*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 17 May 2011 20:35:31 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/17/t1305664351vb73b130d65vwu5.htm/, Retrieved Tue, 17 May 2011 22:32:35 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

56 55 54 52 72 71 56 46 47 47 48 50 44 38 33 33 52 54 39 22 31 31 38 42 41 31 36 34 51 47 31 19 30 33 36 40 32 25 28 29 55 55 40 38 44 41 49 59 61 47 43 39 66 68 63 68 67 59 68 78 82 70 62 68 94 102 100 104 103 93 110 114 120 102 95 103 122 139 135 135 137 130 148 148 145 128 131 133 146 163 151 157 152 149 172 167 160 150 160 165 171 179 171 176 170 169 194 196 188 174 186 191 197 206 197 204 201 190 213 213

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 'Herman Ole Andreas Wold' @ www.yougetit.org Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 56 NA NA 1.05645166091252 NA 2 55 NA NA 0.880295890934327 NA 3 54 NA NA 0.869636406775788 NA 4 52 NA NA 0.871434371109763 NA 5 72 NA NA 1.1625384550494 NA 6 71 NA NA 1.19238812348658 NA 7 56 54.5994908201874 54 1.01110168185532 1.02565059048673 8 46 47.8251637474675 52.7916666666667 0.90592259663711 0.96183674859735 9 47 49.4917507163796 51.2083333333333 0.966478451743784 0.949653211286483 10 47 45.9022798490333 49.5416666666667 0.926538869955257 1.02391428387821 11 48 50.2568590694063 47.9166666666667 1.04883879797022 0.95509351138937 12 50 51.4008764143055 46.375 1.10837469356993 0.972746059755596 13 44 47.4963059218585 44.9583333333333 1.05645166091252 0.926387834716858 14 38 38.0727972829096 43.25 0.880295890934327 0.998087944986844 15 33 36.1623805817599 41.5833333333333 0.869636406775788 0.912550542002897 16 33 35.075233437168 40.25 0.871434371109763 0.940834793276987 17 52 45.5327561561016 39.1666666666667 1.1625384550494 1.14203497415633 18 54 45.8075770772762 38.4166666666667 1.19238812348658 1.17884427523646 19 39 38.3797346737583 37.9583333333333 1.01110168185532 1.01616127186689 20 22 34.0098441487515 37.5416666666667 0.90592259663711 0.646871532365067 21 31 36.1221321339239 37.375 0.966478451743784 0.858199618036569 22 31 34.7838134095703 37.5416666666667 0.926538869955257 0.891219132157338 23 38 39.3751565404653 37.5416666666667 1.04883879797022 0.965075528295308 24 42 41.2407750565811 37.2083333333333 1.10837469356993 1.01840957019788 25 41 38.6485232617162 36.5833333333333 1.05645166091252 1.06084260250671 26 31 31.8006890600026 36.125 0.880295890934327 0.974821644320606 27 36 31.2706757936461 35.9583333333333 0.869636406775788 1.15123831149549 28 34 31.3716373599515 36 0.871434371109763 1.08378149377067 29 51 41.8513843817785 36 1.1625384550494 1.21859768209256 30 47 42.7272410916025 35.8333333333333 1.19238812348658 1.10000081445084 31 31 35.767721995632 35.375 1.01110168185532 0.866703224873693 32 19 31.4808102331396 34.75 0.90592259663711 0.603542280496925 33 30 33.021347101246 34.1666666666667 0.966478451743784 0.908503214845164 34 33 31.1548695022455 33.625 0.926538869955257 1.05922446562074 35 36 35.2235029651665 33.5833333333333 1.04883879797022 1.02204485555004 36 40 37.7771041391751 34.0833333333333 1.10837469356993 1.05884241027675 37 32 36.7557140359146 34.7916666666667 1.05645166091252 0.870612932964172 38 25 31.6539730781802 35.9583333333333 0.880295890934327 0.789790271769489 39 28 32.4664258529628 37.3333333333333 0.869636406775788 0.862429394809556 40 29 33.3323646949484 38.25 0.871434371109763 0.870025282196525 41 55 45.4843170538078 39.125 1.1625384550494 1.20920799876879 42 55 48.2420361627279 40.4583333333333 1.19238812348658 1.1400845481413 43 40 42.9296922421072 42.4583333333333 1.01110168185532 0.931756039023414 44 38 40.3890491000712 44.5833333333333 0.90592259663711 0.940849087727916 45 44 44.5788185866821 46.125 0.966478451743784 0.987015838350302 46 41 43.7017500328896 47.1666666666667 0.926538869955257 0.938177532230258 47 49 50.3879639191526 48.0416666666667 1.04883879797022 0.972454455167516 48 59 54.3565422638252 49.0416666666667 1.10837469356993 1.08542592193663 49 61 53.3948276952867 50.5416666666667 1.05645166091252 1.14243275300212 50 47 46.4356082467858 52.75 0.880295890934327 1.01215428793814 51 43 47.793767522386 54.9583333333333 0.869636406775788 0.899698898603449 52 39 49.3812810295533 56.6666666666667 0.871434371109763 0.789772950131845 53 66 67.6694259043339 58.2083333333333 1.1625384550494 0.975329687195898 54 68 71.2948732168018 59.7916666666667 1.19238812348658 0.953785271392763 55 63 62.1406241973583 61.4583333333333 1.01110168185532 1.01382953283366 56 68 57.3373510121571 63.2916666666667 0.90592259663711 1.18596340430136 57 67 62.8613692988353 65.0416666666667 0.966478451743784 1.06583742523155 58 59 62.1167100732503 67.0416666666667 0.926538869955257 0.949824933265542 59 68 72.806893225766 69.4166666666667 1.04883879797022 0.933977498382462 60 78 79.8029779370349 72 1.10837469356993 0.97740713462526 61 82 79.189855749234 74.9583333333333 1.05645166091252 1.03548616453684 62 70 