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*Unverified author*
R Software Module: rwasp_arimabackwardselection.wasp (opens new window with default values)
Title produced by software: ARIMA Backward Selection
Date of computation: Tue, 09 Dec 2008 10:15:46 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4.htm/, Retrieved Tue, 09 Dec 2008 17:16:24 +0000
 
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/2008/Dec/09/t1228842984n3kcj7ygco1o9t4.htm/},
    year = {2008},
}
@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 = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
 
Feedback Forum:
2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply
test

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Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
235.1 280.7 264.6 240.7 201.4 240.8 241.1 223.8 206.1 174.7 203.3 220.5 299.5 347.4 338.3 327.7 351.6 396.6 438.8 395.6 363.5 378.8 357 369 464.8 479.1 431.3 366.5 326.3 355.1 331.6 261.3 249 205.5 235.6 240.9 264.9 253.8 232.3 193.8 177 213.2 207.2 180.6 188.6 175.4 199 179.6 225.8 234 200.2 183.6 178.2 203.2 208.5 191.8 172.8 148 159.4 154.5 213.2 196.4 182.8 176.4 153.6 173.2 171 151.2 161.9 157.2 201.7 236.4 356.1 398.3 403.7 384.6 365.8 368.1 367.9 347 343.3 292.9 311.5 300.9 366.9 356.9 329.7 316.2 269 289.3 266.2 253.6 233.8 228.4 253.6 260.1 306.6 309.2 309.5 271 279.9 317.9 298.4 246.7 227.3 209.1 259.9 266 320.6 308.5 282.2 262.7 263.5 313.1 284.3 252.6 250.3 246.5 312.7 333.2 446.4 511.6 515.5 506.4 483.2 522.3 509.8 460.7 405.8 375 378.5 406.8 467.8 469.8 429.8 355.8 332.7 378 360.5 334.7 319.5 323.1 363.6 352.1 411.9 388.6 416.4 360.7 338 417.2 388.4 371.1 331.5 353.7 396.7 447 533.5 565.4 542.3 488.7 467.1 531.3 496.1 444 403.4 386.3 394.1 404.1 462.1 448.1 432.3 386.3 395.2 421.9 382.9 384.2 345.5 323.4 372.6 376 462.7 487 444.2 399.3 394.9 455.4 414 375.5 347 339.4 385.8 378.8 451.8 446.1 422.5 383.1 352.8 445.3 367.5 355.1 326.2 319.8 331.8 340.9 394.1 417.2 369.9 349.2 321.4 405.7 342.9 316.5 284.2 270.9 288.8 278.8 324.4 310.9 299 273 279.3 359.2 305 282.1 250.3 246.5 257.9 266.5 315.9 318.4 295.4 266.4 245.8 362.8 324.9 294.2 289.5 295.2 290.3 272 307.4 328.7 292.9 249.1 230.4 361.5 321.7 277.2 260.7 251 257.6 241.8 287.5 292.3 274.7 254.2 230 339 318.2 287 295.8 284 271 262.7 340.6 379.4 373.3 355.2 338.4 466.9 451 422 429.2 425.9 460.7 463.6 541.4 544.2 517.5 469.4 439.4 549 533 506.1 484 457 481.5 469.5 544.7 541.2 521.5 469.7 434.4 542.6 517.3 485.7 465.8 447 426.6 411.6 467.5 484.5 451.2 417.4 379.9 484.7 455 420.8 416.5 376.3 405.6 405.8 500.8 514 475.5 430.1 414.4 538 526 488.5 520.2 504.4 568.5 610.6 818 830.9 835.9 782 762.3 856.9 820.9 769.6 752.2 724.4 723.1 719.5 817.4 803.3 752.5 689 630.4 765.5 757.7 732.2 702.6 683.3 709.5 702.2 784.8 810.9 755.6 656.8 615.1 745.3 694.1 675.7 643.7 622.1 634.6 588 689.7 673.9 647.9 568.8 545.7 632.6 643.8 593.1 579.7 546 562.9 572.5
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.04620.9812-0.03280.9767-0.6823-0.43080.7665
(p-val)(0.4147 )(0 )(0.5305 )(0 )(0 )(0 )(0 )
Estimates ( 2 )0.13780.806600.8730.69550.2998-0.9339
(p-val)(0.1697 )(0 )(NA )(0 )(0 )(0 )(0 )
Estimates ( 3 )00.9682010.68580.31-0.9376
(p-val)(NA )(0 )(NA )(0 )(0 )(0 )(0 )
Estimates ( 4 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(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
22.3785073155383
40.9449857018719
-11.7150027759744
-15.1713693958593
-34.3192406388462
34.6631760058665
-2.57003677564028
-8.71495247639143
-18.7197205153073
-41.5946112279137
24.9125430421334
20.5749334592443
82.5417585252291
24.2163692201392
4.62749609264261
8.01689691681857
48.6340278590096
44.6048971467623
47.1904305314279
-24.8049728805717
-21.3029803937665
10.6098777435555
-6.62922775810455
21.647428568336
86.2790415800455
3.29898544923879
-43.0114635078522
-36.6161689286245
-18.3718890723237
14.0785737885902
-27.6732403804499
-62.0918340479678
-33.2510894698844
-58.289762212394
29.6124226955267
8.06447826142941
13.0251813753167
-44.1704698585794
-6.48022117345881
-14.8070855499533
-18.8694001535685
13.7481110091216
-9.39236539663431
-13.5127335761649
-7.51532768211567
-21.5986124585805
23.8843736213804
-3.91125719388281
39.1765263635293
-14.3497658942172
-22.4525666232903
1.38906581745941
-4.05314616469233
10.5301204298724
-1.86267895039721
3.54638836892512
-33.3781238017944
-33.1588762721732
13.7291653108998
12.6854374355306
41.3224691272465
-39.0774706392475
-6.41378158483881
12.4167534475623
-17.9665878349098
2.19484633401564
-4.91317140051753
-0.442253851361887
-9.4886256079616
-10.7572948819080
44.492834420456
48.7413691598119
111.959525862933
31.3669830862757
26.1937589559767
13.7006007092028
6.56818644899862
7.8333941940835
7.55496104147474
-2.98425628946832
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22.7603298846661
3.0441338005447
54.3507822883106
-31.2361550103708
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2.49482495779286
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13.7889641545598
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4.8967383934535
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16.8444684519205
23.0443832135876
26.4444989021694
-1.87217821624252
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10.2122491248942
34.284482209048
-17.6952382906016
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-13.688644288861
45.0073849358151
10.6072236061903
37.783704900585
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1.56670493216909
4.69437456903167
40.6459033639876
-35.8227197223535
-14.4179092764933
-17.6118160458099
5.29407811006421
57.5689669766441
33.0452626755540
100.907455435996
58.6378729261109
29.8553368917987
22.7483123939092
-0.0208610173209380
55.6320183563976
-6.94868498043625
