test ed

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 28 Apr 2008 12:58:57 -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/Apr/28/t1209409168ipiiju291odhncd.htm/, Retrieved Mon, 28 Apr 2008 20:59:28 +0200

User-defined keywords:

Dataseries X:

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112 118 132 129 121 135 148 148 136 119 104 118 115 126 141 135 125 149 170 170 158 133 114 140 145 150 178 163 172 178 199 199 184 162 146 166 171 180 193 181 183 218 230 242 209 191 172 194 196 196 236 235 229 243 264 272 237 211 180 201 204 188 235 227 234 264 302 293 259 229 203 229 242 233 267 269 270 315 364 347 312 274 237 278 284 277 317 313 318 374 413 405 355 306 271 306 315 301 356 348 355 422 465 467 404 347 305 336 340 318 362 348 363 435 491 505 404 359 310 337 360 342 406 396 420 472 548 559 463 407 362 405 417 391 419 461 472 535 622 606 508 461 390 432

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 4 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 112 NA NA -24.1350694444445 NA 2 118 NA NA -33.6142361111111 NA 3 132 NA NA 1.31076388888889 NA 4 129 NA NA -8.8517361111111 NA 5 121 NA NA -5.72673611111111 NA 6 135 NA NA 32.1024305555556 NA 7 148 194.039930555556 126.791666666667 67.2482638888889 -46.0399305555555 8 148 193.435763888889 127.25 66.1857638888889 -45.4357638888889 9 136 144.481597222222 127.958333333333 16.5232638888889 -8.48159722222222 10 119 105.989930555556 128.583333333333 -22.5934027777778 13.0100694444445 11 104 71.7024305555555 129 -57.2975694444444 32.2975694444445 12 118 98.5982638888889 129.75 -31.1517361111111 19.4017361111111 13 115 107.114930555556 131.25 -24.1350694444445 7.88506944444447 14 126 99.4690972222222 133.083333333333 -33.6142361111111 26.5309027777778 15 141 136.227430555556 134.916666666667 1.31076388888889 4.77256944444446 16 135 127.564930555556 136.416666666667 -8.8517361111111 7.43506944444445 17 125 131.689930555556 137.416666666667 -5.72673611111111 -6.68993055555558 18 149 170.852430555556 138.75 32.1024305555556 -21.8524305555556 19 170 208.164930555556 140.916666666667 67.2482638888889 -38.1649305555555 20 170 209.352430555556 143.166666666667 66.1857638888889 -39.3524305555556 21 158 162.231597222222 145.708333333333 16.5232638888889 -4.23159722222223 22 133 125.823263888889 148.416666666667 -22.5934027777778 7.17673611111113 23 114 94.2440972222222 151.541666666667 -57.2975694444444 19.7559027777778 24 140 123.556597222222 154.708333333333 -31.1517361111111 16.4434027777778 25 145 132.989930555556 157.125 -24.1350694444445 12.0100694444445 26 150 125.927430555556 159.541666666667 -33.6142361111111 24.0725694444444 27 178 163.144097222222 161.833333333333 1.31076388888889 14.8559027777778 28 163 155.273263888889 164.125 -8.8517361111111 7.72673611111114 29 172 160.939930555556 166.666666666667 -5.72673611111111 11.0600694444445 30 178 201.185763888889 169.083333333333 32.1024305555556 -23.1857638888889 31 199 238.498263888889 171.25 67.2482638888889 -39.4982638888889 32 199 239.769097222222 173.583333333333 66.1857638888889 -40.7690972222222 33 184 191.981597222222 175.458333333333 16.5232638888889 -7.9815972222222 34 162 154.239930555556 176.833333333333 -22.5934027777778 7.76006944444444 35 146 120.744097222222 178.041666666667 -57.2975694444444 25.2559027777778 36 166 149.014930555556 180.166666666667 -31.1517361111111 16.9850694444444 37 171 158.989930555556 183.125 -24.1350694444445 12.0100694444445 38 180 152.594097222222 186.208333333333 -33.6142361111111 27.4059027777778 39 193 190.352430555556 189.041666666667 1.31076388888889 2.64756944444449 40 181 182.439930555556 191.291666666667 -8.8517361111111 -1.43993055555552 41 183 187.856597222222 193.583333333333 -5.72673611111111 -4.85659722222226 42 218 227.935763888889 195.833333333333 32.1024305555556 -9.93576388888886 43 230 265.289930555556 198.041666666667 67.2482638888889 -35.2899305555555 44 242 265.935763888889 199.75 66.1857638888889 -23.9357638888889 45 209 