Home » date » 2009 » Dec » 11 »

Bouwvergunningen (BouwV)

*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: Fri, 11 Dec 2009 10:54:33 -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/11/t1260554125qyzpm1729g4qhmu.htm/, Retrieved Fri, 11 Dec 2009 18:55:33 +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/11/t1260554125qyzpm1729g4qhmu.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 «
100 108.1560276 114.0150276 102.1880309 110.3672031 96.8602511 94.1944583 99.51621961 94.06333487 97.5541476 78.15062422 81.2434643 92.36262465 96.06324371 114.0523777 110.6616666 104.9171949 90.00187193 95.7008067 86.02741157 84.85287668 100.04328 80.91713823 74.06539709 77.30281369 97.23043249 90.75515676 100.5614455 92.01293267 99.24012138 105.8672755 90.9920463 93.30624423 91.17419413 77.33295039 91.1277721 85.01249943 83.90390242 104.8626302 110.9039108 95.43714373 111.6238727 108.8925403 96.17511682 101.9740205 99.11953031 86.78158147 118.4195003 118.7441447 106.5296192 134.7772694 104.6778714 105.2954304 139.4139849 103.6060491 99.78182974 103.4610301 120.0594945 96.71377168 107.1308929 105.3608372 111.6942359 132.0519998 126.8037879 154.4824253 141.5570984 109.9506882 127.904198 133.0888617 120.0796299 117.5557142 143.0362309 159.982927 128.5991124 149.7373327 126.8169313 140.9639674 137.6691981 117.9402337 122.3095247 127 etc...
 
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'Gwilym Jenkins' @ 72.249.127.135


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sma1
Estimates ( 1 )0.0587-0.10140.1563-0.7518-0.9161
(p-val)(0.7736 )(0.5142 )(0.2875 )(0 )(0.0077 )
Estimates ( 2 )0-0.13170.1282-1.4152-1.1026
(p-val)(NA )(0.2658 )(0.2421 )(0 )(0.0033 )
Estimates ( 3 )000.1527-1.3143-1.0526
(p-val)(NA )(NA )(0.1674 )(0 )(0.0943 )
Estimates ( 4 )000-0.729-0.9916
(p-val)(NA )(NA )(NA )(0 )(0.2374 )
Estimates ( 5 )000-1.31770
(p-val)(NA )(NA )(NA )(0 )(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
-0.365241700883491
-2.55781256731016
6.53018718238126
9.96073321891981
-2.85167993581617
-3.00690803525539
3.74995276148235
-7.90663401577889
-2.70532534545717
6.33356774781542
4.80813093286882
-3.5573164908859
-9.75064536544949
4.72942513200285
-11.5465879962313
5.96042154592227
-3.68538471314214
14.8652727332249
14.9762538608515
0.488828577156234
4.96850369262909
-5.78538212322108
0.230483196218526
13.0191993746804
-3.17915569746818
-12.2359893024489
4.37323690981063
9.9423969820642
-4.45095667737148
16.9630288137727
7.17036552441887
-0.261905481639858
6.10122776533149
-2.8325232406865
2.38686344664555
26.3436406165974
15.9708121627086
-6.3508578714165
12.1192209640623
-18.3384591840023
-7.92533527550358
25.9797655562251
-14.7863995967881
-7.03478105032627
-2.15657271342692
10.2614310350269
1.04076414027790
0.755508149246256
-3.54462725679137
-0.133958161641347
6.33831152772475
5.19309072799467
32.9699869499103
6.82230488747308
-18.7000774339667
9.38590556922934
10.6454863182601
-9.7196347829989
6.74502577453232
18.7223417179082
27.3753217265385
-13.1525608390406
-3.36697460023247
-18.3584756367688
-1.25941900044546
-6.4882435194857
-13.6905168725603
-3.15796287073729
1.17957163526632
5.98058005497133
-0.132843403734077
-5.68836204407274
-14.2834245317799
-16.6831010447864
-10.3816688157506
-12.1132537181568
-16.5817164112379
12.0876497170745
1.18174572004877
2.99540579566539
-10.5317048368324
44.4215024864494
-0.0126902777855155
-25.8181112146059
-3.46941572993936
8.39046673437901
-21.4906470675242
3.42670942756009
-13.0817930572858
-0.49161455627015
-0.791753210827466
-7.99474852624373
7.68212951983015
-8.8600421384049
2.10563370610555
15.3960023696429
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/1zv9l1260554067.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/1zv9l1260554067.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/2br5x1260554067.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/2br5x1260554067.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/34st01260554067.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/34st01260554067.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/4xlcp1260554067.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/4xlcp1260554067.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/5jx0b1260554067.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/5jx0b1260554067.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/6v9491260554067.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/6v9491260554067.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/7x3mb1260554067.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260554125qyzpm1729g4qhmu/7x3mb1260554067.ps (open in new window)


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