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*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, 06 Dec 2009 04:23:52 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa.htm/, Retrieved Sun, 06 Dec 2009 12:25:59 +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/2009/Dec/06/t12600987514uukim3xqnjlqxa.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
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. etc...
 
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
Iterationar1ar2ar3sma1
Estimates ( 1 )0.13350.24560.0062-0.6949
(p-val)(0.0121 )(0 )(0.9073 )(0 )
Estimates ( 2 )0.1350.24640-0.6953
(p-val)(0.0089 )(0 )(NA )(0 )
Estimates ( 3 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANA
(p-val)(NA )(NA )(NA )(NA )


Estimated ARIMA Residuals
Value
-0.535786632521179
1.80268742313898
5.24599904074085
9.6834222226076
49.0243669302671
-5.03632640514648
20.9997558964846
-27.2777763090155
-17.4033154202424
45.0551817820085
-43.2541058692813
-7.91256984575945
25.0004031174004
-30.6172428606486
-31.5853292191332
-32.840985412483
-17.8444437802683
2.78671870512211
-33.0341866899320
-27.5502770516408
27.4616507382295
-26.9971285964921
27.7369960795368
-4.56582703205924
-65.6672648708867
-33.4292433438226
25.1827094561405
7.96712382317383
1.98774422927147
-0.542860446638895
-10.4204106943212
20.8875193020567
26.9416678850593
-0.806568542375062
1.94364626279324
-33.8138368937879
-15.0914148878643
-0.332269387185860
-3.3390447907939
23.6741118861440
12.5307911580831
-18.1251733628914
2.80038573781672
24.7619446559482
-12.7133965091941
-10.8997874264540
-2.75905556540277
-3.61028488126688
3.39341610106613
-30.1685712241327
17.9829703487789
29.5733603733079
-14.8854627552643
-17.9963879866209
-0.648033694148201
16.1740220582076
23.1174076492978
9.39511722495614
21.0969550458918
27.3393542196195
49.475169536766
20.1459236747378
8.36233764951372
-9.6630139320413
-9.57662704051794
-27.1596279288913
2.95073093209369
13.9779415741083
1.31833299791899
-36.9003430737021
-1.67380261187511
-11.5870232216607
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1.80289681250937
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16.7639854631161
-10.7658231552688
19.6589923538009
3.32724489523893
-2.76894106182013
-28.3692357424402
-2.76254604742177
26.0556681421974
-20.2608134230203
33.5641119630887
17.4197339738798
-23.6742007414948
-32.6195275612705
-2.84116784570023
10.3602386476464
29.7385952156483
-2.59566573667086
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-8.53157942206472
12.0388205756427
19.2873354984682
20.2666066526636
-25.4055755409506
-4.20655608310881
14.6643627057194
14.4496272737015
29.7910745191964
6.89391379741119
40.485313952186
53.5059754841281
-0.525323059556532
-4.61840073449193
-19.8832399032203
4.03983866377683
5.88416315030761
-19.7664257417854
-44.0214983657586
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-25.3674158303719
27.9151503382243
-9.54601587410761
-20.5838169943877
-23.0523433063363
-46.3970626250273
6.12316654114593
25.2049602090880
-1.3627364052291
8.70979215449566
7.19374440946562
19.4002776108996
4.8863762004707
-34.0377739817092
-11.8210225519789
-29.8934066164564
55.6979753578542
-16.7663293240985
-14.2859641365804
46.4436243348601
-16.9829344503045
7.73019352306602
-17.9692360131080
33.317655773596
9.35312703710309
33.3955754004755
9.50765250703184
15.6675437757616
-26.5060063103127
-16.4800179222824
3.05475483447454
16.9239547820924
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-21.0911135414098
2.25856874194261
6.62499218242207
30.0984367865986
-31.7229987023448
-17.7857493058026
45.6301995152682
-9.04942683511678
-23.5081705724505
25.2926072835350
-12.8107580620063
14.1442664507656
21.1779380003945
-37.5522322965583
-0.277681475994018
13.8639009784116
13.4280019252175
-16.0108518538556
-15.9920072679131
9.6051186924677
6.59419103461307
10.5804499514223
-22.553096857587
-1.88481209446568
-10.8826769453796
0.539467844158884
10.1980113430678
-21.5310879566286
43.318653981186
-45.4693146806661
12.1457702379431
11.5340342128919
-0.350614467238063
-27.2709150721804
4.72771850991854
-14.8161831096598
20.1385457942977
