 -0.177060684725011 -0.235947010543113 43 197 198.330302517460 -16.4476313695432 -0.177366105196210 -0.911558783885738 44 193.8 193.111716632261 -6.71853509201076 -0.177335726148020 0.684393571719537 45 196.8 196.219833503228 1.79546682166495 -0.177342985635534 0.598917721873339 46 180.5 181.918216867296 -12.1513634359492 -0.177336691821110 -0.981090197145223 47 174.8 174.604421882307 -7.95999739613025 -0.177336941363778 0.294841771161927 48 181.6 180.694271954965 4.21307219792854 -0.177336741827410 0.856314949706574 49 190 187.703196566553 6.62509742133289 2.08220022277809 0.173685235279959 50 190.6 191.032559698835 3.92344228184305 -0.209810392122436 -0.187799180362499 51 179 180.302734391925 -8.80570224954928 -0.185497013266216 -0.887167336827954 52 174.1 174.088706848288 -6.56221275658529 -0.187788811150374 0.157595420752676 53 161.1 161.733592431604 -11.5805985045597 -0.187106726213236 -0.353017359925939 54 168.6 167.452912979998 3.40889971226353 -0.186554953505889 1.05443311337359 55 169.4 169.677845323893 2.38306927111239 -0.186575020518467 -0.072162036180652 56 152.2 153.794711411814 -13.4431706512623 -0.186616035250958 -1.11329732596853 57 148.3 147.904392124012 -6.89923287119047 -0.186620666220746 0.460333500986748 58 137.7 138.109813995339 -9.4078172140785 -0.186619726653979 -0.176466441632588 59 145 144.006813474961 3.85258212674772 -0.186620381902883 0.932803193713155 60 153.4 153.176721993029 8.45962033149334 -0.18662031922644 0.324082242207736

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u/1rtcw1260004711.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u/1rtcw1260004711.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u/2jual1260004711.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u/2jual1260004711.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u/384wa1260004711.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u/384wa1260004711.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u/4o90m1260004711.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u/4o90m1260004711.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u/58n5n1260004711.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004796n2u6idevqrpr72u/58n5n1260004711.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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