ws9

*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: Fri, 04 Dec 2009 12:58:05 -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/04/t1259956780699ffzkhgdtf705.htm/, Retrieved Fri, 04 Dec 2009 20:59:47 +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/04/t1259956780699ffzkhgdtf705.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:

ws9.10

Dataseries X:

» Textbox « » Textfile « » CSV «

126.51 131.02 136.51 138.04 132.92 129.61 122.96 124.04 121.29 124.56 118.53 113.14 114.15 122.17 129.23 131.19 129.12 128.28 126.83 138.13 140.52 146.83 135.14 131.84 125.7 128.98 133.25 136.76 133.24 128.54 121.08 120.23 119.08 125.75 126.89 126.6 121.89 123.44 126.46 129.49 127.78 125.29 119.02 119.96 122.86 131.89 132.73 135.01 136.71 142.73 144.43 144.93 138.75 130.22 122.19 128.4 140.43 153.5 149.33 142.97

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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Structural Time Series Model t Observed Level Slope Seasonal Stand. Residuals 1 126.51 126.51 0 0 0 2 131.02 130.917202557603 3.92246239849399 0.102797442396775 0.913847618431535 3 136.51 136.475050774094 5.4211963270669 0.0349492259064152 0.323228539519454 4 138.04 138.173301361872 2.03974118144753 -0.133301361871803 -0.735301004209409 5 132.92 133.129974122407 -4.40470661680805 -0.209974122406907 -1.40484469512538 6 129.61 129.480225546292 -3.71771194000801 0.129774453707634 0.149751215420113 7 122.96 123.045183923431 -6.19027272624965 -0.0851839234314419 -0.538970318236795 8 124.04 123.716827700687 0.0535258646267449 0.323172299312794 1.36102692742256 9 121.29 121.422930675273 -2.08245193375971 -0.132930675272830 -0.465601717116753 10 124.56 124.314127141518 2.44319451030242 0.245872858481893 0.986503116255774 11 118.53 118.859869616276 -4.74289199990166 -0.329869616275619 -1.56642743172016 12 113.14 113.072065188506 -5.69368359272046 0.067934811494016 -0.207254119571685 13 114.15 113.820056964336 0.137293918295319 0.329943035663569 1.30617668807452 14 122.17 121.948627069742 6.93900046753001 0.221372930258375 1.47143578272947 15 129.23 129.251133683834 7.26134583979489 -0.0211336838336444 0.0702956150792457 16 131.19 131.227140268583 2.59179533094804 -0.0371402685830076 -1.01410308589435 17 129.12 129.610135228349 -1.13022730994044 -0.490135228349003 -0.81138113179795 18 128.28 127.762037961368 -1.76540834426589 0.517962038632025 -0.138455307850871 19 126.83 127.221648312564 -0.681581329417607 -0.391648312564396 0.236253344661319 20 138.13 136.891532690192 8.47639142970118 1.23846730980816 1.99626004787833 21 140.52 141.496125790248 5.05100180883885 -0.976125790247784 -0.74666856805515 22 146.83 146.18202903809 4.72798641198333 0.647970961910118 -0.0704111087827684 23 135.14 136.055343646103 -8.4155049146763 -0.91534364610312 -2.86507463687213 24 131.84 131.499688352674 -5.00325927790722 0.340311647326001 0.744031465440659 25 125.7 126.360997728313 -5.12301346878176 -0.660997728313063 -0.0264407527440768 26 128.98 128.620932867865 1.19443231899773 0.359067132134668 1.36993223458147 27 133.25 132.851918209884 3.84495003071638 0.398081790116353 0.579569553661896 28 136.76 136.547502748048 3.71454245588471 0.212497251952143 -0.0283266193563843 29 133.24 133.965409953674 -1.78161478605764 -0.725409953673718 -1.19793769414372 30 128.54 127.911717517843 -5.51375707469794 0.62828248215724 -0.813530457709215 31 121.08 122.310659902517 -5.59002397324516 -1.23065990251707 -0.0166246282184823 32 120.23 118.583910619587 -3.96237669087060 1.64608938041287 0.354795577877309 33 119.08 120.204131792144 0.914229817306783 -1.12413179214442 1.06300566305370 34 125.75 123.634521300853 3.11237491401478 2.11547869914734 0.479156498736774 35 126.89 128.025524319659 4.22934775702417 -1.13552431965949 0.243479856030229 36 126.6 126.398505058157 -0.880357027193927 0.201494941842746 -1.11480800848040 37 121.89 123.409157055347 -2.72377114043586 -1.51915705534704 -0.404211138730694 38 123.44 123.244178250926 -0.529131087357738 0.195821749073722 0.476768191036138 39 126.46 126.085898453526 2.39064071431006 0.374101546474418 0.638570867909091 40 129.49 128.850332823347 2.71512017631326 0.639667176652758 0.0705390064677417 41 127.78 128.341115013476 -0.0810618756463612 -0.561115013476304 -0.609307726939597 42 125.29 124.751529462 -3.12645944513900 0.538470538000142 -0.663852200903137 43 119.02 120.215944328442 -4.34972998793681 -1.19594432844207 -0.266645656308553 44 119.96 118.472109226178 -2.08770905950752 1.48789077382238 0.493076694308836 45 122.86 123.708481274012 4.26988405475962 -0.848481274012393 1.38583280540139 46 131.89 130.04782269332 6.06638992233837 1.84217730667989 0.391610356978546 47 132.73 133.583967899546 3.8703892911244 -0.853967899546154 -0.478679383073456 48 135.01 134.687628993331 1.47156315686195 0.322371006669465 -0.523544218885862 49 136.71 138.225458287533 3.2660477324634 -1.51545828753304 0.392236526720319 50 142.73 142.802739606604 4.39140544766192 -0.0727396066035983 0.244751629264758 51 144.43 144.456256767502 2.02957335327447 -0.0262567675020932 -0.516298599702921 52 144.93 144.297405085110 0.136365799289824 0.632594914889814 -0.411906978939660 53 138.75 139.335339537302 -4.26872744002087 -0.585339537301886 -0.959709989279856 54 130.22 129.675203425735 -8.93054893681514 0.544796574264559 -1.01622860367565 55 122.19 123.117440954077 -6.87835319583681 -0.927440954076765 0.447330940491508 56 128.4 126.847246573552 2.29615983216228 1.55275342644837 1.99986096975472 57 140.43 140.854425058853 12.4245091137639 -0.424425058853141 2.20779437417532 58 153.5 151.659603295881 11.0239852637545 1.84039670411948 -0.305292617967822 59 149.33 150.660597521916 0.629629190843078 -1.33059752191579 -2.26577833702963 60 142.97 143.360339514032 -6.22189287681627 -0.390339514032104 -1.49545917193968

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956780699ffzkhgdtf705/1arjq1259956683.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956780699ffzkhgdtf705/1arjq1259956683.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956780699ffzkhgdtf705/2xdcr1259956683.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956780699ffzkhgdtf705/2xdcr1259956683.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956780699ffzkhgdtf705/3gznf1259956683.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956780699ffzkhgdtf705/3gznf1259956683.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956780699ffzkhgdtf705/4ye6o1259956683.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956780699ffzkhgdtf705/4ye6o1259956683.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956780699ffzkhgdtf705/5hs8d1259956683.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956780699ffzkhgdtf705/5hs8d1259956683.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = 12 ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

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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