C decomposition-aantal geboortes per maand (2000-2006)-Olivier Percy

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 06 Jun 2009 08:00:47 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/06/t1244296977sg4kt37ezzi1evn.htm/, Retrieved Sat, 06 Jun 2009 16:02:57 +0200

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/2009/Jun/06/t1244296977sg4kt37ezzi1evn.htm/}, year = {2009}, } @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 = {2009}, 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 «

9.733 9.259 9.864 9.215 10.103 9.380 9.896 10.117 9.451 9.700 9.081 9.084 9.743 8.587 9.731 9.563 9.998 9.437 10.038 9.918 9.252 9.737 9.035 9.133 9.487 8.700 9.627 8.947 9.283 8.829 9.947 9.628 9.318 9.605 8.640 9.214 9.567 8.547 9.185 9.470 9.123 9.278 10.170 9.434 9.655 9.429 8.739 9.552 9.687 9.019 9.672 9.206 9.069 9.788 10.312 10.105 9.863 9.656 9.295 9.946 9.701 9.049 10.190 9.706 9.765 9.893 9.994 10.433 10.073 10.112 9.266 9.820 10.097 9.115 10.411 9.678 10.408 10.153 10.368 10.581 10.597 10.680 9.738 9.556

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 9.733 NA NA 1.01632409597119 NA 2 9.259 NA NA 0.92383779191618 NA 3 9.864 NA NA 1.02324064070637 NA 4 9.215 NA NA 0.983182260561294 NA 5 10.103 NA NA 1.00019173350271 NA 6 9.38 NA NA 0.994618561085224 NA 7 9.896 10.113246212993 9.574 1.05632402475381 0.978518646889671 8 10.117 9.95606002529222 9.54641666666667 1.04291069339723 1.01616502655658 9 9.451 9.58286607286857 9.512875 1.00735750999236 0.986239391027084 10 9.7 9.69115220049708 9.52183333333333 1.01778217085265 1.00091297704544 11 9.081 8.9983222169725 9.53195833333333 0.94401610899885 1.00918813319127 12 9.084 9.4367020518045 9.52995833333333 0.990214408262139 0.962624437025956 13 9.743 9.6939533083972 9.53825 1.01632409597119 1.00505951390959 14 8.587 8.8096017039887 9.535875 0.92383779191618 0.974731921888374 15 9.731 9.7405261040708 9.51929166666667 1.02324064070637 0.999022013393423 16 9.563 9.35256221951683 9.51254166666667 0.983182260561294 1.02250054857096 17 9.998 9.5139904677 9.51216666666666 1.00019173350271 1.05087345146531 18 9.437 9.46110185012297 9.51229166666667 0.994618561085224 0.997452532431763 19 10.038 10.0389514232520 9.50366666666667 1.05632402475381 0.999905226829788 20 9.918 9.90526158360133 9.49770833333334 1.04291069339723 1.00128602523933 21 9.252 9.5679655763666 9.49808333333333 1.00735750999236 0.966976723124187 22 9.737 9.63644640881375 9.46808333333333 1.01778217085265 1.01043471700255 23 9.035 8.8856696279653 9.412625 0.94401610899885 1.01680575334072 24 9.133 9.26593132531296 9.3575 0.990214408262139 0.98565375452872 25 9.487 9.48065228875525 9.328375 1.01632409597119 1.00066954372457 26 8.7 8.60323943721943 9.3125 0.92383779191618 1.01124699172755 27 9.627 9.51937822059813 9.30316666666667 1.02324064070637 1.01130554715948 28 8.947 9.14400468249526 9.30041666666666 0.983182260561294 0.978455316971524 29 9.283 9.28023732464931 9.27845833333333 1.00019173350271 1.00029769447203 30 8.829 9.215513950415 9.265375 0.994618561085224 0.958058340262444 31 9.947 9.79432438451941 9.27208333333333 1.05632402475381 1.01558817224003 32 9.628 9.66678267171116 9.26904166666667 1.04291069339723 0.995988047623678 33 9.318 9.31226466174687 9.24425 1.00735750999236 1.00061589081297 34 9.605 9.4120678477312 9.247625 1.01778217085265 1.02049838094987 35 8.64 8.74418521362909 9.26275 0.94401610899885 0.98808520049796 36 9.214 9.18403234196295 9.27479166666667 0.990214408262139 1.00326301747655 37 9.567 9.45465133063332 9.30279166666667 1.01632409597119 1.01188289926702 38 8.547 8.59538681598814 9.304 0.92383779191618 0.994370606346868 39 9.185 9.5263277299496 9.30995833333333 1.02324064070637 0.964170062208074 40 9.47 9.15998139422939 9.31666666666667 0.983182260561294 1.03384489470316 41 9.123 9.3152440353219 9.31345833333333 1.00019173350271 