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*The author of this computation has been verified*

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

Title produced by software: Structural Time Series Models

Date of computation: Wed, 29 Dec 2010 13:56:47 +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/29/t1293630877x6d0teh9w00k0yl.htm/, Retrieved Wed, 29 Dec 2010 14:54:37 +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/29/t1293630877x6d0teh9w00k0yl.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 «

3106.54 3125.67 3039.71 3051.67 3112.83 3228.01 3223.98 3328.8 3264.26 3394.14 3549.25 3744.63 3839.25 3912.28 3911.06 3675.8 3703.32 3795.91 3906.01 4070.78 4144.38 4140.3 4388.53 4433.57 4305.23 4471.65 4614.76 4697.86 4639.4 4384.47 4350.83 4325.29 4441.82 4162.5 4127.47 3722.23 3757.12 3719.52 3925.43 3751.41 3168.22 2994.38 3136 2672.2 2100.18 1881.46 1908.64 1900.09 1696.58 1748.74 1953.35 2071.37 2030.98 2169.14 2229.85 2480.93 2525.93 2475.14 2529.66 2453.37 2386.53 2517.3 2457.46 2589.73 2679.07 2506.13 2592.31

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	3106.54	3106.54	0	0	0
2	3125.67	3124.68931868883	1.25142511248251	0.980681311167245	0.0701190497998039
3	3039.71	3038.87740252377	-5.77041747953507	0.8325974762276	-0.54236220066074
4	3051.67	3050.81063743179	-3.80283903738118	0.859362568212891	0.108422757246245
5	3112.83	3111.8857531477	4.92877930492268	0.944246852301847	0.391980985245461
6	3228.01	3226.94353498845	21.6451520403553	1.06646501154602	0.658600675593157
7	3223.98	3222.93733404812	17.4387849629822	1.04266595188172	-0.152266368909523
8	3328.8	3327.69029189364	32.4928772627493	1.10970810636111	0.515588687799304
9	3264.26	3263.21148532205	15.2181188833303	1.04851467794636	-0.570545983250874
10	3394.14	3393.03232733386	36.0746689938147	1.10767266614356	0.672615757781974
11	3549.25	3548.09224238842	58.0337769089457	1.15775761158419	0.697169089904114
12	3744.63	3743.42521266667	83.6059240569058	1.2047873333312	0.80356803168967
13	3839.25	3844.94308056049	86.7855505421687	-5.69308056049349	0.122668586958991
14	3912.28	3912.20420463339	83.1972269985107	0.0757953666066259	-0.0964490344360934
15	3911.06	3910.93509835293	67.2652808809289	0.124901647072729	-0.49312600793646
16	3675.8	3675.53237634171	10.0896577318258	0.267623658294913	-1.76692172616402
17	3703.32	3703.05904387715	13.3869450091625	0.260956122850837	0.101792253315551
18	3795.91	3795.67360622699	28.3788430795033	0.236393773007878	0.462508757414791
19	3906.01	3905.79415049052	43.8535518981215	0.215849509477651	0.477191059563207
20	4070.78	4070.58879061794	66.7562787240598	0.191209382057146	0.706040926340599
21	4144.38	4144.18992102251	68.0527679149845	0.190078977488116	0.0399602384641640
22	4140.3	4140.10026392249	54.3862365250903	0.199736077512712	-0.421175029116889
23	4388.53	4388.35129836297	91.1152957864269	0.178701637024952	1.13182186307676
24	4433.57	4433.38724600962	82.384631289529	0.182753990375473	-0.269024541656719
25	4305.23	4342.05554062082	49.9482114462747	-36.8255406208213	-1.10065174031667
26	4471.65	4467.21204086424	63.9034819759635	4.43795913575861	0.397606353006847
27	4614.76	4610.35870593058	78.9267014534757	4.40129406942431	0.462418524703577
28	4697.86	4693.4602714645	79.7180182202672	4.39972853550139	0.0243651508779043
29	4639.4	4634.95826327564	53.5226986406372	4.44173672435856	-0.80674947354213
30	4384.47	4379.9522641173	-4.94509533420086	4.51773588270437	-1.80092209209552
31	4350.83	4346.30653406083	-10.3838032240848	4.52346593917359	-0.167538833016132
32	4325.29	4320.76408113751	-13.2562815124459	4.52591886248543	-0.0884919552986306
33	4441.82	4437.31110530555	11.3405554486716	4.50889469444948	0.757781890025354
34	4162.5	4157.96020455389	-43.743515708324	4.53979544610951	-1.69708175270366
35	4127.47	4122.93095535312	-42.0922140183972	4.53904464687843	0.0508757579892298
36	3722.23	3717.66559458979	-110.911768822306	4.56440541021064	-2.12031998516170
37	3757.12	3784.04264720296	-77.619078703553	-26.9226472029562	1.09406009017536
38	3719.52	3716.95981732916	-75.6537911808403	2.56018267084147	0.0573054390452646
39	3925.43	3922.96779140471	-22.2590058945037	2.46220859529227	1.64384351828422
40	3751.41	3748.90499585756	-51.031564945706	2.50500414243667	-0.886045379760065
41	3168.22	3165.59337741966	-151.908330085841	2.6266225803372	-3.10702481330344
42	2994.38	2991.74931522539	-156.065302492253	2.63068477460612	-0.128050331154495
43	3136	3133.41400249031	-99.645256832255	2.58599750968583	1.73808029465606
44	2672.2	2669.5696973351	-168.660130616372	2.63030266489928	-2.1261831504745
45	2100.18	2097.50992273194	-245.102955491629	2.67007726806079	-2.3550973951135
46	1881.46	1878.79203127967	-240.103080448809	2.66796872032513	0.154042516060985
47	1908.64	1905.98934456817	-189.45048554638	2.65065543183082	1.56059160643903
48	1900.09	1897.44884179425	-155.168468452039	2.64115820574881	1.05622867030751
49	1696.58	1727.11381190715	-158.023333490752	-30.5338119071476	-0.0923402614704458
50	1748.74	1744.47797756568	-125.201320375242	4.26202243431985	0.96902374367647
51	1953.35	1949.17923184803	-62.6659046733559	4.17076815196565	1.92555722464145
52	2071.37	2067.23974768834	-28.4126247492852	4.1302523116587	1.05493282760480
53	2030.98	2026.84757103887	-30.6829354104333	4.13242896112935	-0.0699308060339351
54	2169.14	2165.03243923776	1.31860504833264	4.10756076224194	0.985811634930953
55	2229.85	2225.74952889171	12.5747092012422	4.10047110828534	0.346766638977508
56	2480.93	2476.85260397090	57.7756229441186	4.07739602909665	1.39255898252069
57	2525.93	2521.85160218872	55.3544758297975	4.07839781127486	-0.0745931329995134
58	2475.14	2471.05485634622	35.2390211783279	4.08514365377511	-0.619747716700826
59	2529.66	2525.57584949996	38.8929255246296	4.08415050003521	0.112576340233638
60	2453.37	2449.28104083207	17.0649560720047	4.08895916792645	-0.672521988120713
61	2386.53	2434.0207493835	10.9718434668685	-47.4907493834995	-0.195202294185455
62	2517.3	2512.50885871272	23.6342537011742	4.79114128727589	0.376783040432569
63	2457.46	2452.64970227108	7.80822338694688	4.81029772892422	-0.487358129773467
64	2589.73	2584.94285081711	31.4014277666903	4.7871491828858	0.726674099295957
65	2679.07	2674.29158426867	42.3832324150107	4.77841573133275	0.338280323511151
66	2506.13	2501.32527878227	1.57317091135902	4.80472121772591	-1.25719688562009
67	2592.31	2587.51365605637	17.6079268773612	4.79634394363318	0.493992204352127

