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arima backward selection: bel 20

*Unverified author*
R Software Module: /rwasp_arimabackwardselection.wasp (opens new window with default values)
Title produced by software: ARIMA Backward Selection
Date of computation: Wed, 30 Dec 2009 06:14:35 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb.htm/, Retrieved Wed, 30 Dec 2009 14:18:19 +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/2009/Dec/30/t1262179091gr5vjom8a5k0fhb.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
3032,93 3045,78 3110,52 3013,24 2987,1 2995,55 2833,18 2848,96 2794,83 2845,26 2915,02 2892,63 2604,42 2641,65 2659,81 2638,53 2720,25 2745,88 2735,7 2811,7 2799,43 2555,28 2304,98 2214,95 2065,81 1940,49 2042 1995,37 1946,81 1765,9 1635,25 1833,42 1910,43 1959,67 1969,6 2061,41 2093,48 2120,88 2174,56 2196,72 2350,44 2440,25 2408,64 2472,81 2407,6 2454,62 2448,05 2497,84 2645,64 2756,76 2849,27 2921,44 2981,85 3080,58 3106,22 3119,31 3061,26 3097,31 3161,69 3257,16 3277,01 3295,32 3363,99 3494,17 3667,03 3813,06 3917,96 3895,51 3801,06 3570,12 3701,61 3862,27 3970,1 4138,52 4199,75 4290,89 4443,91 4502,64 4356,98 4591,27 4696,96 4621,4 4562,84 4202,52 4296,49 4435,23 4105,18 4116,68 3844,49 3720,98 3674,4 3857,62 3801,06 3504,37 3032,6 3047,03 2962,34 2197,82 2014,45 1862,83 1905,41 1810,99 1670,07 1864,44 2052,02 2029,60 2070,83 2293,41 2443,27 2513,17 2466,92
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.7246-0.19490.2114-0.45880.3214-0.0258-0.3516
(p-val)(0.0078 )(0.1431 )(0.034 )(0.087 )(0.7923 )(0.8452 )(0.7735 )
Estimates ( 2 )0.7247-0.19390.2118-0.4570.42590-0.4615
(p-val)(0.0076 )(0.1453 )(0.034 )(0.0872 )(0.7259 )(NA )(0.7015 )
Estimates ( 3 )0.7264-0.19210.2101-0.457100-0.0296
(p-val)(0.0075 )(0.1497 )(0.0356 )(0.0874 )(NA )(NA )(0.7714 )
Estimates ( 4 )0.7265-0.18780.2054-0.4588000
(p-val)(0.008 )(0.1572 )(0.0377 )(0.0886 )(NA )(NA )(NA )
Estimates ( 5 )0.293700.1811-0.0207000
(p-val)(0.4916 )(NA )(0.0524 )(0.9674 )(NA )(NA )(NA )
Estimates ( 6 )0.276600.1820000
(p-val)(0.0024 )(NA )(0.0437 )(NA )(NA )(NA )(NA )
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
3.03292826890205
12.0260515677640
59.7475455407064
-114.465065205481
-2.22693508199627
4.35951995596352
-147.147615338686
65.153572198053
-58.9442153086111
94.5064127441215
54.0497539166126
-31.9582830652207
-291.427228013426
103.210014980995
13.4184752845463
25.848767749962
81.7651411018169
0.0342656138418533
-13.8542282976155
63.9062793697858
-37.9084354029856
-239.488697273233
-197.314334937415
-18.3811532105201
-78.8708128245712
-37.8295732489139
153.835701431603
-46.2523335694268
-13.131794384233
-185.299119376881
-72.9117819276823
243.825225144514
56.6148178109725
51.4504706290238
-39.3475780548667
74.1339479106434
-2.27501727330446
16.1353440571070
29.3431180387506
1.19479122514349
142.274824521032
37.8895641544145
-61.2157143422046
44.3522168977665
-99.399814019555
69.8361993067019
-30.5517825502993
62.8935794195445
125.965949022935
71.5094060664719
52.3393013125101
19.3218862161034
19.4930838292207
64.6403579028697
-15.0861494969154
-5.69174869726749
-79.8892245215648
46.8017120289928
52.3915521634676
88.1573151431521
-12.8910808605979
0.555556031137257
46.017345842899
107.370226415589
133.534342762899
85.592463340406
40.2118083758105
-83.726149814921
-116.032498074396
-224.597367333123
198.730124441566
143.260275801969
105.426085784684
115.125784565585
-14.9411382270919
53.3217795102973
96.8611024849115
4.70669897899825
-179.314662214792
245.649258923567
31.3333205409763
-79.5796685631494
-80.4379200791709
-363.924056075888
205.939379895812
126.011618846088
-302.947102260051
85.1467909607727
-298.923496535926
10.0005383240823
-12.1778514262342
245.932788294570
-82.9134977046492
-273.362164089219
-423.468312216033
154.459638009162
-32.0064376545452
-654.887885136994
24.9943895258984
-81.9085187968208
223.842710037987
-69.0844660783794
-87.1663497407853
226.244015203884
152.276241081288
-48.8432924693116
11.6091732540151
176.746655051392
92.2077545126504
20.3296664845438
-106.660701819339
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/1bni91262178865.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/1bni91262178865.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/2784r1262178865.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/2784r1262178865.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/3eotk1262178865.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/3eotk1262178865.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/4sc791262178865.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/4sc791262178865.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/5frf31262178865.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/5frf31262178865.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/6eybo1262178865.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/6eybo1262178865.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/7ifml1262178865.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179091gr5vjom8a5k0fhb/7ifml1262178865.ps (open in new window)


 
Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
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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