Raf Mattheussen aditief decompositie roze garnalen

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 13 Aug 2008 07:18:00 -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/Aug/13/t1218633544plgbx3mhkz9vly7.htm/, Retrieved Wed, 13 Aug 2008 13:19:04 +0000

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

16.8 16.91 16.91 17.16 17.02 17.23 17.22 17.29 17.3 17.22 17.19 17.23 17.36 17.39 17.29 17.28 17.4 17.51 17.54 17.64 17.65 17.5 17.37 17.56 17.49 17.61 17.79 17.83 17.56 17.95 18.09 18.38 18.38 18.44 18.84 19.01 19.06 19.06 18.97 18.98 19.41 19.55 19.64 19.71 19.48 19.48 19.41 19.25 19.14 19.21 19.3 19.53 19.14 19.16 19.24 19.38 19.27 19.27 19.07 19.15 19.24 19.36 19.57 19.59 19.36 19.46 19.65 19.46 19.51 19.64 19.64 19.69 19.28 19.67 19.65 19.6 19.53 19.64 19.67 19.81 19.73 19.87 19.97 20.12 19.94 20.31 20.13 20.22 20.38 20.44 20.34 20.14 19.97 19.82 19.98 20.12

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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 16.8 NA NA -0.0964641203703716 NA 2 16.91 NA NA -0.00931134259259197 NA 3 16.91 NA NA 0.00131365740740666 NA 4 17.16 NA NA 0.00603587962962814 NA 5 17.02 NA NA -0.100005787037037 NA 6 17.23 NA NA 0.00561921296296422 NA 7 17.22 17.2076331018518 17.1466666666667 0.0609664351851841 0.0123668981481515 8 17.29 17.3044386574074 17.19 0.114438657407408 -0.0144386574074069 9 17.3 17.2402719907407 17.2258333333333 0.0144386574074077 0.0597280092592598 10 17.22 17.2509664351852 17.2466666666667 0.00429976851851979 -0.0309664351851886 11 17.19 17.2473553240741 17.2675 -0.0201446759259255 -0.0573553240740736 12 17.23 17.3138136574074 17.295 0.0188136574074073 -0.0838136574074078 13 17.36 17.2235358796296 17.32 -0.0964641203703716 0.136464120370370 14 17.39 17.3386053240741 17.3479166666667 -0.00931134259259197 0.0513946759259269 15 17.29 17.3783969907407 17.3770833333333 0.00131365740740666 -0.088396990740744 16 17.28 17.4093692129630 17.4033333333333 0.00603587962962814 -0.129369212962960 17 17.4 17.3224942129630 17.4225 -0.100005787037037 0.0775057870370368 18 17.51 17.4493692129630 17.44375 0.00561921296296422 0.060630787037038 19 17.54 17.5238831018518 17.4629166666667 0.0609664351851841 0.0161168981481516 20 17.64 17.5919386574074 17.4775 0.114438657407408 0.0480613425925931 21 17.65 17.5219386574074 17.5075 0.0144386574074077 0.128061342592595 22 17.5 17.5555497685185 17.55125 0.00429976851851979 -0.0555497685185209 23 17.37 17.5606886574074 17.5808333333333 -0.0201446759259255 -0.190688657407406 24 17.56 17.6246469907407 17.6058333333333 0.0188136574074073 -0.0646469907407408 25 17.49 17.5506192129630 17.6470833333333 -0.0964641203703716 -0.0606192129629619 26 17.61 17.6915219907407 17.7008333333333 -0.00931134259259197 -0.081521990740736 27 17.79 17.7633969907407 17.7620833333333 0.00131365740740666 0.0266030092592615 28 17.83 17.8377025462963 17.8316666666667 0.00603587962962814 -0.00770254629629719 29 17.56 17.8320775462963 17.9320833333333 -0.100005787037037 -0.272077546296295 30 17.95 18.0593692129630 18.05375 0.00561921296296422 -0.109369212962964 31 18.09 18.2405497685185 18.1795833333333 0.0609664351851841 -0.150549768518516 32 18.38 18.4198553240741 18.3054166666667 0.114438657407408 -0.0398553240740753 33 18.38 18.4294386574074 18.415 0.0144386574074077 -0.0494386574074070 34 18.44 18.5163831018519 18.5120833333333 0.00429976851851979 -0.076383101851853 35 18.84 18.6169386574074 18.6370833333333 -0.0201446759259255 0.223061342592597 36 19.01 18.7996469907407 18.7808333333333 0.0188136574074073 0.210353009259265 37 19.06 18.8156192129630 18.9120833333333 -0.0964641203703716 0.244380787037038 38 19.06 19.0227719907407 19.0320833333333 -0.00931134259259197 0.0372280092592625 39 18.97 19.1346469907407 19.1333333333333 0.00131365740740666 -0.164646990740739 40 18.98 19.2285358796296 19.2225 0.00603587962962814 -0.248535879629628 41 19.41 19.1895775462963 19.2895833333333 -0.100005787037037 0.220422453703701 42 19.55 19.3289525462963 19.3233333333333 0.00561921296296422 0.221047453703701 43 19.64 19.3976331018518 19.3366666666667 0.0609664351851841 0.242366898148152 44 19.71 19.4606886574074 19.34625 0.114438657407408 0.249311342592595 45 19.48 19.3806886574074 19.36625 0.0144386574074077 0.0993113425925962 46 19.48 19.4072164351852 19.4029166666667 0.00429976851851979 0.0727835648148165 47 19.41 19.3944386574074 19.4145833333333 -0.0201446759259255 0.0155613425925942 48 19.25 19.4058969907407 19.3870833333333 0.0188136574074073 -0.155896990740743 49 19.14 19.2577025462963 19.3541666666667 -0.0964641203703716 -0.117702546296300 50 19.21 19.3144386574074 19.32375 -0.00931134259259197 -0.104438657407407 51 19.3 19.3025636574074 19.30125 0.00131365740740666 -0.0025636574074106 52 19.53 19.2897858796296 19.28375 0.00603587962962814 0.240214120370371 53 19.14 19.1608275462963 19.2608333333333 -0.100005787037037 -0.0208275462962959 54 19.16 19.2481192129630 19.2425 0.00561921296296422 -0.0881192129629653 55 19.24 19.3034664351852 19.2425 0.0609664351851841 -0.0634664351851839 56 19.38 19.3673553240741 19.2529166666667 0.114438657407408 0.0126446759259231 57 19.27 19.2848553240741 19.2704166666667 0.0144386574074077 -0.0148553240740732 58 19.27 19.2884664351852 19.2841666666667 0.00429976851851979 -0.0184664351851858 59 19.07 19.2756886574074 19.2958333333333 -0.0201446759259255 -0.205688657407407 60 19.15 19.3363136574074 19.3175 0.0188136574074073 -0.186313657407403 61 19.24 19.2506192129630 19.3470833333333 -0.0964641203703716 -0.0106192129629612 62 19.36 19.3581886574074 19.3675 -0.00931134259259197 0.00181134259258897 63 19.57 19.3821469907407 19.3808333333333 0.00131365740740666 0.187853009259257 64 19.59 19.4122858796296 19.40625 0.00603587962962814 0.177714120370368 65 19.36 19.3454108796296 19.4454166666667 -0.100005787037037 0.0145891203703741 66 19.46 19.4972858796296 19.4916666666667 0.00561921296296422 -0.0372858796296285 67 19.65 19.5767997685185 19.5158333333333 0.0609664351851841 0.0732002314814828 68 19.46 19.6448553240741 19.5304166666667 0.114438657407408 -0.184855324074068 69 19.51 19.5611053240741 19.5466666666667 0.0144386574074077 -0.0511053240740722 70 19.64 19.5547164351852 19.5504166666667 0.00429976851851979 0.0852835648148158 71 19.64 19.5377719907407 19.5579166666667 -0.0201446759259255 0.102228009259264 72 19.69 19.5913136574074 19.5725 0.0188136574074073 0.0986863425925932 73 19.28 19.4843692129630 19.5808333333333 -0.0964641203703716 -0.204369212962963 74 19.67 19.5869386574074 19.59625 -0.00931134259259197 0.0830613425925968 75 19.65 19.6213136574074 19.62 0.00131365740740666 0.0286863425925930 76 19.6 19.6447858796296 19.63875 0.00603587962962814 -0.0447858796296288 77 19.53 19.5620775462963 19.6620833333333 -0.100005787037037 -0.0320775462962892 78 19.64 19.6993692129630 19.69375 0.00561921296296422 -0.059369212962963 79 19.67 19.8001331018519 19.7391666666667 0.0609664351851841 -0.130133101851850 80 19.81 19.9077719907407 19.7933333333333 0.114438657407408 -0.0977719907407426 81 19.73 19.8544386574074 19.84 0.0144386574074077 -0.124438657407406 82 19.87 19.8901331018519 19.8858333333333 0.00429976851851979 -0.0201331018518545 83 19.97 19.9269386574074 19.9470833333333 -0.0201446759259255 0.0430613425925941 84 20.12 20.0346469907407 20.0158333333333 0.0188136574074073 0.0853530092592614 85 19.94 19.9806192129630 20.0770833333333 -0.0964641203703716 -0.0406192129629659 86 20.31 20.1094386574074 20.11875 -0.00931134259259197 0.200561342592593 87 20.13 20.1438136574074 20.1425 0.00131365740740666 -0.0138136574074075 88 20.22 20.1564525462963 20.1504166666667 0.00603587962962814 0.063547453703702 89 20.38 20.0487442129630 20.14875 -0.100005787037037 0.331255787037041 90 20.44 20.1547858796296 20.1491666666667 0.00561921296296422 0.285214120370370 91 20.34 NA NA 0.0609664351851841 NA 92 20.14 NA NA 0.114438657407408 NA 93 19.97 NA NA 0.0144386574074077 NA 94 19.82 NA NA 0.00429976851851979 NA 95 19.98 NA NA -0.0201446759259255 NA 96 20.12 NA NA 0.0188136574074073 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218633544plgbx3mhkz9vly7/189b51218633475.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218633544plgbx3mhkz9vly7/189b51218633475.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218633544plgbx3mhkz9vly7/2wc3f1218633475.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218633544plgbx3mhkz9vly7/2wc3f1218633475.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218633544plgbx3mhkz9vly7/3v0091218633475.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218633544plgbx3mhkz9vly7/3v0091218633475.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218633544plgbx3mhkz9vly7/4jbvk1218633475.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218633544plgbx3mhkz9vly7/4jbvk1218633475.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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$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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