Home » date » 2010 » Dec » 24 »

*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: Fri, 24 Dec 2010 10:51:34 +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/24/t1293187776g4pn6vze5shzw41.htm/, Retrieved Fri, 24 Dec 2010 11:49:41 +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/24/t1293187776g4pn6vze5shzw41.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 «
6,4 7,7 9,2 8,6 7,4 8,6 6,2 6 6,6 5,1 4,7 5 3,6 1,9 -0,1 -5,7 -5,6 -6,4 -7,7 -8 -11,9 -15,4 -15,5 -13,4 -10,9 -10,8 -7,3 -6,5 -5,1 -5,3 -6,8 -8,4 -8,4 -9,7 -8,8 -9,6 -11,5 -11 -14,9 -16,2 -14,4 -17,3 -15,7 -12,6 -9,4 -8,1 -5,4 -4,6 -4,9 -4 -3,1 -1,3 0 -0,4 3 0,4 1,2 0,6 -1,3 -3,2 -1,8 -3,6 -4,2 -6,9 -8 -7,5 -8,2 -7,6 -3,7 -1,7 -0,7 0,2 0,6 2,2 3,3 5,3 5,5 6,3 7,7 6,5 5,5 6,9 5,7 6,9 6,1 4,8 3,7 5,8 6,8 8,5 7,2 5 4,7 2,3 2,4 0,1 1,9 1,7 2 -1,9 0,5 -1,3 -3,3 -2,8 -8 -13,9 -21,9 -28,8 -27,6 -31,4 -31,8 -29,4 -27,6 -23,6 -22,8 -18,2 -17,8 -14,2 -8,8 -7,9 -7 -7 -3,6 -2,4 -4,9 -7,7 -6,5 -5,1 -3,4 -2,8 0,8
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
16.4NANA-0.172916666666667NA
27.7NANA-0.559953703703703NA
39.2NANA-0.579398148148148NA
48.6NANA-0.865972222222222NA
57.4NANA0.274768518518518NA
68.6NANA0.496990740740741NA
76.27.325231481481486.6750.650231481481481-1.12523148148148
867.157175925925936.316666666666670.84050925925926-1.15717592592593
96.66.352083333333335.68750.6645833333333330.247916666666667
105.14.769675925925934.704166666666670.06550925925925970.330324074074073
114.73.491435185185193.56666666666667-0.07523148148148151.20856481481482
1251.660879629629632.4-0.739120370370373.33912037037037
133.61.022916666666671.19583333333333-0.1729166666666672.57708333333333
141.9-0.526620370370370.0333333333333334-0.5599537037037032.42662037037037
15-0.1-1.90023148148148-1.32083333333333-0.5793981481481481.80023148148148
16-5.7-3.81180555555556-2.94583333333333-0.865972222222222-1.88819444444444
17-5.6-4.36689814814815-4.641666666666670.274768518518518-1.23310185185185
18-6.4-5.75300925925926-6.250.496990740740741-0.64699074074074
19-7.7-6.97060185185185-7.620833333333330.650231481481481-0.729398148148149
20-8-7.9136574074074-8.754166666666660.84050925925926-0.0863425925925938
21-11.9-8.91875-9.583333333333330.664583333333333-2.98125
22-15.4-9.8511574074074-9.916666666666670.0655092592592597-5.54884259259259
23-15.5-10.0043981481481-9.92916666666666-0.0752314814814815-5.49560185185186
24-13.4-10.6016203703704-9.8625-0.73912037037037-2.79837962962963
25-10.9-9.95208333333333-9.77916666666667-0.172916666666667-0.947916666666668
26-10.8-10.3182870370370-9.75833333333333-0.559953703703703-0.481712962962964
27-7.3-10.2085648148148-9.62916666666667-0.5793981481481482.90856481481481
28-6.5-10.1118055555556-9.24583333333333-0.8659722222222223.61180555555556
29-5.1-8.45439814814815-8.729166666666670.2747685185185183.35439814814815
30-5.3-7.79467592592593-8.291666666666670.4969907407407412.49467592592593
31-6.8-7.50810185185185-8.158333333333330.6502314814814810.708101851851852
32-8.4-7.35115740740741-8.191666666666670.84050925925926-1.04884259259259
33-8.4-7.85208333333333-8.516666666666670.664583333333333-0.547916666666666
34-9.7-9.17199074074074-9.23750.0655092592592597-0.528009259259258
35-8.8-10.1043981481481-10.0291666666667-0.07523148148148151.30439814814815
36-9.6-11.6557870370370-10.9166666666667-0.739120370370372.05578703703703
37-11.5-11.9604166666667-11.7875-0.1729166666666670.460416666666667
