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Author's title

Author*Unverified author*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationFri, 17 Oct 2014 09:36:37 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Oct/17/t1413535010lfiny6uj5bln9iz.htm/, Retrieved Fri, 10 May 2024 07:30:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=243186, Retrieved Fri, 10 May 2024 07:30:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsEline Van Loon
Estimated Impact130
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [(Partial) Autocorrelation Function] [] [2014-10-17 08:36:37] [9adebd9d8505f0d6c7bd6ecbde218cd8] [Current]
- R P     [(Partial) Autocorrelation Function] [] [2014-11-13 10:27:56] [021f24b0772399b3b291bd2200abf137]
- RMPD    [Bootstrap Plot - Central Tendency] [] [2014-11-13 14:49:32] [021f24b0772399b3b291bd2200abf137]
- RMPD    [Bootstrap Plot - Central Tendency] [] [2014-11-13 14:52:59] [021f24b0772399b3b291bd2200abf137]
- RMPD    [Bootstrap Plot - Central Tendency] [] [2014-11-13 14:56:25] [021f24b0772399b3b291bd2200abf137]
- RMP     [Blocked Bootstrap Plot - Central Tendency] [] [2014-11-13 15:53:40] [021f24b0772399b3b291bd2200abf137]
- RMP     [Blocked Bootstrap Plot - Central Tendency] [] [2014-11-13 15:57:07] [021f24b0772399b3b291bd2200abf137]
- RMP     [Blocked Bootstrap Plot - Central Tendency] [] [2014-11-13 16:00:25] [021f24b0772399b3b291bd2200abf137]
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Dataseries X:
22943
21413
20631
19775
17506
20688
32631
34062
29159
25871
23719
25638
27596
28006
27662
26655
25213
28434
40388
42758
37956
33490
31578
34766
32324
32046
29565
28284
26366
27530
39728
41528
36458
32301
28985
29118
29249
28036
26326
24942
23280
23969
35948
37639
34327
30133
27549
27990
30437
30464
28471
26882
25806
26465
36416
42870
40489
36645
33841
33496
34504
34699
33322
32160
30173
30782
43062
46223
45191
40671
37251
36870




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=243186&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=243186&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=243186&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.3255832.74340.003846
2-0.322028-2.71350.004175
3-0.441229-3.71790.000199
4-0.252003-2.12340.018603
50.0844680.71170.23948
60.2084261.75620.041681
70.1242651.04710.149309
8-0.179031-1.50850.067926
9-0.369383-3.11250.001337
10-0.308861-2.60250.005629
110.2359811.98840.025311
120.7840136.60620
130.3208152.70320.004293
14-0.23976-2.02030.023566
15-0.376698-3.17410.001111
16-0.223083-1.87970.032124
170.0467230.39370.347492
180.1503441.26680.10468

