Home » date » 2010 » Nov » 29 »

*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: Mon, 29 Nov 2010 18:02:06 +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/Nov/29/t1291056101kcru6xb1yc9lx0p.htm/, Retrieved Mon, 29 Nov 2010 19:41:46 +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/Nov/29/t1291056101kcru6xb1yc9lx0p.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 «
9700 9081 9084 9743 8587 9731 9563 9998 9437 10038 9918 9252 9737 9035 9133 9487 8700 9627 8947 9283 8829 9947 9628 9318 9605 8640 9214 9567 8547 9185 9470 9123 9278 10170 9434 9655 9429 8739 9552 9687 9019 9672 9206 9069 9788 10312 10105 9863 9656 9295 9946 9701 9049 10190 9706 9765 9893 9994 10433 10073 10112 9266 9820 10097 9115 10411 9678 10408 10153 10368 10581 10597 10680 9738 9556
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
19700NANA-17.9936868686869NA
29081NANA-264.016414141414NA
39084NANA-65.5012626262627NA
497439622.241161616169309.58333333333312.657828282829120.758838383837
585879240.839646464649374.58333333333-133.743686868687-653.839646464645
697319649.013888888899480.41666666667168.59722222222281.9861111111113
795639516.422979797989534.41666666666-17.993686868686946.5770202020230
899989405.900252525259669.91666666667-264.016414141414592.099747474749
994379675.41540404049740.91666666667-65.5012626262627-238.415404040403
101003810028.15782828289715.5312.6578282828299.84217171717319
1199189516.006313131319649.75-133.743686868687401.993686868687
1292529712.763888888899544.16666666667168.597222222222-460.763888888889
1397379454.922979797989472.91666666667-17.9936868686869282.077020202021
1490359061.483585858589325.5-264.016414141414-26.4835858585848
1591339189.748737373749255.25-65.5012626262627-56.7487373737367
1694879533.32449494959220.66666666667312.657828282829-46.3244949494947
1787009041.756313131319175.5-133.743686868687-341.756313131311
1896279339.430555555569170.83333333333168.597222222222287.569444444443
1989479165.839646464659183.83333333333-17.9936868686869-218.839646464647
2092839035.483585858589299.5-264.016414141414247.516414141415
2188299285.582070707079351.08333333333-65.5012626262627-456.582070707069
2299479692.82449494959380.16666666667312.657828282829254.175505050505
2396289247.672979797989381.41666666667-133.743686868687380.327020202021
2493189528.513888888899359.91666666667168.597222222222-210.513888888889
2596059342.339646464649360.33333333333-17.9936868686869262.660353535355
2686408974.566919191929238.58333333333-264.016414141414-334.566919191919
2792149071.91540404049137.41666666667-65.5012626262627142.084595959597
2895679427.741161616169115.08333333333312.657828282829139.258838383839
2985479010.339646464649144.08333333333-133.743686868687-463.339646464645
3091859358.263888888899189.66666666667168.597222222222-173.263888888889
3194709227.256313131319245.25-17.9936868686869242.743686868687
3291239105.400252525259369.41666666667-264.01641414141417.5997474747492
3392789416.998737373749482.5-65.5012626262627-138.998737373737
34101709830.907828282839518.25312.657828282829339.092171717173
3594349349.089646464659482.83333333333-133.74368686868784.9103535353534
3696559642.263888888899473.66666666667168.59722222222212.7361111111113
3794299438.256313131319456.25-17.9936868686869-9.25631313131271
3887399117.400252525259381.41666666667-264.016414141414-378.400252525251
3995529282.748737373749348.25-65.5012626262627269.251262626263
4096879643.741161616169331.08333333333312.65782828282943.2588383838392
4190199206.256313131319340-133.743686868687-187.256313131313
4296729555.763888888899387.16666666667168.597222222222116.236111111111
4392069440.922979797989458.91666666667-17.9936868686869-234.922979797979
4490699337.483585858589601.5-264.016414141414-268.483585858585
4597889642.41540404049707.91666666667-65.5012626262627145.584595959597
461031210073.99116161629761.33333333333312.657828282829238.008838383841
47101059683.922979797989817.66666666667-133.743686868687421.077020202021
48986310018.26388888899849.66666666666168.597222222222-155.263888888887
4996569793.922979797989811.91666666667-17.9936868686869-137.922979797979
5092959408.983585858589673-264.016414141414-113.983585858585
5199469546.748737373739612.25-65.5012626262627399.251262626265
5297019956.32449494959643.66666666666312.657828282829-255.324494949493
5390499553.256313131319687-133.743686868687-504.256313131313
54101909890.347222222229721.75168.597222222222299.652777777777
5597069723.756313131319741.75-17.9936868686869-17.7563131313109
5697659617.483585858589881.5-264.016414141414147.516414141415
5798939921.582070707079987.08333333333-65.5012626262627-28.5820707070689
58999410323.824494949510011.1666666667312.657828282829-329.824494949495
59104339869.6729797979810003.4166666667-133.743686868687563.327020202021
601007310124.34722222229955.75168.597222222222-51.3472222222208
61101129940.256313131319958.25-17.9936868686869171.743686868687
6292669592.983585858589857-264.016414141414-326.983585858583
6398209709.832070707079775.33333333333-65.5012626262627110.167929292931
641009710079.99116161629767.33333333333312.65782828282917.0088383838392
6591159692.589646464649826.33333333333-133.743686868687-577.589646464645
661041110117.84722222229949.25168.597222222222293.152777777777
6796789981.589646464649999.58333333333-17.9936868686869-303.589646464645
68104089880.3169191919210144.3333333333-264.016414141414527.683080808083
691015310216.498737373710282-65.5012626262627-63.4987373737367
701036810693.657828282810381312.657828282829-325.657828282827
711058110274.922979798010408.6666666667-133.743686868687306.077020202021
721059710471.680555555610303.0833333333168.597222222222125.319444444443
7310680NANANANA
749738NANANANA
759556NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056101kcru6xb1yc9lx0p/1sml41291053721.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056101kcru6xb1yc9lx0p/1sml41291053721.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056101kcru6xb1yc9lx0p/23d3p1291053721.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056101kcru6xb1yc9lx0p/23d3p1291053721.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056101kcru6xb1yc9lx0p/33d3p1291053721.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056101kcru6xb1yc9lx0p/33d3p1291053721.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056101kcru6xb1yc9lx0p/4vnks1291053721.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056101kcru6xb1yc9lx0p/4vnks1291053721.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 6 ;
 
Parameters (R input):
par1 = additive ; par2 = 6 ;
 
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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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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