*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 15 May 2011 10:05:37 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/15/t1305453699gzahfw7vvryu9u8.htm/, Retrieved Sun, 15 May 2011 12:01:43 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

2851 2672 2755 2721 2946 3036 2282 2212 2922 4301 5764 7132 2541 2475 3031 3266 3776 3230 3028 1759 3595 4474 6838 8357 3113 3006 4047 3523 3937 3986 3260 1573 3528 5211 7614 9254 5375 3088 3718 4514 4520 4539 3663 1643 4734 5428 8314 10651 3633 4292 4154 4121 4647 4753 3965 1723 5048 6923 9858 11331 4016 3957 4510 4276 4968 4677 3523 1821 5222 6872 10803 13916 2639 2899 3370 3740 2927 3986 4217 1738 5221 6424 9842 13076 3934 3162 4286 4676 5010 4874 4633 1659 5951 6981 9851 12670

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 'Gwilym Jenkins' @ www.wessa.org Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 2851 NA NA 0.7550230268283 NA 2 2672 NA NA 0.682232053480883 NA 3 2755 NA NA 0.809315043541521 NA 4 2721 NA NA 0.831053012578926 NA 5 2946 NA NA 0.875495129494772 NA 6 3036 NA NA 0.867044777449368 NA 7 2282 2521.62589636132 3453.25 0.730218170234221 0.904971670576868 8 2212 1353.78092227632 3432.125 0.394443944284174 1.63394236364376 9 2922 3133.32607550937 3435.41666666667 0.912065807304123 0.932555351592311 10 4301 4143.42858580034 3469.625 1.19420069483023 1.0380292337461 11 5764 6213.26853736786 3526.91666666667 1.76167148945997 0.92769207790298 12 7132 7807.52420764552 3569.58333333333 2.18723685051351 0.913477795306224 13 2541 2724.68934806663 3608.75 0.7550230268283 0.932583379387097 14 2475 2470.33383931871 3620.95833333333 0.682232053480883 1.00188887858273 15 3031 2937.91477243616 3630.125 0.809315043541521 1.03168411433755 16 3266 3046.12093598148 3665.375 0.831053012578926 1.07218330087334 17 3776 3254.5072280419 3717.33333333333 0.875495129494772 1.16023709133744 18 3230 3306.15011701162 3813.125 0.867044777449368 0.976967132671988 19 3028 2839.08824587065 3888 0.730218170234221 1.06653958516581 20 1759 1551.72604164959 3933.95833333333 0.394443944284174 1.13357638705996 21 3595 3646.81912502159 3998.41666666667 0.912065807304123 0.985790596340232 22 4474 4838.2543567424 4051.45833333333 1.19420069483023 0.92471368186032 23 6838 7168.02108167644 4068.875 1.76167148945997 0.953959247898968 24 8357 8983.16401479653 4107.08333333333 2.18723685051351 0.930295827420589 25 3113 3132.02427104049 4148.25 0.7550230268283 0.993925886457396 26 3006 2831.37672728791 4150.16666666667 0.682232053480883 1.06167433355976 27 4047 3350.26078712057 4139.625 0.809315043541521 1.20796566510819 28 3523 3463.44805713153 4167.54166666667 0.831053012578926 1.01719440912239 29 3937 3703.85510325509 4230.58333333333 0.875495129494772 1.06294654899972 30 3986 3728.54543109237 4300.29166666667 0.867044777449368 1.06904959954644 31 3260 3236.26607896388 4431.91666666667 0.730218170234221 1.00733373599606 32 1573 1786.66671596386 4529.58333333333 0.394443944284174 0.880410423469165 33 3528 4121.89140240113 4519.29166666667 0.912065807304123 0.855917746388183 34 5211 5429.88128430621 4546.875 1.19420069483023 0.959689489908586 35 7614 8125.63634215539 4612.45833333333 1.76167148945997 0.937034304685647 36 9254 10192.0680490491 4659.79166666667 2.18723685051351 0.907960970773088 37 5375 3548.32509245795 4699.625 0.7550230268283 1.51479919678856 38 3088 3219.68047106078 4719.33333333333 0.682232053480883 0.959101385294487 39 3718 3862.45604530191 4772.5 0.809315043541521 0.962599951013652 40 4514 4015.47502073708 4831.79166666667 0.831053012578926 1.12415093524138 41 4520 4263.66128063954 4870 0.875495129494772 1.06012173634065 42 4539 4298.26610360806 4957.375 0.867044777449368 1.05600721095184 43 3663 3609.46841546776 4943 0.730218170234221 1.01483087767241 44 1643 1940.89429817897 4920.58333333333 0.394443944284174 0.84651699041083 45 4734 4550.22030715633 4988.91666666667 0.912065807304123 1.04038918567407 46 5428 5959.9073593617 4990.70833333333 1.19420069483023 0.910752411524286 47 8314 8772.4633907021 4979.625 1.76167148945997 0.947738352355163 48 10651 10922.6962919894 4993.83333333333 2.18723685051351 0.975125529015336 49 3633 3786.6921538862 5015.33333333333 