38-11-12.8932870370370-12.3333333333333-0.5599537037037031.89328703703704
39-14.9-13.1293981481481-12.55-0.579398148148148-1.77060185185185
40-16.2-13.3909722222222-12.525-0.865972222222222-2.80902777777778
41-14.4-12.0418981481481-12.31666666666670.274768518518518-2.35810185185185
42-17.3-11.4696759259259-11.96666666666670.496990740740741-5.83032407407408
43-15.7-10.8331018518519-11.48333333333330.650231481481481-4.86689814814815
44-12.6-10.0761574074074-10.91666666666670.84050925925926-2.52384259259259
45-9.4-9.46875-10.13333333333330.6645833333333330.0687499999999979
46-8.1-8.95532407407407-9.020833333333330.06550925925925970.855324074074073
47-5.4-7.87523148148148-7.8-0.07523148148148152.47523148148148
48-4.6-7.2349537037037-6.49583333333333-0.739120370370372.63495370370370
49-4.9-5.18541666666667-5.0125-0.1729166666666670.285416666666666
50-4-4.25162037037037-3.69166666666667-0.5599537037037030.25162037037037
51-3.1-3.28773148148148-2.70833333333333-0.5793981481481480.187731481481482
52-1.3-2.77013888888889-1.90416666666667-0.8659722222222221.47013888888889
530-1.09606481481481-1.370833333333330.2747685185185181.09606481481481
54-0.4-0.644675925925926-1.141666666666670.4969907407407410.244675925925926
553-0.303935185185185-0.9541666666666670.6502314814814813.30393518518519
560.40.032175925925926-0.8083333333333330.840509259259260.367824074074074
571.2-0.172916666666667-0.83750.6645833333333331.37291666666667
580.6-1.05115740740741-1.116666666666670.06550925925925971.65115740740741
59-1.3-1.75856481481481-1.68333333333333-0.07523148148148150.458564814814814
60-3.2-3.05162037037037-2.3125-0.73912037037037-0.148379629629630
61-1.8-3.24791666666667-3.075-0.1729166666666671.44791666666667
62-3.6-4.4349537037037-3.875-0.5599537037037030.834953703703703
63-4.2-4.99189814814815-4.4125-0.5793981481481480.791898148148148
64-6.9-5.57847222222222-4.7125-0.865972222222222-1.32152777777778
65-8-4.50856481481481-4.783333333333330.274768518518518-3.49143518518519
66-7.5-4.11967592592593-4.616666666666670.496990740740741-3.38032407407407
67-8.2-3.72476851851852-4.3750.650231481481481-4.47523148148148
68-7.6-3.19282407407407-4.033333333333330.84050925925926-4.40717592592592
69-3.7-2.81458333333333-3.479166666666670.664583333333333-0.885416666666667
70-1.7-2.59282407407407-2.658333333333330.06550925925925970.892824074074074
71-0.7-1.66273148148148-1.5875-0.07523148148148150.96273148148148
720.2-1.18912037037037-0.45-0.739120370370371.38912037037037
730.60.6145833333333330.7875-0.172916666666667-0.0145833333333334
742.21.477546296296302.0375-0.5599537037037030.722453703703704
753.32.428935185185183.00833333333333-0.5793981481481480.871064814814816
765.32.884027777777783.75-0.8659722222222222.41597222222222
775.54.649768518518524.3750.2747685185185180.850231481481481
786.35.417824074074084.920833333333330.4969907407407410.882175925925925
797.76.079398148148155.429166666666670.6502314814814811.62060185185185
806.56.607175925925935.766666666666670.84050925925926-0.107175925925926
815.56.556255.891666666666670.664583333333333-1.05625000000000
826.95.994675925925935.929166666666670.06550925925925970.905324074074074
835.75.928935185185196.00416666666667-0.0752314814814815-0.228935185185185
846.95.410879629629636.15-0.739120370370371.48912037037037
856.16.047916666666676.22083333333333-0.1729166666666670.052083333333333
864.85.57754629629636.1375-0.559953703703703-0.777546296296296