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.325583 & 2.7434 & 0.003846 \tabularnewline
2 & -0.322028 & -2.7135 & 0.004175 \tabularnewline
3 & -0.441229 & -3.7179 & 0.000199 \tabularnewline
4 & -0.252003 & -2.1234 & 0.018603 \tabularnewline
5 & 0.084468 & 0.7117 & 0.23948 \tabularnewline
6 & 0.208426 & 1.7562 & 0.041681 \tabularnewline
7 & 0.124265 & 1.0471 & 0.149309 \tabularnewline
8 & -0.179031 & -1.5085 & 0.067926 \tabularnewline
9 & -0.369383 & -3.1125 & 0.001337 \tabularnewline
10 & -0.308861 & -2.6025 & 0.005629 \tabularnewline
11 & 0.235981 & 1.9884 & 0.025311 \tabularnewline
12 & 0.784013 & 6.6062 & 0 \tabularnewline
13 & 0.320815 & 2.7032 & 0.004293 \tabularnewline
14 & -0.23976 & -2.0203 & 0.023566 \tabularnewline
15 & -0.376698 & -3.1741 & 0.001111 \tabularnewline
16 & -0.223083 & -1.8797 & 0.032124 \tabularnewline
17 & 0.046723 & 0.3937 & 0.347492 \tabularnewline
18 & 0.150344 & 1.2668 & 0.10468 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=243186&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]0.325583[/C][C]2.7434[/C][C]0.003846[/C][/ROW]
[ROW][C]2[/C][C]-0.322028[/C][C]-2.7135[/C][C]0.004175[/C][/ROW]
[ROW][C]3[/C][C]-0.441229[/C][C]-3.7179[/C][C]0.000199[/C][/ROW]
[ROW][C]4[/C][C]-0.252003[/C][C]-2.1234[/C][C]0.018603[/C][/ROW]
[ROW][C]5[/C][C]0.084468[/C][C]0.7117[/C][C]0.23948[/C][/ROW]
[ROW][C]6[/C][C]0.208426[/C][C]1.7562[/C][C]0.041681[/C][/ROW]
[ROW][C]7[/C][C]0.124265[/C][C]1.0471[/C][C]0.149309[/C][/ROW]
[ROW][C]8[/C][C]-0.179031[/C][C]-1.5085[/C][C]0.067926[/C][/ROW]
[ROW][C]9[/C][C]-0.369383[/C][C]-3.1125[/C][C]0.001337[/C][/ROW]
[ROW][C]10[/C][C]-0.308861[/C][C]-2.6025[/C][C]0.005629[/C][/ROW]
[ROW][C]11[/C][C]0.235981[/C][C]1.9884[/C][C]0.025311[/C][/ROW]
[ROW][C]12[/C][C]0.784013[/C][C]6.6062[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.320815[/C][C]2.7032[/C][C]0.004293[/C][/ROW]
[ROW][C]14[/C][C]-0.23976[/C][C]-2.0203[/C][C]0.023566[/C][/ROW]
[ROW][C]15[/C][C]-0.376698[/C][C]-3.1741[/C][C]0.001111[/C][/ROW]
[ROW][C]16[/C][C]-0.223083[/C][C]-1.8797[/C][C]0.032124[/C][/ROW]
[ROW][C]17[/C][C]0.046723[/C][C]0.3937[/C][C]0.347492[/C][/ROW]
[ROW][C]18[/C][C]0.150344[/C][C]1.2668[/C][C]0.10468[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=243186&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=243186&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 kACF(k)T-STATP-value
10.3255832.74340.003846
2-0.322028-2.71350.004175
3-0.441229-3.71790.000199
4-0.252003-2.12340.018603
50.0844680.71170.23948
60.2084261.75620.041681
70.1242651.04710.149309
8-0.179031-1.50850.067926
9-0.369383-3.11250.001337
10-0.308861-2.60250.005629
110.2359811.98840.025311
120.7840136.60620
130.3208152.70320.004293
14-0.23976-2.02030.023566
15-0.376698-3.17410.001111
16-0.223083-1.87970.032124
170.0467230.39370.347492
180.1503441.26680.10468







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.3255832.74340.003846
2-0.478786-4.03436.8e-05
3-0.189096-1.59330.057762
4-0.214939-1.81110.037177
50.0016270.01370.494551
6-0.088073-0.74210.230233
7-0.016398-0.13820.445247
8-0.296044-2.49450.007471
9-0.298076-2.51160.007147
10-0.496477-4.18344e-05
110.0513190.43240.333373
120.4878224.11055.2e-05
13-0.160854-1.35540.089796
140.0830210.69950.243249
150.1330861.12140.132947
160.1020530.85990.196365
170.0073980.06230.475236
18-0.056202-0.47360.318631

