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Author's title

Author

*Unverified author*

R Software Module

rwasp_autocorrelation.wasp

Title produced by software

(Partial) Autocorrelation Function

Date of computation

Tue, 02 Dec 2008 11:24:30 -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/02/t1228242301vg95g6cd8ec6o59.htm/, Retrieved Sun, 19 May 2024 11:30:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28218, Retrieved Sun, 19 May 2024 11:30:54 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Severijns Britt

Estimated Impact

167

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

F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD    [(Partial) Autocorrelation Function] [non stationary ti...] [2008-12-02 18:24:30] [7bf28d4d60530086dbc44ae6b648927e] [Current]

Feedback Forum

2008-12-08 18:08:02 [Jessica Alves Pires] [reply] 
Men ziet dat de differentiatie inderdaad een gunstig effect heeft, maar ik zou toch eerst de VRM hebben berekend om zeker te zijn van welke waarden ik best voor d en D gebruik.

Post a new message

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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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28218&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28218&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28218&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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	0.0037	0.0329	0.486923
2	-0.022282	-0.198	0.421758
3	-0.024024	-0.2135	0.415732
4	0.084046	0.747	0.228635
5	0.028545	0.2537	0.400187
6	0.053757	0.4778	0.317056
7	-0.001452	-0.0129	0.494869
8	0.139755	1.2422	0.108924
9	0.052533	0.4669	0.320921
10	-0.121728	-1.0819	0.141284
11	0.052188	0.4639	0.322013
12	-0.260988	-2.3197	0.01147
13	-0.058828	-0.5229	0.301264
14	0.167639	1.49	0.070102
15	0.024853	0.2209	0.41287
16	-0.057848	-0.5142	0.304288
17	-0.058971	-0.5241	0.300821
18	0.054034	0.4803	0.316184
19	0.039321	0.3495	0.363826

