*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 01 Jun 2010 20:54:10 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jun/01/t1275425693ysem4j2clwvc6qv.htm/, Retrieved Tue, 01 Jun 2010 22:54:53 +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/2010/Jun/01/t1275425693ysem4j2clwvc6qv.htm/}, year = {2010}, } @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 = {2010}, 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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

225 243 270 289 273 274 220 271 288 276 266 239 201 242 239 273 280 294 212 264 272 262 238 227 250 245 270 288 298 281 218 284 281 277 276 222 255 267 261 263 264 278 248 320 305 301 274 220 235 252 272 280 305 299 246 307 325 302 274 251 272 253 292 288 258 295 231 250 268

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 225 NA NA 0.893334388469352 NA 2 243 NA NA 0.952782482773724 NA 3 270 NA NA 0.98359741725026 NA 4 289 NA NA 1.04095847967717 NA 5 273 NA NA 1.07965389241574 NA 6 274 NA NA 1.08438888551018 NA 7 220 225.028968097874 260.166666666667 0.864941581413994 0.977651907928197 8 271 284.140126121922 259.125 1.09653690736873 0.953754767757497 9 288 283.798178133833 257.791666666667 1.10088189352061 1.01480566892218 10 276 271.294433491970 255.833333333333 1.06043426772106 1.01734486936375 11 266 252.082373275454 255.458333333333 0.986784693950562 1.05521063033367 12 239 219.559669455852 256.583333333333 0.855705109928623 1.08854235658274 13 201 229.661382368996 257.083333333333 0.893334388469352 0.875201559472695 14 242 244.349007561345 256.458333333333 0.952782482773724 0.990386670341786 15 239 251.309140107441 255.5 0.98359741725026 0.951019926684008 16 273 264.663693457922 254.25 1.04095847967717 1.03149773372071 17 280 272.612607834974 252.5 1.07965389241574 1.02709849784166 18 294 272.000878782137 250.833333333333 1.08438888551018 1.08087886081973 19 212 218.289631609357 252.375 0.864941581413994 0.971186759705507 20 264 279.114331963149 254.541666666667 1.09653690736873 0.945848957820107 21 272 281.779894662379 255.958333333333 1.10088189352061 0.965292432683685 22 262 273.459486788568 257.875 1.06043426772106 0.958094389325656 23 238 255.823931906683 259.25 0.986784693950562 0.930327347508736 24 227 222.019821646897 259.458333333333 0.855705109928623 1.02243123301407 25 250 231.522495678307 259.166666666667 0.893334388469352 1.07980867806197 26 245 247.961641141862 260.25 0.952782482773724 0.988056051217344 27 270 257.169741385224 261.458333333333 0.98359741725026 1.04989023415300 28 288 273.208227645272 262.458333333333 1.04095847967717 1.05414102086974 29 298 285.748396859365 264.666666666667 1.07965389241574 1.04287549212976 30 281 288.492626415937 266.041666666667 1.08438888551018 0.974028360762557 31 218 230.110499888681 266.041666666667 0.864941581413994 0.947370937464653 32 284 292.958110418679 267.166666666667 1.09653690736873 0.969421872615587 33 281 294.715256911246 267.708333333333 1.10088189352061 0.95346268443993 34 277 282.384808541886 266.291666666667 1.06043426772106 0.980930955281585 35 276 260.34669508729 263.833333333333 0.986784693950562 1.06012484586164 36 222 224.444319458362 262.291666666667 0.855705109928623 0.989109461695175 37 255 235.319166829302 263.416666666667 0.893334388469352 1.08363463731356 38 267 253.598937498273 266.166666666667 0.952782482773724 1.05284352779206 39 261 264.259839434570 268.666666666667 0.98359741725026 0.987664264681517 40 263 281.752761832622 270.666666666667 1.04095847967717 0.93344249152822 41 264 293.216002948574 271.583333333333 1.07965389241574 0.90036013500362 42 278 294.321216675554 271.416666666667 1.08438888551018 0.944546244882013 43 248 233.966697772485 270.5 0.864941581413994 1.05997991321466 44 320 295.014117119996 269.041666666667 1.09653690736873 1.08469385507352 45 305 295.999619120353 268.875 1.10088189352061 1.030406731287 46 301 286.36143704584 270.041666666667 1.06043426772106 1.05111918387187 47 274 268.857713072614 272.458333333333 0.986784693950562 1.01912642515857 48 220 235.354559609952 275.041666666667 0.855705109928623 0.934759880431472 49 235 246.411402152796 275.833333333333 0.893334388469352 0.953689634273823 50 252 262.213679113352 275.208333333333 0.952782482773724 0.961048259770854 51 272 270.981088452446 275.5 0.98359741725026 1.00376008360352 52 280 287.694899820779 276.375 1.04095847967717 0.973253263003367 53 305 298.43433009525 276.416666666667 1.07965389241574 1.02200038414701 54 299 301.143830080223 277.708333333333 1.08438888551018 0.992881042657751 55 246 242.652152819184 280.541666666667 0.864941581413994 1.01379689873722 56 307 309.360474991403 282.125 1.09653690736873 0.99236982361283 57 325 311.549575866332 283 1.10088189352061 1.04317266071143 58 302 301.3400710774 284.166666666667 1.06043426772106 1.00218998064293 59 274 278.807792069948 282.541666666667 0.986784693950562 0.982755890593107 60 251 239.953974575818 280.416666666667 0.855705109928623 1.04603393398133 61 272 NA 279.625 NA NA 62 253 NA 276.625 NA NA 63 292 NA 271.875 NA NA 64 288 NA NA NA NA 65 258 NA NA NA NA 66 295 NA NA NA NA 67 231 NA NA NA NA 68 250 NA NA NA NA 69 268 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/01/t1275425693ysem4j2clwvc6qv/1yyhf1275425647.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275425693ysem4j2clwvc6qv/1yyhf1275425647.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275425693ysem4j2clwvc6qv/2yyhf1275425647.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275425693ysem4j2clwvc6qv/2yyhf1275425647.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275425693ysem4j2clwvc6qv/3qqzi1275425647.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275425693ysem4j2clwvc6qv/3qqzi1275425647.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275425693ysem4j2clwvc6qv/4qqzi1275425647.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275425693ysem4j2clwvc6qv/4qqzi1275425647.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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