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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationTue, 02 Dec 2008 11:09:06 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/02/t12282414119dkzwgcywr7k0t3.htm/, Retrieved Fri, 24 May 2024 23:18:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28180, Retrieved Fri, 24 May 2024 23:18:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsnon stationary time series vraag 8 landbouw
Estimated Impact193

Tree of Dependent Computations

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  [Spectral Analysis] [spectral analysis] [2008-12-02 17:36:41] [415d0222c17b651a9576eaac006f530d]
F RMPD      [(Partial) Autocorrelation Function] [autocorrelatie] [2008-12-02 18:09:06] [bb7e3816cefc365f4d7adcd50784b783] [Current]
Feedback Forum
2008-12-06 13:36:47 [Ken Wright] [reply] 
goed, dus hieruit zou je kunnen afleiden dat je enkel de niet seizoenaal moet differentieren.
2008-12-09 17:42:26 [Julian De Ruyter] [reply] 
juiste berekening en conclusie

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3,253
3,233
3,196
3,138
3,091
3,17
3,378
3,468
3,33
3,413
3,356
3,525
3,633
3,597
3,6
3,522
3,503
3,532
3,686
3,748
3,672
3,843
3,905
3,999
4,07
4,084
4,042
3,951
3,933
3,958
4,147
4,221
4,058
4,057
4,089
4,268
4,309
4,303
4,177
4,117
4,065
3,983
4,091
4,067
4,024
3,868
3,8
3,804
3,862
3,792
3,674
3,56
3,489
3,412
3,674
3,672
3,463
3,429
3,4
3,533

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28180&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28180&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28180&T=0

As an alternative you can also use a QR Code:  
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' @ 72.249.127.135






Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.932227.22090
20.8435946.53440
30.7682745.9510
40.7141995.53220
50.6717475.20331e-06
60.6076314.70678e-06
70.5248144.06527.1e-05
80.4324743.34990.000701
90.347912.69490.00456
100.290022.24650.014183
110.2427081.880.032482
120.2005091.55310.062825
130.1208010.93570.176584
140.0259540.2010.420675
15-0.055246-0.42790.335117
16-0.119842-0.92830.178488
17-0.171306-1.32690.09478
18-0.228786-1.77220.040722
19-0.292999-2.26960.013421
20-0.363663-2.81690.003277
21-0.420466-3.25690.000928
22-0.439926-3.40770.000588
23-0.42779-3.31360.000782
24-0.4125-3.19520.001114
25-0.428579-3.31980.000768
26-0.455273-3.52650.000407
27-0.471669-3.65350.000273
28-0.466577-3.61410.000309
29-0.449928-3.48510.000463
30-0.440174-3.40960.000584
31-0.433014-3.35410.000692
32-0.423048-3.27690.000874
33-0.409004-3.16810.001207
34-0.385433-2.98550.002046
35-0.339585-2.63040.00541
36-0.27682-2.14420.018037