873.75.462268518518526.04166666666667-0.579398148148148-1.76226851851852
885.84.950694444444445.81666666666667-0.8659722222222220.849305555555556
896.85.762268518518525.48750.2747685185185181.03773148148148
908.55.563657407407415.066666666666670.4969907407407412.93634259259259
917.25.258564814814814.608333333333330.6502314814814811.94143518518519
9255.144675925925934.304166666666670.84050925925926-0.144675925925926
934.74.768754.104166666666670.664583333333333-0.0687499999999996
942.33.778009259259263.71250.0655092592592597-1.47800925925926
952.43.053935185185193.12916666666667-0.0752314814814815-0.653935185185185
960.11.719212962962962.45833333333333-0.73912037037037-1.61921296296296
971.91.439583333333331.6125-0.1729166666666670.460416666666666
981.70.2900462962962960.85-0.5599537037037031.40995370370370
992-0.583564814814815-0.00416666666666674-0.5793981481481482.58356481481482
100-1.9-2.07430555555556-1.20833333333333-0.8659722222222220.174305555555555
1010.5-2.62106481481481-2.895833333333330.2747685185185183.12106481481481
102-1.3-4.61550925925926-5.11250.4969907407407413.31550925925926
103-3.3-6.89560185185185-7.545833333333330.6502314814814813.59560185185185
104-2.8-9.3136574074074-10.15416666666670.840509259259266.51365740740741
105-8-12.2770833333333-12.94166666666670.6645833333333334.27708333333333
106-13.9-15.4303240740741-15.49583333333330.06550925925925971.53032407407407
107-21.9-17.8877314814815-17.8125-0.0752314814814815-4.01226851851851
108-28.8-20.6516203703704-19.9125-0.73912037037037-8.14837962962963
109-27.6-21.8270833333333-21.6541666666667-0.172916666666667-5.77291666666667
110-31.4-23.6682870370370-23.1083333333333-0.559953703703703-7.73171296296296
111-31.8-24.7377314814815-24.1583333333333-0.579398148148148-7.06226851851853
112-29.4-25.4451388888889-24.5791666666667-0.865972222222222-3.95486111111111
113-27.6-23.7710648148148-24.04583333333330.274768518518518-3.82893518518519
114-23.6-22.1321759259259-22.62916666666670.496990740740741-1.46782407407408
115-22.8-20.2497685185185-20.90.650231481481481-2.55023148148149
116-18.2-18.1844907407407-19.0250.84050925925926-0.0155092592592609
117-17.8-16.16875-16.83333333333330.664583333333333-1.63125
118-14.2-14.4678240740741-14.53333333333330.06550925925925970.267824074074074
119-8.8-12.5377314814815-12.4625-0.07523148148148153.73773148148148
120-7.9-11.5932870370370-10.8541666666667-0.739120370370373.69328703703704
121-7NA-9.5125NANA
122-7NA-8.2875NANA
123-3.6NA-7.14166666666667NANA
124-2.4NA-6.06666666666667NANA
125-4.9NA-5.19166666666667NANA
126-7.7NANANANA
127-6.5NANANANA
128-5.1NANANANA
129-3.4NANANANA
130-2.8NANANANA
1310.8NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187776g4pn6vze5shzw41/1nkje1293187889.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187776g4pn6vze5shzw41/1nkje1293187889.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187776g4pn6vze5shzw41/2xt0z1293187889.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187776g4pn6vze5shzw41/2xt0z1293187889.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187776g4pn6vze5shzw41/3xt0z1293187889.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187776g4pn6vze5shzw41/3xt0z1293187889.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187776g4pn6vze5shzw41/482h21293187889.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187776g4pn6vze5shzw41/482h21293187889.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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