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Author

*Unverified author*

R Software Module

rwasp_autocorrelation.wasp

Title produced by software

(Partial) Autocorrelation Function

Date of computation

Wed, 03 Dec 2008 00:50:35 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/03/t1228290669qnieyrfhxtkyd3l.htm/, Retrieved Fri, 17 May 2024 18:37:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28555, Retrieved Fri, 17 May 2024 18:37:41 +0000

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User-defined keywords

Estimated Impact

251

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F       [(Partial) Autocorrelation Function] [] [2008-12-03 07:50:35] [e02910eed3830f1815f587e12f46cbdb] [Current]
- RMP     [Standard Deviation-Mean Plot] [] [2008-12-06 11:15:17] [996314793dac993597edc1ca2281ff39] 
-   P     [(Partial) Autocorrelation Function] [] [2008-12-06 11:22:40] [996314793dac993597edc1ca2281ff39] 
-   P     [(Partial) Autocorrelation Function] [] [2008-12-06 11:30:49] [996314793dac993597edc1ca2281ff39] 

Feedback Forum

2008-12-06 11:34:41 [Angelique Van de Vijver] [reply] 
Student heeft foute berekening gemaakt.  
Daarom heb ik de berekening opnieuw gemaakt: http://www.freestatistics.org/blog/date/2008/Dec/06/t1228563097giswiravuu78jpy.htm 
met number of time lags= 36(student had hier default); lambda = -1.0 (student had hier 1)en d=2 (ik heb zowel een berekning gemaakt met d=1 en d=2 en hier is de ACF met d=2 beter vind ik )D=0 ;seasonality =12. 
De student heeft de verkeerd lambda-waarde gebruikt(nl.1) 
Ik heb de standard deviation plot gemaakt: 
http://www.freestatistics.org/blog/date/2008/Dec/06/t1228562355iuyo1k5avl81adu.htm 
Hier zien we dat de lambda-waarde gelijk moet zijn aan -0.96, dus heb ik in mijn berekening -1.0 als lambda waarde genomen. 
Op de ACF zien we dan dat de langetermijntrend verdwenen is, er is geen langzaam dalend verloop meer. 
We moesten hier niet seizoenaal differntiëren omdat er hier geen sprake was van seizoenaliteit.

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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	'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28555&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28555&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28555&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

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	'George Udny Yule' @ 72.249.76.132

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	-0.228562	-1.7407	0.043522
2	-0.213432	-1.6255	0.054744
3	0.239281	1.8223	0.036783
4	0.020746	0.158	0.437505
5	-0.076574	-0.5832	0.281018
6	0.014311	0.109	0.456794
7	0.093824	0.7145	0.23888
8	-0.1315	-1.0015	0.16038
9	0.112645	0.8579	0.197245
10	-0.09868	-0.7515	0.227687
11	-0.095612	-0.7282	0.234724
12	0.182579	1.3905	0.084847
13	-0.078194	-0.5955	0.27691
14	0.006005	0.0457	0.48184
15	-0.075301	-0.5735	0.284269
16	0.108537	0.8266	0.205927
17	-0.06654	-0.5068	0.307125

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.228562 & -1.7407 & 0.043522 \tabularnewline
2 & -0.213432 & -1.6255 & 0.054744 \tabularnewline
3 & 0.239281 & 1.8223 & 0.036783 \tabularnewline
4 & 0.020746 & 0.158 & 0.437505 \tabularnewline
5 & -0.076574 & -0.5832 & 0.281018 \tabularnewline
6 & 0.014311 & 0.109 & 0.456794 \tabularnewline
7 & 0.093824 & 0.7145 & 0.23888 \tabularnewline
8 & -0.1315 & -1.0015 & 0.16038 \tabularnewline
9 & 0.112645 & 0.8579 & 0.197245 \tabularnewline
10 & -0.09868 & -0.7515 & 0.227687 \tabularnewline
11 & -0.095612 & -0.7282 & 0.234724 \tabularnewline
12 & 0.182579 & 1.3905 & 0.084847 \tabularnewline
13 & -0.078194 & -0.5955 & 0.27691 \tabularnewline
14 & 0.006005 & 0.0457 & 0.48184 \tabularnewline
15 & -0.075301 & -0.5735 & 0.284269 \tabularnewline
16 & 0.108537 & 0.8266 & 0.205927 \tabularnewline
17 & -0.06654 & -0.5068 & 0.307125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28555&T=1