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.93222 & 7.2209 & 0 \tabularnewline
2 & 0.843594 & 6.5344 & 0 \tabularnewline
3 & 0.768274 & 5.951 & 0 \tabularnewline
4 & 0.714199 & 5.5322 & 0 \tabularnewline
5 & 0.671747 & 5.2033 & 1e-06 \tabularnewline
6 & 0.607631 & 4.7067 & 8e-06 \tabularnewline
7 & 0.524814 & 4.0652 & 7.1e-05 \tabularnewline
8 & 0.432474 & 3.3499 & 0.000701 \tabularnewline
9 & 0.34791 & 2.6949 & 0.00456 \tabularnewline
10 & 0.29002 & 2.2465 & 0.014183 \tabularnewline
11 & 0.242708 & 1.88 & 0.032482 \tabularnewline
12 & 0.200509 & 1.5531 & 0.062825 \tabularnewline
13 & 0.120801 & 0.9357 & 0.176584 \tabularnewline
14 & 0.025954 & 0.201 & 0.420675 \tabularnewline
15 & -0.055246 & -0.4279 & 0.335117 \tabularnewline
16 & -0.119842 & -0.9283 & 0.178488 \tabularnewline
17 & -0.171306 & -1.3269 & 0.09478 \tabularnewline
18 & -0.228786 & -1.7722 & 0.040722 \tabularnewline
19 & -0.292999 & -2.2696 & 0.013421 \tabularnewline
20 & -0.363663 & -2.8169 & 0.003277 \tabularnewline
21 & -0.420466 & -3.2569 & 0.000928 \tabularnewline
22 & -0.439926 & -3.4077 & 0.000588 \tabularnewline
23 & -0.42779 & -3.3136 & 0.000782 \tabularnewline
24 & -0.4125 & -3.1952 & 0.001114 \tabularnewline
25 & -0.428579 & -3.3198 & 0.000768 \tabularnewline
26 & -0.455273 & -3.5265 & 0.000407 \tabularnewline
27 & -0.471669 & -3.6535 & 0.000273 \tabularnewline
28 & -0.466577 & -3.6141 & 0.000309 \tabularnewline
29 & -0.449928 & -3.4851 & 0.000463 \tabularnewline
30 & -0.440174 & -3.4096 & 0.000584 \tabularnewline
31 & -0.433014 & -3.3541 & 0.000692 \tabularnewline
32 & -0.423048 & -3.2769 & 0.000874 \tabularnewline
33 & -0.409004 & -3.1681 & 0.001207 \tabularnewline
34 & -0.385433 & -2.9855 & 0.002046 \tabularnewline
35 & -0.339585 & -2.6304 & 0.00541 \tabularnewline
36 & -0.27682 & -2.1442 & 0.018037 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28180&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]0.93222[/C][C]7.2209[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.843594[/C][C]6.5344[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.768274[/C][C]5.951[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.714199[/C][C]5.5322[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.671747[/C][C]5.2033[/C][C]1e-06[/C][/ROW]
[ROW][C]6[/C][C]0.607631[/C][C]4.7067[/C][C]8e-06[/C][/ROW]
[ROW][C]7[/C][C]0.524814[/C][C]4.0652[/C][C]7.1e-05[/C][/ROW]
[ROW][C]8[/C][C]0.432474[/C][C]3.3499[/C][C]0.000701[/C][/ROW]
[ROW][C]9[/C][C]0.34791[/C][C]2.6949[/C][C]0.00456[/C][/ROW]
[ROW][C]10[/C][C]0.29002[/C][C]2.2465[/C][C]0.014183[/C][/ROW]
[ROW][C]11[/C][C]0.242708[/C][C]1.88[/C][C]0.032482[/C][/ROW]
