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R Software Module

rwasp_autocorrelation.wasp

Title produced by software

(Partial) Autocorrelation Function

Date of computation

Mon, 19 Dec 2016 21:48:16 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/19/t1482180522ea9am3hlqiknr2b.htm/, Retrieved Fri, 17 May 2024 19:37:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301494, Retrieved Fri, 17 May 2024 19:37:09 +0000

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

-       [(Partial) Autocorrelation Function] [] [2016-12-19 20:48:16] [9412b5b3b31fe4708efb1e5c8c74b28f] [Current]

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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	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301494&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

1 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301494&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301494&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	Big Analytics Cloud Computing Center

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	-0.150954	-0.7239	0.238198
2	-0.273989	-1.314	0.1009
3	0.168051	0.8059	0.214265
4	0.355858	1.7066	0.050679
5	-0.287376	-1.3782	0.0907
6	-0.045714	-0.2192	0.4142
7	0.325627	1.5617	0.066013
8	-0.230009	-1.1031	0.1407
9	-0.271906	-1.304	0.102564
10	0.205426	0.9852	0.167392
11	0.194468	0.9326	0.180348
12	-0.308876	-1.4813	0.076046
13	-0.166691	-0.7994	0.216112
14	0.128104	0.6144	0.272502
15	-0.037961	-0.1821	0.428567

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.150954 & -0.7239 & 0.238198 \tabularnewline
2 & -0.273989 & -1.314 & 0.1009 \tabularnewline
3 & 0.168051 & 0.8059 & 0.214265 \tabularnewline
4 & 0.355858 & 1.7066 & 0.050679 \tabularnewline
5 & -0.287376 & -1.3782 & 0.0907 \tabularnewline
6 & -0.045714 & -0.2192 & 0.4142 \tabularnewline
7 & 0.325627 & 1.5617 & 0.066013 \tabularnewline
8 & -0.230009 & -1.1031 & 0.1407 \tabularnewline
9 & -0.271906 & -1.304 & 0.102564 \tabularnewline
10 & 0.205426 & 0.9852 & 0.167392 \tabularnewline
11 & 0.194468 & 0.9326 & 0.180348 \tabularnewline
12 & -0.308876 & -1.4813 & 0.076046 \tabularnewline
13 & -0.166691 & -0.7994 & 0.216112 \tabularnewline
14 & 0.128104 & 0.6144 & 0.272502 \tabularnewline
15 & -0.037961 & -0.1821 & 0.428567 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301494&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]-0.150954[/C][C]-0.7239[/C][C]0.238198[/C][/ROW]
[ROW][C]2[/C][C]-0.273989[/C][C]-1.314[/C][C]0.1009[/C][/ROW]
[ROW][C]3[/C][C]0.168051[/C][C]0.8059[/C][C]0.214265[/C][/ROW]
[ROW][C]4[/C][C]0.355858[/C][C]1.7066[/C][C]0.050679[/C][/ROW]
[ROW][C]5[/C][C]-0.287376[/C][C]-1.3782[/C][C]0.0907[/C][/ROW]
[ROW][C]6[/C][C]-0.045714[/C][C]-0.2192[/C][C]0.4142[/C][/ROW]
[ROW][C]7[/C][C]0.325627[/C][C]1.5617[/C][C]0.066013[/C][/ROW]
[ROW][C]8[/C][C]-0.230009[/C][C]-1.1031[/C][C]0.1407[/C][/ROW]
[ROW][C]9[/C][C]-0.271906[/C][C]-1.304[/C][C]0.102564[/C][/ROW]
[ROW][C]10[/C][C]0.205426[/C][C]0.9852[/C][C]0.167392[/C][/ROW]
[ROW][C]11[/C][C]0.194468[/C][C]0.9326[/C][C]0.180348[/C][/ROW]
[ROW][C]12[/C][C]-0.308876[/C][C]-1.4813[/C][C]0.076046[/C][/ROW]
[ROW][C]13[/C][C]-0.166691[/C][C]-0.7994[/C][C]0.216112[/C][/ROW]
[ROW][C]14[/C][C]0.128104[/C][C]0.6144[/C][C]0.272502[/C][/ROW]
[ROW][C]15[/C][C]-0.037961[/C][C]-0.1821[/C][C]0.428567[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301494&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301494&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	-0.150954	-0.7239	0.238198
2	-0.273989	-1.314	0.1009
3	0.168051	0.8059	0.214265
4	0.355858	1.7066	0.050679
5	-0.287376	-1.3782	0.0907
6	-0.045714	-0.2192	0.4142
7	0.325627	1.5617	0.066013
8	-0.230009	-1.1031	0.1407
9	-0.271906	-1.304	0.102564
10	0.205426	0.9852	0.167392
11	0.194468	0.9326	0.180348
12	-0.308876	-1.4813	0.076046
13	-0.166691	-0.7994	0.216112
14	0.128104	0.6144	0.272502
15	-0.037961	-0.1821	0.428567

