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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 computationSat, 20 Dec 2008 06:26:46 -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/20/t1229779657qz7j9f26nvspa3a.htm/, Retrieved Fri, 01 May 2026 18:14:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35354, Retrieved Fri, 01 May 2026 18:14:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact481

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [ARIMA Backward Selection] [ARIMA Backward Se...] [2008-12-08 10:48:08] [58bf45a666dc5198906262e8815a9722]
- RMPD    [Variance Reduction Matrix] [Variance Reductio...] [2008-12-08 11:24:27] [58bf45a666dc5198906262e8815a9722]
- RMP       [ARIMA Backward Selection] [Backward Selectio...] [2008-12-08 18:32:45] [58bf45a666dc5198906262e8815a9722]
-   P         [ARIMA Backward Selection] [ARIMA Backward Se...] [2008-12-15 11:53:10] [58bf45a666dc5198906262e8815a9722]
- RMPD            [(Partial) Autocorrelation Function] [ACF hoeveelheid u...] [2008-12-20 13:26:46] [6797a1f4a60918966297e9d9220cabc2] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100.03
100.49
108.76
105.28
104.55
108.91
105.83
82.12
113.71
117.53
104.75
104.3
102.82
106.86
123.1
112.02
103.66
121.78
107.81
93.54
119.41
117.99
116.82
112.62
105.76
107.86
122.06
114.29
109.95
119.99
103.77
96.02
120.83
110.14
119.77
113.61
106.83
108
126.39
105.09
118.95
120.6
106.07
96.59
118.74
121.88
120.89
105.66
113.76
111.92
127.19
109.19
118.05
123.23
114.68
104.57
115.73
129.87
120.31
104.23
123.92
124.1
125.61
133.91
124.47
129.64
128.58
103.55
129.57

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35354&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35354&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35354&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 time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.1795891.49180.070157
20.0203080.16870.433267
30.2939582.44180.008591
40.2300491.91090.030084
50.1840851.52910.065403
60.3357172.78870.003417
70.0720650.59860.275694
80.184731.53450.064741
90.0753350.62580.266763
10-0.138432-1.14990.127077
110.0963910.80070.213032
120.4961684.12155.2e-05
13-0.001346-0.01120.495556
14-0.123336-1.02450.154587
150.0424420.35260.36275
160.0654930.5440.294088
17-0.006575-0.05460.478303
180.1816641.5090.067931
19-0.014624-0.12150.451833
200.0505080.41950.338059
21-0.067598-0.56150.288135
22-0.166344-1.38180.085751
230.0617190.51270.304909
240.2739872.27590.01298
25-0.036366-0.30210.381752
26-0.099275-0.82460.206208
27-0.030651-0.25460.399893
280.0635850.52820.299535
29-0.051763-0.430.334278
300.0740090.61480.270365
310.0039710.0330.48689
32-0.004395-0.03650.485492
33-0.105497-0.87630.191948
34-0.13826-1.14850.127369
35-0.040087-0.3330.370077
360.1990561.65350.051388

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.179589 & 1.4918 & 0.070157 \tabularnewline
