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W9 voor paper

*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: Sun, 05 Dec 2010 12:30:04 +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/05/t1291552096capw6jv30xla94y.htm/, Retrieved Sun, 05 Dec 2010 13:28:23 +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/05/t1291552096capw6jv30xla94y.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 «
17848 19592 21092 20889 25890 24965 22225 20977 22897 22785 22769 19637 20203 20450 23083 21738 26766 25280 22574 22729 21378 22902 24989 21116 15169 15846 20927 18273 22538 15596 14034 11366 14861 15149 13577 13026 13190 13196 15826 14733 16307 15703 14589 12043 15057 14053 12698 10888 10045 11549 13767 12424 13116 14211 12266 12602 15714 13742 12745 10491 10057 10900 11771 11992 11993 14504 11727 11477 13578 11555 11846 11397 10066 10269 14279 13870 13695 14420 11424 9704 12464 14301 13464 9893 11572 12380 16692 16052 16459 14761 13654 13480 18068 16560 14530 10650 11651 13735 13360 17818 20613 16231 13862 12004 17734 15034 12609 12320 10833 11350 13648 14890 16325 18045 15616 11926 16855 15083 12520 12355
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ 72.249.76.132


ARIMA Parameter Estimation and Backward Selection
Iterationma1sma1
Estimates ( 1 )-0.4871-0.724
(p-val)(0 )(0 )
Estimates ( 2 )0-0.6703
(p-val)(NA )(0 )
Estimates ( 3 )NANA
(p-val)(NA )(NA )


Estimated ARIMA Residuals
Value
-65.7886692023958
-1090.24367484819
430.585659502419
-716.196274911575
-324.815604320244
-612.262829694972
-270.607050591343
1004.55534842077
-2160.15087708942
272.903385308934
1836.27628767198
294.209023532974
-5036.42498500461
-2425.76148024945
1584.03717564569
-935.315370369908
-1145.25649894006
-5824.61639558718
-1769.05823118654
-2846.68855984107
1643.7163579688
378.254513714072
-2264.21551035044
1630.85888964840
3713.04669981441
928.935564757552
-146.517517985878
337.775796603019
-2867.36564083224
1324.18461343758
1760.82554732474
-297.863141200508
1335.33532324400
-863.610221452122
-1781.9791967927
-326.348368170571
755.873576664157
1273.27285812770
-193.225760322829
-59.6357581870998
-2966.78867113702
2081.56597632501
961.312485704218
2505.96571285172
2328.69489276303
-850.321697167459
-993.449669411257
-549.289086189081
752.999974905661
352.650616905747
-1732.65445548433
729.385131239915
-2395.84331415130
2716.32982956748
462.14022282523
1078.31837345603
303.540103346865
-1298.84052721797
235.379425563868
1856.20344784013
737.793811585268
-286.259346901873
1624.62649826189
1286.60435993284
-1511.2679988553
257.411004567131
-706.777150102872
-1198.17265399614
-86.0915684537755
2766.58864342742
844.53491991253
-1452.48017436344
2169.0298152043
1194.31269726531
2150.18551098837
1172.82959824073
-384.468180382503
-1890.05446553883
365.502071321458
1106.43038767528
2719.66374227134
6.49798380689502
-1548.18680799867
-2401.56163031232
218.756409982652
1481.53027364114
-2827.51747929102
3804.4225054705
3548.56341280605
-2179.88266855881
-1394.55524739916
-1691.44337410457
1889.24878643206
-1220.81367988151
-2107.90839533671
1371.25993003001
-816.210118086015
-969.342424715293
-365.420240735014
352.287899280744
37.6386809742901
3292.01653218924
1303.12504419209
-1927.29076084498
221.170251137824
-511.93914653335
-1480.66326641426
1135.36167531019
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/1xgmz1291552192.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/1xgmz1291552192.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/2xgmz1291552192.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/2xgmz1291552192.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/3xgmz1291552192.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/3xgmz1291552192.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/48p3k1291552192.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/48p3k1291552192.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/58p3k1291552192.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/58p3k1291552192.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/68p3k1291552192.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/68p3k1291552192.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/78p3k1291552192.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t1291552096capw6jv30xla94y/78p3k1291552192.ps (open in new window)


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