[ROW][C]12[/C][C]0.200509[/C][C]1.5531[/C][C]0.062825[/C][/ROW]
[ROW][C]13[/C][C]0.120801[/C][C]0.9357[/C][C]0.176584[/C][/ROW]
[ROW][C]14[/C][C]0.025954[/C][C]0.201[/C][C]0.420675[/C][/ROW]
[ROW][C]15[/C][C]-0.055246[/C][C]-0.4279[/C][C]0.335117[/C][/ROW]
[ROW][C]16[/C][C]-0.119842[/C][C]-0.9283[/C][C]0.178488[/C][/ROW]
[ROW][C]17[/C][C]-0.171306[/C][C]-1.3269[/C][C]0.09478[/C][/ROW]
[ROW][C]18[/C][C]-0.228786[/C][C]-1.7722[/C][C]0.040722[/C][/ROW]
[ROW][C]19[/C][C]-0.292999[/C][C]-2.2696[/C][C]0.013421[/C][/ROW]
[ROW][C]20[/C][C]-0.363663[/C][C]-2.8169[/C][C]0.003277[/C][/ROW]
[ROW][C]21[/C][C]-0.420466[/C][C]-3.2569[/C][C]0.000928[/C][/ROW]
[ROW][C]22[/C][C]-0.439926[/C][C]-3.4077[/C][C]0.000588[/C][/ROW]
[ROW][C]23[/C][C]-0.42779[/C][C]-3.3136[/C][C]0.000782[/C][/ROW]
[ROW][C]24[/C][C]-0.4125[/C][C]-3.1952[/C][C]0.001114[/C][/ROW]
[ROW][C]25[/C][C]-0.428579[/C][C]-3.3198[/C][C]0.000768[/C][/ROW]
[ROW][C]26[/C][C]-0.455273[/C][C]-3.5265[/C][C]0.000407[/C][/ROW]
[ROW][C]27[/C][C]-0.471669[/C][C]-3.6535[/C][C]0.000273[/C][/ROW]
[ROW][C]28[/C][C]-0.466577[/C][C]-3.6141[/C][C]0.000309[/C][/ROW]
[ROW][C]29[/C][C]-0.449928[/C][C]-3.4851[/C][C]0.000463[/C][/ROW]
[ROW][C]30[/C][C]-0.440174[/C][C]-3.4096[/C][C]0.000584[/C][/ROW]
[ROW][C]31[/C][C]-0.433014[/C][C]-3.3541[/C][C]0.000692[/C][/ROW]
[ROW][C]32[/C][C]-0.423048[/C][C]-3.2769[/C][C]0.000874[/C][/ROW]
[ROW][C]33[/C][C]-0.409004[/C][C]-3.1681[/C][C]0.001207[/C][/ROW]
[ROW][C]34[/C][C]-0.385433[/C][C]-2.9855[/C][C]0.002046[/C][/ROW]
[ROW][C]35[/C][C]-0.339585[/C][C]-2.6304[/C][C]0.00541[/C][/ROW]
[ROW][C]36[/C][C]-0.27682[/C][C]-2.1442[/C][C]0.018037[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28180&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28180&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.932227.22090
20.8435946.53440
30.7682745.9510
40.7141995.53220
50.6717475.20331e-06
60.6076314.70678e-06
70.5248144.06527.1e-05
80.4324743.34990.000701
90.347912.69490.00456
100.290022.24650.014183
110.2427081.880.032482
120.2005091.55310.062825
130.1208010.93570.176584
140.0259540.2010.420675
15-0.055246-0.42790.335117
16-0.119842-0.92830.178488
17-0.171306-1.32690.09478
18-0.228786-1.77220.040722
19-0.292999-2.26960.013421
20-0.363663-2.81690.003277
21-0.420466-3.25690.000928
22-0.439926-3.40770.000588
23-0.42779-3.31360.000782
24-0.4125-3.19520.001114
25-0.428579-3.31980.000768
26-0.455273-3.52650.000407
27-0.471669-3.65350.000273
28-0.466577-3.61410.000309
29-0.449928-3.48510.000463
30-0.440174-3.40960.000584
31-0.433014-3.35410.000692
32-0.423048-3.27690.000874
33-0.409004-3.16810.001207
34-0.385433-2.98550.002046
35-0.339585-2.63040.00541
36-0.27682-2.14420.018037