2 & 0.020308 & 0.1687 & 0.433267 \tabularnewline
3 & 0.293958 & 2.4418 & 0.008591 \tabularnewline
4 & 0.230049 & 1.9109 & 0.030084 \tabularnewline
5 & 0.184085 & 1.5291 & 0.065403 \tabularnewline
6 & 0.335717 & 2.7887 & 0.003417 \tabularnewline
7 & 0.072065 & 0.5986 & 0.275694 \tabularnewline
8 & 0.18473 & 1.5345 & 0.064741 \tabularnewline
9 & 0.075335 & 0.6258 & 0.266763 \tabularnewline
10 & -0.138432 & -1.1499 & 0.127077 \tabularnewline
11 & 0.096391 & 0.8007 & 0.213032 \tabularnewline
12 & 0.496168 & 4.1215 & 5.2e-05 \tabularnewline
13 & -0.001346 & -0.0112 & 0.495556 \tabularnewline
14 & -0.123336 & -1.0245 & 0.154587 \tabularnewline
15 & 0.042442 & 0.3526 & 0.36275 \tabularnewline
16 & 0.065493 & 0.544 & 0.294088 \tabularnewline
17 & -0.006575 & -0.0546 & 0.478303 \tabularnewline
18 & 0.181664 & 1.509 & 0.067931 \tabularnewline
19 & -0.014624 & -0.1215 & 0.451833 \tabularnewline
20 & 0.050508 & 0.4195 & 0.338059 \tabularnewline
21 & -0.067598 & -0.5615 & 0.288135 \tabularnewline
22 & -0.166344 & -1.3818 & 0.085751 \tabularnewline
23 & 0.061719 & 0.5127 & 0.304909 \tabularnewline
24 & 0.273987 & 2.2759 & 0.01298 \tabularnewline
25 & -0.036366 & -0.3021 & 0.381752 \tabularnewline
26 & -0.099275 & -0.8246 & 0.206208 \tabularnewline
27 & -0.030651 & -0.2546 & 0.399893 \tabularnewline
28 & 0.063585 & 0.5282 & 0.299535 \tabularnewline
29 & -0.051763 & -0.43 & 0.334278 \tabularnewline
30 & 0.074009 & 0.6148 & 0.270365 \tabularnewline
31 & 0.003971 & 0.033 & 0.48689 \tabularnewline
32 & -0.004395 & -0.0365 & 0.485492 \tabularnewline
33 & -0.105497 & -0.8763 & 0.191948 \tabularnewline
34 & -0.13826 & -1.1485 & 0.127369 \tabularnewline
35 & -0.040087 & -0.333 & 0.370077 \tabularnewline
36 & 0.199056 & 1.6535 & 0.051388 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35354&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]0.179589[/C][C]1.4918[/C][C]0.070157[/C][/ROW]
[ROW][C]2[/C][C]0.020308[/C][C]0.1687[/C][C]0.433267[/C][/ROW]
[ROW][C]3[/C][C]0.293958[/C][C]2.4418[/C][C]0.008591[/C][/ROW]
[ROW][C]4[/C][C]0.230049[/C][C]1.9109[/C][C]0.030084[/C][/ROW]
[ROW][C]5[/C][C]0.184085[/C][C]1.5291[/C][C]0.065403[/C][/ROW]
[ROW][C]6[/C][C]0.335717[/C][C]2.7887[/C][C]0.003417[/C][/ROW]
[ROW][C]7[/C][C]0.072065[/C][C]0.5986[/C][C]0.275694[/C][/ROW]
[ROW][C]8[/C][C]0.18473[/C][C]1.5345[/C][C]0.064741[/C][/ROW]
[ROW][C]9[/C][C]0.075335[/C][C]0.6258[/C][C]0.266763[/C][/ROW]
[ROW][C]10[/C][C]-0.138432[/C][C]-1.1499[/C][C]0.127077[/C][/ROW]
[ROW][C]11[/C][C]0.096391[/C][C]0.8007[/C][C]0.213032[/C][/ROW]
[ROW][C]12[/C][C]0.496168[/C][C]4.1215[/C][C]5.2e-05[/C][/ROW]
[ROW][C]13[/C][C]-0.001346[/C][C]-0.0112[/C][C]0.495556[/C][/ROW]
[ROW][C]14[/C][C]-0.123336[/C][C]-1.0245[/C][C]0.154587[/C][/ROW]
[ROW][C]15[/C][C]0.042442[/C][C]0.3526[/C][C]0.36275[/C][/ROW]
[ROW][C]16[/C][C]0.065493[/C][C]0.544[/C][C]0.294088[/C][/ROW]
[ROW][C]17[/C][C]-0.006575[/C][C]-0.0546[/C][C]0.478303[/C][/ROW]