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.0037 & 0.0329 & 0.486923 \tabularnewline
2 & -0.022282 & -0.198 & 0.421758 \tabularnewline
3 & -0.024024 & -0.2135 & 0.415732 \tabularnewline
4 & 0.084046 & 0.747 & 0.228635 \tabularnewline
5 & 0.028545 & 0.2537 & 0.400187 \tabularnewline
6 & 0.053757 & 0.4778 & 0.317056 \tabularnewline
7 & -0.001452 & -0.0129 & 0.494869 \tabularnewline
8 & 0.139755 & 1.2422 & 0.108924 \tabularnewline
9 & 0.052533 & 0.4669 & 0.320921 \tabularnewline
10 & -0.121728 & -1.0819 & 0.141284 \tabularnewline
11 & 0.052188 & 0.4639 & 0.322013 \tabularnewline
12 & -0.260988 & -2.3197 & 0.01147 \tabularnewline
13 & -0.058828 & -0.5229 & 0.301264 \tabularnewline
14 & 0.167639 & 1.49 & 0.070102 \tabularnewline
15 & 0.024853 & 0.2209 & 0.41287 \tabularnewline
16 & -0.057848 & -0.5142 & 0.304288 \tabularnewline
17 & -0.058971 & -0.5241 & 0.300821 \tabularnewline
18 & 0.054034 & 0.4803 & 0.316184 \tabularnewline
19 & 0.039321 & 0.3495 & 0.363826 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28218&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]0.0037[/C][C]0.0329[/C][C]0.486923[/C][/ROW]
[ROW][C]2[/C][C]-0.022282[/C][C]-0.198[/C][C]0.421758[/C][/ROW]
[ROW][C]3[/C][C]-0.024024[/C][C]-0.2135[/C][C]0.415732[/C][/ROW]
[ROW][C]4[/C][C]0.084046[/C][C]0.747[/C][C]0.228635[/C][/ROW]
[ROW][C]5[/C][C]0.028545[/C][C]0.2537[/C][C]0.400187[/C][/ROW]
[ROW][C]6[/C][C]0.053757[/C][C]0.4778[/C][C]0.317056[/C][/ROW]
[ROW][C]7[/C][C]-0.001452[/C][C]-0.0129[/C][C]0.494869[/C][/ROW]
[ROW][C]8[/C][C]0.139755[/C][C]1.2422[/C][C]0.108924[/C][/ROW]
[ROW][C]9[/C][C]0.052533[/C][C]0.4669[/C][C]0.320921[/C][/ROW]
[ROW][C]10[/C][C]-0.121728[/C][C]-1.0819[/C][C]0.141284[/C][/ROW]
[ROW][C]11[/C][C]0.052188[/C][C]0.4639[/C][C]0.322013[/C][/ROW]
[ROW][C]12[/C][C]-0.260988[/C][C]-2.3197[/C][C]0.01147[/C][/ROW]
[ROW][C]13[/C][C]-0.058828[/C][C]-0.5229[/C][C]0.301264[/C][/ROW]
[ROW][C]14[/C][C]0.167639[/C][C]1.49[/C][C]0.070102[/C][/ROW]
[ROW][C]15[/C][C]0.024853[/C][C]0.2209[/C][C]0.41287[/C][/ROW]
[ROW][C]16[/C][C]-0.057848[/C][C]-0.5142[/C][C]0.304288[/C][/ROW]
[ROW][C]17[/C][C]-0.058971[/C][C]-0.5241[/C][C]0.300821[/C][/ROW]
[ROW][C]18[/C][C]0.054034[/C][C]0.4803[/C][C]0.316184[/C][/ROW]
[ROW][C]19[/C][C]0.039321[/C][C]0.3495[/C][C]0.363826[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28218&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28218&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	0.0037	0.0329	0.486923
2	-0.022282	-0.198	0.421758
3	-0.024024	-0.2135	0.415732
4	0.084046	0.747	0.228635
5	0.028545	0.2537	0.400187
6	0.053757	0.4778	0.317056
7	-0.001452	-0.0129	0.494869
8	0.139755	1.2422	0.108924
9	0.052533	0.4669	0.320921
10	-0.121728	-1.0819	0.141284
11	0.052188	0.4639	0.322013
12	-0.260988	-2.3197	0.01147
13	-0.058828	-0.5229	0.301264
14	0.167639	1.49	0.070102
15	0.024853	0.2209	0.41287
16	-0.057848	-0.5142	0.304288
17	-0.058971	-0.5241	0.300821
18	0.054034	0.4803	0.316184
19	0.039321	0.3495	0.363826

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.0037	0.0329	0.486923
2	-0.022296	-0.1982	0.421709
3	-0.023869	-0.2122	0.416266
4	0.083808	0.7449	0.22927
5	0.026986	0.2399	0.405532
6	0.057027	0.5069	0.306829
7	0.003504	0.0311	0.487616
8	0.138157	1.228	0.111553
9	0.051795	0.4604	0.323259
10	-0.127949	-1.1372	0.12944
11	0.061264	0.5445	0.293806
12	-0.305753	-2.7176	0.004039
13	-0.08137	-0.7232	0.235837
14	0.178862	1.5898	0.057941
15	-0.012583	-0.1118	0.455616
16	-0.004383	-0.039	0.48451
17	-0.042134	-0.3745	0.354519
18	0.108578	0.9651	0.168729
19	0.0353	0.3138	0.377268

