| TIJDREEKS A - STAP 21 | *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 Aug 2010 19:29:22 +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/Aug/19/t1282246147uzl5vqvi79abweu.htm/, Retrieved Thu, 19 Aug 2010 21:29:09 +0200 | | 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/Aug/19/t1282246147uzl5vqvi79abweu.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: | Mertens Jeroen | | Dataseries X: | » Textbox « » Textfile « » CSV « | 349
348
347
345
365
364
349
339
340
340
341
343
341
343
341
335
355
357
337
325
336
338
337
328
326
327
319
310
320
322
303
292
303
315
311
307
308
312
309
310
309
304
287
275
290
298
294
286
294
292
287
281
280
271
264
259
271
279
279
273
286
286
280
277
269
255
252
245
257
267
261
258
271
262
258
253
236
228
235
226
231
235
227
222
233
221
218
220
204
196
208
190
191
194
179
162
179
176
168
170
153
142
155
136
136
144
135
114
135
132
123
123
103
97
113
108
111
121
111
97 | | Output produced by software: |
Autocorrelation Function | Time lag k | ACF(k) | T-STAT | P-value | 1 | -0.083521 | -0.9111 | 0.182041 | 2 | -0.405411 | -4.4225 | 1.1e-05 | 3 | 0.186442 | 2.0338 | 0.022095 | 4 | -0.026504 | -0.2891 | 0.386497 | 5 | -0.221066 | -2.4115 | 0.008707 | 6 | 0.247226 | 2.6969 | 0.004007 | 7 | -0.190761 | -2.081 | 0.019792 | 8 | -0.049643 | -0.5415 | 0.294573 | 9 | 0.222953 | 2.4321 | 0.00825 | 10 | -0.288968 | -3.1523 | 0.001025 | 11 | -0.095916 | -1.0463 | 0.148767 | 12 | 0.757133 | 8.2594 | 0 | 13 | -0.075041 | -0.8186 | 0.207324 | 14 | -0.424823 | -4.6343 | 5e-06 | 15 | 0.121142 | 1.3215 | 0.094435 | 16 | 0.076044 | 0.8295 | 0.204228 | 17 | -0.156116 | -1.703 | 0.045587 | 18 | 0.168034 | 1.833 | 0.034648 | 19 | -0.124241 | -1.3553 | 0.088942 | 20 | -0.082197 | -0.8967 | 0.185855 | 21 | 0.142529 | 1.5548 | 0.061324 | 22 | -0.135301 | -1.476 | 0.071299 | 23 | -0.070914 | -0.7736 | 0.220356 | 24 | 0.509725 | 5.5604 | 0 | 25 | -0.0446 | -0.4865 | 0.313744 | 26 | -0.343428 | -3.7464 | 0.000139 | 27 | 0.063918 | 0.6973 | 0.2435 | 28 | 0.127302 | 1.3887 | 0.083758 | 29 | -0.107209 | -1.1695 | 0.122267 | 30 | 0.066628 | 0.7268 | 0.234379 | 31 | -0.11525 | -1.2572 | 0.105567 | 32 | -0.06629 | -0.7231 | 0.235507 | 33 | 0.094026 | 1.0257 | 0.153557 | 34 | -0.021439 | -0.2339 | 0.407744 | 35 | -0.018313 | -0.1998 | 0.421001 | 36 | 0.303944 | 3.3156 | 0.000606 | 37 | -0.063544 | -0.6932 | 0.244773 | 38 | -0.250283 | -2.7303 | 0.003645 | 39 | -0.002919 | -0.0318 | 0.487326 | 40 | 0.120911 | 1.319 | 0.094853 | 41 | -0.04614 | -0.5033 | 0.307831 | 42 | -0.002928 | -0.0319 | 0.487285 | 43 | -0.108422 | -1.1827 | 0.119634 | 44 | 0.009297 | 0.1014 | 0.459695 | 45 | 0.082597 | 0.901 | 0.184697 | 46 | 0.032754 | 0.3573 | 0.36075 | 47 | -0.015306 | -0.167 | 0.43384 | 48 | 0.144746 | 1.579 | 0.058497 |
