Home » date » 2009 » Dec » 19 »

ARIMA Backward Selection – Aantal overnachtingen in België

*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: Sat, 19 Dec 2009 06:31:59 -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/19/t1261229576gdjhd0i8r4tb5zu.htm/, Retrieved Sat, 19 Dec 2009 14:33:04 +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/19/t1261229576gdjhd0i8r4tb5zu.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 «
897262 1133132 1384548 2324057 2502808 2516762 5579822 4945991 2019915 1830905 1251016 949902 923000 1215747 1479112 2371781 2521576 2350559 5673323 4414295 2016902 1958302 1284086 1186305 957833 1255719 1482709 2361136 2508100 2254488 5669953 4227480 2067790 1958419 1318158 1287921 1076982 1293669 1582053 2393005 2310531 2597899 5507587 4194133 2185092 2122018 1413348 1338342 1052655 1370046 1887027 2448017 2550796 2655837 5269499 4247405 2109722 2143145 1582013 1413221 1118520 1478655 2000108 2085234 2651805 2522176 5170142 4150129 2104254 2211398 1505900 1524305 1093144 1449647 1771197 2445932 2678945 2400737 4796880 4118001 2125714 2125515 1508760 1508765 1091075 1514814 1748997 2424406 2747942 2377332 5210706 3882821 2197469 2271155 1618917 1391579 1143249 1445785 1870242 2597788 2436231 2684184 4705109 4331347 2369192 2283947 1749607 1598601 1221234 1497778 1823567 2489908 2532837 2456065 46 etc...
 
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'Gwilym Jenkins' @ 72.249.127.135


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-1.1357-0.07190.19370.95790.61010.2193-0.7596
(p-val)(0 )(0.5934 )(0.0286 )(0 )(0.0062 )(0.0185 )(8e-04 )
Estimates ( 2 )-1.09600.230.95570.60390.22-0.7615
(p-val)(0 )(NA )(1e-04 )(0 )(0.01 )(0.0186 )(0.0014 )
Estimates ( 3 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(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
949.901258469299
23214.0771525810
83626.8513840072
104338.590457970
53988.623891673
11734.2060311839
-162420.093205995
49492.3388343657
-465045.369270008
-117015.710325351
173220.827061331
109272.104928773
173136.413609575
117803.983559204
-6151.02265347944
38065.0481755778
-24429.2262138674
117.556754856897
-133717.490403963
3821.24237482901
-258285.950104907
26530.4529033301
27494.1407669064
85720.9283173034
84980.3773381325
182692.577056512
-4826.32785977677
115331.651170460
-6297.32738244722
-164819.33118248
261853.473641101
-46857.0776676673
-84415.4403278456
128822.932202675
196958.07022199
82371.025844515
50716.1300798511
-48066.903919035
81721.9765782375
302125.080344026
125010.975192841
165741.372171821
156493.640957528
-263567.056599137
33905.7403350114
-9190.35764226551
26836.4629082422
157825.789904899
109164.584401630
-2428.88728323623
126435.651139973
131701.644583739
-329987.302492531
55106.0019709733
-71932.3251412496
-134462.942491615
-82924.8392678675
26093.1195299001
246.07579867591
-14495.9422192043
36608.19915473
25373.7276183706
-68031.1429064264
-249652.331956607
243976.990128718
123594.106521618
-172755.396679139
-424443.610500978
-20297.2866568498
29442.4547656571
-42738.9697118827
-107431.969740674
16940.1588395914
-58233.1109172248
70874.3071044393
-127639.335591397
73635.9189054084
41865.8315137485
3406.93349325621
336741.695775387
-87474.2528950495
-37027.5181677898
132953.763827125
134798.675178801
-182640.790178617
25783.5895908483
-82315.8921735858
131919.352246586
135314.559272857
-248867.191057889
182498.048190385
-244609.529440579
338387.654596629
271451.831341905
70593.4964794687
22024.6970008348
263410.112515911
16737.9834934554
81025.6154276781
-113635.022670921
-36155.5029223402
-45323.8736582488
-93252.5558967648
-229820.183609770
99119.8664821792
-42067.057724424
-38785.7074125163
-150698.633995079
22926.6342389310
-135949.883597595
128589.294400226
46278.1472136066
-191741.994906531
162889.936877358
3541.01173131233
64567.7160924263
139225.486589257
-3064.71129687157
-1552.14009980892
55343.9937937638
-66430.2179510958
16458.0505177427
-137769.297405884
-99442.1289207871
101741.273119671
-96615.8368411662
79772.8990810322
127917.242436739
-3955.95575022967
-68035.5075689983
48304.9925303004
-37292.0507685887
18572.9980538275
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/1cpv91261229507.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/1cpv91261229507.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/2c4vz1261229507.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/2c4vz1261229507.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/3o0td1261229507.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/3o0td1261229507.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/4rsqk1261229507.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/4rsqk1261229507.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/5fnvm1261229507.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/5fnvm1261229507.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/6bnq21261229507.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/6bnq21261229507.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/75gtf1261229507.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/19/t1261229576gdjhd0i8r4tb5zu/75gtf1261229507.ps (open in new window)


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