Opdracht 6 bis IKO - Autocorrelatie Bouwvergunningen - Nathan Jacobs

*Unverified author*

R Software Module: /rwasp_autocorrelation.wasp (opens new window with default values)

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Tue, 26 Apr 2011 14:57:27 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Apr/26/t1303829664amhde3mwuxpini7.htm/, Retrieved Tue, 26 Apr 2011 16:54:25 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W12

Dataseries X:

» Textbox « » Textfile « » CSV «

1394 1657 2411 3595 3336 3249 2920 2113 2040 1853 1832 2093 2164 2368 2072 2521 1819 1947 2226 1754 1787 2072 1846 2137 2467 2154 2289 2628 2074 2798 2194 2442 2565 2063 2069 2539 1898 2139 2408 2725 2201 2311 2548 2276 2351 2280 2057 2479 2379 2295 2456 2546 2844 2260 2981 2678 3440 2842 2450 2669 2570 2540 2318 2930 2947 2799 2695 2498 2260 2160 2058 2533 2150 2172 2155 3016 2333 2355 2825 2214 2360 2299 1746 2069 2267 1878 2266 2282 2085 2277 2251 1828 1954 1851 1570 1852 2187 1855 2218

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Autocorrelation Function Time lag k ACF(k) T-STAT P-value 1 0.515124 5.1254 1e-06 2 0.344965 3.4324 0.000437 3 0.211458 2.104 0.018957 4 -0.002146 -0.0214 0.491504 5 -0.044339 -0.4412 0.330028 6 0.02672 0.2659 0.395448 7 0.052406 0.5214 0.301613 8 0.142804 1.4209 0.079247 9 0.226869 2.2573 0.013092 10 0.141801 1.4109 0.080705 11 0.111268 1.1071 0.135467 12 0.162657 1.6184 0.054377 13 -0.015278 -0.152 0.439741 14 -0.017031 -0.1695 0.432892 15 -0.013329 -0.1326 0.447381 16 -0.112547 -1.1198 0.132748 17 -0.095688 -0.9521 0.171687 18 -0.094564 -0.9409 0.174524 19 -0.061361 -0.6105 0.271453 20 -0.043801 -0.4358 0.33196 21 0.060571 0.6027 0.274051 22 -0.017507 -0.1742 0.431033 23 0.016207 0.1613 0.436111 24 0.084286 0.8386 0.201847 25 0.026911 0.2678 0.39472 26 -0.024992 -0.2487 0.402065 27 -0.043732 -0.4351 0.332208 28 -0.123506 -1.2289 0.111017 29 -0.169058 -1.6821 0.04785 30 -0.19441 -1.9344 0.027962 31 -0.246365 -2.4513 0.007993 32 -0.158563 -1.5777 0.058915 33 -0.127583 -1.2694 0.10363 34 -0.141778 -1.4107 0.080738 35 -0.157959 -1.5717 0.059608 36 -0.040121 -0.3992 0.345304 37 -0.158427 -1.5763 0.05907 38 -0.224772 -2.2365 0.013783 39 -0.152951 -1.5218 0.065618 40 -0.186115 -1.8518 0.033516 41 -0.204916 -2.0389 0.022064 42 -0.181984 -1.8107 0.036608 43 -0.175237 -1.7436 0.042168 44 -0.137336 -1.3665 0.087442 45 -0.111519 -1.1096 0.13493 46 -0.045252 -0.4503 0.326757 47 -0.098069 -0.9758 0.165777 48 0.030797 0.3064 0.379963 Partial Autocorrelation Function Time lag k PACF(k) T-STAT P-value 1 0.515124 5.1254 1e-06 2 0.108368 1.0782 0.141773 3 -0.003867 -0.0385 0.484694 4 -0.186446 -1.8551 0.033278 5 -0.004536 -0.0451 0.482048 6 0.136736 1.3605 0.08838 7 0.062157 0.6185 0.268848 8 0.095034 0.9456 0.173332 9 0.102148 1.0164 0.155967 10 -0.075638 -0.7526 0.226741 11 -0.006083 -0.0605 0.47593 12 0.149194 1.4845 0.070432 13 -0.144327 -1.436 0.077073 14 -0.001334 -0.0133 0.494718 15 -0.012427 -0.1236 0.450923 16 -0.103235 -1.0272 0.15342 17 -0.042693 -0.4248 0.335956 18 -0.064256 -0.6393 0.262039 19 0.062664 0.6235 0.267195 20 -0.047695 -0.4746 0.318073 21 0.090185 0.8973 0.185861 22 -0.084238 -0.8382 0.201981 23 0.039859 0.3966 0.34626 24 0.103881 1.0336 0.151919 25 0.050931 0.5068 0.306725 26 -0.093507 -0.9304 0.177218 27 -0.051019 -0.5076 0.30642 28 -0.043496 -0.4328 0.333058 29 -0.096056 -0.9557 0.170764 30 -0.09175 -0.9129 0.181757 31 -0.14519 -1.4446 0.075861 32 0.050561 0.5031 0.308015 33 -0.126403 -1.2577 0.10573 34 -0.049962 -0.4971 0.310104 35 -0.134149 -1.3348 0.092507 36 0.143468 1.4275 0.078292 37 -0.117429 -1.1684 0.122725 38 -0.124623 -1.24 0.108957 39 0.099003 0.9851 0.163495 40 -0.009676 -0.0963 0.461749 41 -0.051774 -0.5151 0.303798 42 -0.108668 -1.0812 0.14111 43 0.069489 0.6914 0.245463 44 -0.0845 -0.8408 0.201253 45 -0.06282 -0.625 0.266688 46 0.076657 0.7627 0.22372 47 -0.12024 -1.1964 0.117204 48 0.060541 0.6024 0.27415