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.325583 & 2.7434 & 0.003846 \tabularnewline
2 & -0.478786 & -4.0343 & 6.8e-05 \tabularnewline
3 & -0.189096 & -1.5933 & 0.057762 \tabularnewline
4 & -0.214939 & -1.8111 & 0.037177 \tabularnewline
5 & 0.001627 & 0.0137 & 0.494551 \tabularnewline
6 & -0.088073 & -0.7421 & 0.230233 \tabularnewline
7 & -0.016398 & -0.1382 & 0.445247 \tabularnewline
8 & -0.296044 & -2.4945 & 0.007471 \tabularnewline
9 & -0.298076 & -2.5116 & 0.007147 \tabularnewline
10 & -0.496477 & -4.1834 & 4e-05 \tabularnewline
11 & 0.051319 & 0.4324 & 0.333373 \tabularnewline
12 & 0.487822 & 4.1105 & 5.2e-05 \tabularnewline
13 & -0.160854 & -1.3554 & 0.089796 \tabularnewline
14 & 0.083021 & 0.6995 & 0.243249 \tabularnewline
15 & 0.133086 & 1.1214 & 0.132947 \tabularnewline
16 & 0.102053 & 0.8599 & 0.196365 \tabularnewline
17 & 0.007398 & 0.0623 & 0.475236 \tabularnewline
18 & -0.056202 & -0.4736 & 0.318631 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=243186&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]0.325583[/C][C]2.7434[/C][C]0.003846[/C][/ROW]
[ROW][C]2[/C][C]-0.478786[/C][C]-4.0343[/C][C]6.8e-05[/C][/ROW]
[ROW][C]3[/C][C]-0.189096[/C][C]-1.5933[/C][C]0.057762[/C][/ROW]
[ROW][C]4[/C][C]-0.214939[/C][C]-1.8111[/C][C]0.037177[/C][/ROW]
[ROW][C]5[/C][C]0.001627[/C][C]0.0137[/C][C]0.494551[/C][/ROW]
[ROW][C]6[/C][C]-0.088073[/C][C]-0.7421[/C][C]0.230233[/C][/ROW]
[ROW][C]7[/C][C]-0.016398[/C][C]-0.1382[/C][C]0.445247[/C][/ROW]
[ROW][C]8[/C][C]-0.296044[/C][C]-2.4945[/C][C]0.007471[/C][/ROW]
[ROW][C]9[/C][C]-0.298076[/C][C]-2.5116[/C][C]0.007147[/C][/ROW]
[ROW][C]10[/C][C]-0.496477[/C][C]-4.1834[/C][C]4e-05[/C][/ROW]
[ROW][C]11[/C][C]0.051319[/C][C]0.4324[/C][C]0.333373[/C][/ROW]
[ROW][C]12[/C][C]0.487822[/C][C]4.1105[/C][C]5.2e-05[/C][/ROW]
[ROW][C]13[/C][C]-0.160854[/C][C]-1.3554[/C][C]0.089796[/C][/ROW]
[ROW][C]14[/C][C]0.083021[/C][C]0.6995[/C][C]0.243249[/C][/ROW]
[ROW][C]15[/C][C]0.133086[/C][C]1.1214[/C][C]0.132947[/C][/ROW]
[ROW][C]16[/C][C]0.102053[/C][C]0.8599[/C][C]0.196365[/C][/ROW]
[ROW][C]17[/C][C]0.007398[/C][C]0.0623[/C][C]0.475236[/C][/ROW]
[ROW][C]18[/C][C]-0.056202[/C][C]-0.4736[/C][C]0.318631[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=243186&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=243186&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 kPACF(k)T-STATP-value
10.3255832.74340.003846
2-0.478786-4.03436.8e-05
3-0.189096-1.59330.057762
4-0.214939-1.81110.037177
50.0016270.01370.494551
6-0.088073-0.74210.230233
7-0.016398-0.13820.445247
8-0.296044-2.49450.007471
9-0.298076-2.51160.007147
10-0.496477-4.18344e-05
110.0513190.43240.333373
120.4878224.11055.2e-05
13-0.160854-1.35540.089796
140.0830210.69950.243249
150.1330861.12140.132947
160.1020530.85990.196365
170.0073980.06230.475236
18-0.056202-0.47360.318631



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 0 ; 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)
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('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')