| Gemiddelde temperatuur Nederland | | *Unverified author* | | R Software Module: /rwasp_decompose.wasp (opens new window with default values) | | Title produced by software: Classical Decomposition | | Date of computation: Mon, 16 May 2011 19:09:09 +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/16/t1305572774qo71gd40z8w23p9.htm/, Retrieved Mon, 16 May 2011 21:06:21 +0200 | | | | Original text written by user: | | | | | IsPrivate? | | No (this computation is public) | | | | User-defined keywords: | | KDGP2W92 | | | | Dataseries X: | | » Textbox « » Textfile « » CSV « | | 17
16.7
15.4
15.1
16.1
17
16.1
14.3
16.1
14.8
15.9
17.6
15.9
14.8
16.5
15.6
14.6
17.1
15.2
14.8
15.4
16.6
15.1
15.4
15.2
16.6
16.1
15.7
15.8
15.7
16.9
15.9
17.1
17
16.6
17.1
16.6
16.6
16.5
17
15.9
17
16.1
16.1
16.8
16.7
15.7
18.7
16.1
16.3
17.2
16.1
16.5
16.5
15.1
16.7
14.4
16.2
15.9
17.3
15.6
15.6
14.7
15.8
15.8
14.8
16.1
16.3
16.1
17.4
16.7
16.1
15.4
16.9
15.5
17.6
18.4
15.9
15.2
15.5
15.9
15.8
17.6
18.2
15.9
15.7
16.4
15.6
15.8
17
16.8
16.6
17.7
15.7
18
18.2
16.4
18
16.3 | | | | Output produced by software: |
| Classical Decomposition by Moving Averages | | t | Observations | Fit | Trend | Seasonal | Random | | 1 | 17 | NA | NA | -0.318204365079364 | NA | | 2 | 16.7 | NA | NA | -0.0789186507936503 | NA | | 3 | 15.4 | NA | NA | -0.0449900793650803 | NA | | 4 | 15.1 | NA | NA | 0.0115575396825393 | NA | | 5 | 16.1 | NA | NA | -0.0920138888888894 | NA | | 6 | 17 | NA | NA | 0.0633432539682545 | NA | | 7 | 16.1 | 15.6478670634921 | 15.9625 | -0.314632936507938 | 0.452132936507939 | | 8 | 14.3 | 15.5722718253968 | 15.8375 | -0.265228174603174 | -1.27227182539682 | | 9 | 16.1 | 15.7353670634921 | 15.8041666666667 | -0.068799603174602 | 0.364632936507935 | | 10 | 14.8 | 16.2716765873016 | 15.8708333333333 | 0.400843253968253 | -1.47167658730159 | | 11 | 15.9 | 15.6835813492063 | 15.8291666666667 | -0.145585317460317 | 0.216418650793653 | | 12 | 17.6 | 16.6234623015873 | 15.7708333333333 | 0.852628968253969 | 0.9765376984127 | | 13 | 15.9 | 15.4192956349206 | 15.7375 | -0.318204365079364 | 0.480704365079369 | | 14 | 14.8 | 15.6419146825397 | 15.7208333333333 | -0.0789186507936503 | -0.84191468253968 | | 15 | 16.5 | 15.6675099206349 | 15.7125 | -0.0449900793650803 | 0.832490079365083 | | 16 | 15.6 | 15.7698908730159 | 15.7583333333333 | 0.0115575396825393 | -0.16989087301587 | | 17 | 14.6 | 15.7079861111111 | 15.8 | -0.0920138888888894 | -1.10798611111111 | | 18 | 17.1 | 15.7383432539683 | 15.675 | 0.0633432539682545 | 1.36165674603174 | | 19 | 15.2 | 15.2395337301587 | 15.5541666666667 | -0.314632936507938 | -0.0395337301587322 | | 20 | 14.8 | 15.3347718253968 | 15.6 | -0.265228174603174 | -0.534771825396822 | | 21 | 15.4 | 15.5895337301587 | 15.6583333333333 | -0.068799603174602 | -0.189533730158729 | | 22 | 16.6 | 16.0466765873016 | 15.6458333333333 | 0.400843253968253 | 0.553323412698415 | | 23 | 15.1 | 15.5544146825397 | 15.7 | -0.145585317460317 | -0.45441468253968 | | 24 | 15.4 | 16.5442956349206 | 15.6916666666667 | 0.852628968253969 | -1.14429563492063 | | 25 | 15.2 | 15.3859623015873 | 15.7041666666667 | -0.318204365079364 | -0.185962301587303 | | 26 | 16.6 | 15.7419146825397 | 15.8208333333333 | -0.0789186507936503 | 0.858085317460318 | | 27 | 16.1 | 15.8925099206349 | 15.9375 | -0.0449900793650803 | 0.207490079365083 | | 28 | 15.7 | 16.0365575396825 | 16.025 | 0.0115575396825393 | -0.336557539682538 | | 29 | 15.8 | 16.0121527777778 | 16.1041666666667 | -0.0920138888888894 | -0.212152777777778 | | 30 | 15.7 | 16.3008432539683 | 16.2375 | 0.0633432539682545 | -0.600843253968252 | | 31 | 16.9 | 16.0520337301587 | 16.3666666666667 | -0.314632936507938 | 0.847966269841269 | | 32 | 15.9 | 16.1597718253968 | 16.425 | -0.265228174603174 | -0.259771825396825 | | 33 | 17.1 | 16.3728670634921 | 16.4416666666667 | -0.068799603174602 | 0.727132936507935 | | 34 | 17 | 16.9133432539683 | 16.5125 | 0.400843253968253 | 0.0866567460317462 | | 35 | 16.6 | 16.425248015873 | 16.5708333333333 | -0.145585317460317 | 0.174751984126985 | | 36 | 17.1 | 17.4817956349206 | 16.6291666666667 | 0.852628968253969 | -0.381795634920628 | | 37 | 16.6 | 16.3317956349206 | 16.65 | -0.318204365079364 | 0.268204365079367 | | 38 | 16.6 | 16.5460813492064 | 16.625 | -0.0789186507936503 | 0.0539186507936513 | | 39 | 16.5 | 16.5758432539683 | 16.6208333333333 | -0.0449900793650803 | -0.0758432539682516 | | 40 | 17 | 16.6073908730159 | 16.5958333333333 | 0.0115575396825393 | 0.39260912698413 | | 41 | 15.9 | 16.4538194444444 | 16.5458333333333 | -0.0920138888888894 | -0.553819444444441 | | 42 | 17 | 16.6383432539683 | 16.575 | 0.0633432539682545 | 0.361656746031748 | | 43 | 16.1 | 16.3062003968254 | 16.6208333333333 | -0.314632936507938 | -0.206200396825391 | | 44 | 16.1 | 16.3222718253968 | 16.5875 | -0.265228174603174 | -0.222271825396824 | | 45 | 16.8 | 16.5353670634921 | 16.6041666666667 | -0.068799603174602 | 0.264632936507937 | | 46 | 16.7 | 16.9966765873016 | 16.5958333333333 | 0.400843253968253 | -0.296676587301587 | | 47 | 15.7 | 16.437748015873 | 16.5833333333333 | -0.145585317460317 | -0.737748015873018 | | 48 | 18.7 | 17.440128968254 | 16.5875 | 0.852628968253969 | 1.25987103174603 | | 49 | 16.1 | 16.2067956349206 | 16.525 | -0.318204365079364 | -0.106795634920637 | | 50 | 16.3 | 16.4294146825397 | 16.5083333333333 | -0.0789186507936503 | -0.129414682539682 | | 51 | 17.2 | 16.3883432539683 | 16.4333333333333 | -0.0449900793650803 | 0.811656746031748 | | 52 | 16.1 | 16.3240575396825 | 16.3125 | 0.0115575396825393 | -0.224057539682541 | | 53 | 16.5 | 16.2079861111111 | 16.3 | -0.0920138888888894 | 0.292013888888889 | | 54 | 16.5 | 16.3133432539683 | 16.25 | 0.0633432539682545 | 0.186656746031744 | | 55 | 15.1 | 15.8562003968254 | 16.1708333333333 | -0.314632936507938 | -0.756200396825397 | | 56 | 16.7 | 15.8556051587302 | 16.1208333333333 | -0.265228174603174 | 0.844394841269839 | | 57 | 14.4 | 15.9187003968254 | 15.9875 | -0.068799603174602 | -1.51870039682539 | | 58 | 16.2 | 16.2716765873016 | 15.8708333333333 | 0.400843253968253 | -0.071676587301587 | | 59 | 15.9 | 15.6835813492063 | 15.8291666666667 | -0.145585317460317 | 0.216418650793653 | | 60 | 17.3 | 16.5817956349206 | 15.7291666666667 | 0.852628968253969 | 0.71820436507937 | | 61 | 15.6 | 15.3817956349206 | 15.7 | -0.318204365079364 | 0.218204365079368 | | 62 | 15.6 | 15.6460813492063 | 15.725 | -0.0789186507936503 | -0.0460813492063465 | | 63 | 14.7 | 15.7341765873016 | 15.7791666666667 | -0.0449900793650803 | -1.03417658730159 | | 64 | 15.8 | 15.9115575396825 | 15.9 | 0.0115575396825393 | -0.111557539682536 | | 65 | 15.8 | 15.8913194444444 | 15.9833333333333 | -0.0920138888888894 | -0.0913194444444425 | | 66 | 14.8 | 16.0300099206349 | 15.9666666666667 | 0.0633432539682545 | -1.23000992063492 | | 67 | 16.1 | 15.5937003968254 | 15.9083333333333 | -0.314632936507938 | 0.506299603174606 | | 68 | 16.3 | 15.6889384920635 | 15.9541666666667 | -0.265228174603174 | 0.611061507936508 | | 69 | 16.1 | 15.9728670634921 | 