[TABLE]
[ROW][C]Autocorrelation Function[/C][/ROW]
[ROW][C]Time lag k[/C][C]ACF(k)[/C][C]T-STAT[/C][C]P-value[/C][/ROW]
[ROW][C]1[/C][C]-0.228562[/C][C]-1.7407[/C][C]0.043522[/C][/ROW]
[ROW][C]2[/C][C]-0.213432[/C][C]-1.6255[/C][C]0.054744[/C][/ROW]
[ROW][C]3[/C][C]0.239281[/C][C]1.8223[/C][C]0.036783[/C][/ROW]
[ROW][C]4[/C][C]0.020746[/C][C]0.158[/C][C]0.437505[/C][/ROW]
[ROW][C]5[/C][C]-0.076574[/C][C]-0.5832[/C][C]0.281018[/C][/ROW]
[ROW][C]6[/C][C]0.014311[/C][C]0.109[/C][C]0.456794[/C][/ROW]
[ROW][C]7[/C][C]0.093824[/C][C]0.7145[/C][C]0.23888[/C][/ROW]
[ROW][C]8[/C][C]-0.1315[/C][C]-1.0015[/C][C]0.16038[/C][/ROW]
[ROW][C]9[/C][C]0.112645[/C][C]0.8579[/C][C]0.197245[/C][/ROW]
[ROW][C]10[/C][C]-0.09868[/C][C]-0.7515[/C][C]0.227687[/C][/ROW]
[ROW][C]11[/C][C]-0.095612[/C][C]-0.7282[/C][C]0.234724[/C][/ROW]
[ROW][C]12[/C][C]0.182579[/C][C]1.3905[/C][C]0.084847[/C][/ROW]
[ROW][C]13[/C][C]-0.078194[/C][C]-0.5955[/C][C]0.27691[/C][/ROW]
[ROW][C]14[/C][C]0.006005[/C][C]0.0457[/C][C]0.48184[/C][/ROW]
[ROW][C]15[/C][C]-0.075301[/C][C]-0.5735[/C][C]0.284269[/C][/ROW]
[ROW][C]16[/C][C]0.108537[/C][C]0.8266[/C][C]0.205927[/C][/ROW]
[ROW][C]17[/C][C]-0.06654[/C][C]-0.5068[/C][C]0.307125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28555&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28555&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	-0.228562	-1.7407	0.043522
2	-0.213432	-1.6255	0.054744
3	0.239281	1.8223	0.036783
4	0.020746	0.158	0.437505
5	-0.076574	-0.5832	0.281018
6	0.014311	0.109	0.456794
7	0.093824	0.7145	0.23888
8	-0.1315	-1.0015	0.16038
9	0.112645	0.8579	0.197245
10	-0.09868	-0.7515	0.227687
11	-0.095612	-0.7282	0.234724
12	0.182579	1.3905	0.084847
13	-0.078194	-0.5955	0.27691
14	0.006005	0.0457	0.48184
15	-0.075301	-0.5735	0.284269
16	0.108537	0.8266	0.205927
17	-0.06654	-0.5068	0.307125

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.228562	-1.7407	0.043522
2	-0.280317	-2.1348	0.018506
3	0.129114	0.9833	0.164771
4	0.069699	0.5308	0.298788
5	0.035367	0.2693	0.394308
6	-0.015855	-0.1208	0.452153
7	0.072396	0.5514	0.291754
8	-0.10253	-0.7808	0.219034
9	0.110997	0.8453	0.200701
10	-0.144708	-1.1021	0.137493
11	-0.086182	-0.6563	0.2571
12	0.077616	0.5911	0.278374
13	-0.014998	-0.1142	0.454729
14	0.088666	0.6753	0.251096
15	-0.123678	-0.9419	0.175074
16	0.07141	0.5438	0.294316
17	-0.056728	-0.432	0.33366

