| Stephanie De Coninck - decompositie scheermachines | | R Software Module: rwasp_decompose.wasp (opens new window with default values) | | Title produced by software: Classical Decomposition | | Date of computation: Mon, 19 May 2008 13:08:52 -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/19/t1211224288gtqjkjayggmv5gk.htm/, Retrieved Mon, 19 May 2008 21:11:33 +0200 | | | | User-defined keywords: | | opgave 9 | | | | Dataseries X: | | » Textbox « » Textfile « » CSV « | | 68,64
68,61
68,61
68,61
68,58
68,75
68,54
68,5
68,47
68,47
68,47
68,59
68,32
67,86
67,91
67,91
68,05
68,15
68,25
68,25
68,31
68,31
69,65
69,65
70,18
70,08
70,08
70,09
70,04
70,14
70,26
70,23
70,54
70,54
70,57
70,61
70,63
70,45
70,4
70,4
70,33
70,51
70,45
70,39
70,59
70,59
70,32
70,94
70,44
70,57
70,61
70,61
70,68
69,96
70,11
70,22
70,49
70,49
70,58
70,85
70,69
70,7
70,7
70,7
70,67
70,64
70,98
70,75
70,88
70,88
70,92
70,89
71,02
71,01
71,02
| | | | Text written by user: | | | | | Output produced by software: |
| Classical Decomposition by Moving Averages | | t | Observations | Fit | Trend | Seasonal | Random | | 1 | 68.64 | NA | NA | 0.229687499999998 | NA | | 2 | 68.61 | NA | NA | 0.0429166666666638 | NA | | 3 | 68.61 | NA | NA | 0.0139583333333283 | NA | | 4 | 68.61 | NA | NA | -0.0256249999999974 | NA | | 5 | 68.58 | NA | NA | -0.0461458333333370 | NA | | 6 | 68.75 | NA | NA | -0.176666666666662 | NA | | 7 | 68.54 | 68.4092708333334 | 68.5566666666667 | -0.147395833333327 | 0.130729166666654 | | 8 | 68.5 | 68.3154166666667 | 68.5120833333333 | -0.196666666666665 | 0.184583333333336 | | 9 | 68.47 | 68.4063541666667 | 68.4516666666667 | -0.0453124999999937 | 0.0636458333333394 | | 10 | 68.47 | 68.2898958333333 | 68.3933333333333 | -0.103437499999998 | 0.180104166666666 | | 11 | 68.47 | 68.4797916666666 | 68.3420833333333 | 0.137708333333326 | -0.00979166666664355 | | 12 | 68.59 | 68.6119791666667 | 68.295 | 0.316979166666663 | -0.0219791666666538 | | 13 | 68.32 | 68.4876041666667 | 68.2579166666667 | 0.229687499999998 | -0.167604166666663 | | 14 | 67.86 | 68.2783333333333 | 68.2354166666667 | 0.0429166666666638 | -0.418333333333337 | | 15 | 67.91 | 68.2322916666667 | 68.2183333333333 | 0.0139583333333283 | -0.322291666666672 | | 16 | 67.91 | 68.179375 | 68.205 | -0.0256249999999974 | -0.269375000000025 | | 17 | 68.05 | 68.2013541666667 | 68.2475 | -0.0461458333333370 | -0.151354166666664 | | 18 | 68.15 | 68.1641666666667 | 68.3408333333333 | -0.176666666666662 | -0.0141666666666538 | | 19 | 68.25 | 68.3151041666667 | 68.4625 | -0.147395833333327 | -0.0651041666666714 | | 20 | 68.25 | 68.4358333333333 | 68.6325 | -0.196666666666665 | -0.185833333333349 | | 21 | 68.31 | 68.7701041666667 | 68.8154166666667 | -0.0453124999999937 | -0.460104166666667 | | 22 | 68.31 | 68.8932291666667 | 68.9966666666667 | -0.103437499999998 | -0.583229166666669 | | 23 | 69.65 | 69.308125 | 69.1704166666667 | 0.137708333333326 | 0.341875000000016 | | 24 | 69.65 | 69.6532291666667 | 69.33625 | 0.316979166666663 | -0.00322916666667084 | | 25 | 70.18 | 69.7326041666667 | 69.5029166666667 | 0.229687499999998 | 0.447395833333346 | | 26 | 70.08 | 69.7120833333333 | 69.6691666666667 | 0.0429166666666638 | 0.367916666666659 | | 27 | 70.08 | 69.8585416666667 | 69.8445833333333 | 0.0139583333333283 | 0.221458333333317 | | 28 | 70.09 | 70.0047916666667 | 70.0304166666667 | -0.0256249999999974 | 0.0852083333333269 | | 29 | 70.04 | 70.1155208333333 | 70.1616666666667 | -0.0461458333333370 | -0.0755208333333286 | | 30 | 70.14 | 70.0633333333333 | 70.24 | -0.176666666666662 | 0.076666666666668 | | 31 | 70.26 | 70.1513541666667 | 70.29875 | -0.147395833333327 | 0.108645833333327 | | 32 | 70.23 | 70.13625 | 70.3329166666667 | -0.196666666666665 | 0.09375 | | 33 | 70.54 | 70.3163541666667 | 70.3616666666667 | -0.0453124999999937 | 0.223645833333350 | | 34 | 70.54 | 70.2844791666667 | 70.3879166666667 | -0.103437499999998 | 0.255520833333335 | | 35 | 70.57 | 70.550625 | 70.4129166666667 | 0.137708333333326 | 0.0193750000000108 | | 36 | 70.61 | 70.7573958333333 | 70.4404166666667 | 0.316979166666663 | -0.147395833333334 | | 37 | 