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ARIMA berekening parameters

*The author of this computation has been verified*
R Software Module: /rwasp_arimabackwardselection.wasp (opens new window with default values)
Title produced by software: ARIMA Backward Selection
Date of computation: Sun, 19 Dec 2010 10:52:23 +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/19/t1292755857u8iufups7l5izlg.htm/, Retrieved Sun, 19 Dec 2010 11:50:59 +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/19/t1292755857u8iufups7l5izlg.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 «
126.64 126.81 125.84 126.77 124.34 124.4 120.48 118.54 117.66 116.97 120.11 119.16 116.9 116.11 114.98 113.65 115.82 117.59 118.57 118.07 114.98 114.04 115.02 114.28 115.04 116.7 119.21 118.39 116.5 115.46 117.59 117.33 116.2 116.83 118.99 118.62 121.09 122.4 123.76 125.33 123.23 122.52 123.64 124.67 124.71 122.53 124.4 125.45 125.35 124.3 127.03 128.51 128.1 128.94 129.67 129.87 131.12 132.68 132.24 133.63 129.91 127.93 131.17 130.86 133.48 134.08 136.02 132.8 132.37 133.05 132.57 130.7 130.5 129.67 127.8 126.82 126.85 128.28 128.3 126.82 125.08 128.53 130.34 131.52 132.59 131.17 132.72 133.36 132.82 132.9 130.9 129.41 128.67 129.28 130.91 131.06 130.84 131.41 133.22 132.06 132.48 134.38 135.22 134.89 136.09 136.33 136.32 137.48 136.53 136.8 138.03 137.39 137.55 136.08 134.78 133.28 133.57 134.84 133.02 133.49 133.77 134.34 134.5 134.03 135.51 136.53 135.95 134.32 132.44 133.61 13 etc...
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.0051-0.0097-0.0033-0.0049-0.0096-0.0622-0.009
(p-val)(0.9997 )(0.9488 )(0.9828 )(0.9997 )(0.9836 )(0.1937 )(0.9846 )
Estimates ( 2 )-0.0102-0.0097-0.00330-0.0096-0.0621-0.009
(p-val)(0.8225 )(0.833 )(0.9424 )(NA )(0.9834 )(0.1944 )(0.9846 )
Estimates ( 3 )-0.0102-0.0097-0.00340-0.0187-0.06220
(p-val)(0.8226 )(0.8321 )(0.9409 )(NA )(0.681 )(0.186 )(NA )
Estimates ( 4 )-0.0102-0.009700-0.0188-0.06240
(p-val)(0.8228 )(0.833 )(NA )(NA )(0.6784 )(0.1843 )(NA )
Estimates ( 5 )-0.0102000-0.0186-0.06390
(p-val)(0.822 )(NA )(NA )(NA )(0.6808 )(0.1682 )(NA )
Estimates ( 6 )0000-0.0184-0.06290
(p-val)(NA )(NA )(NA )(NA )(0.6852 )(0.1732 )(NA )
Estimates ( 7 )00000-0.06240
(p-val)(NA )(NA )(NA )(NA )(NA )(0.1763 )(NA )
Estimates ( 8 )0000000
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )


Estimated ARIMA Residuals
Value
9.8829021514408
25.1727721362117
-143.224749290191
137.299009879993
-356.830797785976
8.73577374074307
-562.757695748934
-272.502931586513
-122.29636417686
-95.3176400264975
437.838155990306
-132.254586972353
-323.481254152358
-100.079169834866
-176.594062029674
-179.298516652076
259.208413889482
226.799993088809
128.791030920731
-75.6997889519106
-397.71661374814
-135.656282554545
113.110882164298
-107.110152644543
93.4184352410888
215.989295398737
367.49191918076
-99.5704527956987
-253.342460781329
-146.825360095868
266.496580798974
-43.9724811325479
-147.459808383219
80.4019595096013
306.764124861866
-37.6159412038419
370.319918666718
180.305099205004
180.201012846463
220.430237603478
-287.860479852892
-104.702616971652
152.139640922538
155.432244311505
24.5936265213595
-319.525768753207
292.762459327067
165.490368802721
-2.4296621857451
-139.366000536308
382.875693997246
214.813595399582
-51.409237816454
135.577207012978
110.653533109465
10.5727686811715
207.328313919524
249.536183538232
-68.8505522993543
205.70812050831
-546.560582649917
-284.530257845247
486.493995486841
-39.514435192027
410.597562237765
95.3586953849259
316.674019285569
-488.753594991385
-70.6856897501727
118.603772077774
-109.964071256035
-305.717629160136
0.115301514530859
-129.034983865266
-256.201217427831
-140.16739519337
23.4774801700292
181.986606561765
-1.14688089370861
-214.317082570723
-261.368948315224
494.227696421889
271.799391495076
172.420674139113
147.18351899653
-227.487416513597
238.750743547959
112.522081902066
-83.542188957932
-1.40094421723321
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-194.475664867237
-94.5102648946245
103.199730862031
257.896633237473
9.29723697670344
-18.7339648516091
93.3973324879904
273.951390710062
-178.542788372859
45.5624372844611
281.14764520602
