Home » date » 2008 » Dec » 08 »

Gilliam Schoorel

*Unverified author*
R Software Module: rwasp_arimabackwardselection.wasp (opens new window with default values)
Title produced by software: ARIMA Backward Selection
Date of computation: Mon, 08 Dec 2008 05:23:52 -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/08/t1228739073ywo6gctmf7k3er7.htm/, Retrieved Mon, 08 Dec 2008 12:24:41 +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/08/t1228739073ywo6gctmf7k3er7.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

Post a new message
 
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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.0460.9813-0.03270.9769-0.6824-0.43080.7666
(p-val)(0.4158 )(0 )(0.5307 )(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
22.3785064886151
40.9449854923112
-11.7150027486239
-15.1713694101681
-34.3192405750359
34.6631751993281
-2.5700365450742
-8.71495255139827
-18.7197199380602
-41.5946118328445
24.9125431097621
20.5749333157631
82.5417586414395
24.2163691190038
4.62749597678372
8.01689679873285
48.6340273815247
44.6048966919696
47.1904304522605
-24.8049727845385
-21.3029805312793
10.6098774215995
-6.62922807089883
21.6474287757540
86.2790415614575
3.29898528994313
-43.0114634321702
-36.6161689135998
-18.3718895838211
14.0785736665574
-27.6732402552926
-62.0918336579236
-33.2510892460472
-58.2897623151476
29.6124227123327
8.06447856680947
13.0251813681365
-44.1704696147069
-6.48022140887149
-14.8070855083806
-18.8694003575766
13.7481110553071
-9.39236542249925
-13.5127333135030
-7.51532769208293
-21.5986126264778
23.8843735429063
-3.9112570521293
39.1765261980725
-14.3497658608138
-22.4525668259679
1.38906596524935
-4.05314648410401
10.5301203574855
-1.86267892641382
3.54638847751916
-33.3781238177425
-33.1588764220521
13.729165209263
12.6854375108193
41.3224691204883
-39.0774706357962
-6.41378169116752
12.4167535249528
-17.9665881769272
2.19484627195182
-4.9131714361086
-0.442253734708405
-9.48862555319863
-10.7572951419594
44.4928343529694
48.7413692719579
111.959525653481
31.3669828326349
26.1937586166492
13.7006006280804
6.56818582516025
7.83339381526781
7.55496087423243
-2.98425607582805
-20.5845053349657
-56.2156557555713
22.7603298222581
3.04413403914670
54.3507822178595
-31.2361549820276
-14.9995226910323
2.49482523392631
-40.7524743218049
13.7889640103591
-29.6563757301657
4.89673867539702
-50.1295209619479
-2.03264673170375
16.8444685392781
23.0443833767602
26.4444989540797
-1.87217851794234
9.23334114424557
-17.3603588798825
10.2122489069995
34.2844818830984
-17.6952382797121
-41.567418407035
-30.5342905982939
-13.6886446391841
45.0073850510075
10.6072238234693
37.7837049156396
-24.2270549144156
-8.66491597432785
1.56670496758333
4.69437423389259
40.6459032378483
-35.822719640298
-14.4179091084217
-17.6118160593976
5.29407776417126
57.5689670685297
33.0452627610603
100.90745536152
58.6378727355779
29.8553364971637
22.7483122463651
-0.0208615653862098
55.6320179705152
-6.94868507239816
-22.3207533378475
-73.601179246201
-23.5693026537418
-0.0420882021667346
40.2537972896144
32.9859122879408
-11.1791199892917
-35.7622259541634
-40.5942831522767
-17.3565978835991
38.7503088349158
-27.6194796385694
-21.1403633794899
-36.3060306715591
8.25111057447377
35.734659816826
6.4353256493502
44.3489094404115
-32.6181384830482
44.4963999771848
-36.6472011621712
-12.4497949129337
60.2256701402524
-19.0469572318363
-5.60839183025759
-57.4226879517737
35.1358099493672
31.4587761608162
74.9891907109532
65.4246364769504
30.3004542607972
-7.84745073272175
-8.30445720313011
-3.9798851130882
63.8814575771785
-40.1280602686094
-34.8342986219901
-57.3539560553383
-10.9255640335868
-2.68663474049469
24.7060957030662
36.5885724761504
-33.1605601652453
-9.22927834976195
-16.4721848265453
17.7496899438851
6.76065208026216
-38.6841235745173
14.6871703483882
-47.8339360451761
-27.9395523924859
39.6100944427891
27.958458369271
66.5830298755682
3.72135529391909
-21.2697790528745
-19.4581815597893
11.6773425646361
53.2530727671757
-48.0961463925165
-23.4546760274113
-43.4324308144439
-4.98183481685973
38.0609408475339
7.92836949024416
54.2320661829685
-24.9063265395049
-1.10564018654323
-16.9671999073641
-18.7554640918991
73.9259534092603
-79.6821450122355
6.8537785907898
-54.6357640337714
5.4083245196717
-8.23205922849942
34.3817468754066
29.5923401021473
4.35599640065295
-36.1368178496713
