Classical Decomposition

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 08 May 2008 15:51:18 -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/May/08/t1210283575ayjbj9xcvy3exzp.htm/, Retrieved Thu, 08 May 2008 23:53:01 +0200

User-defined keywords:

Dataseries X:

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56421 53152 53536 52408 41454 38271 35306 26414 31917 38030 27534 18387 50556 43901 48572 43899 37532 40357 35489 29027 34485 42598 30306 26451 47460 50104 61465 53726 39477 43895 31481 29896 33842 39120 33702 25094 51442 45594 52518 48564 41745 49585 32747 33379 35645 37034 35681 20972 58552 54955 65540 51570 51145 46641 35704 33253 35193 41668 34865 21210 56126 49231 59723 48103 47472 50497 40059 34149 36860 46356 36577

Text written by user:

zie word-file

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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 56421 NA NA 10725.9745370370 NA 2 53152 NA NA 7378.44675925926 NA 3 53536 NA NA 14881.9328703704 NA 4 52408 NA NA 9388.6550925926 NA 5 41454 NA NA 144.335648148148 NA 6 38271 NA NA 5022.94675925926 NA 7 35306 32660.7800925926 39158.125 -6497.34490740741 2645.21990740741 8 26414 29294.6967592593 38528.2916666667 -9233.5949074074 -2880.69675925926 9 31917 32203.2106481481 37936 -5732.78935185185 -286.210648148146 10 38030 36226.2939814815 37374.625 -1148.33101851852 1803.70601851852 11 27534 29058.3912037037 36856.6666666667 -7798.27546296296 -1524.39120370371 12 18387 19648.2106481481 36780.1666666667 -17131.9560185185 -1261.21064814815 13 50556 47600.6828703704 36874.7083333333 10725.9745370370 2955.31712962964 14 43901 44369.6550925926 36991.2083333333 7378.44675925926 -468.655092592591 15 48572 52089.0162037037 37207.0833333333 14881.9328703704 -3517.01620370371 16 43899 46893.0717592593 37504.4166666667 9388.6550925926 -2994.07175925926 17 37532 37954.5856481481 37810.25 144.335648148148 -422.585648148146 18 40357 43284.6967592593 38261.75 5022.94675925926 -2927.69675925926 19 35489 31971.4050925926 38468.75 -6497.34490740741 3517.59490740741 20 29027 29364.6134259259 38598.2083333333 -9233.5949074074 -337.613425925927 21 34485 33661.0856481481 39393.875 -5732.78935185185 823.914351851854 22 42598 39192.2106481481 40340.5416666667 -1148.33101851852 3405.78935185185 23 30306 33032.7662037037 40831.0416666667 -7798.27546296296 -2726.7662037037 24 26451 23927.5439814815 41059.5 -17131.9560185185 2523.45601851852 25 47460 51765.8912037037 41039.9166666667 10725.9745370370 -4305.8912037037 26 50104 48287.5717592593 40909.125 7378.44675925926 1816.42824074074 27 61465 55800.474537037 40918.5416666667 14881.9328703704 5664.52546296296 28 53726 50135.4884259259 40746.8333333333 9388.6550925926 3590.51157407408 29 39477 40887.7523148148 40743.4166666667 144.335648148148 -1410.75231481481 30 43895 45851.3217592593 40828.375 5022.94675925926 -1956.32175925925 31 31481 34440.4050925926 40937.75 -6497.34490740741 -2959.4050925926 32 29896 31682.1550925926 40915.75 -9233.5949074074 -1786.15509259259 33 33842 34622.2523148148 40355.0416666667 -5732.78935185185 -780.25231481481 34 39120 38618.8356481482 39767.1666666667 -1148.33101851852 501.164351851847 35 33702 31848.3078703704 39646.5833333333 -7798.27546296296 1853.69212962964 36 25094 22846.2106481481 39978.1666666667 -17131.9560185185 2247.78935185185 37 51442 50993.974537037 40268 10725.9745370370 448.025462962964 38 45594 47844.3217592593 40465.875 7378.44675925926 -2250.32175925925 39 52518 55568.0578703704 40686.125 14881.9328703704 -3050.05787037037 40 48564 50062.9884259259 40674.3333333333 9388.6550925926 -1498.98842592592 41 41745 40814.2106481481 40669.875 144.335648148148 930.789351851854 42 49585 45603.5300925926 40580.5833333333 5022.94675925926 3981.46990740741 43 32747 34207.7384259259 40705.0833333333 -6497.34490740741 -1460.73842592592 44 33379 32157.7800925926 41391.375 -9233.5949074074 1221.21990740741 45 35645 36591.2106481481 42324 -5732.78935185185 -946.210648148146 46 37034 41843.5023148148 42991.8333333333 -1148.33101851852 -4809.50231481482 47 35681 35710.4745370370 43508.75 -7798.27546296296 -29.4745370370365 48 20972 26645.7939814815 43777.75 -17131.9560185185 -5673.79398148148 49 58552 54504.2662037037 43778.2916666667 10725.9745370370 4047.7337962963 50 54955 51274.6967592592 43896.25 7378.44675925926 3680.30324074076 51 65540 58754.099537037 43872.1666666667 14881.9328703704 6785.90046296297 52 51570 53435.0717592593 44046.4166666667 9388.6550925926 -1865.07175925926 53 51145 44349.8356481481 44205.5 144.335648148148 6795.16435185185 54 46641 49204.3634259259 44181.4166666667 5022.94675925926 -2563.36342592593 55 35704 37592.9050925926 44090.25 -6497.34490740741 -1888.90509259258 56 33253 34517.0717592592 43750.6666666667 -9233.5949074074 -1264.07175925925 57 35193 37537.0023148148 43269.7916666667 -5732.78935185185 -2344.00231481481 58 41668 41734.6273148148 42882.9583333333 -1148.33101851852 -66.6273148148102 59 34865 34787.1828703704 42585.4583333333 -7798.27546296296 77.8171296296277 60 21210 25461.1273148148 42593.0833333333 -17131.9560185185 -4251.12731481482 61 56126 NA 42935.2083333333 NA NA 62 49231 NA 43154 NA NA 63 59723 NA 43260.7916666667 NA NA 64 48103 NA 43525.5833333333 NA NA 65 47472 NA 43792.25 NA NA 66 50497 NA NA NA NA 67 40059 NA NA NA NA 68 34149 NA NA NA NA 69 36860 NA NA NA NA 70 46356 NA NA NA NA 71 36577 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/08/t1210283575ayjbj9xcvy3exzp/1apz61210283470.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/08/t1210283575ayjbj9xcvy3exzp/1apz61210283470.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/08/t1210283575ayjbj9xcvy3exzp/2zkw41210283470.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/08/t1210283575ayjbj9xcvy3exzp/2zkw41210283470.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/08/t1210283575ayjbj9xcvy3exzp/3u70y1210283470.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/08/t1210283575ayjbj9xcvy3exzp/3u70y1210283470.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/08/t1210283575ayjbj9xcvy3exzp/4twhj1210283470.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/08/t1210283575ayjbj9xcvy3exzp/4twhj1210283470.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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