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Apr/26/t1303829664amhde3mwuxpini7/1pfol1303829843.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/26/t1303829664amhde3mwuxpini7/1pfol1303829843.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/26/t1303829664amhde3mwuxpini7/2m81o1303829843.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/26/t1303829664amhde3mwuxpini7/2m81o1303829843.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/26/t1303829664amhde3mwuxpini7/36r601303829843.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/26/t1303829664amhde3mwuxpini7/36r601303829843.ps (open in new window)

Parameters (Session):

par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

R code (references can be found in the software module):

if (par1 == 'Default') { par1 = 10*log10(length(x)) } else { par1 <- as.numeric(par1) } par2 <- as.numeric(par2) par3 <- as.numeric(par3) par4 <- as.numeric(par4) par5 <- as.numeric(par5) if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma' par7 <- as.numeric(par7) if (par8 != '') par8 <- as.numeric(par8) ox <- x if (par8 == '') { if (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } } else { x <- log(x,base=par8) } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='picts.png') op <- par(mfrow=c(2,1)) plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value') if (par8=='') { mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } else { mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } plot(x,type='l', main=mytitle,xlab='time',ylab='value') par(op) dev.off() bitmap(file='pic1.png') racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub) dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub) dev.off() (myacf <- c(racf$acf)) (mypacf <- c(rpacf$acf)) lengthx <- length(x) sqrtn <- sqrt(lengthx) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Autocorrelation Function',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Time lag k',header=TRUE) a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,'P-value',header=TRUE) a<-table.row.end(a) for (i in 2:(par1+1)) { a<-table.row.start(a) a<-table.element(a,i-1,header=TRUE) a<-table.element(a,round(myacf[i],6)) mytstat <- myacf[i]*sqrtn a<-table.element(a,round(mytstat,4)) a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) 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,'Partial Autocorrelation Function',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Time lag k',header=TRUE) a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,'P-value',header=TRUE) a<-table.row.end(a) for (i in 1:par1) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,round(mypacf[i],6)) mytstat <- mypacf[i]*sqrtn a<-table.element(a,round(mytstat,4)) a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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