16.0416666666667 | -0.068799603174602 | 0.127132936507937 | | 70 | 17.4 | 16.5508432539683 | 16.15 | 0.400843253968253 | 0.849156746031742 | | 71 | 16.7 | 16.187748015873 | 16.3333333333333 | -0.145585317460317 | 0.512251984126983 | | 72 | 16.1 | 17.340128968254 | 16.4875 | 0.852628968253969 | -1.24012896825396 | | 73 | 15.4 | 16.177628968254 | 16.4958333333333 | -0.318204365079364 | -0.77762896825397 | | 74 | 16.9 | 16.3460813492063 | 16.425 | -0.0789186507936503 | 0.553918650793651 | | 75 | 15.5 | 16.3383432539683 | 16.3833333333333 | -0.0449900793650803 | -0.838343253968253 | | 76 | 17.6 | 16.3198908730159 | 16.3083333333333 | 0.0115575396825393 | 1.28010912698413 | | 77 | 18.4 | 16.1871527777778 | 16.2791666666667 | -0.0920138888888894 | 2.21284722222222 | | 78 | 15.9 | 16.4675099206349 | 16.4041666666667 | 0.0633432539682545 | -0.567509920634919 | | 79 | 15.2 | 16.1978670634921 | 16.5125 | -0.314632936507938 | -0.997867063492063 | | 80 | 15.5 | 16.2181051587302 | 16.4833333333333 | -0.265228174603174 | -0.718105158730159 | | 81 | 15.9 | 16.4020337301587 | 16.4708333333333 | -0.068799603174602 | -0.502033730158729 | | 82 | 15.8 | 16.8258432539683 | 16.425 | 0.400843253968253 | -1.02584325396825 | | 83 | 17.6 | 16.087748015873 | 16.2333333333333 | -0.145585317460317 | 1.51225198412699 | | 84 | 18.2 | 17.0234623015873 | 16.1708333333333 | 0.852628968253969 | 1.1765376984127 | | 85 | 15.9 | 15.965128968254 | 16.2833333333333 | -0.318204365079364 | -0.065128968253962 | | 86 | 15.7 | 16.3169146825397 | 16.3958333333333 | -0.0789186507936503 | -0.616914682539683 | | 87 | 16.4 | 16.4716765873016 | 16.5166666666667 | -0.0449900793650803 | -0.0716765873015852 | | 88 | 15.6 | 16.5990575396825 | 16.5875 | 0.0115575396825393 | -0.999057539682537 | | 89 | 15.8 | 16.5079861111111 | 16.6 | -0.0920138888888894 | -0.707986111111108 | | 90 | 17 | 16.6800099206349 | 16.6166666666667 | 0.0633432539682545 | 0.319990079365081 | | 91 | 16.8 | 16.3228670634921 | 16.6375 | -0.314632936507938 | 0.477132936507939 | | 92 | 16.6 | 16.4889384920635 | 16.7541666666667 | -0.265228174603174 | 0.111061507936512 | | 93 | 17.7 | 16.7770337301587 | 16.8458333333333 | -0.068799603174602 | 0.922966269841268 | | 94 | 15.7 | NA | NA | 0.400843253968253 | NA | | 95 | 18 | NA | NA | -0.145585317460317 | NA | | 96 | 18.2 | NA | NA | 0.852628968253969 | NA | | 97 | 16.4 | NA | NA | NA | NA | | 98 | 18 | NA | NA | NA | NA | | 99 | 16.3 | NA | NA | NA | NA |
| | | | Charts produced by software: |  | | http://www.freestatistics.org/blog/date/2011/May/16/t1305572774qo71gd40z8w23p9/1aioh1305572945.png (open in new window) | | http://www.freestatistics.org/blog/date/2011/May/16/t1305572774qo71gd40z8w23p9/1aioh1305572945.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2011/May/16/t1305572774qo71gd40z8w23p9/2lpk61305572945.png (open in new window) | | http://www.freestatistics.org/blog/date/2011/May/16/t1305572774qo71gd40z8w23p9/2lpk61305572945.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2011/May/16/t1305572774qo71gd40z8w23p9/3g0ia1305572945.png (open in new window) | | http://www.freestatistics.org/blog/date/2011/May/16/t1305572774qo71gd40z8w23p9/3g0ia1305572945.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2011/May/16/t1305572774qo71gd40z8w23p9/429g11305572945.png (open in new window) | | http://www.freestatistics.org/blog/date/2011/May/16/t1305572774qo71gd40z8w23p9/429g11305572945.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')
| | |
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