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.932227.22090
2-0.19426-1.50470.06882
30.0807980.62590.26689
40.0898560.6960.244551
50.0240180.1860.426519
6-0.196842-1.52470.06629
7-0.107867-0.83550.203365
8-0.108355-0.83930.202313
9-0.032342-0.25050.401521
100.0872190.67560.250946
11-0.008696-0.06740.473261
120.0423120.32770.372123
13-0.30004-2.32410.011762
14-0.07353-0.56960.28555
15-0.025339-0.19630.422528
16-0.057962-0.4490.327534
17-0.063397-0.49110.312584
18-0.056348-0.43650.332032
19-0.018884-0.14630.442097
20-0.111904-0.86680.194751
210.0369210.2860.387936
220.109310.84670.200259
230.14531.12550.132432
24-0.054307-0.42070.337752
25-0.165777-1.28410.102021
26-0.002675-0.02070.491767
27-0.074904-0.58020.281976
28-0.02194-0.16990.432812
29-0.072556-0.5620.288099
30-0.013847-0.10730.457472
310.040480.31360.377474
320.0826570.64030.26222
33-0.021029-0.16290.435577
34-0.094861-0.73480.232665
350.0706130.5470.293216
360.0713680.55280.291221

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.93222 & 7.2209 & 0 \tabularnewline
2 & -0.19426 & -1.5047 & 0.06882 \tabularnewline
3 & 0.080798 & 0.6259 & 0.26689 \tabularnewline
4 & 0.089856 & 0.696 & 0.244551 \tabularnewline
5 & 0.024018 & 0.186 & 0.426519 \tabularnewline
6 & -0.196842 & -1.5247 & 0.06629 \tabularnewline
7 & -0.107867 & -0.8355 & 0.203365 \tabularnewline
8 & -0.108355 & -0.8393 & 0.202313 \tabularnewline
9 & -0.032342 & -0.2505 & 0.401521 \tabularnewline
10 & 0.087219 & 0.6756 & 0.250946 \tabularnewline
11 & -0.008696 & -0.0674 & 0.473261 \tabularnewline
12 & 0.042312 & 0.3277 & 0.372123 \tabularnewline
13 & -0.30004 & -2.3241 & 0.011762 \tabularnewline
14 & -0.07353 & -0.5696 & 0.28555 \tabularnewline
15 & -0.025339 & -0.1963 & 0.422528 \tabularnewline
16 & -0.057962 & -0.449 & 0.327534 \tabularnewline
17 & -0.063397 & -0.4911 & 0.312584 \tabularnewline
18 & -0.056348 & -0.4365 & 0.332032 \tabularnewline
19 & -0.018884 & -0.1463 & 0.442097 \tabularnewline
20 & -0.111904 & -0.8668 & 0.194751 \tabularnewline
21 & 0.036921 & 0.286 & 0.387936 \tabularnewline
22 & 0.10931 & 0.8467 & 0.200259 \tabularnewline
23 & 0.1453 & 1.1255 & 0.132432 \tabularnewline
24 & -0.054307 & -0.4207 & 0.337752 \tabularnewline
25 & -0.165777 & -1.2841 & 0.102021 \tabularnewline
26 & -0.002675 & -0.0207 & 0.491767 \tabularnewline
27 & -0.074904 & -0.5802 & 0.281976 \tabularnewline
28 & -0.02194 & -0.1699 & 0.432812 \tabularnewline
29 & -0.072556 & -0.562 & 0.288099 \tabularnewline
30 & -0.013847 & -0.1073 & 0.457472 \tabularnewline
31 & 0.04048 & 0.3136 & 0.377474 \tabularnewline
32 & 0.082657 & 0.6403 & 0.26222 \tabularnewline
33 & -0.021029 & -0.1629 & 0.435577 \tabularnewline
34 & -0.094861 & -0.7348 & 0.232665 \tabularnewline
35 & 0.070613 & 0.547 & 0.293216 \tabularnewline
36 & 0.071368 & 0.5528 & 0.291221 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28180&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]0.93222[/C][C]7.2209[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.19426[/C][C]-1.5047[/C][C]0.06882[/C][/ROW]
[ROW][C]3[/C][C]0.080798[/C][C]0.6259[/C][C]0.26689[/C][/ROW]
[ROW][C]4[/C][C]0.089856[/C][C]0.696[/C][C]0.244551[/C][/ROW]
[ROW][C]5[/C][C]0.024018[/C][C]0.186[/C][C]0.426519[/C][/ROW]
