*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 10 Dec 2010 19:31:37 +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/10/t12920097509xvqeyj1udfphrs.htm/, Retrieved Fri, 10 Dec 2010 20:35:51 +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/10/t12920097509xvqeyj1udfphrs.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 «

42.33600 42.14710 40.25640 39.18980 39.13170 38.15070 38.27070 39.13350 40.12190 41.28450 42.57490 43.90190 43.18350 43.61880 44.76240 45.19720 44.38810 43.55520 43.56780 44.21350 45.14510 45.80790 42.32820 37.89990 34.79640 35.21440 36.37270 36.25020 36.82610 36.77230 36.90420 37.04940 36.82590 36.13570 36.03000 35.79270 35.91740 35.40080 35.17230 34.92110 35.02920 34.77390 34.89990 34.90540 34.56800 34.40600 34.45780 34.73160 34.26020 33.88490 34.05490 34.27550 34.13930 34.15870 34.53860 33.79870 33.49730 33.68020 34.32840 34.15380 33.91840 34.32620 34.77500 35.01190 34.55130 34.69510 35.47300 35.97940 36.47890 36.39100 36.67040 37.41620 37.11850 36.30010 35.70020 35.58590 35.67770 35.24080 34.80160 34.43890 34.98810 36.06800 36.35660 36.12540 34.87100 35.21990 34.33900 33.82340 34.51990 35.53000 35.79660 33.84840 33.98710 34.11610 33.82350 32.45400 31.87740 31.11500 31.04360 30.88680 31.30010 30.05070 28.6 etc...

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 42.336 NA NA -0.614778125 NA 2 42.1471 NA NA -0.567256875 NA 3 40.2564 NA NA -0.305278958333334 NA 4 39.1898 NA NA -0.179240208333333 NA 5 39.1317 NA NA -0.041592291666666 NA 6 38.1507 NA NA -0.0445681250000004 NA 7 38.2707 40.593725625 40.5769041666667 0.0168214583333331 -2.32302562500001 8 39.1335 40.7258510416667 40.6735375 0.0523135416666637 -1.59235104166666 9 40.1219 41.2729227083333 40.9226083333333 0.350314375000002 -1.15102270833334 10 41.2845 42.047785625 41.3606666666667 0.687118958333333 -0.763285625000002 11 42.5749 42.497473125 41.8299916666667 0.667481458333333 0.0774268750000005 12 43.9019 42.252860625 42.2741958333333 -0.021335208333332 1.649039375 13 43.1835 42.1053177083333 42.7200958333333 -0.614778125 1.07818229166668 14 43.6188 42.585218125 43.152475 -0.567256875 1.033581875 15 44.7624 43.2681627083333 43.5734416666667 -0.305278958333334 1.49423729166667 16 45.1972 43.7919764583333 43.9712166666667 -0.179240208333333 1.40522354166667 17 44.3881 44.1078202083333 44.1494125 -0.041592291666666 0.280279791666672 18 43.5552 43.844481875 43.88905 -0.0445681250000004 -0.289281875 19 43.5678 43.306325625 43.2895041666667 0.0168214583333331 0.261474375000006 20 44.2135 42.642171875 42.5898583333333 0.0523135416666637 1.57132812500001 21 45.1451 42.2404185416667 41.8901041666667 0.350314375000002 2.90468145833334 22 45.8079 41.854860625 41.1677416666667 0.687118958333333 3.953039375 23 42.3282 41.147348125 40.4798666666667 0.667481458333333 1.18085187499999 24 37.8999 39.8608272916667 39.8821625 -0.021335208333332 -1.96092729166666 25 34.7964 38.7071135416667 39.3218916666667 -0.614778125 -3.91071354166667 26 35.2144 38.178480625 38.7457375 -0.567256875 -2.96408062500001 27 36.3727 37.7953210416667 38.1006 -0.305278958333334 -1.42262104166666 28 36.2502 37.171718125 37.3509583333333 -0.179240208333333 -0.921518124999999 29 36.8261 36.6439327083333 36.685525 -0.041592291666666 0.182167291666666 30 36.7723 36.290731875 36.3353 -0.0445681250000004 0.481568124999995 31 36.9042 36.3110297916667 36.2942083333333 0.0168214583333331 0.59317020833334 32 37.0494 36.400996875 36.3486833333333 0.0523135416666637 0.648403125000002 33 36.8259 36.6567477083333 36.3064333333333 0.350314375000002 0.169152291666656 34 36.1357 36.8881564583333 36.2010375 0.687118958333333 -0.752456458333327 35 36.03 36.7382689583333 36.0707875 0.667481458333333 -0.708268958333335 36 35.7927 35.8913147916667 35.91265 -0.021335208333332 -0.0986147916666624 37 35.9174 35.1310927083333 35.7458708333333 -0.614778125 0.786307291666674 38 35.4008 35.005768125 35.573025 -0.567256875 0.395031874999994 39 35.1723 35.0843335416667 35.3896125 -0.305278958333334 0.0879664583333266 40 34.9211 35.0442222916667 35.2234625 -0.179240208333333 -0.123122291666661 41 35.0292 35.0442910416667 35.0858833333333 -0.041592291666666 -0.0150910416666576 42 34.7739 34.931594375 34.9761625 -0.0445681250000004 -0.157694374999998 43 34.8999 34.8797214583333 34.8629 0.0168214583333331 0.0201785416666738 44 34.9054 34.7830010416667 34.7306875 0.0523135416666637 0.122398958333335 45 34.568 34.9712810416667 34.6209666666667 0.350314375000002 -0.403281041666673 46 34.406 35.2346272916667 34.5475083333333 0.687118958333333 -0.82862729166667 47 34.4578 35.151010625 34.4835291666667 0.667481458333333 -0.693210624999999 48 34.7316 34.3994814583333 34.4208166666667 -0.021335208333332 0.332118541666674 49 34.2602 33.7653510416667 34.3801291666667 -0.614778125 0.494848958333328 50 33.8849 33.751705625 34.3189625 -0.567256875 0.133194375000002 51 34.0549 33.9229585416667 34.2282375 -0.305278958333334 0.13194145833333 52 34.2755 33.974143125 34.1533833333333 -0.179240208333333 0.301356874999996 53 34.1393 34.0761577083333 34.11775 -0.041592291666666 0.0631422916666651 54 34.1587 34.0437152083333 34.0882833333333 -0.0445681250000004 0.114984791666664 55 34.5386 34.066788125 34.0499666666667 0.0168214583333331 0.471811874999993 56 33.7987 34.1064260416667 34.0541125 0.0523135416666637 -0.307726041666669 57 33.4973 34.4528185416667 34.1025041666667 0.350314375000002 -0.955518541666663 58 33.6802 34.850310625 34.1631916666667 0.687118958333333 -1.17011062499999 59 34.3284 34.878523125 34.2110416666667 0.667481458333333 -0.550123124999999 60 34.1538 34.229223125 34.2505583333333 -0.021335208333332 -0.0754231250000075 61 33.9184 33.6970635416667 34.3118416666667 -0.614778125 0.221336458333333 62 34.3262 33.874380625 34.4416375 -0.567256875 0.451819375000007 63 34.775 34.351454375 34.6567333333333 -0.305278958333334 0.423545624999996 64 35.0119 34.7146764583333 34.8939166666667 -0.179240208333333 0.297223541666661 65 34.5513 35.0628577083333 35.10445 -0.041592291666666 -0.511557708333328 66 34.6951 35.2933985416667 35.3379666666667 -0.0445681250000004 -0.598298541666665 67 35.473 35.6240589583333 35.6072375 0.0168214583333331 -0.15105895833333 68 35.9794 35.875134375 35.8228208333333 0.0523135416666637 0.104265625000004 69 36.4789 36.2939310416667 35.9436166666667 0.350314375000002 0.184968958333343 70 36.391 36.6932022916667 36.0060833333333 0.687118958333333 -0.302202291666667 71 36.6704 36.7444147916667 36.0769333333333 0.667481458333333 -0.0740147916666629 72 37.4162 36.1252689583333 36.1466041666667 -0.021335208333332 1.29093104166667 73 37.1185 35.5265885416667 36.1413666666667 -0.614778125 1.59191145833333 74 36.3001 35.4819472916667 36.0492041666667 -0.567256875 0.818152708333329 75 35.7002 35.6176210416667 35.9229 -0.305278958333334 0.082578958333336 76 35.5859 35.6680847916667 35.847325 -0.179240208333333 -0.0821847916666627 77 35.6777 35.779199375 35.8207916666667 -0.041592291666666 -0.101499374999996 78 35.2408 35.7093652083333 35.7539333333333 -0.0445681250000004 -0.468565208333331 79 34.8016 35.623325625 35.6065041666667 0.0168214583333331 -0.821725624999999 80 34.4389 35.5201635416667 35.46785 0.0523135416666637 -1.08126354166667 81 34.9881 35.716439375 35.366125 0.350314375000002 -0.728339374999997 82 36.068 35.9230897916667 35.2359708333333 0.687118958333333 0.144910208333336 83 36.3566 35.781773125 35.1142916666667 0.667481458333333 0.574826875000007 84 36.1254 35.0567647916667 35.0781 -0.021335208333332 1.06863520833333 85 34.871 34.5168302083333 35.1316083333333 -0.614778125 0.354169791666678 86 35.2199 34.581205625 35.1484625 -0.567256875 0.638694375000007 87 34.339 34.7768710416667 35.08215 -0.305278958333334 -0.437871041666668 88 33.8234 34.7798722916667 34.9591125 -0.179240208333333 -0.956472291666664 89 34.5199 34.7306452083333 34.7722375 -0.041592291666666 -0.210745208333336 90 35.53 34.4691485416667 34.5137166666667 -0.0445681250000004 1.06085145833332 91 35.7966 34.2528297916667 34.2360083333333 0.0168214583333331 1.54377020833333 92 33.8484 33.9925510416667 33.9402375 0.0523135416666637 -0.14415104166666 93 33.9871 33.9822060416667 33.6318916666667 0.350314375000002 0.00489395833334072 94 34.1161 34.0593439583333 33.372225 0.687118958333333 0.0567560416666737 95 