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630877x6d0teh9w00k0yl/1s90x1293631002.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630877x6d0teh9w00k0yl/1s90x1293631002.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630877x6d0teh9w00k0yl/2s90x1293631002.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630877x6d0teh9w00k0yl/2s90x1293631002.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630877x6d0teh9w00k0yl/3l0h01293631002.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630877x6d0teh9w00k0yl/3l0h01293631002.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630877x6d0teh9w00k0yl/4eayl1293631002.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630877x6d0teh9w00k0yl/4eayl1293631002.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630877x6d0teh9w00k0yl/5eayl1293631002.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630877x6d0teh9w00k0yl/5eayl1293631002.ps (open in new window)

Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 12 ;

R code (references can be found in the software module):

par1 <- as.numeric(par1)

nx <- length(x)

x <- ts(x,frequency=par1)

m <- StructTS(x,type='BSM')

m$coef

m$fitted

m$resid

mylevel <- as.numeric(m$fitted[,'level'])

myslope <- as.numeric(m$fitted[,'slope'])

myseas <- as.numeric(m$fitted[,'sea'])

myresid <- as.numeric(m$resid)

myfit <- mylevel+myseas

mylagmax <- nx/2

bitmap(file='test2.png')

op <- par(mfrow = c(2,2))

acf(as.numeric(x),lag.max = mylagmax,main='Observed')

acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')

acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')

acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')

par(op)

dev.off()

bitmap(file='test3.png')

op <- par(mfrow = c(2,2))

spectrum(as.numeric(x),main='Observed')

spectrum(mylevel,main='Level')

spectrum(myseas,main='Seasonal')

spectrum(myresid,main='Standardized Residals')

par(op)

dev.off()

bitmap(file='test4.png')

op <- par(mfrow = c(2,2))

cpgram(as.numeric(x),main='Observed')

cpgram(mylevel,main='Level')

cpgram(myseas,main='Seasonal')

cpgram(myresid,main='Standardized Residals')

par(op)

dev.off()

bitmap(file='test1.png')

plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')

grid()

dev.off()

bitmap(file='test5.png')

op <- par(mfrow = c(2,2))

hist(m$resid,main='Residual Histogram')

plot(density(m$resid),main='Residual Kernel Density')

qqnorm(m$resid,main='Residual Normal QQ Plot')

qqline(m$resid)

plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')

par(op)

dev.off()

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Structural Time Series Model',6,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'t',header=TRUE)

a<-table.element(a,'Observed',header=TRUE)

a<-table.element(a,'Level',header=TRUE)

a<-table.element(a,'Slope',header=TRUE)

a<-table.element(a,'Seasonal',header=TRUE)

a<-table.element(a,'Stand. Residuals',header=TRUE)

a<-table.row.end(a)

for (i in 1:nx) {

a<-table.row.start(a)

a<-table.element(a,i,header=TRUE)

a<-table.element(a,x[i])

a<-table.element(a,mylevel[i])

a<-table.element(a,myslope[i])

a<-table.element(a,myseas[i])

a<-table.element(a,myresid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable.tab')

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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