Decompositie Toegekende bouwvergunningen voor het aantal woningen

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 11 Jan 2010 08:26:40 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/11/t1263223677ccg86pvxgvrw8sd.htm/, Retrieved Mon, 11 Jan 2010 16:28:02 +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/Jan/11/t1263223677ccg86pvxgvrw8sd.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

2766 3851 3289 3848 3348 3682 4058 3655 3811 3341 3032 3475 3353 3186 3902 4164 3499 4145 3796 3711 3949 3740 3243 4407 4814 3908 5250 3937 4004 5560 3922 3759 4138 4634 3996 4308 4143 4429 5219 4929 5761 5592 4163 4962 5208 4755 4491 5732 5731 5040 6102 4904 5369 5578 4619 4731 5011 5299 4146 4625 4736 4219 5116 4205 4121 5103 4300 4578 3809 5526 4248 3830 4430 4837 4408 4569 4104 4807 3944 3794 4390 4041 4104 4823

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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 2766 NA NA 75.3975694444447 NA 2 3851 NA NA -189.442708333333 NA 3 3289 NA NA 535.237847222222 NA 4 3848 NA NA -21.8107638888888 NA 5 3348 NA NA -9.11631944444453 NA 6 3682 NA NA 628.578125 NA 7 4058 3297.49479166667 3537.45833333333 -239.963541666667 760.505208333334 8 3655 3365.50868055556 3534.20833333333 -168.699652777778 289.491319444444 9 3811 3437.05729166667 3532.04166666667 -94.9843750000001 373.942708333334 10 3341 3691.15451388889 3570.75 120.404513888889 -350.154513888889 11 3032 3010.52256944444 3590.20833333333 -579.685763888889 21.4774305555552 12 3475 3559.87673611111 3615.79166666667 -55.9149305555556 -84.8767361111109 13 3353 3699.56423611111 3624.16666666667 75.3975694444447 -346.564236111111 14 3186 3426.140625 3615.58333333333 -189.442708333333 -240.140625000000 15 3902 4158.90451388889 3623.66666666667 535.237847222222 -256.904513888888 16 4164 3624.23090277778 3646.04166666667 -21.8107638888888 539.769097222222 17 3499 3662.34201388889 3671.45833333333 -9.11631944444453 -163.342013888889 18 4145 4347.66145833333 3719.08333333333 628.578125 -202.661458333333 19 3796 3578.828125 3818.79166666667 -239.963541666667 217.171875 20 3711 3741.05034722222 3909.75 -168.699652777778 -30.0503472222222 21 3949 3901.015625 3996 -94.9843750000001 47.9843749999995 22 3740 4163.11284722222 4042.70833333333 120.404513888889 -423.112847222222 23 3243 3474.60590277778 4054.29166666667 -579.685763888889 -231.605902777778 24 4407 4078.37673611111 4134.29166666667 -55.9149305555556 328.623263888890 25 4814 4273.89756944444 4198.5 75.3975694444447 540.102430555556 26 3908 4016.30729166667 4205.75 -189.442708333333 -108.307291666667 27 5250 4750.86284722222 4215.625 535.237847222222 499.137152777778 28 3937 4238.93923611111 4260.75 -21.8107638888888 -301.939236111110 29 4004 4320.25868055556 4329.375 -9.11631944444453 -316.258680555556 30 5560 4985.203125 4356.625 628.578125 574.796875 31 3922 4084.578125 4324.54166666667 -239.963541666667 -162.578124999999 32 3759 4149.59201388889 4318.29166666667 -168.699652777778 -390.592013888888 33 4138 4243.72395833333 4338.70833333333 -94.9843750000001 -105.723958333333 34 4634 4499.15451388889 4378.75 120.404513888889 134.845486111110 35 3996 3913.60590277778 4493.29166666667 -579.685763888889 82.3940972222226 36 4308 4511.91840277778 4567.83333333333 -55.9149305555556 -203.918402777777 37 4143 4654.60590277778 4579.20833333333 75.3975694444447 -511.605902777778 38 4429 4449.93229166667 4639.375 -189.442708333333 -20.9322916666670 39 5219 5269.32118055556 4734.08333333333 535.237847222222 -50.3211805555566 40 4929 4761.89756944444 4783.70833333333 -21.8107638888888 167.102430555556 41 5761 4800.25868055556 4809.375 -9.11631944444453 960.741319444444 42 5592 5517.91145833333 