0.979362426298984 42 9.278 9.28144887252695 9.33166666666667 0.994618561085224 0.999628412268998 43 10.17 9.87742187446671 9.35075 1.05632402475381 1.02962089999311 44 9.434 9.7777222967213 9.37541666666667 1.04291069339723 0.964846383821255 45 9.655 9.48464871564431 9.415375 1.00735750999236 1.01796073734125 46 9.429 9.59225769956256 9.42466666666667 1.01778217085265 0.982980263387836 47 8.739 8.88452894183359 9.41141666666667 0.94401610899885 0.983619959731534 48 9.552 9.33813445924874 9.43041666666667 0.990214408262139 1.02290238394880 49 9.687 9.6119698313222 9.45758333333333 1.01632409597119 1.00780590971408 50 9.019 8.7685679087311 9.49145833333333 0.92383779191618 1.02856020434301 51 9.672 9.74952209470367 9.52808333333333 1.02324064070637 0.992048626183864 52 9.206 9.38566268895572 9.54620833333333 0.983182260561294 0.980857751348007 53 9.069 9.58066991660018 9.57883333333333 1.00019173350271 0.946593513704754 54 9.788 9.56665574491814 9.61841666666666 0.994618561085224 1.02313705656226 55 10.312 10.1781221135133 9.63541666666667 1.05632402475381 1.01315349580145 56 10.105 10.0507910799425 9.63725 1.04291069339723 1.00539349784772 57 9.863 9.73115749298536 9.66008333333333 1.00735750999236 1.01354849175031 58 9.656 9.87503151269779 9.7025 1.01778217085265 0.97781966443184 59 9.295 9.20635976699311 9.75233333333333 0.94401610899885 1.00962815219591 60 9.946 9.68994938671754 9.78570833333333 0.990214408262139 1.02642434991801 61 9.701 9.936431298961 9.77683333333333 1.01632409597119 0.976306252025754 62 9.049 9.03259305101247 9.77725 0.92383779191618 1.00181641627104 63 10.19 10.0274171987088 9.79966666666667 1.02324064070637 1.01621382635920 64 9.706 9.66214173381107 9.82741666666667 0.983182260561294 1.00453918679701 65 9.765 9.84709598961197 9.84520833333333 1.00019173350271 0.991662923800218 66 9.893 9.78580336787725 9.83875 0.994618561085224 1.01095430064277 67 9.994 10.4047916438251 9.85 1.05632402475381 0.960518993759106 68 10.433 10.2927463608106 9.86925 1.04291069339723 1.01362645442458 69 10.073 9.95390942238242 9.88120833333334 1.00735750999236 1.01196420145735 70 10.112 10.0651023331045 9.88925 1.01778217085265 1.00465943269561 71 9.266 9.35980171870996 9.914875 0.94401610899885 0.989978236555753 72 9.82 9.85510889822893 9.9525 0.990214408262139 0.9964374926151 73 10.097 10.1418134600218 9.97891666666666 1.01632409597119 0.995581316872127 74 9.115 9.23899381102308 10.0006666666667 0.92383779191618 0.986579294936301 75 10.411 10.2617393054306 10.0286666666667 1.02324064070637 1.01454536020910 76 9.678 9.90474195660456 10.0741666666667 0.983182260561294 0.977107737122483 77 10.408 10.1194398637136 10.1175 1.00019173350271 1.02851542577184 78 10.153 10.0716733193092 10.1261666666667 0.994618561085224 1.00807479334491 79 10.368 NA NA 1.05632402475381 NA 80 10.581 NA NA 1.04291069339723 NA 81 10.597 NA NA 1.00735750999236 NA 82 10.68 NA NA 1.01778217085265 NA 83 9.738 NA NA 0.94401610899885 NA 84 9.556 NA NA 0.990214408262139 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244296977sg4kt37ezzi1evn/1atd31244296843.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244296977sg4kt37ezzi1evn/1atd31244296843.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244296977sg4kt37ezzi1evn/20b391244296843.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244296977sg4kt37ezzi1evn/20b391244296843.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244296977sg4kt37ezzi1evn/33u491244296843.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244296977sg4kt37ezzi1evn/33u491244296843.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244296977sg4kt37ezzi1evn/4tn9v1244296843.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244296977sg4kt37ezzi1evn/4tn9v1244296843.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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