-22.4054381209128
21.9981471304361
-9.31446207887975
17.1212633540534
-16.2316035617490
-5.56346525738514
2.84988061997096
-3.34347699684979
-11.2076579811831
-14.8863298121983
-16.7526826751381
-16.9358795455631
26.7012335581169
14.2984522854783
19.8653425094112
4.02855063792803
-10.4356622651241
-0.644048928904996
-0.0721818922427731
6.19694957756419
-15.7008074383192
6.78648213230716
-8.78639284879386
-0.804797063326985
4.27036039787276
4.46393209513173
-10.0673764861451
44.2958992899991
10.7221156793516
-19.3705602223908
23.8577034168595
12.0038915002675
-35.0873791571011
-22.5093244667386
-12.5694623289276
26.8182258931058
-8.7368446351607
-14.5208892065400
-0.0921174747365669
48.3422202905191
3.29333422850648
-30.4822841388074
7.00043730328802
-2.08166597073887
-7.8425085839915
-10.8209887585579
-1.49794378762279
0.0758008153865243
11.7857858864924
14.7693546608004
-13.0430829213303
6.39115113690185
25.4456929331316
-4.95571339175896
23.8585457717002
-10.3084128947578
-31.0664480744388
2.95637562726829
34.9855058349589
28.0330886666013
7.1951119698985
2.57708775861874
-5.01911648517603
22.2927284179217
18.1465093937820
-6.73364470195542
13.3613981041401
0.979565260487808
25.4566950862536
4.79540954679668
10.9219530408368
-19.5531993592174
-10.8391064968938
-16.6154428215117
-7.40020478192257
5.84997773060864
18.460959612088
2.15842774703921
-20.1540948648834
-19.6232376969211
17.7379885349736
-4.18979948383121
9.65533692193458
-15.8166516739497
1.03969680542699
-14.6168686366907
-11.6290432456711
4.23825969118618
5.04018881387003
-1.58197181148581
-8.8845122798381
-4.51783537855804
-34.1798714497597
-1.94561122019763
-1.21129686462669
13.0999153335679
-10.8546047372559
4.74183448205642
-9.469832456662
-4.49867590138663
-0.0145667877647714
-2.26315737136980
10.8750895351050
-25.9560298518237
24.9891613169692
12.3738216999596
24.1529728836655
-3.95744012616563
-21.9342584755680
-6.9190221900331
18.0687617640549
15.6453828799114
9.89690470265054
-11.988351888823
39.5334838044379
2.25867943873742
40.0849676061669
39.636778339766
114.891327204345
-28.5620976472100
0.428474695424663
-19.7360880599300
-0.99302748677134
-15.7750145882124
-12.2162691240802
-11.7784891750315
-13.7112707062195
-0.337751379577586
-23.7966240796736
-6.17480619291195
-7.42218696990015
-20.6002129974372
-24.717394283938
-8.5564260964737
-24.4346584455418
37.4339194767463
23.9194367248457
4.14256574919023
-32.3495852675315
3.38192717959361
12.6664220068972
-13.6744374755610
-26.7715262063425
28.6662141814793
-23.2612247734427
-50.4254627240437
5.48948921177846
27.5558751437550
-30.057446302018
16.8713714059679
-15.1362384966254
-1.10528845903530
-4.04542670183625
-46.3937081096853
9.1222393493091
-14.7910258000548
14.2802285735170
-9.07750520858237
12.8465161279387
-31.6547102106307
42.6024971773380
-18.3845410975124
-2.66691451411512
-7.80245964005941
-1.16521983568894
26.2309819972669
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/1mx511260098627.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/1mx511260098627.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/2v29u1260098627.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/2v29u1260098627.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/3gin51260098627.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/3gin51260098627.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/4dmye1260098627.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/4dmye1260098627.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/5aq691260098627.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/5aq691260098627.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/6j32z1260098627.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/6j32z1260098627.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/7kfbp1260098627.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t12600987514uukim3xqnjlqxa/7kfbp1260098627.ps (open in new window)


 
Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 1 ;
 
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; 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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