[ROW][C]6[/C][C]-0.196842[/C][C]-1.5247[/C][C]0.06629[/C][/ROW]
[ROW][C]7[/C][C]-0.107867[/C][C]-0.8355[/C][C]0.203365[/C][/ROW]
[ROW][C]8[/C][C]-0.108355[/C][C]-0.8393[/C][C]0.202313[/C][/ROW]
[ROW][C]9[/C][C]-0.032342[/C][C]-0.2505[/C][C]0.401521[/C][/ROW]
[ROW][C]10[/C][C]0.087219[/C][C]0.6756[/C][C]0.250946[/C][/ROW]
[ROW][C]11[/C][C]-0.008696[/C][C]-0.0674[/C][C]0.473261[/C][/ROW]
[ROW][C]12[/C][C]0.042312[/C][C]0.3277[/C][C]0.372123[/C][/ROW]
[ROW][C]13[/C][C]-0.30004[/C][C]-2.3241[/C][C]0.011762[/C][/ROW]
[ROW][C]14[/C][C]-0.07353[/C][C]-0.5696[/C][C]0.28555[/C][/ROW]
[ROW][C]15[/C][C]-0.025339[/C][C]-0.1963[/C][C]0.422528[/C][/ROW]
[ROW][C]16[/C][C]-0.057962[/C][C]-0.449[/C][C]0.327534[/C][/ROW]
[ROW][C]17[/C][C]-0.063397[/C][C]-0.4911[/C][C]0.312584[/C][/ROW]
[ROW][C]18[/C][C]-0.056348[/C][C]-0.4365[/C][C]0.332032[/C][/ROW]
[ROW][C]19[/C][C]-0.018884[/C][C]-0.1463[/C][C]0.442097[/C][/ROW]
[ROW][C]20[/C][C]-0.111904[/C][C]-0.8668[/C][C]0.194751[/C][/ROW]
[ROW][C]21[/C][C]0.036921[/C][C]0.286[/C][C]0.387936[/C][/ROW]
[ROW][C]22[/C][C]0.10931[/C][C]0.8467[/C][C]0.200259[/C][/ROW]
[ROW][C]23[/C][C]0.1453[/C][C]1.1255[/C][C]0.132432[/C][/ROW]
[ROW][C]24[/C][C]-0.054307[/C][C]-0.4207[/C][C]0.337752[/C][/ROW]
[ROW][C]25[/C][C]-0.165777[/C][C]-1.2841[/C][C]0.102021[/C][/ROW]
[ROW][C]26[/C][C]-0.002675[/C][C]-0.0207[/C][C]0.491767[/C][/ROW]
[ROW][C]27[/C][C]-0.074904[/C][C]-0.5802[/C][C]0.281976[/C][/ROW]
[ROW][C]28[/C][C]-0.02194[/C][C]-0.1699[/C][C]0.432812[/C][/ROW]
[ROW][C]29[/C][C]-0.072556[/C][C]-0.562[/C][C]0.288099[/C][/ROW]
[ROW][C]30[/C][C]-0.013847[/C][C]-0.1073[/C][C]0.457472[/C][/ROW]
[ROW][C]31[/C][C]0.04048[/C][C]0.3136[/C][C]0.377474[/C][/ROW]
[ROW][C]32[/C][C]0.082657[/C][C]0.6403[/C][C]0.26222[/C][/ROW]
[ROW][C]33[/C][C]-0.021029[/C][C]-0.1629[/C][C]0.435577[/C][/ROW]
[ROW][C]34[/C][C]-0.094861[/C][C]-0.7348[/C][C]0.232665[/C][/ROW]
[ROW][C]35[/C][C]0.070613[/C][C]0.547[/C][C]0.293216[/C][/ROW]
[ROW][C]36[/C][C]0.071368[/C][C]0.5528[/C][C]0.291221[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28180&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28180&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.932227.22090
2-0.19426-1.50470.06882
30.0807980.62590.26689
40.0898560.6960.244551
50.0240180.1860.426519
6-0.196842-1.52470.06629
7-0.107867-0.83550.203365
8-0.108355-0.83930.202313
9-0.032342-0.25050.401521
100.0872190.67560.250946
11-0.008696-0.06740.473261
120.0423120.32770.372123
13-0.30004-2.32410.011762
14-0.07353-0.56960.28555
15-0.025339-0.19630.422528
16-0.057962-0.4490.327534
17-0.063397-0.49110.312584
18-0.056348-0.43650.332032
19-0.018884-0.14630.442097
20-0.111904-0.86680.194751
210.0369210.2860.387936
220.109310.84670.200259
230.14531.12550.132432
24-0.054307-0.42070.337752
25-0.165777-1.28410.102021
26-0.002675-0.02070.491767
27-0.074904-0.58020.281976
28-0.02194-0.16990.432812
29-0.072556-0.5620.288099
30-0.013847-0.10730.457472
310.040480.31360.377474
320.0826570.64030.26222
33-0.021029-0.16290.435577
34-0.094861-0.73480.232665
350.0706130.5470.293216
360.0713680.55280.291221


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 0 ; 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')