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ARIMA backward selection Brussel

*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 13:10:56 +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/t1293634013xq0m1mucaxqmxff.htm/, Retrieved Wed, 29 Dec 2010 15:46:55 +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/t1293634013xq0m1mucaxqmxff.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 «
45990 42904 49968 42831 42110 45002 42091 39457 44448 48208 49603 48093 43130 45599 52287 49732 49571 48933 49203 45018 49405 56007 61858 55740 48827 52043 60348 55615 56852 55630 56457 50013 56291 52477 59846 55732 49114 55382 61102 61219 55785 57941 58844 51479 59968 60747 61532 61292 55164 56292 66015 60829 57571 57619 55304 54181 61033 63886 67365 63707 53473 52531 62703 61004 60438 65272 64463 62449 67373 70307 75544 71966 66263 69550 75388 57716 55779 52927 45655 46487 48683 50010 48944 41341 32411 34763 39106 34472 32642 34248 32280 29990 29656 34071 34105 33717
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.52620.06810.18650.40380.97380.025-0.9257
(p-val)(0.3389 )(0.6296 )(0.0951 )(0.4686 )(0 )(0.8574 )(0.0017 )
Estimates ( 2 )-0.20160.10120.14940.08550.99660-0.8813
(p-val)(0.782 )(0.4344 )(0.3461 )(0.9075 )(0 )(NA )(0 )
Estimates ( 3 )-0.12660.11350.136600.9970-0.8886
(p-val)(0.2218 )(0.3002 )(0.1933 )(NA )(0 )(NA )(0 )
Estimates ( 4 )-0.134900.11900.99530-0.8724
(p-val)(0.1945 )(NA )(0.2538 )(NA )(0 )(NA )(0 )
Estimates ( 5 )-0.12590000.99470-0.8691
(p-val)(0.2265 )(NA )(NA )(NA )(0 )(NA )(0 )
Estimates ( 6 )00000.99390-0.8617
(p-val)(NA )(NA )(NA )(NA )(0 )(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
45.9899414402288
-1933.7951305075
4216.69926411884
-3946.53279731439
-1022.86747575328
1769.44086266471
-1608.83387295718
-1895.23559570252
2943.16267693219
2772.17753987218
1177.63400254345
-823.251426485695
-3421.9535796419
3376.6603217248
2216.9535059914
1936.59381518176
469.942596794449
-2051.18546388062
1525.96568095569
-1830.16709017497
697.470427605816
3575.34856506055
4534.54356450836
-3722.51321896329
-3792.24372125852
2704.46198481391
3009.56495699314
-528.542127480231
1313.35240575076
-1700.07250754662
1425.1984643654
-3175.18561568065
1915.66840533085
-6803.31004421834
3157.13326785749
-459.34556555734
-2042.11507856978
4889.28931628209
184.936212652445
3825.5437847524
-4702.75522061982
1125.19360000236
1513.53189903151
-3203.94618607179
3385.62308252936
-433.281882654359
-3273.93959389521
2500.10419837084
-649.145587377812
-1025.53648674365
3383.11394859865
-1611.37262124216
-2237.08362919018
-871.569317974005
-2147.46645053155
3053.49993613019
1853.15407266988
1457.77633905808
286.214884138523
-1002.10728387364
-4866.3337870369
-3312.08172804069
2954.53113095623
2116.07176656928
1213.23598516053
4261.40776187716
302.986822410372
1769.9398202864
-451.255974615804
1035.08585354817
1962.76262030842
-531.683872025235
437.752908266227
1953.15402022382
-1232.9858283461
-14407.610107127
-2310.95215846491
-4150.191087261
-7014.98741126489
3432.62051860672
-2663.90025906447
-1036.3727502668
-4714.8721247651
-5162.11735851243
-3324.72228268081
330.252147993009
-2584.35754731008
511.637166209623
-233.270056790774
913.975480697933
-132.679504916125
453.972261351583
-5089.35368038946
1866.37329311159
-2480.49221236851
2879.23985485672
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/17i751293628238.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/17i751293628238.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/27i751293628238.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/27i751293628238.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/37i751293628238.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/37i751293628238.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/47i751293628238.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/47i751293628238.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/57i751293628238.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/57i751293628238.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/67i751293628238.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/67i751293628238.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/7zso81293628238.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293634013xq0m1mucaxqmxff/7zso81293628238.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 4 ;
 
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; 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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