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.228562 & -1.7407 & 0.043522 \tabularnewline
2 & -0.280317 & -2.1348 & 0.018506 \tabularnewline
3 & 0.129114 & 0.9833 & 0.164771 \tabularnewline
4 & 0.069699 & 0.5308 & 0.298788 \tabularnewline
5 & 0.035367 & 0.2693 & 0.394308 \tabularnewline
6 & -0.015855 & -0.1208 & 0.452153 \tabularnewline
7 & 0.072396 & 0.5514 & 0.291754 \tabularnewline
8 & -0.10253 & -0.7808 & 0.219034 \tabularnewline
9 & 0.110997 & 0.8453 & 0.200701 \tabularnewline
10 & -0.144708 & -1.1021 & 0.137493 \tabularnewline
11 & -0.086182 & -0.6563 & 0.2571 \tabularnewline
12 & 0.077616 & 0.5911 & 0.278374 \tabularnewline
13 & -0.014998 & -0.1142 & 0.454729 \tabularnewline
14 & 0.088666 & 0.6753 & 0.251096 \tabularnewline
15 & -0.123678 & -0.9419 & 0.175074 \tabularnewline
16 & 0.07141 & 0.5438 & 0.294316 \tabularnewline
17 & -0.056728 & -0.432 & 0.33366 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28555&T=2

[TABLE]
[ROW][C]Partial Autocorrelation Function[/C][/ROW]
[ROW][C]Time lag k[/C][C]PACF(k)[/C][C]T-STAT[/C][C]P-value[/C][/ROW]
[ROW][C]1[/C][C]-0.228562[/C][C]-1.7407[/C][C]0.043522[/C][/ROW]
[ROW][C]2[/C][C]-0.280317[/C][C]-2.1348[/C][C]0.018506[/C][/ROW]
[ROW][C]3[/C][C]0.129114[/C][C]0.9833[/C][C]0.164771[/C][/ROW]
[ROW][C]4[/C][C]0.069699[/C][C]0.5308[/C][C]0.298788[/C][/ROW]
[ROW][C]5[/C][C]0.035367[/C][C]0.2693[/C][C]0.394308[/C][/ROW]
[ROW][C]6[/C][C]-0.015855[/C][C]-0.1208[/C][C]0.452153[/C][/ROW]
[ROW][C]7[/C][C]0.072396[/C][C]0.5514[/C][C]0.291754[/C][/ROW]
[ROW][C]8[/C][C]-0.10253[/C][C]-0.7808[/C][C]0.219034[/C][/ROW]
[ROW][C]9[/C][C]0.110997[/C][C]0.8453[/C][C]0.200701[/C][/ROW]
[ROW][C]10[/C][C]-0.144708[/C][C]-1.1021[/C][C]0.137493[/C][/ROW]
[ROW][C]11[/C][C]-0.086182[/C][C]-0.6563[/C][C]0.2571[/C][/ROW]
[ROW][C]12[/C][C]0.077616[/C][C]0.5911[/C][C]0.278374[/C][/ROW]
[ROW][C]13[/C][C]-0.014998[/C][C]-0.1142[/C][C]0.454729[/C][/ROW]
[ROW][C]14[/C][C]0.088666[/C][C]0.6753[/C][C]0.251096[/C][/ROW]
[ROW][C]15[/C][C]-0.123678[/C][C]-0.9419[/C][C]0.175074[/C][/ROW]
[ROW][C]16[/C][C]0.07141[/C][C]0.5438[/C][C]0.294316[/C][/ROW]
[ROW][C]17[/C][C]-0.056728[/C][C]-0.432[/C][C]0.33366[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28555&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28555&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.228562	-1.7407	0.043522
2	-0.280317	-2.1348	0.018506
3	0.129114	0.9833	0.164771
4	0.069699	0.5308	0.298788
5	0.035367	0.2693	0.394308
6	-0.015855	-0.1208	0.452153
7	0.072396	0.5514	0.291754
8	-0.10253	-0.7808	0.219034
9	0.110997	0.8453	0.200701
10	-0.144708	-1.1021	0.137493
11	-0.086182	-0.6563	0.2571
12	0.077616	0.5911	0.278374
13	-0.014998	-0.1142	0.454729
14	0.088666	0.6753	0.251096
15	-0.123678	-0.9419	0.175074
16	0.07141	0.5438	0.294316
17	-0.056728	-0.432	0.33366

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = Default ; par2 = 1 ; par3 = 2 ; par4 = 0 ; par5 = 12 ;

Parameters (R input):

par1 = Default ; par2 = 1 ; par3 = 2 ; par4 = 0 ; par5 = 12 ;

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 (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='lags',ylab='ACF')
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('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('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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Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code