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ARIMA Backward selection

*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: Wed, 29 Dec 2010 08:06:55 +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/29/t129360989914xgsyszue0vx3j.htm/, Retrieved Wed, 29 Dec 2010 09:04:59 +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/29/t129360989914xgsyszue0vx3j.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 «
11974 10106 12069 11412 11180 10508 11288 10928 10199 11030 11234 13747 13912 12376 12264 11675 11271 10672 10933 10379 10187 10747 10970 12175 14200 11676 11258 10872 11148 10690 10684 11658 10178 10981 10773 11665 11359 10716 12928 12317 11641 10459 10953 10703 10703 11101 11334 13268 13145 12334 13153 11289 11374 10914 11299 11284 10694 11077 11104 12820 14915 11773 11608 11468 11511 11200 11164 10960 10667 11556 11372 12333 13102 11115 12572 11557 12059 11420 11185 11113 10706 11523 11391 12634 13469 11735 13281 11968 11623 11084 11509 11134 10438 11530 11491 13093 13106 11305 13113 12203 11309 11088 11234 11619 10942 11445 11291 13281 13726 11300 11983 11092 11093 10692 10786 11166 10553 11103 10969 12090 12544 12264 13783 11214 11453 10883 10381 10348 10024 10805 10796 11907 12261 11377 12689 11474 10992 10764 12164 10409 10398 10349 10865 11630 12221 10884 12 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time25 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )1.2967-0.47740.1743-0.871-0.1993-0.2876-1
(p-val)(0 )(2e-04 )(0.0356 )(0 )(0.019 )(7e-04 )(0 )
Estimates ( 2 )-0.35520.375800.867-0.0929-0.2045-0.881
(p-val)(0.0811 )(0.0045 )(NA )(0 )(0.4466 )(0.072 )(0 )
Estimates ( 3 )0.04760.151300.45960-0.1615-0.9995
(p-val)(0.9518 )(0.7102 )(NA )(0.5526 )(NA )(0.0631 )(0 )
Estimates ( 4 )00.175500.50640-0.1626-0.9999
(p-val)(NA )(0.0504 )(NA )(0 )(NA )(0.0572 )(0 )
Estimates ( 5 )00.140600.508300-1
(p-val)(NA )(0.1162 )(NA )(0 )(NA )(NA )(0 )
Estimates ( 6 )0000.460800-1
(p-val)(NA )(NA )(NA )(0 )(NA )(NA )(0 )
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
13.7469728330636
1209.50934298781
968.398193887581
-543.390011649639
236.129489007366
-74.7189679623947
128.506322390243
-323.493075317273
-235.131479866183
159.598401313763
-191.593279051861
5.93211400465926
-861.290079456048
1389.35375138644
-242.835877538415
-762.508433051323
-210.678061834247
148.302722386461
83.9725575273376
-380.463987105886
1006.38276723675
-463.794911038816
226.595339690994
-304.014003669818
-706.327716415642
-1349.56147414826
220.532765313825
1053.44605877792
409.975063077425
44.3606283196777
-286.031565701078
79.0721896988307
-265.495663867574
587.784472279061
-93.9550937529569
316.426109968812
552.280925672084
-64.9424192111464
929.463258811555
407.276726312866
-597.644883983244
232.414537319997
214.018541019041
182.942276945909
195.016982357741
200.340401127005
-37.2133902576372
24.0828441256222
142.981370302593
1736.49045726601
-604.170185305794
-612.338791486962
227.637725842125
149.423286506141
433.435825434497
-122.765453712250
-34.3773242666585
255.834937822019
405.505703501036
54.7100567855338
-380.471668182581
12.3835394710572
-321.023577413825
514.567428877574
-164.111445502435
689.35394479509
272.230177174372
-107.827078638546
86.0289454885467
191.037763982256
305.025412376317
77.4278371626142
-53.8122615595009
213.355468564442
159.157345679393
838.400513122016
-38.8321838388924
43.4218659121934
148.777126473501
311.251023315826
-67.2820043290516
-55.715334549384
380.661812139353
137.174840325010
349.124912745021
-365.985024696370
-43.6341284781502
722.63699006056
252.824202879086
-381.386926763889
317.138868484086
-37.6620880704178
556.045371666327
149.676631504870
90.1681028314645
-9.90054711474404
562.464726399464
160.184952146273
-315.619396210850
-367.764949319118
-311.759814193689
-121.935675911256
-55.2414171884395
-257.624180617026
234.273500705066
-41.6065619400796
-93.844368662053
-173.100756158600
-496.55243984963
-429.852288038836
1084.59635760168
845.67774943552
-893.788447289315
302.618252755597
-94.0872987045807
-645.032077869429
-384.286687138428
-184.831118683617
-185.020605122464
-200.630145917937
-564.650884683613
-583.681545224472
262.407824050942
132.505623518320
-122.569602681387
-370.270307354508
93.77376689171
1088.97976814252
-1129.21835127600
344.860014762152
-875.203107345908
190.587561891634
-912.705170600055
-385.464330682986
-272.202642895159
-255.397444641263
-289.628313697769
-345.629837160966
-177.625502387069
-452.201443266578
-214.355351660211
-347.662205857157
199.525022867473
130.118331570386
-91.4741729519243
-189.958852610644
25.3884115666621
-404.697473692508
82.6234455379081
3.35587194338512
-355.298672704957
-36.7827869720814
-410.82298480131
-41.4839232564157
-10.6368298994247
-173.073544467070
-42.2081049718465
1321.73125265008
-417.746394433656
-874.063728116162
-142.045548533041
-448.87904122257
-224.274322353485
-270.454029616332
-424.927166392136
133.31704127
107.333304981576
-416.291597707802
-463.49746603191
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/1d82l1293609988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/1d82l1293609988.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/2d82l1293609988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/2d82l1293609988.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/3nh161293609988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/3nh161293609988.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/4nh161293609988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/4nh161293609988.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/5nh161293609988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/5nh161293609988.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/6nh161293609988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/6nh161293609988.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/7nh161293609988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t129360989914xgsyszue0vx3j/7nh161293609988.ps (open in new window)


 
Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; 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 <- 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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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.


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