[ROW][C]18[/C][C]0.181664[/C][C]1.509[/C][C]0.067931[/C][/ROW]
[ROW][C]19[/C][C]-0.014624[/C][C]-0.1215[/C][C]0.451833[/C][/ROW]
[ROW][C]20[/C][C]0.050508[/C][C]0.4195[/C][C]0.338059[/C][/ROW]
[ROW][C]21[/C][C]-0.067598[/C][C]-0.5615[/C][C]0.288135[/C][/ROW]
[ROW][C]22[/C][C]-0.166344[/C][C]-1.3818[/C][C]0.085751[/C][/ROW]
[ROW][C]23[/C][C]0.061719[/C][C]0.5127[/C][C]0.304909[/C][/ROW]
[ROW][C]24[/C][C]0.273987[/C][C]2.2759[/C][C]0.01298[/C][/ROW]
[ROW][C]25[/C][C]-0.036366[/C][C]-0.3021[/C][C]0.381752[/C][/ROW]
[ROW][C]26[/C][C]-0.099275[/C][C]-0.8246[/C][C]0.206208[/C][/ROW]
[ROW][C]27[/C][C]-0.030651[/C][C]-0.2546[/C][C]0.399893[/C][/ROW]
[ROW][C]28[/C][C]0.063585[/C][C]0.5282[/C][C]0.299535[/C][/ROW]
[ROW][C]29[/C][C]-0.051763[/C][C]-0.43[/C][C]0.334278[/C][/ROW]
[ROW][C]30[/C][C]0.074009[/C][C]0.6148[/C][C]0.270365[/C][/ROW]
[ROW][C]31[/C][C]0.003971[/C][C]0.033[/C][C]0.48689[/C][/ROW]
[ROW][C]32[/C][C]-0.004395[/C][C]-0.0365[/C][C]0.485492[/C][/ROW]
[ROW][C]33[/C][C]-0.105497[/C][C]-0.8763[/C][C]0.191948[/C][/ROW]
[ROW][C]34[/C][C]-0.13826[/C][C]-1.1485[/C][C]0.127369[/C][/ROW]
[ROW][C]35[/C][C]-0.040087[/C][C]-0.333[/C][C]0.370077[/C][/ROW]
[ROW][C]36[/C][C]0.199056[/C][C]1.6535[/C][C]0.051388[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35354&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35354&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
10.1795891.49180.070157
20.0203080.16870.433267
30.2939582.44180.008591
40.2300491.91090.030084
50.1840851.52910.065403
60.3357172.78870.003417
70.0720650.59860.275694
80.184731.53450.064741
90.0753350.62580.266763
10-0.138432-1.14990.127077
110.0963910.80070.213032
120.4961684.12155.2e-05
13-0.001346-0.01120.495556
14-0.123336-1.02450.154587
150.0424420.35260.36275
160.0654930.5440.294088
17-0.006575-0.05460.478303
180.1816641.5090.067931
19-0.014624-0.12150.451833
200.0505080.41950.338059
21-0.067598-0.56150.288135
22-0.166344-1.38180.085751
230.0617190.51270.304909
240.2739872.27590.01298
25-0.036366-0.30210.381752
26-0.099275-0.82460.206208
27-0.030651-0.25460.399893
280.0635850.52820.299535
29-0.051763-0.430.334278
300.0740090.61480.270365
310.0039710.0330.48689
32-0.004395-0.03650.485492
33-0.105497-0.87630.191948
34-0.13826-1.14850.127369
35-0.040087-0.3330.370077
360.1990561.65350.051388






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.1795891.49180.070157
2-0.012342-0.10250.459319
30.3022762.51090.007195
40.1394351.15820.125381
50.1594581.32460.094844
60.2641192.19390.015805
7-0.090318-0.75020.227831
80.1500721.24660.108382
9-0.208224-1.72960.044083
10-0.288071-2.39290.009721
11-0.037009-0.30740.379725
120.4656113.86770.000123
130.0371160.30830.379387
14-0.112105-0.93120.177494
15-0.133422-1.10830.135794
16-0.027651-0.22970.409506
17-0.147382-1.22420.112512
180.1122020.9320.177288
190.0244950.20350.419683
200.0658680.54710.293025
21-0.068981-0.5730.284254
