| aankoop nieuwe en tweedehands wagens | | R Software Module: rwasp_decompose.wasp (opens new window with default values) | | Title produced by software: Classical Decomposition | | Date of computation: Sun, 01 Jun 2008 11:19:14 -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/Jun/01/t1212340812pgqdbjlxs4d10fe.htm/, Retrieved Sun, 01 Jun 2008 17:20:12 +0000 | | | | IsPrivate? | | No (this computation is public) | | | | User-defined keywords: | | | | | Dataseries X: | | » Textbox « » Textfile « » CSV « | | 102,8
103,1
103,1
103,3
103,5
103,3
103,5
103,8
103,9
103,9
104,2
104,6
104,9
105,2
105,2
105,6
105,6
106,2
106,3
106,4
106,9
107,2
107,3
107,3
107,4
107,55
107,87
108,37
108,38
107,92
108,03
108,14
108,3
108,64
108,66
109,04
109,03
109,03
109,54
109,75
109,83
109,65
109,82
109,95
110,12
110,15
110,2
109,99
110,14
110,14
110,81
110,97
110,99
109,73
109,81
110,02
110,18
110,21
110,25
110,36
110,51
110,64
110,95
111,18
111,19
111,69
111,7
111,83
111,77
111,73
112,01
111,86
112,04
| | | | Text written by user: | | | | | Output produced by software: |
| Classical Decomposition by Moving Averages | | t | Observations | Fit | Trend | Seasonal | Random | | 1 | 102.8 | NA | NA | -0.0494704861111028 | NA | | 2 | 103.1 | NA | NA | -0.067491319444434 | NA | | 3 | 103.1 | NA | NA | 0.177300347222223 | NA | | 4 | 103.3 | NA | NA | 0.363654513888887 | NA | | 5 | 103.5 | NA | NA | 0.262404513888884 | NA | | 6 | 103.3 | NA | NA | -0.185616319444440 | NA | | 7 | 103.5 | 103.481779513889 | 103.670833333333 | -0.189053819444441 | 0.0182204861111046 | | 8 | 103.8 | 103.679175347222 | 103.845833333333 | -0.16665798611111 | 0.120824652777770 | | 9 | 103.9 | 103.985112847222 | 104.020833333333 | -0.0357204861111153 | -0.0851128472222058 | | 10 | 103.9 | 104.225425347222 | 104.204166666667 | 0.0212586805555492 | -0.325425347222193 | | 11 | 104.2 | 104.344904513889 | 104.3875 | -0.0425954861111197 | -0.144904513888875 | | 12 | 104.6 | 104.507821180556 | 104.595833333333 | -0.0880121527777806 | 0.092178819444456 | | 13 | 104.9 | 104.783862847222 | 104.833333333333 | -0.0494704861111028 | 0.116137152777796 | | 14 | 105.2 | 104.990842013889 | 105.058333333333 | -0.067491319444434 | 0.209157986111109 | | 15 | 105.2 | 105.468967013889 | 105.291666666667 | 0.177300347222223 | -0.268967013888897 | | 16 | 105.6 | 105.917821180556 | 105.554166666667 | 0.363654513888887 | -0.317821180555555 | | 17 | 105.6 | 106.083237847222 | 105.820833333333 | 0.262404513888884 | -0.483237847222213 | | 18 | 106.2 | 105.876883680556 | 106.0625 | -0.185616319444440 | 0.323116319444452 | | 19 | 106.3 | 106.090112847222 | 106.279166666667 | -0.189053819444441 | 0.209887152777767 | | 20 | 106.4 | 106.314592013889 | 106.48125 | -0.16665798611111 | 0.0854079861111217 | | 21 | 106.9 | 106.654696180556 | 106.690416666667 | -0.0357204861111153 | 0.245303819444459 | | 22 | 107.2 | 106.938342013889 | 106.917083333333 | 0.0212586805555492 | 0.261657986111132 | | 23 | 107.3 | 107.105737847222 | 107.148333333333 | -0.0425954861111197 | 0.194262152777796 | | 24 | 107.3 | 107.247821180556 | 107.335833333333 | -0.0880121527777806 | 0.0521788194444639 | | 25 | 107.4 | 107.430112847222 | 107.479583333333 | -0.0494704861111028 | -0.030112847222199 | | 26 | 107.55 | 107.556675347222 | 107.624166666667 | -0.067491319444434 | -0.0066753472222274 | | 27 | 107.87 | 107.932300347222 | 107.755 | 0.177300347222223 | -0.0623003472222194 | | 28 | 108.37 | 108.236987847222 | 107.873333333333 | 0.363654513888887 | 0.133012152777795 | | 29 | 108.38 | 108.252404513889 | 107.99 | 0.262404513888884 | 0.127595486111119 | | 30 | 107.92 | 107.933550347222 | 108.119166666667 | -0.185616319444440 | -0.0135503472222211 | | 31 | 108.03 | 108.070529513889 | 108.259583333333 | -0.189053819444441 | -0.0405295138888562 | | 32 | 108.14 | 108.222508680556 | 108.389166666667 | -0.16665798611111 | -0.0825086805555486 | | 33 | 108.3 | 108.484696180556 | 108.520416666667 | -0.0357204861111153 | -0.184696180555562 | | 34 | 108.64 | 108.668758680556 | 108.6475 | 0.0212586805555492 | -0.0287586805555406 | | 35 | 108.66 | 108.722821180556 | 108.765416666667 | -0.0425954861111197 | -0.0628211805555452 | | 36 | 109.04 | 108.809904513889 | 108.897916666667 | -0.0880121527777806 | 0.230095486111125 | | 37 | 109.03 | 108.995112847222 | 109.044583333333 | -0.0494704861111028 | 0.0348871527777703 | | 38 | 109.03 | 109.127092013889 | 109.194583333333 | -0.067491319444434 | -0.0970920138888687 | | 39 | 109.54 | 109.523133680556 | 109.345833333333 | 0.177300347222223 | 0.0168663194444463 | | 40 | 109.75 | 109.848237847222 | 109.484583333333 | 0.363654513888887 | -0.0982378472222365 | | 41 | 109.83 | 109.874071180556 | 109.611666666667 | 0.262404513888884 | -0.0440711805555623 | | 42 | 109.65 | 109.529800347222 | 109.715416666667 | -0.185616319444440 | 0.120199652777757 | | 43 | 109.82 | 109.612196180556 | 109.80125 | -0.189053819444441 | 0.207803819444436 | | 44 | 109.95 | 109.727092013889 | 109.89375 | -0.16665798611111 | 0.222907986111110 | | 45 | 110.12 | 109.957196180556 | 109.992916666667 | -0.0357204861111153 | 0.162803819444449 | | 46 | 110.15 | 110.117925347222 | 110.096666666667 | 0.0212586805555492 | 0.03207465277778 | | 47 | 110.2 | 110.153237847222 | 110.195833333333 | -0.0425954861111197 | 0.0467621527777879 | | 48 | 109.99 | 110.159487847222 | 110.2475 | -0.0880121527777806 | -0.169487847222214 | | 49 | 110.14 | 110.200946180556 | 110.250416666667 | -0.0494704861111028 | -0.0609461805555611 | | 50 | 110.14 | 110.185425347222 | 110.252916666667 | -0.067491319444434 | -0.0454253472222348 | | 51 | 110.81 | 110.435633680556 | 110.258333333333 | 0.177300347222223 | 0.374366319444448 | | 52 | 110.97 | 110.626987847222 | 110.263333333333 | 0.363654513888887 | 0.343012152777789 | | 53 | 110.99 | 110.530321180556 | 110.267916666667 | 0.262404513888884 | 0.459678819444463 | | 54 | 109.73 | 110.099800347222 | 110.285416666667 | -0.185616319444440 | -0.36980034722221 | | 55 | 109.81 | 110.127196180556 | 110.31625 | -0.189053819444441 | -0.317196180555555 | | 56 | 110.02 | 110.185842013889 | 110.3525 | -0.16665798611111 | -0.165842013888906 | | 57 | 110.18 | 110.343446180556 | 110.379166666667 | -0.0357204861111153 | -0.163446180555539 | | 58 | 110.21 | 110.415008680556 | 110.39375 | 0.0212586805555492 | -0.205008680555565 | | 59 | 110.25 | 110.368237847222 | 110.410833333333 | -0.0425954861111197 | -0.118237847222218 | | 60 | 110.36 | 110.412821180556 | 110.500833333333 | -0.0880121527777806 | -0.0528211805555827 | | 61 | 110.51 | 110.611779513889 | 110.66125 | -0.0494704861111028 | -0.1017795138889 | | 62 | 110.64 | 110.747925347222 | 110.815416666667 | -0.067491319444434 | -0.107925347222221 | | 63 | 110.95 | 111.134383680556 | 110.957083333333 | 0.177300347222223 | -0.184383680555541 | | 64 | 111.18 | 111.450321180556 | 111.086666666667 | 0.363654513888887 | -0.270321180555527 | | 65 | 111.19 | 111.485737847222 | 111.223333333333 | 0.262404513888884 | -0.295737847222213 | | 66 | 111.69 | 111.173550347222 | 111.359166666667 | -0.185616319444440 | 0.51644965277778 | | 67 | 111.7 | 111.296362847222 | 111.485416666667 | -0.189053819444441 | 0.403637152777762 | | 68 | 111.83 | NA | NA | -0.16665798611111 | NA | | 69 | 111.77 | NA | NA | -0.0357204861111153 | NA | | 70 | 111.73 | NA | NA | 0.0212586805555492 | NA | | 71 | 112.01 | NA | NA | -0.0425954861111197 | NA | | 72 | 111.86 | NA | NA | -0.0880121527777806 | NA | | 73 | 112.04 | NA | NA | NA | NA |
| | | Charts produced by software: |  | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212340812pgqdbjlxs4d10fe/1u3qf1212340749.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212340812pgqdbjlxs4d10fe/1u3qf1212340749.ps (open in new window) |
 | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212340812pgqdbjlxs4d10fe/2xbcs1212340749.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212340812pgqdbjlxs4d10fe/2xbcs1212340749.ps (open in new window) |
 | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212340812pgqdbjlxs4d10fe/3cgzd1212340749.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212340812pgqdbjlxs4d10fe/3cgzd1212340749.ps (open in new window) |
 | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212340812pgqdbjlxs4d10fe/4zqkk1212340749.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212340812pgqdbjlxs4d10fe/4zqkk1212340749.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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