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.0037 & 0.0329 & 0.486923 \tabularnewline
2 & -0.022296 & -0.1982 & 0.421709 \tabularnewline
3 & -0.023869 & -0.2122 & 0.416266 \tabularnewline
4 & 0.083808 & 0.7449 & 0.22927 \tabularnewline
5 & 0.026986 & 0.2399 & 0.405532 \tabularnewline
6 & 0.057027 & 0.5069 & 0.306829 \tabularnewline
7 & 0.003504 & 0.0311 & 0.487616 \tabularnewline
8 & 0.138157 & 1.228 & 0.111553 \tabularnewline
9 & 0.051795 & 0.4604 & 0.323259 \tabularnewline
10 & -0.127949 & -1.1372 & 0.12944 \tabularnewline
11 & 0.061264 & 0.5445 & 0.293806 \tabularnewline
12 & -0.305753 & -2.7176 & 0.004039 \tabularnewline
13 & -0.08137 & -0.7232 & 0.235837 \tabularnewline
14 & 0.178862 & 1.5898 & 0.057941 \tabularnewline
15 & -0.012583 & -0.1118 & 0.455616 \tabularnewline
16 & -0.004383 & -0.039 & 0.48451 \tabularnewline
17 & -0.042134 & -0.3745 & 0.354519 \tabularnewline
18 & 0.108578 & 0.9651 & 0.168729 \tabularnewline
19 & 0.0353 & 0.3138 & 0.377268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28218&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]0.0037[/C][C]0.0329[/C][C]0.486923[/C][/ROW]
[ROW][C]2[/C][C]-0.022296[/C][C]-0.1982[/C][C]0.421709[/C][/ROW]
[ROW][C]3[/C][C]-0.023869[/C][C]-0.2122[/C][C]0.416266[/C][/ROW]
[ROW][C]4[/C][C]0.083808[/C][C]0.7449[/C][C]0.22927[/C][/ROW]
[ROW][C]5[/C][C]0.026986[/C][C]0.2399[/C][C]0.405532[/C][/ROW]
[ROW][C]6[/C][C]0.057027[/C][C]0.5069[/C][C]0.306829[/C][/ROW]
[ROW][C]7[/C][C]0.003504[/C][C]0.0311[/C][C]0.487616[/C][/ROW]
[ROW][C]8[/C][C]0.138157[/C][C]1.228[/C][C]0.111553[/C][/ROW]
[ROW][C]9[/C][C]0.051795[/C][C]0.4604[/C][C]0.323259[/C][/ROW]
[ROW][C]10[/C][C]-0.127949[/C][C]-1.1372[/C][C]0.12944[/C][/ROW]
[ROW][C]11[/C][C]0.061264[/C][C]0.5445[/C][C]0.293806[/C][/ROW]
[ROW][C]12[/C][C]-0.305753[/C][C]-2.7176[/C][C]0.004039[/C][/ROW]
[ROW][C]13[/C][C]-0.08137[/C][C]-0.7232[/C][C]0.235837[/C][/ROW]
[ROW][C]14[/C][C]0.178862[/C][C]1.5898[/C][C]0.057941[/C][/ROW]
[ROW][C]15[/C][C]-0.012583[/C][C]-0.1118[/C][C]0.455616[/C][/ROW]
[ROW][C]16[/C][C]-0.004383[/C][C]-0.039[/C][C]0.48451[/C][/ROW]
[ROW][C]17[/C][C]-0.042134[/C][C]-0.3745[/C][C]0.354519[/C][/ROW]
[ROW][C]18[/C][C]0.108578[/C][C]0.9651[/C][C]0.168729[/C][/ROW]
[ROW][C]19[/C][C]0.0353[/C][C]0.3138[/C][C]0.377268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28218&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28218&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	0.0037	0.0329	0.486923
2	-0.022296	-0.1982	0.421709
3	-0.023869	-0.2122	0.416266
4	0.083808	0.7449	0.22927
5	0.026986	0.2399	0.405532
6	0.057027	0.5069	0.306829
7	0.003504	0.0311	0.487616
8	0.138157	1.228	0.111553
9	0.051795	0.4604	0.323259
10	-0.127949	-1.1372	0.12944
11	0.061264	0.5445	0.293806
12	-0.305753	-2.7176	0.004039
13	-0.08137	-0.7232	0.235837
14	0.178862	1.5898	0.057941
15	-0.012583	-0.1118	0.455616
16	-0.004383	-0.039	0.48451
17	-0.042134	-0.3745	0.354519
18	0.108578	0.9651	0.168729
19	0.0353	0.3138	0.377268

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 1 ; 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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Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code