Home » date » 2010 » Dec » 29 »

Arima- backward foutieve gegevens

*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 20:17:01 +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/t12936537162brk3iiwdxsopc6.htm/, Retrieved Wed, 29 Dec 2010 21:15:28 +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/t12936537162brk3iiwdxsopc6.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 «
206010 198112 194519 185705 180173 176142 203401 221902 197378 185001 176356 180449 180144 173666 165688 161570 156145 153730 182698 200765 176512 166618 158644 159585 163095 159044 155511 153745 150569 150605 179612 194690 189917 184128 175335 179566 181140 177876 175041 169292 166070 166972 206348 215706 202108 195411 193111 195198 198770 194163 190420 189733 186029 191531 232571 243477 227247 217859 208679 213188 216234 213586 209465 204045 200237 203666 241476 260307 243324 244460 233575 237217 235243 230354 227184 221678 217142 219452 256446 265845 248624 241114 229245 231805 219277 219313 212610 214771 211142 211457 240048 240636 230580 208795 197922 194596 194581 185686 178106 172608 167302 168053 202300 202388 182516 173476 166444 171297 169701 164182 161914 159612 151001 158114 186530 187069 174330 169362 166827 178037 186413 189226 191563 188906 186005 195309 223532 226899 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time25 seconds
R Server'George Udny Yule' @ 72.249.76.132


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.11630.1014-0.0588-0.8404-0.0118-0.1359-0.5075
(p-val)(0.3221 )(0.3942 )(0.5653 )(0 )(0.958 )(0.312 )(0.0214 )
Estimates ( 2 )-0.11650.1008-0.0584-0.84060-0.1314-0.5179
(p-val)(0.3212 )(0.3949 )(0.5669 )(0 )(NA )(0.2043 )(0 )
Estimates ( 3 )-0.0980.1290-0.86450-0.1298-0.5236
(p-val)(0.3844 )(0.2346 )(NA )(0 )(NA )(0.2075 )(0 )
Estimates ( 4 )00.16750-1.11330-0.1304-0.5309
(p-val)(NA )(0.0964 )(NA )(0 )(NA )(0.2047 )(0 )
Estimates ( 5 )00.18820-1.115800-0.5723
(p-val)(NA )(0.0593 )(NA )(0 )(NA )(NA )(0 )
Estimates ( 6 )000-1.169200-0.5888
(p-val)(NA )(NA )(NA )(0 )(NA )(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
771.389484344918
-3281.65694519371
3994.37394743971
490.555472509792
243.947651221478
917.065594589796
-1061.88000974403
-395.748216644954
1679.72037294076
-98.5435437054915
-3233.88003807822
2463.58721705362
2227.95990650297
566.725234912552
2063.42456086961
86.1479005472823
507.728028605563
-1245.31797342205
-4603.60433959705
15806.8310652725
2263.78978886917
-6598.26602144456
-1378.12328132725
-2386.37109399243
-471.058460297129
-330.963357334583
-3929.42738447182
-919.75281224926
1002.70300659338
7692.10432755103
-9428.78667360814
-1868.00302326715
1611.31880853065
3801.84923276073
-3106.98730816543
-978.58496897836
-1470.54740956366
-1559.05335886530
2417.17352206447
-1412.81207280886
3293.41224668329
5096.37881889485
-5585.1183201479
-4544.9696046271
-2251.52694411230
-3717.8011198
1315.76621128704
263.273654410274
560.127191059162
-1238.66079576039
-3250.86921549741
-409.604690563
1201.56984504019
488.513814164840
4971.99951004581
-2860.81781833504
6684.03817425988
-4464.38918400475
-2503.82822493670
-4421.66375971382
-1427.09574651144
1284.70852365333
-1471.07295268509
-1002.31192200728
-176.962161025442
-114.507248725556
-5163.05141377259
-621.153802729883
-1351.63471190373
-1870.38888242760
508.914000015807
-10565.6998988198
5865.64321631878
739.489671803976
6588.14188251013
1453.2822199329
-2774.79987522725
-7063.19148762174
-9100.60059937427
9532.16782736551
-11055.5687611001
704.374761591528
-668.874638274081
7007.07598139324
-3003.54206815292
-1161.51394285816
-110.680406569660
1356.48954882324
1920.39527104811
2771.17560130865
-4975.39181208992
-3567.26749052899
6846.10959683642
5800.08897392726
4535.81043506126
1172.94510364249
-550.431239949031
3763.93091919042
1173.0212189402
-4268.59853207317
5598.42650580579
-4212.46970834935
-4044.76249605023
4930.91121057795
6363.58097248712
4764.83853500815
6074.72552736334
6957.46284813835
3535.2175812337
1654.66432906125
-3944.89737910637
-515.690168086844
2614.14557653161
-6099.24087123822
-2319.34508929563
655.045922840503
-800.40512000236
1315.75659063684
6753.14365367717
-10641.0998919188
-3006.47265561915
-4419.82729630356
-1434.70607709344
-1126.75194859068
-3213.68487362528
629.8734129375
-1925.60334182790
-3357.74894340599
498.269954437012
-1862.82689981172
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/16y5b1293653796.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/16y5b1293653796.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/26y5b1293653796.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/26y5b1293653796.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/36y5b1293653796.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/36y5b1293653796.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/46y5b1293653796.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/46y5b1293653796.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/5zp5e1293653796.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/5zp5e1293653796.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/6zp5e1293653796.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/6zp5e1293653796.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/7zp5e1293653796.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936537162brk3iiwdxsopc6/7zp5e1293653796.ps (open in new window)


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