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arima backward selection laaggeschoolden

*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, 22 Dec 2010 19:29:00 +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/22/t12930460561x7maclm1t74g15.htm/, Retrieved Wed, 22 Dec 2010 20:27:37 +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/22/t12930460561x7maclm1t74g15.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 «
104708 101817 97898 95559 92822 90848 101141 105841 93647 90923 89130 90212 93196 91861 90593 89895 88819 87924 96906 101217 98709 98139 95529 98577 100772 100180 99200 96251 94514 93780 105192 107682 99687 99436 102049 102673 105813 105056 103916 103513 101893 102503 113149 116696 108500 107800 105941 108742 111680 111270 110698 108517 107127 107088 116321 125045 116779 122887 120162 123198 123610 122293 121289 119393 117494 116693 125062 127281 120195 119804 117113 119240 115823 116281 113816 114632 112987 111633 116721 114850 112797 105368 102524 101327 102612 98873 95993 93244 90403 88539 98106 96963 90781 89253 87794 89810 90864 89025 87621 87718 83433 84535 92223 91052 88456 88706 89137 94066 99258 100673 102269 100833 99314 101764 108242 108148 104761 103772 103737 111043 109906 109335 107247 105690 102755 102280 110590 109122 102803 101424 99138
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sma1
Estimates ( 1 )0.60380.3066-0.0351-0.7024-0.679
(p-val)(0.0022 )(0.0037 )(0.7732 )(0 )(0 )
Estimates ( 2 )0.56460.29490-0.6692-0.6803
(p-val)(4e-04 )(0.0022 )(NA )(0 )(0 )
Estimates ( 3 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )


Estimated ARIMA Residuals
Value
-379.757988900563
1241.0354595946
2241.50124874447
1207.12750954307
821.501947904502
362.067376367431
-1688.1640021554
-1028.57589657243
7967.08506436016
2774.83637679673
-2148.36485860075
422.610713627592
-987.7104818259
308.268874772065
957.949704601282
-1969.86981160254
-794.546789404751
511.65981359214
1652.61234073001
-1875.30105033668
-1565.59554750845
1382.16124174848
5037.60832003173
-906.916748461226
-557.880261121985
361.074686033048
309.649666259166
1237.38339065811
-101.991599877773
1066.6345788555
68.1121571259719
-739.230384624971
-1205.54101814999
30.0180481219233
-1463.01192130587
1097.11339666832
667.193679796504
599.658115605079
945.585919428443
-876.455305095726
-120.755823396225
437.029604429193
-1277.08881883574
4751.41226144998
143.284325409644
5825.73057197777
-1583.8815621231
-1038.18653134978
-2935.75719297185
-1462.83102319817
204.232148201908
-150.806436534166
-352.977466818907
-454.094653552572
-1611.43709678373
-3102.86333173426
1004.85539502573
-698.096928743572
-927.942907158746
493.047091189936
-4623.11281574118
1495.64682996392
483.924489353112
3050.54846304544
1105.24106895635
-947.489595329479
-4458.20885510661
-6384.17924652785
6127.26955120536
-5556.44485157359
-1794.46900460235
-1383.99058102624
2172.87781107783
-1289.20319728664
-727.098776810152
-538.000773933657
-144.424999054607
21.3295634071992
2472.18501771356
-2323.52721310319
-407.789944477305
1362.16334205956
1497.47714471805
1389.98545003513
718.119845368922
-299.207845468879
422.980376317561
1641.88120590551
-2235.0023733686
1528.87807706302
-386.388020062517
-2876.61407157692
3090.12112195292
2843.53155052477
2079.87167310673
3041.80808791162
3822.68909275291
2069.43428355978
1752.19090198903
-2053.05340358904
-855.303041798431
1754.53646637946
-2683.30488631671
-2161.02560496658
926.539033709919
-26.2386751852243
599.991690195714
4503.91906175728
-3355.80016876026
-1683.90834678012
-1441.08906567313
-750.598785852534
-411.981077514924
-852.682982007366
771.192757988392
-1278.73106545025
-2005.49138149402
-2.36487528047451
-746.523472664195
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/1pyvj1293046133.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/1pyvj1293046133.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/207c41293046133.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/207c41293046133.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/307c41293046133.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/307c41293046133.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/407c41293046133.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/407c41293046133.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/507c41293046133.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/507c41293046133.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/6tzcp1293046133.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/6tzcp1293046133.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/7tzcp1293046133.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930460561x7maclm1t74g15/7tzcp1293046133.ps (open in new window)


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