124.785996132636
-46.1086467414106
204.53699473908
39.4282679932035
-3.68264802188257
189.933585661661
-133.77135182877
31.6836970725595
200.328014634149
-83.9002975681264
33.7654171453529
-236.899608355485
-192.956325298887
-231.688050852507
44.9687775655947
209.918116261126
-293.238260511046
75.6329590069107
55.8247276748507
82.5708222621747
26.625936782599
-88.0382571673429
219.306342291242
146.677269702649
-89.0322024154151
-243.848107413192
-309.78704160029
185.887533612943
-396.76579781618
-142.249549809934
-276.086300597571
118.710311140091
212.923670269894
198.0277602953
157.524635253794
-103.928056694542
351.199179035417
-41.8212848385921
-274.917107671602
261.062409817666
90.7323006793104
116.25522701185
-157.314097182527
-92.931114187884
14.8658110170979
220.083760509983
106.627120813591
53.6153411802572
168.767020765514
5.70402231245653
-60.1416885363711
185.548577057796
-216.385078600751
-156.973185142943
278.151688835967
-289.025501957786
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187.061948192986
-232.331584627925
241.580601841515
8.31862180781488
227.721424091698
-94.682128239864
-123.799510075575
-93.3976145744179
159.146612275674
-283.329965580411
36.2772912172818
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144.450891812383
-2.30812555226413
149.779965623523
32.195864374609
80.1359072224209
-39.6789559981208
-190.702488966218
-65.4640856242308
150.089597532049
-117.930816689242
284.815276599592
-33.0149902109166
210.856741275588
-55.888908287107
-14.9733905149322
-175.685358263032
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-224.519145699523
-27.4233322327779
18.0758748523778
-27.0343571689969
-128.369846870053
235.316351677703
-34.27093333877
-82.260441504908
-44.3676818431904
88.5385147178837
274.100969603567
2.35873273819514
58.9411910250174
103.12792277612
232.146734911865
23.3616427872956
-22.4104199658228
40.6964521023383
169.064604762783
177.062982707857
37.1972526227794
-188.12156172169
-335.965303169582
64.6985392817552
-58.7833131756977
-265.834125285238
-109.45782497938
321.114648782128
10.6825777816471
82.677986527417
9.04156200899286
-58.7694176838751
72.9215808929548
-120.212728710027
-130.898548536657
62.8100997472963
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117.538200512134
-207.253068388893
-23.2014601893583
179.574015836321
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91.1816264217832
178.063831976795
87.2713707249523
237.012429752848
-15.6868518440433
35.9978166763836
-389.194109341018
-130.811616121024
4.81989561785278
153.454501852376
-235.043887086269
113.508467156955
64.442727566805
-187.741671958057
61.1449605816768
65.2611073058485
87.1647787516664
10.4474898235405
120.647107221573
30.4449160538532
74.3539034811562
-7.54605350236868
129.015502334244
58.7784304274092
-163.576874023971
27.1599644977489
132.537479900087
-20.61352549257
142.999466329536
-281.390882951229
99.9206567807762
-22.5539671647098
-318.069841080452
-61.0641849109393
-92.462903064181
-170.682445898258
81.6570457604835
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66.6585561379122
-230.253454397422
-47.2970991913563
59.2295228156541
-51.3925870429309
-85.1973014927171
83.3782113080257
67.7446602448548
-150.599879494797
-481.152420915677
-56.7100071148916
178.4540889762
-59.7330876936225
6.67031476961107
-102.8048428167
7.87732594755434
42.9524857920783
296.664644693889
-211.910718297252
97.3479361384955
26.8476745656295
-160.896610157745
-151.933721068383
-213.94776153755
30.6494531304762
-34.717685737759
3.75583048259215
77.9349473034278
-46.7728292049959
96.233745987631
289.678279678548
-264.247813145797
197.868836404557
85.209534698684
108.671566279121
-41.6235675102775
-11.5725498525807
34.35260529218
-162.031058474254
-62.4167831575483
-212.968729548336
114.369135101023
79.0707084732375
124.613901759207
87.9474785743971
106.954930514092
6.58758865247771
167.942955927099
41.7188934016035
-39.7043702853742
-161.528330825655
-178.642132759716
234.919710674531
251.220898037525
-91.320958908944
167.238840123492
-183.613183133463
-119.284293339932
-259.415238414914
263.365933583769
-107.356912197852
-102.083233454655
172.183625600811