218.731597222222 202.208333333333 16.5232638888889 -9.7315972222222 46 191 183.656597222222 206.25 -22.5934027777778 7.34340277777784 47 172 153.119097222222 210.416666666667 -57.2975694444444 18.8809027777778 48 194 182.223263888889 213.375 -31.1517361111111 11.7767361111111 49 196 191.698263888889 215.833333333333 -24.1350694444445 4.30173611111118 50 196 184.885763888889 218.5 -33.6142361111111 11.1142361111111 51 236 222.227430555556 220.916666666667 1.31076388888889 13.7725694444445 52 235 214.064930555556 222.916666666667 -8.8517361111111 20.9350694444444 53 229 218.356597222222 224.083333333333 -5.72673611111111 10.6434027777778 54 243 256.810763888889 224.708333333333 32.1024305555556 -13.8107638888889 55 264 292.581597222222 225.333333333333 67.2482638888889 -28.5815972222222 56 272 291.519097222222 225.333333333333 66.1857638888889 -19.5190972222222 57 237 241.481597222222 224.958333333333 16.5232638888889 -4.48159722222223 58 211 201.989930555556 224.583333333333 -22.5934027777778 9.01006944444447 59 180 167.160763888889 224.458333333333 -57.2975694444444 12.8392361111111 60 201 194.389930555556 225.541666666667 -31.1517361111111 6.61006944444443 61 204 203.864930555556 228 -24.1350694444445 0.135069444444468 62 188 196.844097222222 230.458333333333 -33.6142361111111 -8.84409722222225 63 235 233.560763888889 232.25 1.31076388888889 1.43923611111111 64 227 225.064930555556 233.916666666667 -8.8517361111111 1.93506944444445 65 234 229.898263888889 235.625 -5.72673611111111 4.10173611111114 66 264 269.852430555556 237.75 32.1024305555556 -5.85243055555551 67 302 307.748263888889 240.5 67.2482638888889 -5.74826388888886 68 293 310.144097222222 243.958333333333 66.1857638888889 -17.1440972222222 69 259 263.689930555556 247.166666666667 16.5232638888889 -4.68993055555549 70 229 227.656597222222 250.25 -22.5934027777778 1.34340277777778 71 203 196.202430555556 253.5 -57.2975694444444 6.79756944444443 72 229 225.973263888889 257.125 -31.1517361111111 3.02673611111112 73 242 237.698263888889 261.833333333333 -24.1350694444445 4.30173611111115 74 233 233.052430555556 266.666666666667 -33.6142361111111 -0.0524305555555316 75 267 272.435763888889 271.125 1.31076388888889 -5.43576388888891 76 269 266.356597222222 275.208333333333 -8.8517361111111 2.64340277777779 77 270 272.773263888889 278.5 -5.72673611111111 -2.77326388888883 78 315 314.060763888889 281.958333333333 32.1024305555556 0.939236111111143 79 364 352.998263888889 285.75 67.2482638888889 11.0017361111111 80 347 355.519097222222 289.333333333333 66.1857638888889 -8.51909722222223 81 312 309.773263888889 293.25 16.5232638888889 2.22673611111111 82 274 274.573263888889 297.166666666667 -22.5934027777778 -0.573263888888846 83 237 243.702430555556 301 -57.2975694444444 -6.70243055555557 84 278 274.306597222222 305.458333333333 -31.1517361111111 3.69340277777781 85 284 285.823263888889 309.958333333333 -24.1350694444445 -1.82326388888890 86 277 280.802430555556 314.416666666667 -33.6142361111111 -3.80243055555553 87 317 319.935763888889 318.625 1.31076388888889 -2.93576388888891 88 313 312.898263888889 321.75 -8.8517361111111 0.101736111111052 89 318 318.773263888889 324.5 -5.72673611111111 -0.773263888888891 90 374 359.185763888889 327.083333333333 32.1024305555556 14.8142361111111 91 413 396.789930555556 329.541666666667 67.2482638888889 16.2100694444445 92 405 398.019097222222 331.833333333333 66.1857638888889 6.98090277777777 93 355 350.981597222222 334.458333333333 16.5232638888889 4.01840277777779 94 306 314.948263888889 337.541666666667 -22.5934027777778 -8.94826388888896 95 271 283.244097222222 340.541666666667 -57.2975694444444 -12.2440972222223 96 306 312.931597222222 344.083333333333 -31.1517361111111 -6.9315972222222 97 315 324.114930555556 348.25 -24.1350694444445 -9.11493055555553 98 301 319.385763888889 353 -33.6142361111111 -18.3857638888888 99 356 358.935763888889 357.625 1.31076388888889 -2.93576388888891 100 348 352.523263888889 361.375 -8.8517361111111 -4.52326388888883 101 355 358.773263888889 364.5 -5.72673611111111 -3.77326388888889 102 422 399.269097222222 367.166666666667 32.1024305555556 22.7309027777778 103 465 436.706597222222 369.458333333333 67.2482638888889 28.2934027777778 104 467 437.394097222222 371.208333333333 66.1857638888889 29.6059027777778 105 404 388.689930555555 372.166666666667 16.5232638888889 15.3100694444445 106 347 349.823263888889 372.416666666667 -22.5934027777778 -2.82326388888885 107 305 315.452430555556 372.75 -57.2975694444444 -10.4524305555556 108 336 342.473263888889 373.625 -31.1517361111111 -6.47326388888888 109 340 351.114930555556 375.25 -24.1350694444445 -11.1149305555556 110 318 344.302430555556 377.916666666667 -33.6142361111111 -26.3024305555556 111 362 380.810763888889 379.5 1.31076388888889 -18.8107638888889 112 348 371.148263888889 380 -8.8517361111111 -23.1482638888889 113 363 374.981597222222 380.708333333333 -5.72673611111111 -11.9815972222222 114 435 413.060763888889 380.958333333333 32.1024305555556 21.9392361111112 115 491 449.081597222222 381.833333333333 67.2482638888889 41.9184027777777 116 505 449.852430555556 383.666666666667 66.1857638888889 55.1475694444444 117 404 403.023263888889 386.5 16.5232638888889 0.976736111111165 118 359 367.739930555556 390.333333333333 -22.5934027777778 -8.73993055555553 119 310 337.410763888889 394.708333333333 -57.2975694444444 -27.4107638888889 120 337 367.473263888889 398.625 -31.1517361111111 -30.4732638888889 121 360 378.406597222222 402.541666666667 -24.1350694444445 -18.4065972222222 122 342 373.552430555556 407.166666666667 -33.6142361111111 -31.5524305555556 123 406 413.185763888889 411.875 1.31076388888889 -7.18576388888886 124 396 407.481597222222 416.333333333333 -8.8517361111111 -11.4815972222221 125 420 414.773263888889 420.5 -5.72673611111111 5.22673611111117 126 472 457.602430555556 425.5 32.1024305555556 14.3975694444445 127 548 497.956597222222 430.708333333333 67.2482638888889 50.0434027777778 128 559 501.310763888889 435.125 66.1857638888889 57.6892361111111 129 463 454.231597222222 437.708333333333 16.5232638888889 8.7684027777778 130 407 418.364930555555 440.958333333333 -22.5934027777778 -11.3649305555555 131 362 388.535763888889 445.833333333333 -57.2975694444444 -26.5357638888888 132 405 419.473263888889 450.625 -31.1517361111111 -14.4732638888889 133 417 432.198263888889 456.333333333333 -24.1350694444445 -15.1982638888888 134 391 427.760763888889 461.375 -33.6142361111111 -36.7607638888889 135 419 466.519097222222 465.208333333333 1.31076388888889 -47.5190972222222 136 461 460.481597222222 469.333333333333 -8.8517361111111 0.518402777777794 137 472 467.023263888889 472.75 -5.72673611111111 4.97673611111117 138 535 507.144097222222 475.041666666667 32.1024305555556 27.8559027777778 139 622 NA NA 67.2482638888889 NA 140 606 NA NA 66.1857638888889 NA 141 508 NA NA 16.5232638888889 NA 142 461 NA NA -22.5934027777778 NA 143 390 NA NA -57.2975694444444 NA 144 432 NA NA -31.1517361111111 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209409168ipiiju291odhncd/1u5s31209409131.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209409168ipiiju291odhncd/1u5s31209409131.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209409168ipiiju291odhncd/2sz0i1209409131.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209409168ipiiju291odhncd/2sz0i1209409131.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209409168ipiiju291odhncd/3j9j91209409131.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209409168ipiiju291odhncd/3j9j91209409131.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209409168ipiiju291odhncd/4q7ac1209409131.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209409168ipiiju291odhncd/4q7ac1209409131.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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) a<-table.element(a,m$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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