Van Passen Glenn 2 mar 01 A verbetering oefening 9 vraag 2

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 01 Jun 2008 10:57:46 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Jun/01/t1212339538c4s56wagw2eyvew.htm/, Retrieved Sun, 01 Jun 2008 16:59:02 +0000

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

11835.70 11542.20 13093.70 11180.20 12035.70 12112.00 10875.20 9897.30 11672.10 12385.70 11405.60 9830.90 11025.10 10853.80 12252.60 11839.40 11669.10 11601.40 11178.40 9516.40 12102.80 12989.00 11610.20 10205.50 11356.20 11307.10 12648.60 11947.20 11714.10 12192.50 11268.80 9097.40 12639.80 13040.10 11687.30 11191.70 11391.90 11793.10 13933.20 12778.10 11810.30 13698.40 11956.60 10723.80 13938.90 13979.80 13807.40 12973.90 12509.80 12934.10 14908.30 13772.10 13012.60 14049.90 11816.50 11593.20 14466.20 13615.90 14733.90 13880.70 13527.50 13584.00 16170.20 13260.60 14741.90 15486.50 13154.50 12621.20 15031.60 15452.40 15428.00 13105.90 14716.80 14180.00 16202.20 14392.40 15140.60 15960.10 14729.90 13705.20 15728.50 17315.60 16152.80

Text written by user:

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 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 11835.7 NA NA 0.962375680416726 NA 2 11542.2 NA NA 0.971725251223278 NA 3 13093.7 NA NA 1.1087688053701 NA 4 11180.2 NA NA 1.03593610730050 NA 5 12035.7 NA NA 0.989578018080147 NA 6 12112 NA NA 1.04906445953755 NA 7 10875.2 10754.4459840122 11455.0833333333 0.938836119394931 1.01122828792551 8 9897.3 9388.96728372105 11392.625 0.82412677356808 1.05414149404486 9 11672.1 12074.0646409929 11328.8958333333 1.06577594309474 0.966708423969492 10 12385.7 12158.1791820437 11321.3166666667 1.0739191862587 1.01871339569434 11 11405.6 11660.2049631582 11333.5083333333 1.02882572811669 0.978164623695494 12 9830.9 10744.1747507026 11296.9583333333 0.95106792763857 0.914998148122742 13 11025.1 10863.6014328428 11288.3166666667 0.962375680416726 1.01486602469315 14 10853.8 10965.9963883037 11285.0791666667 0.971725251223278 0.98976870096151 15 12252.6 12514.8444414032 11287.1541666667 1.1087688053701 0.979045329517995 16 11839.4 11737.4021305401 11330.2375 1.03593610730050 1.00868998678971 17 11669.1 11245.4656396610 11363.9 0.989578018080147 1.03767157127268 18 11601.4 11946.7810340289 11388.0333333333 1.04906445953755 0.97109003395600 19 11178.4 10719.1027159342 11417.4375 0.938836119394931 1.04284848239985 20 9516.4 9436.35113933965 11450.1208333333 0.82412677356808 1.00848303115032 21 12102.8 12240.9784758808 11485.5083333333 1.06577594309474 0.98871181122056 22 12989 12357.0511166857 11506.5 1.0739191862587 1.05114075173331 23 11610.2 11844.7334310437 11512.8666666667 1.02882572811669 0.980199349153017 24 10205.5 10974.7255047113 11539.3708333333 0.95106792763857 0.929909362709704 25 11356.2 11132.5373167353 11567.7666666667 0.962375680416726 1.02009089903777 26 11307.1 11227.3864320276 11554.075 0.971725251223278 1.00709992200367 27 12648.6 12816.2493815329 11558.9916666667 1.1087688053701 0.98691899817629 28 11947.2 11999.7615825149 11583.4958333333 1.03593610730050 0.99561978109703 29 11714.1 11468.0588451029 11588.8375 0.989578018080147 1.02145447265491 30 12192.5 12203.9154752654 11633.1416666667 1.04906445953755 0.999064605512181 31 11268.8 10961.5884383052 11675.7208333333 0.938836119394931 1.02802619012964 32 9097.4 9640.18859519705 11697.4583333333 0.82412677356808 0.943695230665147 33 12639.8 12545.4973072215 11771.2333333333 1.06577594309474 1.00751685568687 34 13040.1 12736.0148242000 11859.3791666667 1.0739191862587 1.02387600674131 35 11687.3 12240.9770866801 11898.0083333333 1.02882572811669 0.954768554604797 36 11191.7 11379.3018755627 11964.7625 0.95106792763857 0.983513762301574 37 11391.9 11602.5615990508 12056.1666666667 0.962375680416726 0.98184352677188 38 11793.1 11808.9801903056 12152.5916666667 0.971725251223278 0.998655244563911 39 13933.2 13609.5688419052 12274.4875 1.1087688053701 1.02377967750883 40 12778.1 12812.2203730680 12367.7708333333 1.03593610730050 0.997336888371068 41 11810.3 12365.0371001412 12495.2625 0.989578018080147 0.955136640865004 42 13698.4 13278.9093113612 12657.8583333333 1.04906445953755 1.0315907488185 43 11956.6 11997.1012070948 12778.6958333333 0.938836119394931 0.99662408390196 44 10723.8 10608.8328662334 12872.8166666667 0.82412677356808 1.01083692572182 45 13938.9 13813.5086762516 12960.9875 1.06577594309474 1.00907744199444 46 13979.8 14007.1637436784 13043.0333333333 1.0739191862587 0.998046446505577 47 13807.4 13513.1586804612 13134.5458333333 1.02882572811669 1.02177442939113 48 12973.9 12553.4190089307 13199.2875 0.95106792763857 1.03349533627215 49 12509.8 12711.1502146135 13208.0958333333 0.962375680416726 0.984159559818434 50 12934.1 12864.1685428985 13238.4833333333 0.971725251223278 1.00543614279215 51 14908.3 14742.9430750145 13296.6791666667 1.1087688053701 1.01121600511812 52 13772.1 13781.5630542708 13303.4875 1.03593610730050 0.999313354063429 53 13012.6 13188.0361518445 13326.9291666667 0.989578018080147 0.98669732552864 54 14049.9 14060.9431549272 13403.3166666667 1.04906445953755 0.999214622034556 55 11816.5 12658.8007276787 13483.5041666667 0.938836119394931 0.933461253889793 56 11593.2 11169.3798605835 13552.9875 0.82412677356808 1.03794482278395 57 14466.2 14529.3459698974 13632.6458333333 1.06577594309474 0.995653901419361 58 13615.9 14673.9377931101 13663.9125 1.0739191862587 0.927896805341041 59 14733.9 14109.9890588894 13714.6541666667 1.02882572811669 1.04421767717229 60 13880.7 13169.0254645760 13846.5666666667 0.95106792763857 1.05404154903781 61 13527.5 13436.8576657224 13962.175 0.962375680416726 1.00674579849936 62 13584 13663.1939238481 14060.7583333333 0.971725251223278 0.99420384982534 63 16170.2 15663.7432287842 14127.15 1.1087688053701 1.03233306137738 64 13260.6 14738.5004005888 14227.2291666667 1.03593610730050 0.899725185031055 65 14741.9 14183.2959970451 14332.6708333333 0.989578018080147 1.03938463972487 66 15486.5 15032.3681022552 14329.3083333333 1.04906445953755 1.03021026990928 67 13154.5 13469.0867114255 14346.5791666667 0.938836119394931 0.976643797893243 68 12621.2 11884.7047307328 14420.9666666667 0.82412677356808 1.06197000985331 69 15031.6 15397.4071533488 14447.1333333333 1.06577594309474 0.976242288736958 70 15452.4 15567.1298043113 14495.625 1.0739191862587 0.99262999629646 71 15428 14979.0810191683 14559.3958333333 1.02882572811669 1.02996972779954 72 13105.9 13881.5417792646 14595.7416666667 0.95106792763857 0.944124234065758 73 14716.8 NA 14681.1166666667 NA NA 74 14180 NA 14791.925 NA NA 75 16202.2 NA 14866.1291666667 NA NA 76 14392.4 NA 14972.8 NA NA 77 15140.6 NA 15080.6333333333 NA NA 78 15960.1 NA NA NA NA 79 14729.9 NA NA NA NA 80 13705.2 NA NA NA NA 81 15728.5 NA NA NA NA 82 17315.6 NA NA NA NA 83 16152.8 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212339538c4s56wagw2eyvew/1m6kh1212339459.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212339538c4s56wagw2eyvew/1m6kh1212339459.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212339538c4s56wagw2eyvew/20pvl1212339459.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212339538c4s56wagw2eyvew/20pvl1212339459.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212339538c4s56wagw2eyvew/311rv1212339459.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212339538c4s56wagw2eyvew/311rv1212339459.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212339538c4s56wagw2eyvew/435kg1212339459.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212339538c4s56wagw2eyvew/435kg1212339459.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ;

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

par2 <- as.numeric(par2) x <- ts(x,freq=par2) m <- decompose(x,type=par1) m$figure bitmap(file='test1.png') plot(m) dev.off() mylagmax <- length(x)/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend') acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'t',header=TRUE) a<-table.element(a,'Observations',header=TRUE) a<-table.element(a,'Fit',header=TRUE) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Random',header=TRUE) a<-table.row.end(a) for (i in 1:length(m$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[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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