4889.33333333333 628.578125 74.088541666667 43 4163 4774.86979166667 5014.83333333333 -239.963541666667 -611.869791666666 44 4962 4937.75868055556 5106.45833333333 -168.699652777778 24.2413194444443 45 5208 5073.72395833333 5168.70833333333 -94.9843750000001 134.276041666667 46 4755 5324.86284722222 5204.45833333333 120.404513888889 -569.862847222222 47 4491 4607.39756944445 5187.08333333333 -579.685763888889 -116.397569444445 48 5732 5114.25173611111 5170.16666666667 -55.9149305555556 617.748263888889 49 5731 5263.98090277778 5188.58333333333 75.3975694444447 467.019097222223 50 5040 5008.515625 5197.95833333333 -189.442708333333 31.484375 51 6102 5715.36284722222 5180.125 535.237847222222 386.637152777777 52 4904 5172.77256944444 5194.58333333333 -21.8107638888888 -268.772569444444 53 5369 5193.75868055556 5202.875 -9.11631944444453 175.241319444444 54 5578 5770.953125 5142.375 628.578125 -192.953124999999 55 4619 4814.828125 5054.79166666667 -239.963541666667 -195.828125 56 4731 4810.42534722222 4979.125 -168.699652777778 -79.4253472222217 57 5011 4808.84895833333 4903.83333333333 -94.9843750000001 202.151041666666 58 5299 4954.02951388889 4833.625 120.404513888889 344.970486111111 59 4146 4172.81423611111 4752.5 -579.685763888889 -26.8142361111104 60 4625 4624.79340277778 4680.70833333333 -55.9149305555556 0.206597222221717 61 4736 4723.02256944444 4647.625 75.3975694444447 12.9774305555566 62 4219 4438.515625 4627.95833333333 -189.442708333333 -219.515625 63 5116 5106.73784722222 4571.5 535.237847222222 9.26215277777737 64 4205 4509.06423611111 4530.875 -21.8107638888888 -304.064236111111 65 4121 4535.46701388889 4544.58333333333 -9.11631944444453 -414.467013888889 66 5103 5144.28645833333 4515.70833333333 628.578125 -41.286458333333 67 4300 4229.86979166667 4469.83333333333 -239.963541666667 70.130208333334 68 4578 4314.13368055555 4482.83333333333 -168.699652777778 263.866319444445 69 3809 4384.09895833333 4479.08333333333 -94.9843750000001 -575.098958333333 70 5526 4585.15451388889 4464.75 120.404513888889 940.845486111112 71 4248 3899.52256944444 4479.20833333333 -579.685763888889 348.477430555556 72 3830 4410.25173611111 4466.16666666667 -55.9149305555556 -580.25173611111 73 4430 4514.39756944444 4439 75.3975694444447 -84.3975694444434 74 4837 4202.05729166667 4391.5 -189.442708333333 634.942708333333 75 4408 4918.27951388889 4383.04166666667 535.237847222222 -510.279513888888 76 4569 4323.56423611111 4345.375 -21.8107638888888 245.435763888889 77 4104 4268.38368055556 4277.5 -9.11631944444453 -164.383680555556 78 4807 4941.453125 4312.875 628.578125 -134.453125 79 3944 NA NA -239.963541666667 NA 80 3794 NA NA -168.699652777778 NA 81 4390 NA NA -94.9843750000001 NA 82 4041 NA NA 120.404513888889 NA 83 4104 NA NA -579.685763888889 NA 84 4823 NA NA -55.9149305555556 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/11/t1263223677ccg86pvxgvrw8sd/1bu191263223597.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263223677ccg86pvxgvrw8sd/1bu191263223597.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263223677ccg86pvxgvrw8sd/2ogex1263223597.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263223677ccg86pvxgvrw8sd/2ogex1263223597.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263223677ccg86pvxgvrw8sd/38m4t1263223597.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263223677ccg86pvxgvrw8sd/38m4t1263223597.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263223677ccg86pvxgvrw8sd/4uftd1263223597.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263223677ccg86pvxgvrw8sd/4uftd1263223597.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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