220.0035510.02950.488276
230.0202660.16830.433403
24-0.041083-0.34130.366972
250.0791650.65760.256494
260.0212670.17670.430147
27-0.005932-0.04930.48042
280.0716460.59510.276851
29-0.125835-1.04530.149775
30-0.093148-0.77370.220862
31-0.099031-0.82260.20678
320.018410.15290.439452
330.0740820.61540.270168
340.0127940.10630.457837
35-0.032536-0.27030.39388
360.0565670.46990.319961

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.179589 & 1.4918 & 0.070157 \tabularnewline
2 & -0.012342 & -0.1025 & 0.459319 \tabularnewline
3 & 0.302276 & 2.5109 & 0.007195 \tabularnewline
4 & 0.139435 & 1.1582 & 0.125381 \tabularnewline
5 & 0.159458 & 1.3246 & 0.094844 \tabularnewline
6 & 0.264119 & 2.1939 & 0.015805 \tabularnewline
7 & -0.090318 & -0.7502 & 0.227831 \tabularnewline
8 & 0.150072 & 1.2466 & 0.108382 \tabularnewline
9 & -0.208224 & -1.7296 & 0.044083 \tabularnewline
10 & -0.288071 & -2.3929 & 0.009721 \tabularnewline
11 & -0.037009 & -0.3074 & 0.379725 \tabularnewline
12 & 0.465611 & 3.8677 & 0.000123 \tabularnewline
13 & 0.037116 & 0.3083 & 0.379387 \tabularnewline
14 & -0.112105 & -0.9312 & 0.177494 \tabularnewline
15 & -0.133422 & -1.1083 & 0.135794 \tabularnewline
16 & -0.027651 & -0.2297 & 0.409506 \tabularnewline
17 & -0.147382 & -1.2242 & 0.112512 \tabularnewline
18 & 0.112202 & 0.932 & 0.177288 \tabularnewline
19 & 0.024495 & 0.2035 & 0.419683 \tabularnewline
20 & 0.065868 & 0.5471 & 0.293025 \tabularnewline
21 & -0.068981 & -0.573 & 0.284254 \tabularnewline
22 & 0.003551 & 0.0295 & 0.488276 \tabularnewline
23 & 0.020266 & 0.1683 & 0.433403 \tabularnewline
24 & -0.041083 & -0.3413 & 0.366972 \tabularnewline
25 & 0.079165 & 0.6576 & 0.256494 \tabularnewline
26 & 0.021267 & 0.1767 & 0.430147 \tabularnewline
27 & -0.005932 & -0.0493 & 0.48042 \tabularnewline
28 & 0.071646 & 0.5951 & 0.276851 \tabularnewline
29 & -0.125835 & -1.0453 & 0.149775 \tabularnewline
30 & -0.093148 & -0.7737 & 0.220862 \tabularnewline
31 & -0.099031 & -0.8226 & 0.20678 \tabularnewline
32 & 0.01841 & 0.1529 & 0.439452 \tabularnewline
33 & 0.074082 & 0.6154 & 0.270168 \tabularnewline
34 & 0.012794 & 0.1063 & 0.457837 \tabularnewline
35 & -0.032536 & -0.2703 & 0.39388 \tabularnewline
36 & 0.056567 & 0.4699 & 0.319961 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35354&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]0.179589[/C][C]1.4918[/C][C]0.070157[/C][/ROW]
[ROW][C]2[/C][C]-0.012342[/C][C]-0.1025[/C][C]0.459319[/C][/ROW]
[ROW][C]3[/C][C]0.302276[/C][C]2.5109[/C][C]0.007195[/C][/ROW]
[ROW][C]4[/C][C]0.139435[/C][C]1.1582[/C][C]0.125381[/C][/ROW]
[ROW][C]5[/C][C]0.159458[/C][C]1.3246[/C][C]0.094844[/C][/ROW]
[ROW][C]6[/C][C]0.264119[/C][C]2.1939[/C][C]0.015805[/C][/ROW]
[ROW][C]7[/C][C]-0.090318[/C][C]-0.7502[/C][C]0.227831[/C][/ROW]
[ROW][C]8[/C][C]0.150072[/C][C]1.2466[/C][C]0.108382[/C][/ROW]
[ROW][C]9[/C][C]-0.208224[/C][C]-1.7296[/C][C]0.044083[/C][/ROW]