70.63 | 70.6934375 | 70.46375 | 0.229687499999998 | -0.0634375000000063 | | 38 | 70.45 | 70.52125 | 70.4783333333333 | 0.0429166666666638 | -0.071249999999992 | | 39 | 70.4 | 70.5010416666667 | 70.4870833333333 | 0.0139583333333283 | -0.10104166666666 | | 40 | 70.4 | 70.465625 | 70.49125 | -0.0256249999999974 | -0.0656249999999972 | | 41 | 70.33 | 70.4367708333333 | 70.4829166666667 | -0.0461458333333370 | -0.106770833333329 | | 42 | 70.51 | 70.3095833333333 | 70.48625 | -0.176666666666662 | 0.200416666666669 | | 43 | 70.45 | 70.3446875 | 70.4920833333333 | -0.147395833333327 | 0.105312499999997 | | 44 | 70.39 | 70.2925 | 70.4891666666667 | -0.196666666666665 | 0.0974999999999966 | | 45 | 70.59 | 70.4576041666667 | 70.5029166666667 | -0.0453124999999937 | 0.132395833333334 | | 46 | 70.59 | 70.4169791666667 | 70.5204166666667 | -0.103437499999998 | 0.173020833333339 | | 47 | 70.32 | 70.6814583333333 | 70.54375 | 0.137708333333326 | -0.361458333333331 | | 48 | 70.94 | 70.8523958333333 | 70.5354166666667 | 0.316979166666663 | 0.0876041666666652 | | 49 | 70.44 | 70.7280208333333 | 70.4983333333333 | 0.229687499999998 | -0.288020833333334 | | 50 | 70.57 | 70.52 | 70.4770833333333 | 0.0429166666666638 | 0.049999999999983 | | 51 | 70.61 | 70.4797916666667 | 70.4658333333333 | 0.0139583333333283 | 0.130208333333329 | | 52 | 70.61 | 70.431875 | 70.4575 | -0.0256249999999974 | 0.178125000000009 | | 53 | 70.68 | 70.4180208333333 | 70.4641666666667 | -0.0461458333333370 | 0.261979166666677 | | 54 | 69.96 | 70.2945833333333 | 70.47125 | -0.176666666666662 | -0.334583333333342 | | 55 | 70.11 | 70.3305208333333 | 70.4779166666667 | -0.147395833333327 | -0.220520833333339 | | 56 | 70.22 | 70.2970833333333 | 70.49375 | -0.196666666666665 | -0.0770833333333343 | | 57 | 70.49 | 70.4576041666667 | 70.5029166666667 | -0.0453124999999937 | 0.0323958333333252 | | 58 | 70.49 | 70.4069791666667 | 70.5104166666667 | -0.103437499999998 | 0.083020833333336 | | 59 | 70.58 | 70.6514583333333 | 70.51375 | 0.137708333333326 | -0.0714583333333394 | | 60 | 70.85 | 70.8586458333333 | 70.5416666666667 | 0.316979166666663 | -0.00864583333334679 | | 61 | 70.69 | 70.8359375 | 70.60625 | 0.229687499999998 | -0.145937500000002 | | 62 | 70.7 | 70.7075 | 70.6645833333333 | 0.0429166666666638 | -0.00749999999999318 | | 63 | 70.7 | 70.716875 | 70.7029166666667 | 0.0139583333333283 | -0.0168749999999989 | | 64 | 70.7 | 70.7097916666667 | 70.7354166666667 | -0.0256249999999974 | -0.00979166666667197 | | 65 | 70.67 | 70.7196875 | 70.7658333333333 | -0.0461458333333370 | -0.0496874999999903 | | 66 | 70.64 | 70.605 | 70.7816666666667 | -0.176666666666662 | 0.0349999999999966 | | 67 | 70.98 | 70.6496875 | 70.7970833333333 | -0.147395833333327 | 0.330312500000005 | | 68 | 70.75 | 70.6270833333333 | 70.82375 | -0.196666666666665 | 0.122916666666654 | | 69 | 70.88 | 70.8046875 | 70.85 | -0.0453124999999937 | 0.0753124999999812 | | 70 | 70.88 | NA | NA | -0.103437499999998 | NA | | 71 | 70.92 | NA | NA | 0.137708333333326 | NA | | 72 | 70.89 | NA | NA | 0.316979166666663 | NA | | 73 | 71.02 | NA | NA | NA | NA | | 74 | 71.01 | NA | NA | NA | NA | | 75 | 71.02 | NA | NA | NA | NA |
| | | Charts produced by software: |  | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224288gtqjkjayggmv5gk/1ycob1211224130.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224288gtqjkjayggmv5gk/1ycob1211224130.ps (open in new window) |
 | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224288gtqjkjayggmv5gk/2pcxq1211224130.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224288gtqjkjayggmv5gk/2pcxq1211224130.ps (open in new window) |
 | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224288gtqjkjayggmv5gk/3ipmq1211224130.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224288gtqjkjayggmv5gk/3ipmq1211224130.ps (open in new window) |
 | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224288gtqjkjayggmv5gk/4vwwj1211224130.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224288gtqjkjayggmv5gk/4vwwj1211224130.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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