33.8235 33.7831897916667 33.1157083333333 0.667481458333333 0.040310208333338 96 32.454 32.731910625 32.7532458333333 -0.021335208333332 -0.277910624999997 97 31.8774 31.613634375 32.2284125 -0.614778125 0.263765625000005 98 31.115 31.1061014583333 31.6733583333333 -0.567256875 0.00889854166666737 99 31.0436 30.8284002083333 31.1336791666667 -0.305278958333334 0.215199791666667 100 30.8868 30.3707347916667 30.549975 -0.179240208333333 0.516065208333337 101 31.3001 29.9221785416667 29.9637708333333 -0.041592291666666 1.37792145833334 102 30.0507 29.4049152083333 29.4494833333333 -0.0445681250000004 0.645784791666667 103 28.6799 29.0114714583333 28.99465 0.0168214583333331 -0.331571458333332 104 27.6438 28.642346875 28.5900333333333 0.0523135416666637 -0.998546874999999 105 27.2394 28.5847727083333 28.2344583333333 0.350314375000002 -1.34537270833334 106 26.8549 28.561460625 27.8743416666667 0.687118958333333 -1.706560625 107 27.0158 28.101560625 27.4340791666667 0.667481458333333 -1.08576062500001 108 26.9188 26.9419272916667 26.9632625 -0.021335208333332 -0.0231272916666647 109 26.4966 25.996371875 26.61115 -0.614778125 0.500228125 110 26.785 25.8292847916667 26.3965416666667 -0.567256875 0.955715208333334 111 26.8398 25.9665335416667 26.2718125 -0.305278958333334 0.873266458333333 112 26.4478 26.026768125 26.2060083333333 -0.179240208333333 0.421031875000004 113 25.1728 26.130386875 26.1719791666667 -0.041592291666666 -0.957586874999997 114 24.8784 26.0401777083333 26.0847458333333 -0.0445681250000004 -1.16177770833333 115 25.4015 25.918563125 25.9017416666667 0.0168214583333331 -0.517063124999996 116 25.7716 25.7215427083333 25.6692291666667 0.0523135416666637 0.0500572916666684 117 26.1181 25.790176875 25.4398625 0.350314375000002 0.327923125000002 118 26.3969 25.963660625 25.2765416666667 0.687118958333333 0.433239374999999 119 26.6571 25.9294772916667 25.2619958333333 0.667481458333333 0.727622708333335 120 25.1839 25.3622939583333 25.3836291666667 -0.021335208333332 -0.178393958333334 121 23.8394 24.8599552083333 25.4747333333333 -0.614778125 -1.02055520833333 122 23.8619 24.9734264583333 25.5406833333333 -0.567256875 -1.11152645833333 123 24.2581 25.3468627083333 25.6521416666667 -0.305278958333334 -1.08876270833333 124 25.1098 25.6079222916667 25.7871625 -0.179240208333333 -0.498122291666668 125 26.1617 25.909249375 25.9508416666667 -0.041592291666666 0.252450624999998 126 26.8087 26.0767902083333 26.1213583333333 -0.0445681250000004 0.731909791666673 127 25.6577 26.3672047916667 26.3503833333333 0.0168214583333331 -0.709504791666671 128 27.0982 26.703484375 26.6511708333333 0.0523135416666637 0.394715625 129 27.4665 27.266126875 26.9158125 0.350314375000002 0.200373124999999 130 28.289 27.7575064583333 27.0703875 0.687118958333333 0.53149354166667 131 28.6933 27.7460522916667 27.0785708333333 0.667481458333333 0.947247708333332 132 27.2401 26.9579064583333 26.9792416666667 -0.021335208333332 0.282193541666675 133 27.2798 NA 26.9441625 NA NA 134 27.6404 NA 26.9746291666667 NA NA 135 26.831 NA 26.9327416666667 NA NA 136 26.2467 NA 26.7858291666667 NA NA 137 25.2212 NA 26.6027291666667 NA NA 138 25.3653 NA NA NA NA 139 26.2592 NA NA NA NA 140 27.2279 NA NA NA NA 141 26.3315 NA NA NA NA 142 25.8981 NA NA NA NA 143 26.6898 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t12920097509xvqeyj1udfphrs/1m8zd1292009493.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920097509xvqeyj1udfphrs/1m8zd1292009493.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920097509xvqeyj1udfphrs/2m8zd1292009493.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920097509xvqeyj1udfphrs/2m8zd1292009493.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920097509xvqeyj1udfphrs/3sxsa1292009493.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920097509xvqeyj1udfphrs/3sxsa1292009493.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920097509xvqeyj1udfphrs/4sxsa1292009493.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920097509xvqeyj1udfphrs/4sxsa1292009493.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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