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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: Tue, 28 Dec 2010 08:48:40 +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/28/t1293526102j20hi3qf8exzqa5.htm/, Retrieved Tue, 28 Dec 2010 09:48:24 +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/28/t1293526102j20hi3qf8exzqa5.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 «
20503 22885 26217 26583 27751 28158 27373 28367 26851 26733 26849 26733 27951 29781 32914 33488 35652 36488 35387 35676 34844 32447 31068 29010 29812 30951 32974 32936 34012 32946 31948 30599 27691 25073 23406 22248 22896 25317 26558 26471 27543 26198 24725 25005 23462 20780 19815 19761 21454 23899 24939 23580 24562 24696 23785 23812 21917 19713 19282 18788 21453 24482 27474 27264 27349 30632 29429 30084 26290 24379 23335 21346 21106 24514 28353 30805 31348 34556 33855 34787 32529 29998 29257 28155 30466 35704 39327 39351 42234 43630 43722 43121 37985 37135 34646 33026 35087 38846 42013 43908 42868 44423 44167 43636 44382 42142 43452 36912 42413 45344 44873 47510 49554 47369 45998 48140 48441 44928 40454 38661 37246 36843 36424 37594 38144 38737 34560 36080 33508 35462 33374 32110 35533 35532 37903 36763 40399 44164 44496 43110 43880 43930 44327
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time21 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.56650.1750.0214-0.65490.1438-0.0416-0.9999
(p-val)(0.0846 )(0.0982 )(0.8496 )(0.0353 )(0.1722 )(0.6903 )(0 )
Estimates ( 2 )0.60380.18410-0.68940.1423-0.0401-0.9998
(p-val)(0.011 )(0.0543 )(NA )(0.0021 )(0.1753 )(0.6993 )(0 )
Estimates ( 3 )0.6160.18060-0.69930.1440-1.0001
(p-val)(0.008 )(0.0582 )(NA )(0.0013 )(0.171 )(NA )(0 )
Estimates ( 4 )0.69590.13430-0.742600-1.0008
(p-val)(0.0029 )(0.1476 )(NA )(8e-04 )(NA )(NA )(0.0158 )
Estimates ( 5 )-0.6587000.600500-1.0001
(p-val)(0.1179 )(NA )(NA )(0.1737 )(NA )(NA )(7e-04 )
Estimates ( 6 )-0.037400000-1
(p-val)(0.6704 )(NA )(NA )(NA )(NA )(NA )(0 )
Estimates ( 7 )000000-1
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0 )
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
-62.321381126758
-390.088749557945
-155.337368874004
141.78084661056
709.741418516009
329.620568764402
-212.189416541707
-506.969604366085
464.927791945498
-1593.57790647935
-1117.52936416192
-1413.19078384949
-338.45472739476
-798.74038234253
-1017.11307755271
-451.737909199677
-497.288012586387
-1395.92759094214
-96.4758675263218
-1627.0215503915
-1476.67701271934
-1163.88991495856
-887.117993106668
-90.0297585792944
-288.777124314963
541.805694615512
-1354.95211521141
-387.185162063238
-356.690746325884
-1228.83119339613
-488.629150431658
244.927189740032
190.721546569678
-834.231039133104
-21.3998797954502
915.245084873905
734.892196440462
474.348403097308
-1228.52339788642
-1444.37258434554
-399.325711806788
368.031300966376
173.644742349217
-17.7832498657755
-175.573335200881
-230.415951632902
477.042329487877
333.276374799065
1423.11562786152
951.41089493545
798.81241383719
-63.7938458274309
-1105.6998173208
3144.58426304238
-17.3153498277729
548.812716307257
-1855.50529376333
14.5364933302269
-160.101020806881
-1113.53354231872
-1542.18558810317
1055.9714552549
1472.42594719982
2439.99283135841
-418.314562338369
2604.0728028311
447.542735410159
737.664657904132
-136.506964243455
-508.573000981219
123.75941666081
-109.361109060897
1088.19226038051
2714.66002559656
1137.10032443033
-165.697908710922
1741.74358709365
642.013120029304
1066.01901205731
-767.456203879512
-2864.01506178994
1031.38762916915
-1469.18175242878
-640.327364234324
688.639977374673
990.382705271232
520.76688434506
1601.8337818069
-2096.70844693665
577.838920094179
617.638940820678
-623.100786016039
3022.3794703219
-193.615312331436
2237.12769581985
-5069.52576688584
3676.77832698638
220.495358021104
-3014.97082465778
2007.63669552485
1076.77295455738
-2921.94706175993
-638.018696315082
1939.02826294356
2375.92642063677
-1396.6599647477
-3531.51722516781
-235.950815922162
-3104.36658127538
-3224.81311959252
-2796.40367905345
419.065665356569
-502.82148504883
-47.4721813737582
-3153.56825794044
1060.82806983594
-612.429750508933
3846.9693220275
-724.48814475154
375.977746223589
1824.89896869593
-2386.30119418338
132.919160569482
-1729.29339251285
2412.99733907329
3104.15432366185
1551.87877963368
-1652.55815795726
2536.71401949951
1808.42341461426
1649.75041923491
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/1b5hq1293526097.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/1b5hq1293526097.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/23xhb1293526097.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/23xhb1293526097.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/33xhb1293526097.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/33xhb1293526097.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/43xhb1293526097.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/43xhb1293526097.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/53xhb1293526097.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/53xhb1293526097.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/6e6ge1293526097.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/6e6ge1293526097.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/7e6ge1293526097.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293526102j20hi3qf8exzqa5/7e6ge1293526097.ps (open in new window)


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