68.6630794928775 78 0.880295890934327 1.01947073328194 63 62 70.4405489488389 81 0.869636406775788 0.880174855608106 64 68 73.1278676422943 83.9166666666667 0.871434371109763 0.929878064168679 65 94 101.237723793885 87.0833333333333 1.1625384550494 0.928507640011534 66 102 107.712393821621 90.3333333333333 1.19238812348658 0.946966234627732 67 100 94.4537487799847 93.4166666666667 1.01110168185532 1.05871922810533 68 104 87.2705434760417 96.3333333333333 0.90592259663711 1.191696486095 69 103 95.721636658124 99.0416666666667 0.966478451743784 1.07603676238708 70 93 94.3911473766918 101.875 0.926538869955257 0.98526188720707 71 110 109.603654387888 104.5 1.04883879797022 1.00361617150747 72 114 118.827003606476 107.208333333333 1.10837469356993 0.95937789004205 73 120 116.4297767964 110.208333333333 1.05645166091252 1.03066417631156 74 102 99.4367566801234 112.958333333333 0.880295890934327 1.02577762394365 75 95 100.587944383733 115.666666666667 0.869636406775788 0.944447175872136 76 103 103.373902272896 118.625 0.871434371109763 0.996383010946916 77 122 141.539056902265 121.75 1.1625384550494 0.86195289604228 78 139 148.750418404951 124.75 1.19238812348658 0.934451153082427 79 135 128.620559779346 127.208333333333 1.01110168185532 1.04959891506924 80 135 117.165989165066 129.333333333333 0.90592259663711 1.15221149893429 81 137 127.494615759201 131.916666666667 0.966478451743784 1.07455518167726 82 130 124.773901153975 134.666666666667 0.926538869955257 1.04188455115767 83 148 143.603512088756 136.916666666667 1.04883879797022 1.03061546230518 84 148 153.971717848423 138.916666666667 1.10837469356993 0.961215488585368 85 145 148.519495996618 140.583333333333 1.05645166091252 0.976302801372973 86 128 125.148732494497 142.166666666667 0.880295890934327 1.02278303142725 87 131 124.973998623737 143.708333333333 0.869636406775788 1.04821804089349 88 133 126.466913107304 145.125 0.871434371109763 1.05165846727952 89 146 170.796274687675 146.916666666667 1.1625384550494 0.854819581205632 90 163 177.31805053015 148.708333333333 1.19238812348658 0.919252154603878 91 151 151.79163998853 150.125 1.01110168185532 0.994784693092518 92 157 137.398260489962 151.666666666667 0.90592259663711 1.14266366575631 93 152 148.636331891096 153.791666666667 0.966478451743784 1.02263018782896 94 149 144.848910003005 156.333333333333 0.926538869955257 1.0286580685827 95 172 166.45945756119 158.708333333333 1.04883879797022 1.03328463591066 96 167 177.801773760176 160.416666666667 1.10837469356993 0.939248222716013 97 160 171.057131429418 161.916666666667 1.05645166091252 0.935360009038965 98 150 143.965057163218 163.541666666667 0.880295890934327 1.04191949738151 99 160 143.56247681857 165.083333333333 0.869636406775788 1.11449735018297 100 165 145.239061851627 166.666666666667 0.871434371109763 1.13605801288196 101 171 195.790851471237 168.416666666667 1.1625384550494 0.87338095071884 102 179 203.351857892941 170.541666666667 1.19238812348658 0.8802476744237 103 171 174.836332487483 172.916666666667 1.01110168185532 0.978057578577053 104 176 158.611947961214 175.083333333333 0.90592259663711 1.10962636965431 105 170 171.227765700607 177.166666666667 0.966478451743784 0.992829634285167 106 169 166.159304011976 179.333333333333 0.926538869955257 1.01709621982901 107 194 190.364241831595 181.5 1.04883879797022 1.01909895542053 108 196 203.617667664576 183.708333333333 1.10837469356993 0.962588375792987 109 188 196.411971291318 185.916666666667 1.05645166091252 0.957171799478343 110 174 165.642343477476 188.166666666667 0.880295890934327 1.0504560388791 111 186 165.774440041635 190.625 0.869636406775788 1.12200650446043 112 191 168.00528479687 192.791666666667 0.871434371109763 1.13686899927542 113 197 226.065290404815 194.458333333333 1.1625384550494 0.871429663736668 114 206 233.658389364891 195.958333333333 1.19238812348658 0.881628947969428 115 197 NA NA 1.01110168185532 NA 116 204 NA NA 0.90592259663711 NA 117 201 NA NA 0.966478451743784 NA 118 190 NA NA 0.926538869955257 NA 119 213 NA NA 1.04883879797022 NA 120 213 NA NA 1.10837469356993 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/17/t1305664351vb73b130d65vwu5/16hwz1305664529.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305664351vb73b130d65vwu5/16hwz1305664529.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305664351vb73b130d65vwu5/25ens1305664529.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305664351vb73b130d65vwu5/25ens1305664529.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305664351vb73b130d65vwu5/3x4lr1305664529.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305664351vb73b130d65vwu5/3x4lr1305664529.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305664351vb73b130d65vwu5/4ohyr1305664529.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305664351vb73b130d65vwu5/4ohyr1305664529.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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