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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: Thu, 10 Dec 2009 08:26:41 -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/10/t1260458970zanxseeno4lq3l2.htm/, Retrieved Thu, 10 Dec 2009 16:29:38 +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/10/t1260458970zanxseeno4lq3l2.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 «
299.9 339.2 374.2 393.5 389.2 381.7 375.2 369 357.4 352.1 346.5 342.9 340.3 328.3 322.9 314.3 308.9 294 285.6 281.2 280.3 278.8 274.5 270.4 263.4 259.9 258 262.7 284.7 311.3 322.1 327 331.3 333.3 321.4 327 320 314.7 316.7 314.4 321.3 318.2 307.2 301.3 287.5 277.7 274.4 258.8 253.3 251 248.4 249.5 246.1 244.5 243.6 244 240.8 249.8 248 259.4 260.5 260.8 261.3 259.5 256.6 257.9 256.5 254.2 253.3 253.8 255.5 257.1 257.3 253.2 252.8 252 250.7 252.2 250 251 253.4 251.2 255.6 261.1 258.9 259.9 261.2 264.7 267.1 266.4 267.7 268.6 267.5 268.5 268.5 270.5 270.9 270.1 269.3 269.8 270.1 264.9 263.7 264.8 263.7 255.9 276.2 360.1 380.5 373.7 369.8 366.6 359.3 345.8 326.2 324.5 328.1 327.5 324.4 316.5 310.9 301.5 291.7 290.4 287.4 277.7 281.6 288 276 272.9 283 283.3 276.8 284.5 282.7 281.2 287.4 283.1 284 285.5 289.2 292.5 296.4 305.2 303.9 311.5 316.3 316.7 322.5 317.1 309.8 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
Iterationar1ar2ar3ma1
Estimates ( 1 )-0.30490.25610.00920.7814
(p-val)(0.15 )(0.0312 )(0.892 )(2e-04 )
Estimates ( 2 )-0.31660.260600.7947
(p-val)(0.0538 )(0.0102 )(NA )(0 )
Estimates ( 3 )00.111700.4867
(p-val)(NA )(0.0586 )(NA )(0 )
Estimates ( 4 )0000.4498
(p-val)(NA )(NA )(NA )(0 )
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
0.299899812188875
35.1158479949783
17.6958673414598
6.34433450410502
-11.2873428264445
-4.16314083790895
-3.99374476507493
-3.41880184081738
-9.21026357377827
-0.125069753736144
-4.24384424614576
-0.942713537301115
-1.51587246071497
-10.8602413014214
0.175993355322484
-7.34570670701513
-1.22187311657464
-13.3450184270107
-1.30201273790276
-2.10254124585825
1.06126958562489
-1.52520440898148
-3.45718800845309
-2.24989562727427
-5.42482910104508
-0.40192391796694
-0.922746951460738
5.53991872239476
19.5158844495207
16.5768207116984
0.275500776734816
1.79569342958047
2.22008318492561
0.372341337566866
-12.5613666769152
11.4902826018774
-11.2635287839635
-0.443358834602293
2.99741939044947
-3.16703083875240
8.21806660832004
-6.8429030864346
-8.44003510078807
-1.44608791459041
-11.8679034335579
-3.36509225040857
-0.121270710813576
-14.446685855235
1.89967685171882
-1.48263739640203
-1.26425817689324
1.97213705966436
-4.0695152731825
0.257801269420071
-0.645819427203179
0.892979699226913
-3.5341163454199
10.6753867237961
-6.63838270369828
13.6259338904965
-5.33073912752857
1.62151494783870
-0.412018741768748
-1.63296952480931
-2.16106620749349
2.55278162176131
-2.31861580483201
-1.31669263430763
-0.102839314627886
0.806875396812501
1.40779055203353
0.858998212661334
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-4.08013532072746
1.56346622255472
-1.10312185220155
-0.718446476145118
1.93899728631746
-2.99854732605743
2.29189702632101
1.53019282736417
-3.05640609360825
5.61956065958083
3.01062150829742
-4.15658184013523
2.40886182196738
0.373266151700193
3.20666922549583
0.694155410744997
-1.42866339615807
1.72733954646662
0.137468876249272
-1.31206715080646
1.53808588428046
-0.625756541842748
2.19289279851182
-0.667279230617055
-0.698560567835102
-0.504676082158767
0.834955400741819
-0.0170422068707126
-5.24753678452907
1.32047337026268
1.03797125083008
-1.47118489348537
-7.20680411868369
23.9303746655414
73.1240720706958
-17.4561766406797
-7.67256993315505
-2.44367965056864
-1.25135849941847
-6.25548132037216
-10.0981423088547
-13.8701062246859
6.55801274531797
2.59680383117416
-1.67403629240522
-2.68723256679351
-6.52512852999627
-2.07807146297239
-7.50647105224272
-5.52129674844156
2.43683767399557
-3.09171522950584
-8.05010343176986
8.15296640070443
3.51508309329148
-14.1462716903164
3.0703399689516
9.9456170321945
-4.19437060136789
-5.58639358083087
10.3853947111926
-6.128757806502
0.62306100176761
6.09774906027144
-7.10027611565158
3.66339185248574
0.197178451673096
3.50353721572736
1.42733750390727
2.79216496024191
7.07256946258337
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9.13735204383698
0.498018975529362
-0.691019894937199
5.600339199855
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-3.46427157950711
-10.9988059827571
9.42308493451543
-5.07876539353106
6.89217894131411
-10.5310933114651
3.98949532838725
4.18461766528475
1.93034727183709
