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arima backward selection middengeschoolden

*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 19:34:28 +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/t1293046400ls3wev8supdoj46.htm/, Retrieved Wed, 22 Dec 2010 20:33:20 +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/t1293046400ls3wev8supdoj46.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 «
56190 54300 51362 49802 48088 46696 56586 64148 56449 52538 49359 49583 51050 49610 48321 47692 46243 46248 56381 62329 60673 58393 55742 57135 57961 56571 55615 53494 52623 52820 66825 70695 65660 63238 61741 63642 65521 64006 62728 62438 61109 63422 78094 82030 75892 72431 69194 71171 72545 71503 69624 67407 66103 67466 81088 86781 79964 80407 76589 78083 78000 76431 75461 73739 71988 72929 85785 89261 84012 80924 76588 77546 73054 73430 71093 72202 70872 70452 80506 80400 77613 69056 65321 64018 64767 61099 58329 56396 54656 55259 66912 66631 59907 56274 54045 55792 55499 53216 52259 51257 48150 51125 61046 61022 56742 54485 53862 58228 61951 62874 64013 62937 61897 65267 75228 76161 71480 69070 68293 74685 72664 71965 69238 67738 65187 66170 77309 77134 70957 67749 65081
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sma1
Estimates ( 1 )0.79370.2569-0.1456-0.798-0.566
(p-val)(0 )(0.0277 )(0.183 )(0 )(0 )
Estimates ( 2 )0.12880.27260-0.1115-0.5697
(p-val)(0.794 )(0.0045 )(NA )(0.8319 )(0 )
Estimates ( 3 )0.02590.276900-0.5701
(p-val)(0.7701 )(0.0022 )(NA )(NA )(0 )
Estimates ( 4 )00.27800-0.5669
(p-val)(NA )(0.0021 )(NA )(NA )(0 )
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
-204.111005343425
375.716462470718
1363.60140442283
663.620855626287
-186.582877824764
984.698872825405
119.308640293455
-1739.90575792591
5240.44335930055
1683.54667635205
-985.255532717327
666.591195708317
-550.827242380818
-26.5008992128547
1086.06239771469
-1141.13380222409
415.951449786125
1037.46636355519
3624.9725801716
-2976.19070741960
-1732.19439115759
1305.16256588149
1551.70598712848
819.514395458812
458.811888102428
-289.54341300662
-6.7987982750674
1233.1797304406
-185.833475631346
2164.25756597423
2695.68836771278
-2144.43575335811
-2213.2136781487
-308.966835285498
-549.517132922459
847.234905343392
224.002346638446
303.457278327302
-475.346380521525
-1342.56321231477
136.114037050763
799.180482138085
484.486951129422
836.047321873514
-1674.320415942
3249.05197922628
-800.86394680647
-1066.47780176230
-1154.33559412089
-183.321306979955
1052.86885688914
-144.922718515031
-633.435665057468
-93.4965926216568
-355.699135992729
-1603.54156069270
885.58192756533
-1111.23918087092
-1313.80999984403
-150.694295826699
-4900.93022803229
2101.81603585562
400.383722893299
2244.44203932845
365.368443991074
-2208.13947041067
-3084.62340688241
-4043.96110780677
3833.34122486209
-5171.49165908579
-686.89122157336
-847.315861717433
2341.70566946805
-2356.01985857369
-1550.74600599895
-631.931267035364
-3.10185883907091
617.519745614417
-71.763944992993
-2803.82113216134
-2190.48681588939
2127.08216762723
2076.85866265731
1164.38711243214
-203.643755290904
-775.353226697436
1181.67768805748
140.326659305790
-1894.81226947906
2501.50863448056
-1455.77137634689
-1953.05748911830
1668.25316930157
2453.88861402476
2077.38374918143
2859.98160937324
3387.28683184575
1934.8186140022
1574.60830822403
-936.005919507582
408.38295753287
1787.93858174247
-1372.43048291006
-266.765646108852
514.137024162693
991.25952269867
1145.22486907121
3702.6908154436
-3822.8074066021
-931.187110013866
-1335.87000255598
-408.332704856568
-196.734760393621
-1211.18966306875
875.82063230116
-629.64169904142
-1500.37696375183
112.642682915076
-803.22796526992
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/1jdex1293046460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/1jdex1293046460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/2jdex1293046460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/2jdex1293046460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/3jdex1293046460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/3jdex1293046460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/4c4di1293046460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/4c4di1293046460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/5c4di1293046460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/5c4di1293046460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/6c4di1293046460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/6c4di1293046460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/7c4di1293046460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293046400ls3wev8supdoj46/7c4di1293046460.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 = 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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