Sarah Geerts - decompositie - eigen reeks

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 20 May 2011 04:04:52 +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/20/t130586409455k8fosbi2azc55.htm/, Retrieved Fri, 20 May 2011 06:01:38 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

31.514 27.071 29.462 26.105 22.397 23.843 21.705 18.089 20.764 25.316 17.704 15.548 28.029 29.383 36.438 32.034 22.679 24.319 18.004 17.537 20.366 22.782 19.169 13.807 29.743 25.591 29.096 26.482 22.405 27.044 17.970 18.730 19.684 19.785 18.479 10.698 31.956 29.506 34.506 27.165 26.736 23.691 18.157 17.328 18.205 20.995 17.382 9.367 31.124 26.551 30.651 25.859 25.100 25.778 20.418 18.688 20.424 24.776 19.814 12.738 31.566 30.111 30.019 31.934 25.826 26.835 20.205 17.789 20.520 22.518 15.572 11.509 25.447 24.090 27.786 26.195 20.516 22.759 19.028 16.971 20.036 22.485 18.730 14.538 27.561 25.985 34.670 32.066 27.186 29.586 21.359 21.553 19.573 24.256 22.380 16.167 27.297 28.287

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 31.514 NA NA 1.2605490810558 NA 2 27.071 NA NA 1.17093781869857 NA 3 29.462 NA NA 1.36524305933105 NA 4 26.105 NA NA 1.23380943922305 NA 5 22.397 NA NA 1.04174825738425 NA 6 23.843 NA NA 1.09954886097311 NA 7 21.705 19.1231152860426 23.1479583333333 0.826125354584947 1.13501381314382 8 18.089 18.1475675475694 23.0990833333333 0.785640160940126 0.996772705354816 9 20.764 20.5348637535887 23.4860833333333 0.874341773472462 1.0111584011056 10 25.316 22.5882170097483 24.0237916666667 0.940243626949603 1.12076132388291 11 17.704 19.4922144325757 24.2825833333333 0.80272408273045 0.908260067692092 12 15.548 14.5663372640209 24.3141666666667 0.599088484656581 1.06739255848509 13 28.029 30.4798141655374 24.1797916666667 1.2605490810558 0.919592220863719 14 29.383 28.105532571464 24.0025833333333 1.17093781869857 1.04545252523815 15 36.438 32.7153194307499 23.963 1.36524305933105 1.11379013361401 16 32.034 29.4150452056102 23.8408333333333 1.23380943922305 1.08903453236544 17 22.679 24.7897453759573 23.7962916666667 1.04174825738425 0.914854092127768 18 24.319 26.152540585566 23.7847916666667 1.09954886097311 0.929890536654862 19 18.004 19.6482900583302 23.7836666666667 0.826125354584947 0.91631383425994 20 17.537 18.6173803638116 23.6970833333333 0.785640160940126 0.941969259761614 21 20.366 20.3137281467146 23.2331666666667 0.874341773472462 1.00257322796228 22 22.782 21.3396910036126 22.6959166666667 0.940243626949603 1.06758809188677 23 19.169 18.0236976168939 22.4531666666667 0.80272408273045 1.06354425198704 24 13.807 13.5126155055706 22.5552916666667 0.599088484656581 1.02178590031723 25 29.743 28.5733912490756 22.6674166666667 1.2605490810558 1.04093349440845 26 25.591 26.5986819660263 22.7157083333333 1.17093781869857 0.962115342131863 27 29.096 31.0415314400101 22.737 1.36524305933105 0.937324888632833 28 26.482 27.863992514327 22.5837083333333 1.23380943922305 0.950402207665812 29 22.405 23.3665002254835 22.4300833333333 1.04174825738425 0.958851337761104 30 27.044 24.4889231589137 22.2717916666667 1.09954886097311 1.10433602263791 31 17.97 18.3684497746292 22.2344583333333 0.826125354584947 0.978307925844696 32 18.73 17.6688835445099 22.4897916666667 0.785640160940126 1.06005565959032 33 19.684 20.0034825407608 22.8783333333333 0.874341773472462 0.984028654005132 34 19.785 21.7499114626872 23.1322083333333 0.940243626949603 0.90965887534494 35 18.479 18.7364831555218 23.341125 0.80272408273045 0.98625765820701 36 10.698 14.0078120621796 23.381875 0.599088484656581 0.763716699832379 37 31.956 29.307713611669 23.2499583333333 1.2605490810558 1.09036141213269 38 29.506 27.1649767685944 23.1993333333333 1.17093781869857 1.086177994973 39 34.506 31.5088427621936 23.0792916666667 1.36524305933105 1.0951211461629 40 27.165 28.4616189614506 23.0680833333333 1.23380943922305 0.95444324642225 41 26.736 24.0360405117398 23.0727916666667 1.04174825738425 1.11232962795771 42 23.691 25.2584241034513 22.971625 1.09954886097311 0.937944501326305 43 18.157 18.9029873009355 22.8815 0.826125354584947 0.960536010046489 44 17.328 17.8526578721565 22.7237083333333 0.785640160940126 0.970611778038118 45 18.205 19.6201929658148 22.4399583333333 0.874341773472462 0.927870588822415 46 20.995 20.8968362553193 22.2249166666667 0.940243626949603 1.00469754098091 47 17.382 17.7420752512026 22.1023333333333 0.80272408273045 0.979705009357446 48 9.367 13.2525112551488 22.121125 0.599088484656581 