paper trend depositos 2 jaar

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 25 Dec 2010 23:00:31 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/25/t1293317914rva3hii37rhnl7v.htm/, Retrieved Sat, 25 Dec 2010 23:58:38 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/25/t1293317914rva3hii37rhnl7v.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

61,2 62 65,1 63,2 66,3 61,9 62,1 66,3 72 65,3 67,6 70,5 74,2 77,8 78,5 77,8 81,4 84,5 88 93,9 98,9 96,7 98,9 102,2 105,4 105,1 116,6 112 108,8 106,9 109,5 106,7 118,9 117,5 113,7 119,6 120,6 117,5 120,3 119,8 108 98,8 94,6 84,6 84,4 79,1 73,3 74,3 67,8 64,8 66,5 57,7 53,8 51,8 50,9 49 48,1 42,6 40,9 43,3 43,7

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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 61.2 NA NA 1.42256944444444 NA 2 62 NA NA 1.01944444444443 NA 3 65.1 NA NA 5.6236111111111 NA 4 63.2 NA NA 2.45902777777778 NA 5 66.3 NA NA -0.851388888888885 NA 6 61.9 NA NA -2.78993055555555 NA 7 62.1 63.8944444444444 65.8333333333333 -1.93888888888889 -1.79444444444442 8 66.3 62.9371527777778 67.0333333333333 -4.09618055555555 3.36284722222223 9 72 69.4142361111111 68.25 1.16423611111112 2.58576388888888 10 65.3 69.8111111111111 69.4166666666667 0.394444444444451 -4.51111111111111 11 67.6 67.7288194444444 70.6541666666667 -2.92534722222222 -0.128819444444446 12 70.5 72.7434027777778 72.225 0.518402777777778 -2.24340277777779 13 74.2 75.6684027777778 74.2458333333334 1.42256944444444 -1.46840277777778 14 77.8 77.4944444444444 76.475 1.01944444444443 0.305555555555557 15 78.5 84.3694444444444 78.7458333333333 5.6236111111111 -5.86944444444444 16 77.8 83.6340277777778 81.175 2.45902777777778 -5.83402777777776 17 81.4 82.9361111111111 83.7875 -0.851388888888885 -1.53611111111110 18 84.5 83.6225694444445 86.4125 -2.78993055555555 0.877430555555549 19 88 87.0944444444444 89.0333333333333 -1.93888888888889 0.905555555555551 20 93.9 87.3746527777778 91.4708333333333 -4.09618055555555 6.52534722222221 21 98.9 95.3600694444445 94.1958333333333 1.16423611111112 3.53993055555554 22 96.7 97.6027777777778 97.2083333333333 0.394444444444451 -0.902777777777757 23 98.9 96.8496527777778 99.775 -2.92534722222222 2.05034722222223 24 102.2 102.368402777778 101.85 0.518402777777778 -0.168402777777757 25 105.4 105.101736111111 103.679166666667 1.42256944444444 0.298263888888897 26 105.1 106.127777777778 105.108333333333 1.01944444444443 -1.02777777777779 27 116.6 112.098611111111 106.475 5.6236111111111 4.5013888888889 28 112 110.634027777778 108.175 2.45902777777778 1.36597222222224 29 108.8 108.806944444444 109.658333333333 -0.851388888888885 -0.00694444444442865 30 106.9 108.210069444444 111 -2.78993055555555 -1.31006944444441 31 109.5 110.419444444444 112.358333333333 -1.93888888888889 -0.919444444444409 32 106.7 109.412152777778 113.508333333333 -4.09618055555555 -2.71215277777776 33 118.9 115.343402777778 114.179166666667 1.16423611111112 3.55659722222224 34 117.5 115.052777777778 114.658333333333 0.394444444444451 2.44722222222224 35 113.7 112.024652777778 114.95 -2.92534722222222 1.67534722222224 36 119.6 115.097569444444 114.579166666667 0.518402777777778 4.50243055555556 37 120.6 115.043402777778 113.620833333333 1.42256944444444 5.55659722222224 38 117.5 113.098611111111 112.079166666667 1.01944444444443 4.40138888888892 39 120.3 115.344444444444 109.720833333333 5.6236111111111 4.95555555555556 40 119.8 109.142361111111 106.683333333333 2.45902777777778 10.6576388888889 41 108 102.548611111111 103.4 -0.851388888888885 5.4513888888889 42 98.8 97.0392361111111 99.8291666666667 -2.78993055555555 1.76076388888889 43 94.6 93.8027777777778 95.7416666666667 -1.93888888888889 0.797222222222231 44 84.6 87.2496527777778 91.3458333333333 -4.09618055555555 -2.64965277777777 45 84.4 88.0725694444445 86.9083333333333 1.16423611111112 -3.67256944444445 46 79.1 82.4736111111111 82.0791666666667 0.394444444444451 -3.3736111111111 47 73.3 74.3079861111111 77.2333333333333 -2.92534722222222 -1.00798611111111 48 74.3 73.5350694444444 73.0166666666667 0.518402777777778 0.764930555555551 49 67.8 70.6600694444444 69.2375 1.42256944444444 -2.86006944444443 50 64.8 66.9527777777778 65.9333333333333 1.01944444444443 -2.15277777777777 51 66.5 68.5611111111111 62.9375 5.6236111111111 -2.06111111111110 52 57.7 62.3631944444445 59.9041666666667 2.45902777777778 -4.66319444444445 53 53.8 56.1819444444444 57.0333333333333 -0.851388888888885 -2.38194444444444 54 51.8 51.6017361111111 54.3916666666667 -2.78993055555555 0.198263888888896 55 50.9 50.1569444444444 52.0958333333333 -1.93888888888889 0.743055555555557 56 49 NA NA -4.09618055555555 NA 57 48.1 NA NA 1.16423611111112 NA 58 42.6 NA NA 0.394444444444451 NA 59 40.9 NA NA -2.92534722222222 NA 60 43.3 NA NA 0.518402777777778 NA 61 43.7 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/25/t1293317914rva3hii37rhnl7v/1b81u1293318028.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293317914rva3hii37rhnl7v/1b81u1293318028.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293317914rva3hii37rhnl7v/2b81u1293318028.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293317914rva3hii37rhnl7v/2b81u1293318028.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293317914rva3hii37rhnl7v/3lijf1293318028.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293317914rva3hii37rhnl7v/3lijf1293318028.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293317914rva3hii37rhnl7v/4lijf1293318028.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293317914rva3hii37rhnl7v/4lijf1293318028.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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