Partial Autocorrelation Function | Time lag k | PACF(k) | T-STAT | P-value | 1 | -0.083521 | -0.9111 | 0.182041 | 2 | -0.415284 | -4.5302 | 7e-06 | 3 | 0.126357 | 1.3784 | 0.085335 | 4 | -0.205282 | -2.2394 | 0.013495 | 5 | -0.133682 | -1.4583 | 0.073696 | 6 | 0.150398 | 1.6406 | 0.051755 | 7 | -0.384296 | -4.1922 | 2.7e-05 | 8 | 0.223324 | 2.4362 | 0.008163 | 9 | -0.157592 | -1.7191 | 0.044096 | 10 | -0.31387 | -3.4239 | 0.000424 | 11 | 0.135538 | 1.4786 | 0.070951 | 12 | 0.565476 | 6.1686 | 0 | 13 | 0.044468 | 0.4851 | 0.314254 | 14 | -0.012428 | -0.1356 | 0.446194 | 15 | -0.108689 | -1.1857 | 0.11906 | 16 | 0.206021 | 2.2474 | 0.013228 | 17 | 0.065509 | 0.7146 | 0.238123 | 18 | -0.047504 | -0.5182 | 0.302637 | 19 | 0.086792 | 0.9468 | 0.172831 | 20 | -0.109275 | -1.1921 | 0.117806 | 21 | -0.09897 | -1.0796 | 0.141243 | 22 | 0.111666 | 1.2181 | 0.112792 | 23 | 0.030081 | 0.3281 | 0.371691 | 24 | -0.055438 | -0.6048 | 0.273246 | 25 | -0.052503 | -0.5727 | 0.28395 | 26 | 0.12643 | 1.3792 | 0.085211 | 27 | 0.036053 | 0.3933 | 0.347404 | 28 | -0.054175 | -0.591 | 0.277827 | 29 | 0.020364 | 0.2221 | 0.412292 | 30 | -0.027889 | -0.3042 | 0.38074 | 31 | -0.125233 | -1.3661 | 0.087238 | 32 | 0.016016 | 0.1747 | 0.430799 | 33 | 0.011042 | 0.1205 | 0.452163 | 34 | 0.024657 | 0.269 | 0.394208 | 35 | -0.015161 | -0.1654 | 0.434459 | 36 | -0.032934 | -0.3593 | 0.360014 | 37 | -0.008376 | -0.0914 | 0.463675 | 38 | -0.053603 | -0.5847 | 0.279916 | 39 | -0.084436 | -0.9211 | 0.179435 | 40 | -0.004714 | -0.0514 | 0.479535 | 41 | -0.051483 | -0.5616 | 0.287718 | 42 | -0.043591 | -0.4755 | 0.317645 | 43 | -0.029355 | -0.3202 | 0.374679 | 44 | 0.063342 | 0.691 | 0.245461 | 45 | 0.040391 | 0.4406 | 0.330148 | 46 | 0.027853 | 0.3038 | 0.38089 | 47 | -0.054393 | -0.5934 | 0.277034 | 48 | 0.024422 | 0.2664 | 0.395192 |
| | Charts produced by software: | | http://www.freestatistics.org/blog/date/2010/Aug/19/t1282246147uzl5vqvi79abweu/14pho1282246159.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Aug/19/t1282246147uzl5vqvi79abweu/14pho1282246159.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Aug/19/t1282246147uzl5vqvi79abweu/24pho1282246159.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Aug/19/t1282246147uzl5vqvi79abweu/24pho1282246159.ps (open in new window) |
| | Parameters (Session): | par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ; | | Parameters (R input): | par1 = 48 ; 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 (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='pic1.png')
racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')
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')
| |
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