66.6143577697381
-117.45516996951
-22.1330436474654
29.4319777662165
146.096733193449
-142.766386139072
-171.52161926893
-150.730101590288
-478.138932327139
-199.799030443454
443.374425759606
-510.935214060447
92.4058259170807
-145.784701736162
-188.090205972288
-195.293858665036
144.29140714511
-390.466345562635
-103.958258144308
6.82916225309702
-32.1975249719658
-307.056576040527
244.474962904346
-83.0451806805802
288.830961002997
-70.5029289344419
-251.020708763124
202.299933347073
180.098554414015
-267.169156603378
-249.57008406883
-87.499025447688
102.158609116072
-24.1845667693807
187.964390497348
168.605543764332
-103.963447036411
64.5427596671194
110.179565402546
-119.388417370059
115.392920435408
-271.427811867371
-37.9837065497413
-154.265213342602
62.7162606865224
44.7467212439024
-257.380665482884
65.100463597948
-77.6783748631023
219.02952445611
14.6856799876608
52.6716167271928
-120.277525060799
243.625365108596
50.9203006481278
-64.0422233039901
-68.4615369800063
207.848715521541
77.6043363811025
-62.1013481338016
40.4544615956784
-173.806476816936
-4.6931563989365
22.4152173474262
123.805941192717
-26.7694577706602
194.842148006418
42.3828540779969
-44.6688267878656
-141.392836532752
89.4450152319165
21.0987516252915
-78.9244950680549
99.668976979859
-62.2670372541404
44.1984113796206
-21.1835419268396
-352.304472193776
-141.600927210998
27.937495152024
-32.3398269104383
24.1388060898737
30.2717462974955
-8.15846095366446
-170.42737644392
-37.1097262970396
-262.466370653915
15.9061844418859
75.3186867263258
77.728374472026
-27.9760120958033
-58.654579590975
62.2331400661732
33.6997394202275
62.9263944381416
2.66248401700575
-216.077830946214
-54.7854800503329
11.5805617978099
52.9820108751535
106.9397401315
-86.8168896934736
522.11562102648
119.821803081602
11.2097316656699
12.2721463846916
-15.128333130577
141.826781984961
-86.459735517564
97.908462080307
49.7197875813875
-45.4492831060915
117.019703838967
-15.5443648602552
78.6296758660052
-20.8666157884524
94.5900045256564
33.5246735705698
10.898221499247
-114.746623146289
6.7341671513744
-122.367571903719
123.78748937198
25.6190053423944
-15.4079195700951
-173.512568709149
27.339066495731
-74.6759842278556
143.004299551815
-70.635142718731
4.27387551033689
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166.693522266067
61.0381367391743
209.139584795657
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-205.546514969949
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136.374390927212
-95.5210929227566
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96.8922765821132
-3.08929330578903
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175.904639830942
43.6022744944424
-29.0347932189484
-200.749913303746
-153.133867227745
3.38901553561396
-58.2705148202512
-18.9738628248714
-226.558351063969
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/13z7x1292755936.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/13z7x1292755936.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/23z7x1292755936.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/23z7x1292755936.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/3er6i1292755936.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/3er6i1292755936.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/4er6i1292755936.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/4er6i1292755936.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/5er6i1292755936.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/5er6i1292755936.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/6er6i1292755936.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/6er6i1292755936.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/7er6i1292755936.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292755857u8iufups7l5izlg/7er6i1292755936.ps (open in new window)


 
Parameters (Session):
par1 = FALSE ; par2 = 1.9 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
Parameters (R input):
par1 = FALSE ; par2 = 1.9 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- 5 #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 





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