[ROW][C]10[/C][C]-0.288071[/C][C]-2.3929[/C][C]0.009721[/C][/ROW]
[ROW][C]11[/C][C]-0.037009[/C][C]-0.3074[/C][C]0.379725[/C][/ROW]
[ROW][C]12[/C][C]0.465611[/C][C]3.8677[/C][C]0.000123[/C][/ROW]
[ROW][C]13[/C][C]0.037116[/C][C]0.3083[/C][C]0.379387[/C][/ROW]
[ROW][C]14[/C][C]-0.112105[/C][C]-0.9312[/C][C]0.177494[/C][/ROW]
[ROW][C]15[/C][C]-0.133422[/C][C]-1.1083[/C][C]0.135794[/C][/ROW]
[ROW][C]16[/C][C]-0.027651[/C][C]-0.2297[/C][C]0.409506[/C][/ROW]
[ROW][C]17[/C][C]-0.147382[/C][C]-1.2242[/C][C]0.112512[/C][/ROW]
[ROW][C]18[/C][C]0.112202[/C][C]0.932[/C][C]0.177288[/C][/ROW]
[ROW][C]19[/C][C]0.024495[/C][C]0.2035[/C][C]0.419683[/C][/ROW]
[ROW][C]20[/C][C]0.065868[/C][C]0.5471[/C][C]0.293025[/C][/ROW]
[ROW][C]21[/C][C]-0.068981[/C][C]-0.573[/C][C]0.284254[/C][/ROW]
[ROW][C]22[/C][C]0.003551[/C][C]0.0295[/C][C]0.488276[/C][/ROW]
[ROW][C]23[/C][C]0.020266[/C][C]0.1683[/C][C]0.433403[/C][/ROW]
[ROW][C]24[/C][C]-0.041083[/C][C]-0.3413[/C][C]0.366972[/C][/ROW]
[ROW][C]25[/C][C]0.079165[/C][C]0.6576[/C][C]0.256494[/C][/ROW]
[ROW][C]26[/C][C]0.021267[/C][C]0.1767[/C][C]0.430147[/C][/ROW]
[ROW][C]27[/C][C]-0.005932[/C][C]-0.0493[/C][C]0.48042[/C][/ROW]
[ROW][C]28[/C][C]0.071646[/C][C]0.5951[/C][C]0.276851[/C][/ROW]
[ROW][C]29[/C][C]-0.125835[/C][C]-1.0453[/C][C]0.149775[/C][/ROW]
[ROW][C]30[/C][C]-0.093148[/C][C]-0.7737[/C][C]0.220862[/C][/ROW]
[ROW][C]31[/C][C]-0.099031[/C][C]-0.8226[/C][C]0.20678[/C][/ROW]
[ROW][C]32[/C][C]0.01841[/C][C]0.1529[/C][C]0.439452[/C][/ROW]
[ROW][C]33[/C][C]0.074082[/C][C]0.6154[/C][C]0.270168[/C][/ROW]
[ROW][C]34[/C][C]0.012794[/C][C]0.1063[/C][C]0.457837[/C][/ROW]
[ROW][C]35[/C][C]-0.032536[/C][C]-0.2703[/C][C]0.39388[/C][/ROW]
[ROW][C]36[/C][C]0.056567[/C][C]0.4699[/C][C]0.319961[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35354&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35354&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
10.1795891.49180.070157
2-0.012342-0.10250.459319
30.3022762.51090.007195
40.1394351.15820.125381
50.1594581.32460.094844
60.2641192.19390.015805
7-0.090318-0.75020.227831
80.1500721.24660.108382
9-0.208224-1.72960.044083
10-0.288071-2.39290.009721
11-0.037009-0.30740.379725
120.4656113.86770.000123
130.0371160.30830.379387
14-0.112105-0.93120.177494
15-0.133422-1.10830.135794
16-0.027651-0.22970.409506
17-0.147382-1.22420.112512
180.1122020.9320.177288
190.0244950.20350.419683
200.0658680.54710.293025
21-0.068981-0.5730.284254
220.0035510.02950.488276
230.0202660.16830.433403
24-0.041083-0.34130.366972
250.0791650.65760.256494
260.0212670.17670.430147
27-0.005932-0.04930.48042
280.0716460.59510.276851
29-0.125835-1.04530.149775
30-0.093148-0.77370.220862
31-0.099031-0.82260.20678
320.018410.15290.439452
330.0740820.61540.270168
340.0127940.10630.457837
35-0.032536-0.27030.39388
360.0565670.46990.319961


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')