*Unverified author*

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

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Thu, 19 May 2011 23:12:50 +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/May/20/t1305846527j15awg311dg26yo.htm/, Retrieved Fri, 20 May 2011 01:08:50 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W12

Dataseries X:

» Textbox « » Textfile « » CSV «

90 51 47 59 54 79 59 80 46 62 55 77 72 72 71 50 66 78 59 52 71 98 70 84 90 98 98 78 59 0 58 55 62 80 91 86 61 49 61 56 73 85 82 32 39 30 51 48 57 59 32 56 54 74 62 78 72 48 59 61 80 69 58 63 27 23 34 45 51 51 73 37 35 66 54 30 66 61 37 55 64 53 63 70 72 52 53 50 60 73 66 78

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 1 seconds R Server 'Herman Ole Andreas Wold' @ www.yougetit.org Autocorrelation Function Time lag k ACF(k) T-STAT P-value 1 -0.279575 -2.667 0.004529 2 -0.068203 -0.6506 0.258466 3 -0.060988 -0.5818 0.281074 4 -0.034938 -0.3333 0.369841 5 -0.170885 -1.6301 0.053265 6 -0.077277 -0.7372 0.231455 7 0.396672 3.784 0.000138 8 -0.1656 -1.5797 0.05882 9 -0.00861 -0.0821 0.467359 10 0.064921 0.6193 0.268632 11 -0.08218 -0.784 0.217552 12 -0.11215 -1.0698 0.143759 13 -0.083774 -0.7992 0.213141 14 0.368483 3.5151 0.000344 15 -0.122316 -1.1668 0.123165 16 0.006983 0.0666 0.473517 17 -0.054171 -0.5168 0.303291 18 -0.029212 -0.2787 0.390566 19 -0.002245 -0.0214 0.491481 20 -0.161321 -1.5389 0.063649 21 0.429109 4.0934 4.6e-05 22 -0.176345 -1.6822 0.047977 23 0.015993 0.1526 0.439542 24 -0.192133 -1.8328 0.035049 25 0.097179 0.927 0.178182 26 -0.095801 -0.9139 0.181598 27 -0.034746 -0.3315 0.370533 28 0.315188 3.0067 0.001707 29 -0.076128 -0.7262 0.234785 30 -0.006414 -0.0612 0.475672 31 -0.088753 -0.8467 0.199705 32 -0.042919 -0.4094 0.341598 33 -0.040891 -0.3901 0.348695 34 -0.131351 -1.253 0.106707 35 0.311861 2.975 0.001876 36 -0.006303 -0.0601 0.476092 37 0.001718 0.0164 0.49348 38 -0.055389 -0.5284 0.299261 39 -0.013394 -0.1278 0.449305 40 -0.038049 -0.363 0.358736 41 -0.150781 -1.4384 0.076881 42 0.244721 2.3345 0.010885 43 0.018229 0.1739 0.431167 44 -0.020067 -0.1914 0.424311 45 -0.154072 -1.4697 0.072539 46 0.143909 1.3728 0.086593 47 -0.087903 -0.8385 0.201962 48 -0.064215 -0.6126 0.270843 49 0.188244 1.7957 0.037928 50 -0.024802 -0.2366 0.406751 51 -0.067181 -0.6409 0.26161 52 -0.038341 -0.3658 0.357699 53 0.061874 0.5902 0.278245 54 -0.100566 -0.9593 0.169964 55 -0.068101 -0.6496 0.258781 56 0.190011 1.8126 0.036596 57 -0.036495 -0.3481 0.364268 58 -0.017768 -0.1695 0.43289 59 0.026224 0.2502 0.401515 60 -0.028699 -0.2738 0.39244 Partial Autocorrelation Function Time lag k PACF(k) T-STAT P-value 1 -0.279575 -2.667 0.004529 2 -0.158776 -1.5146 0.066667 3 -0.141858 -1.3532 0.089666 4 -0.124578 -1.1884 0.118884 5 -0.282745 -2.6972 0.004166 6 -0.332543 -3.1723 0.001031 7 0.204077 1.9468 0.027323 8 -0.076742 -0.7321 0.233002 9 -0.100913 -0.9626 0.169138 10 0.021765 0.2076 0.417993 11 -0.103295 -0.9854 0.163528 12 -0.094599 -0.9024 0.184608 13 -0.20086 -1.9161 0.029247 14 0.137443 1.3111 0.096558 15 0.068685 0.6552 0.256992 16 -0.010046 -0.0958 0.461933 17 -0.148068 -1.4125 0.08061 18 -0.070537 -0.6729 0.251365 19 0.11827 1.1282 0.131095 20 -0.157725 -1.5046 0.067945 21 0.232646 2.2193 0.014476 22 0.039063 0.3726 0.355142 23 0.025153 0.2399 0.405457 24 -0.260071 -2.4809 0.007471 25 -0.015531 -0.1482 0.441273 26 -0.00621 -0.0592 0.476445 27 0.043514 0.4151 0.339525 28 0.019606 0.187 0.426025 29 0.00232 0.0221 0.491194 30 0.059188 0.5646 0.286863 31 -0.028806 -0.2748 0.392048 32 -0.117873 -1.1244 0.131892 33 0.035361 0.3373 0.368327 34 -0.063719 -0.6078 0.272402 35 -0.07749 -0.7392 0.230842 36 0.063132 0.6022 0.274255 37 -0.02068 -0.1973 0.422028 38 0.120996 1.1542 0.125714 39 0.006498 0.062 0.475354 40 0.021748 0.2075 0.418058 41 -0.016066 -0.1533 0.439265 42 -0.035551 -0.3391 0.367646 43 0.13553 1.2929 0.099664 44 -0.016768 -0.16 0.436636 45 -0.051457 -0.4909 0.312352 46 0.054488 0.5198 0.302239 47 0.036659 0.3497 0.363685 48 0.056355 0.5376 0.296084 49 0.032367 0.3088 0.379105 50 -0.037859 -0.3611 0.359412 51 -0.008763 -0.0836 0.46678 52 0.017528 0.1672 0.433789 53 0.019238 0.1835 0.427399 54 -0.071397 -0.6811 0.248773 55 0.003646 0.0348 0.486167 56 -0.118179 -1.1274 0.131278 57 -0.154736 -1.4761 0.071686 58 -0.087301 -0.8328 0.203569 59 0.127085 1.2123 0.114265 60 0.002383 0.0227 0.490959

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/20/t1305846527j15awg311dg26yo/191xi1305846769.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305846527j15awg311dg26yo/191xi1305846769.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305846527j15awg311dg26yo/26l7q1305846769.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305846527j15awg311dg26yo/26l7q1305846769.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305846527j15awg311dg26yo/3beyt1305846769.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305846527j15awg311dg26yo/3beyt1305846769.ps (open in new window)

Parameters (Session):

par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 60 ; par2 = 1 ; par3 = 1 ; 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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