0.706809435559677 49 31.124 28.1131332658551 22.3022916666667 1.2605490810558 1.1070982272119 50 26.551 26.2912619995421 22.4531666666667 1.17093781869857 1.00987925191504 51 30.651 30.8576218228927 22.6022916666667 1.36524305933105 0.993304026341415 52 25.859 28.1953731662116 22.8522916666667 1.23380943922305 0.917136292098753 53 25.1 24.076017601117 23.1111666666667 1.04174825738425 1.04253121989891 54 25.778 25.6777187357691 23.3529583333333 1.09954886097311 1.00390538058551 55 20.418 19.4237216494422 23.5118333333333 0.826125354584947 1.05118886938881 56 18.688 18.6028460208342 23.6785833333333 0.785640160940126 1.00457747051556 57 20.424 20.8098442413458 23.8005833333333 0.874341773472462 0.98145857139194 58 24.776 22.5915862160782 24.027375 0.940243626949603 1.09669147456176 59 19.814 19.5148244942393 24.31075 0.80272408273045 1.01533067877956 60 12.738 14.6087976603709 24.3850416666667 0.599088484656581 0.871940340070161 61 31.566 30.7828711737746 24.4202083333333 1.2605490810558 1.02544040878463 62 30.111 28.5402920257316 24.373875 1.17093781869857 1.05503475482494 63 30.019 33.2305849153924 24.3404166666667 1.36524305933105 0.903354547517915 64 31.934 29.9202901709721 24.2503333333333 1.23380943922305 1.06730248328212 65 25.826 24.9806023379456 23.9795 1.04174825738425 1.03384216483724 66 26.835 26.1159805859386 23.7515416666667 1.09954886097311 1.02753177931402 67 20.205 19.3688187352521 23.445375 0.826125354584947 1.04317151583571 68 17.789 18.0222252068927 22.9395416666667 0.785640160940126 0.987059022722481 69 20.52 19.7562988352187 22.595625 0.874341773472462 1.03865608488468 70 22.518 20.9330748117747 22.2634583333333 0.940243626949603 1.07571392174712 71 15.572 17.5018600694456 21.8030833333333 0.80272408273045 0.88973400188391 72 11.509 12.8276826334667 21.412 0.599088484656581 0.897200244880837 73 25.447 26.7149742434508 21.193125 1.2605490810558 0.952536946811333 74 24.09 24.7184973527268 21.11 1.17093781869857 0.9745738042342 75 27.786 28.7462165465097 21.05575 1.36524305933105 0.96659676778834 76 26.195 25.9522047882508 21.0342083333333 1.23380943922305 1.00935547533361 77 20.516 22.0479941810542 21.1644166666667 1.04174825738425 0.93051548506074 78 22.759 23.5547647724453 21.4222083333333 1.09954886097311 0.966216399096621 79 19.028 17.8744612344772 21.6365 0.826125354584947 1.06453558238151 80 16.971 17.1297379840647 21.8035416666667 0.785640160940126 0.990733192520959 81 20.036 19.3835742233688 22.1693333333333 0.874341773472462 1.03365869313434 82 22.485 21.344274691294 22.7007916666667 0.940243626949603 1.05344408864693 83 18.73 18.6419289479435 23.2233333333333 0.80272408273045 1.00472435295202 84 14.538 14.2497439619001 23.7857083333333 0.599088484656581 1.02022885736548 85 27.561 30.4640573020242 24.1672916666667 1.2605490810558 0.904705493649681 86 25.985 28.6356746688798 24.4553333333333 1.17093781869857 0.9074345305452 87 34.67 33.6217839370183 24.6269583333333 1.36524305933105 1.03117669380498 88 32.066 30.4522162654571 24.6814583333333 1.23380943922305 1.05299396669442 89 27.186 25.9471710960886 24.9073333333333 1.04174825738425 1.04774427621892 90 29.586 27.6286849314224 25.1272916666667 1.09954886097311 1.07084358424717 91 21.359 20.8052786174264 25.1841666666667 0.826125354584947 1.02661446610524 92 21.553 19.8524066968095 25.2690833333333 0.785640160940126 1.08566182071335 93 19.573 NA NA 0.874341773472462 NA 94 24.256 NA NA 0.940243626949603 NA 95 22.38 NA NA 0.80272408273045 NA 96 16.167 NA NA 0.599088484656581 NA 97 27.297 NA NA NA NA 98 28.287 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/20/t130586409455k8fosbi2azc55/1ombj1305864290.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t130586409455k8fosbi2azc55/1ombj1305864290.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t130586409455k8fosbi2azc55/266451305864290.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t130586409455k8fosbi2azc55/266451305864290.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t130586409455k8fosbi2azc55/3n4681305864290.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t130586409455k8fosbi2azc55/3n4681305864290.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t130586409455k8fosbi2azc55/4frb81305864290.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t130586409455k8fosbi2azc55/4frb81305864290.ps (open in new window)

Parameters (Session):

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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