0.7550230268283 0.959412556489847 50 4292 3432.48001907569 5031.25 0.682232053480883 1.25040786141437 51 4154 4085.15256811642 5047.66666666667 0.809315043541521 1.01685308705994 52 4121 4257.5192106507 5123.04166666667 0.831053012578926 0.967934563792648 53 4647 4596.05759813772 5249.66666666667 0.875495129494772 1.01108393460581 54 4753 4632.04221606034 5342.33333333333 0.867044777449368 1.02611327321678 55 3965 3933.41145123791 5386.625 0.730218170234221 1.00803082747729 56 1723 2125.51049926831 5388.625 0.394443944284174 0.81062878804557 57 5048 4915.57866846557 5389.5 0.912065807304123 1.02693911347283 58 6923 6461.57116791496 5410.79166666667 1.19420069483023 1.07141124350317 59 9858 9566.97723244855 5430.625 1.76167148945997 1.03041951083195 60 11331 11900.3911641689 5440.83333333333 2.18723685051351 0.952153575767886 61 4016 4091.65853813926 5419.25 0.7550230268283 0.981509078180882 62 3957 3687.40739639305 5404.91666666667 0.682232053480883 1.07311169464775 63 4510 4383.45260458176 5416.25 0.809315043541521 1.02886934269255 64 4276 4505.45002607007 5421.375 0.831053012578926 0.949072784129799 65 4968 4778.9996012384 5458.625 0.875495129494772 1.03954810933917 66 4677 4860.40013432107 5605.70833333333 0.867044777449368 0.962266453532084 67 3523 4130.14439660185 5656.04166666667 0.730218170234221 0.852996811176532 68 1821 2190.97175885513 5554.58333333333 0.394443944284174 0.831138052163457 69 5222 4982.61550530242 5463 0.912065807304123 1.04804394287354 70 6872 6440.52338066857 5393.16666666667 1.19420069483023 1.06699403042717 71 10803 9311.82847839177 5285.79166666667 1.76167148945997 1.16013734843468 72 13916 11312.2978559871 5171.95833333333 2.18723685051351 1.2301656283418 73 2639 3905.04201334153 5172.08333333333 0.7550230268283 0.675792985320999 74 2899 3545.92952430245 5197.54166666667 0.682232053480883 0.817557139850456 75 3370 4203.61605761481 5194.04166666667 0.809315043541521 0.801690723846028 76 3740 4300.9763577668 5175.33333333333 0.831053012578926 0.869569997344027 77 2927 4479.58026695118 5116.625 0.875495129494772 0.653409432485094 78 3986 4371.27849924244 5041.58333333333 0.867044777449368 0.911861369777924 79 4217 3695.29947622737 5060.54166666667 0.730218170234221 1.14117949766422 80 1738 2021.70600126419 5125.45833333333 0.394443944284174 0.859670000936444 81 5221 4719.56052537913 5174.58333333333 0.912065807304123 1.10624706938801 82 6424 6271.64349907467 5251.75 1.19420069483023 1.02429291475955 83 9842 9473.46183754972 5377.54166666667 1.76167148945997 1.0389021636198 84 13076 12032.718993625 5501.33333333333 2.18723685051351 1.08670367910426 85 3934 4194.65626271575 5555.66666666667 0.7550230268283 0.937859923104403 86 3162 3799.83355353958 5569.70833333333 0.682232053480883 0.832141712379629 87 4286 4529.6014128613 5596.83333333333 0.809315043541521 0.946220121671275 88 4676 4695.83042036836 5650.45833333333 0.831053012578926 0.995777015225603 89 5010 4967.59584371706 5674.04166666667 0.875495129494772 1.00853615262131 90 4874 4905.3058284198 5657.5 0.867044777449368 0.993617966032123 91 4633 NA NA 0.730218170234221 NA 92 1659 NA NA 0.394443944284174 NA 93 5951 NA NA 0.912065807304123 NA 94 6981 NA NA 1.19420069483023 NA 95 9851 NA NA 1.76167148945997 NA 96 12670 NA NA 2.18723685051351 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/15/t1305453699gzahfw7vvryu9u8/187ph1305453934.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/15/t1305453699gzahfw7vvryu9u8/187ph1305453934.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/15/t1305453699gzahfw7vvryu9u8/2ku6v1305453934.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/15/t1305453699gzahfw7vvryu9u8/2ku6v1305453934.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/15/t1305453699gzahfw7vvryu9u8/3sysz1305453934.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/15/t1305453699gzahfw7vvryu9u8/3sysz1305453934.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/15/t1305453699gzahfw7vvryu9u8/44ruz1305453934.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/15/t1305453699gzahfw7vvryu9u8/44ruz1305453934.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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