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.150954	-0.7239	0.238198
2	-0.303696	-1.4565	0.079389
3	0.076978	0.3692	0.357687
4	0.36012	1.7271	0.048781
5	-0.120144	-0.5762	0.285042
6	0.030232	0.145	0.442992
7	0.18869	0.9049	0.187443
8	-0.310275	-1.488	0.075163
9	-0.175845	-0.8433	0.203867
10	0.012648	0.0607	0.476078
11	0.064369	0.3087	0.380162
12	0.047242	0.2266	0.411383
13	-0.173502	-0.8321	0.206958
14	-0.227657	-1.0918	0.143112
15	-0.049742	-0.2386	0.406782

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.150954 & -0.7239 & 0.238198 \tabularnewline
2 & -0.303696 & -1.4565 & 0.079389 \tabularnewline
3 & 0.076978 & 0.3692 & 0.357687 \tabularnewline
4 & 0.36012 & 1.7271 & 0.048781 \tabularnewline
5 & -0.120144 & -0.5762 & 0.285042 \tabularnewline
6 & 0.030232 & 0.145 & 0.442992 \tabularnewline
7 & 0.18869 & 0.9049 & 0.187443 \tabularnewline
8 & -0.310275 & -1.488 & 0.075163 \tabularnewline
9 & -0.175845 & -0.8433 & 0.203867 \tabularnewline
10 & 0.012648 & 0.0607 & 0.476078 \tabularnewline
11 & 0.064369 & 0.3087 & 0.380162 \tabularnewline
12 & 0.047242 & 0.2266 & 0.411383 \tabularnewline
13 & -0.173502 & -0.8321 & 0.206958 \tabularnewline
14 & -0.227657 & -1.0918 & 0.143112 \tabularnewline
15 & -0.049742 & -0.2386 & 0.406782 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301494&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]-0.150954[/C][C]-0.7239[/C][C]0.238198[/C][/ROW]
[ROW][C]2[/C][C]-0.303696[/C][C]-1.4565[/C][C]0.079389[/C][/ROW]
[ROW][C]3[/C][C]0.076978[/C][C]0.3692[/C][C]0.357687[/C][/ROW]
[ROW][C]4[/C][C]0.36012[/C][C]1.7271[/C][C]0.048781[/C][/ROW]
[ROW][C]5[/C][C]-0.120144[/C][C]-0.5762[/C][C]0.285042[/C][/ROW]
[ROW][C]6[/C][C]0.030232[/C][C]0.145[/C][C]0.442992[/C][/ROW]
[ROW][C]7[/C][C]0.18869[/C][C]0.9049[/C][C]0.187443[/C][/ROW]
[ROW][C]8[/C][C]-0.310275[/C][C]-1.488[/C][C]0.075163[/C][/ROW]
[ROW][C]9[/C][C]-0.175845[/C][C]-0.8433[/C][C]0.203867[/C][/ROW]
[ROW][C]10[/C][C]0.012648[/C][C]0.0607[/C][C]0.476078[/C][/ROW]
[ROW][C]11[/C][C]0.064369[/C][C]0.3087[/C][C]0.380162[/C][/ROW]
[ROW][C]12[/C][C]0.047242[/C][C]0.2266[/C][C]0.411383[/C][/ROW]
[ROW][C]13[/C][C]-0.173502[/C][C]-0.8321[/C][C]0.206958[/C][/ROW]
[ROW][C]14[/C][C]-0.227657[/C][C]-1.0918[/C][C]0.143112[/C][/ROW]
[ROW][C]15[/C][C]-0.049742[/C][C]-0.2386[/C][C]0.406782[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301494&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301494&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	-0.150954	-0.7239	0.238198
2	-0.303696	-1.4565	0.079389
3	0.076978	0.3692	0.357687
4	0.36012	1.7271	0.048781
5	-0.120144	-0.5762	0.285042
6	0.030232	0.145	0.442992
7	0.18869	0.9049	0.187443
8	-0.310275	-1.488	0.075163
9	-0.175845	-0.8433	0.203867
10	0.012648	0.0607	0.476078
11	0.064369	0.3087	0.380162
12	0.047242	0.2266	0.411383
13	-0.173502	-0.8321	0.206958
14	-0.227657	-1.0918	0.143112
15	-0.049742	-0.2386	0.406782

Figure 1

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Postscript link

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Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ; par8 = ;

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)
x <- na.omit(x)
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,'ACF(k)',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,'PACF(k)',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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Input Parameters & R Code