9.17797035835814
-26.9730313003033
73.8037230426676
-70.452809894436
-1.90263248090317
-58.4316663529561
-7.43715978132497
5.43953056234499
8.3367150103227
22.1932226006127
-40.1728182986949
4.8018370027543
-7.8136256549169
8.75618820251726
51.7668101223151
-46.0839614068583
-5.75332415233752
-44.010396530341
-3.924523382771
5.26588057889939
24.8166909873333
27.5991602331611
-23.0653090331093
-5.8131969754846
-6.0423549204824
-17.0155073864105
92.8546513601721
-32.9853563161068
-12.7726320683495
-23.8083820056398
14.3440593272324
-3.83509896076848
-2.20211006781624
22.9008217341529
-5.78485452876235
-21.3651457335157
-25.7147708218192
-19.0820615807527
106.792565747689
-36.453053901185
-27.2684673908959
-34.7362515596583
-3.68954167946275
9.23618466290733
-1.65926455572803
30.9441435483516
-33.8422258526126
3.03289173462433
-1.15898144546924
-19.2283726819272
75.1383794014399
-9.9654949091202
-7.83702770223287
-7.87734529667658
-15.2994228799268
-1.96341188281787
9.43898719155064
72.0838776060879
-1.90505667036179
12.5505470498203
5.66117619109626
-1.13871465178613
104.370356813131
2.57579001127325
-0.78544339154057
-6.11413576630202
-0.936773765022123
51.1722628541877
25.6034007756113
75.648273267973
-34.6973908916322
10.2195137298779
-18.4841878881763
-10.8855763772888
74.1733331186918
-1.35848909607401
-6.45869271660725
-37.3148618362936
-30.0158535090323
40.2104108322034
11.6125619914086
65.0658329295034
-48.291086929222
12.3811129140797
-26.8423058546248
-16.3320042453507
70.6437612819805
-11.0435768256039
-12.0171715558186
-37.0535979061938
-22.4154288573814
-10.2135232215070
8.72223670055793
42.7357374916201
-25.2945605597388
-18.5584753079700
-7.20183932004707
-29.1160714591336
73.737129806159
-22.4524358694435
-8.05098551733854
-25.6150831694156
-45.9798873901229
38.0070685466039
20.8954464069369
87.3590284768338
-36.8433038018391
-6.00028478976395
-16.6828217950041
5.44520054551831
90.3490136325772
-2.75556800036073
-15.1188908141312
17.7559613696221
-16.4604846474355
82.8462211657965
64.1866081630914
212.141801606751
-27.5162871061304
59.0925851958034
-9.33494969992572
38.8452513534614
65.8918577392988
-3.48146105285567
-27.4368126741983
-32.2175370399562
-30.5667954444818
14.0696868915968
19.9566336806135
82.1444341841618
-52.5175605305678
-21.3237648151169
-28.2367619460549
-42.9761822181632
101.610245178895
-0.498610702839564
-0.079997811920639
-63.2860147148329
-7.55096938014204
42.8766809657915
26.9560965144466
61.0727678395122
-9.59667980466775
-23.8125051304427
-60.6272470502508
-30.6038087685784
104.704868679268
-48.3380132804681
-0.392843747144909
-62.6178787075433
-15.9593324342879
14.3216276827599
-8.43637649344316
77.2693447113406
-59.9668887155805
2.24656280965031
-55.0245021329475
-6.50577456560929
42.0329845100611
24.9639061093985
-32.3035835946257
-34.6340724681287
-48.0315549651446
38.9703539428678
33.97735328507
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/1kgd81228739027.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/1kgd81228739027.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/2260h1228739027.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/2260h1228739027.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/32gmj1228739027.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/32gmj1228739027.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/4e8db1228739027.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/4e8db1228739027.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/52meu1228739027.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/52meu1228739027.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/6f3hf1228739027.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/6f3hf1228739027.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/7uz0i1228739027.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/08/t1228739073ywo6gctmf7k3er7/7uz0i1228739027.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')
 





Copyright

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.


Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

  • personalize online software applications according to your needs
  • enforce strict security rules with respect to the data that you upload (e.g. statistical data)
  • manage user sessions of online applications
  • alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.


FreeStatistics.org is powered by