6.16869061183627
-6.13778041918545
1.92745230038901
-0.136600992681565
-8.41118391703372
3.63788549976738
-0.421425433231775
2.84977240347996
-9.13164699754884
7.15404322789419
-7.12206662413502
1.83131704217362
-6.48881966838962
-4.79673532626191
-0.184291819055488
2.39416034373880
-4.10791622331618
-2.95700774588863
6.10765939381912
-0.436613458596639
-5.96764900269079
0.781125304708951
5.05630275103277
-5.74874003090088
4.96192768199649
-5.92414787470022
-5.01821035949393
-1.62215743066895
-8.461862787553
1.92086300133730
-1.0070920269429
10.7916394639481
-4.11828767628216
2.38774316100711
2.42622482162534
0.551664613691997
0.818354279289082
-5.41045100648859
1.76576868394145
-8.12341860501951
5.83182525702972
2.73262208331369
3.46904257511048
-2.31319374720417
-0.832482525056378
0.516334844385881
1.80502762784755
5.41032820555813
71.4546388317525
78.4196092032173
-48.6632815233162
10.1660310932332
-1.50214213876495
-9.17957864023651
5.71037406522055
-18.0626103763198
-3.98760136960334
1.37202630640655
-0.960817564375247
-0.0643810092947206
-2.9788396868106
3.93912891537309
-7.05985123335421
-9.93196568431284
0.348023372151033
-4.90660491406163
-14.5424808024575
9.07012121649188
-6.46032851714165
0.699075742527953
-8.19358991616434
3.64463746296656
-6.64704409388156
4.30210868054456
-7.14619089468113
4.46638315978663
-7.63730939948448
5.99424391390488
-2.93625307720072
2.76108227933594
18.3343470869207
29.098027556475
5.14942890488408
25.1282722237115
5.36934714687385
-2.07528852533380
-6.42320721605574
-19.8316459613177
1.33269138555329
-12.3362152625843
-6.60217796441145
-0.401118624305013
7.71383313020635
-10.5736720042718
-3.62377656699095
-6.61000878590704
2.62155182644068
-6.94861292090843
-4.85062182015912
-8.59106784797945
3.0192304692805
-3.76300674187405
0.965943570113382
-0.768139252400715
5.29668145019099
-3.49972707031111
4.06733481185933
-9.26790628326779
4.08686178890105
-0.362770515813565
-3.31227362757249
10.4227510736789
-19.1819263999275
9.34203315791342
16.3723448704029
5.61992997537527
-9.29030041553682
-1.90819321463295
-3.87996915855979
-7.5644761167706
5.77343554357759
-1.89330234910943
-9.7460248499517
2.56571524922759
-7.47627614696665
0.784355163067744
1.64455690717722
-1.15425105263904
-0.772221816822082
-7.24599653961548
0.560615829214441
6.48694939615012
-8.71103973537089
-1.51915201034529
-0.701821571745654
-0.0889456061834153
-2.4222191457298
-6.20944538649991
5.62362082425631
-0.899543708199076
-8.51901635413049
15.734536250137
-3.18652352114009
-7.05557185905099
6.94312287794202
-7.3640768235444
2.34860703502767
-5.50708558014185
3.76962423811591
-5.68752731219513
18.7564527212804
-16.1374363682760
6.95631273834118
4.95183616696886
-9.6105510204473
3.23997955403814
0.515907685452873
-2.48409441574131
0.863845677758832
-9.56360944203766
0.0769338111456932
10.6121831863216
-2.95129419320079
-3.23556669473271
8.98492168453566
-6.37096970453393
-5.44788841192513
-5.18052709489126
16.1811592212088
-6.9708920321948
1.56344870157051
-4.16092927499864
-4.13021396619627
2.18982419741116
26.9265213018819
-23.9827846264886
15.5240184924571
16.7615926480152
-0.028324935869648
6.13438366325602
-12.1793953882671
19.7562392106773
-12.1997144579642
4.28498768217321
-17.0946816998486
2.51996837448655
0.193134713379891
-5.24635643981082
15.286896353534
-7.69247856732807
8.02581055334866
-22.9056596106788
10.1116910003538
1.41140015688273
-2.94226239502922
-2.53698535872127
3.49157239564096
-5.80852709295465
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/16r011260458796.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/16r011260458796.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/2vdnk1260458796.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/2vdnk1260458796.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/30fza1260458796.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/30fza1260458796.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/43us91260458796.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/43us91260458796.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/5lp4t1260458796.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/5lp4t1260458796.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/624fy1260458796.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/624fy1260458796.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/7qati1260458796.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260458970zanxseeno4lq3l2/7qati1260458796.ps (open in new window)


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