-22.3207533358726
-73.6011794163601
-23.5693025302208
-0.0420883446120209
40.2537971632014
32.9859120834091
-11.1791200517189
-35.7622260344795
-40.5942829999971
-17.3565976609718
38.7503089084374
-27.6194798364843
-21.1403636429916
-36.3060308539261
8.25111074072547
35.7346597135289
6.43532572587367
44.3489094004393
-32.6181384804374
44.4964001237788
-36.6472010905169
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60.2256699548748
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-57.4226879268804
35.1358100955122
31.4587759769194
74.9891908670934
65.424636539736
30.3004542766643
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63.8814576024815
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-10.9255640148812
-2.68663500680216
24.7060957571466
36.5885723702298
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-9.22927838605097
-16.4721845876306
17.7496902544707
6.7606518530155
-38.6841235962382
14.6871702436634
-47.8339359791294
-27.9395524905455
39.6100943414783
27.9584584695252
66.5830298245495
3.72135513644457
-21.2697787188670
-19.4581815686667
11.677343028692
53.2530727597229
-48.0961463697999
-23.4546762294311
-43.4324308687283
-4.98183479341622
38.0609408596577
7.92836944865667
54.2320661014988
-24.9063267400510
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-16.9671998787758
-18.7554637283523
73.9259531846022
-79.6821448660293
6.85377846424661
-54.6357641943138
5.40832465667085
-8.23205937430834
34.3817470139747
29.5923399467688
4.35599622563679
-36.1368175854271
9.17797054799642
-26.9730311540657
73.8037230184729
-70.4528098362244
-1.90263241263762
-58.4316666422502
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5.43953050235441
8.33671514594836
22.1932223601896
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4.80183731445241
-7.81362555085556
8.75618838349404
51.7668099177915
-46.0839610998987
-5.75332420375346
-44.0103965069239
-3.9245233013528
5.26588070327126
24.8166909885527
27.5991601648533
-23.0653092698723
-5.81319653888257
-6.0423548718899
-17.0155070792119
92.854651159328
-32.9853559477900
-12.7726321703392
-23.8083820124742
14.3440595196021
-3.83509872664396
-2.20211005650556
22.900821814823
-5.78485485042189
-21.3651453398833
-25.7147707883298
-19.0820613471411
106.792565459412
-36.4530535486912
-27.2684673850334
-34.7362515714659
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9.23618490754326
-1.65926442290369
30.9441435986003
-33.8422264211101
3.03289219053773
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-19.2283723203471
75.1383789246267
-9.96549447856856
-7.83702756987274
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9.4389873061347
72.0838778589513
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12.5505475008919
5.66117634984376
-1.13871414005465
104.370356488589
2.57579058557871
-0.785443247636723
-6.11413568927418
-0.936773788178205
51.1722632391585
25.6034009562255
75.6482735062605
-34.6973914308347
10.2195143135337
-18.4841877131055
-10.8855758848541
74.1733326032288
-1.35848872522844
-6.4586927781758
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-30.0158536250917
40.2104110930385
11.6125620909040
65.0658330563124
-48.2910875237654
12.3811133154180
-26.8423058159273
-16.3320037284632
70.6437608189373
-11.0435765600839
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-22.4154288878162
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8.72223674574374
42.7357375823068
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-29.116071123637
73.7371295478034
-22.4524357503357
-8.05098547078489
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38.0070685403358
20.8954465284085
87.359028569194
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5.4452010124629
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17.7559613108654
-16.4604843984246
82.8462213594343
64.1866082666554
212.141801857737
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42.8766810058263
26.9560967013053
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104.704868699463
-48.3380134802108
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-62.6178792208532
-15.9593320937794
14.3216273793884
-8.43637607823851
77.2693442940271
-59.9668889316547
2.24656280082611
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42.0329842749658
24.9639060823505
-32.3035835699007
-34.6340727771691
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38.9703539833647
33.9773534432384
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/1bwv61228842935.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/1bwv61228842935.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/2tcb51228842935.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/2tcb51228842935.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/3h4a81228842935.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/3h4a81228842935.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/4mom01228842935.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/4mom01228842935.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/51dyf1228842935.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/51dyf1228842935.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/6ps2e1228842935.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/6ps2e1228842935.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/76lcs1228842935.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228842984n3kcj7ygco1o9t4/76lcs1228842935.ps (open in new window)


 
Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 4 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 4 ; 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 <- as.numeric(par5) #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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