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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 computationWed, 02 Dec 2009 03:07:08 -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/2009/Dec/02/t1259748487r77xag3vljwp5fp.htm/, Retrieved Sat, 27 Apr 2024 17:56:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62313, Retrieved Sat, 27 Apr 2024 17:56:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordscvm
Estimated Impact158

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [(Partial) Autocorrelation Function] [Identifying Integ...] [2009-11-22 12:16:10] [b98453cac15ba1066b407e146608df68]
-    D        [(Partial) Autocorrelation Function] [BBWS8-ACF1] [2009-11-28 15:24:25] [408e92805dcb18620260f240a7fb9d53]
-    D          [(Partial) Autocorrelation Function] [W8: d,D=0, Lamda 1] [2009-12-01 14:27:42] [03d5b865e91ca35b5a5d21b8d6da5aba]
-   PD              [(Partial) Autocorrelation Function] [W9: Autocorrelati...] [2009-12-02 10:07:08] [a5ada8bd39e806b5b90f09589c89554a] [Current]
-   PD                [(Partial) Autocorrelation Function] [Review: Autocorre...] [2009-12-08 16:11:45] [03d5b865e91ca35b5a5d21b8d6da5aba]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
114.1
110.3
103.9
101.6
94.6
95.9
104.7
102.8
98.1
113.9
80.9
95.7
113.2
105.9
108.8
102.3
99
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102
106
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100
110.7
112.8
109.8
117.3
109.1
115.9
96
99.8
116.8
115.7
99.4
94.3
91
93.2
103.1
94.1
91.8
102.7
82.6
89.1

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=62313&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=62313&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62313&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
1-0.617592-3.65370.00042
2-0.010299-0.06090.475882
30.3793092.2440.015629
4-0.310262-1.83550.037468
50.026630.15750.437859
60.1950761.15410.128143
7-0.245101-1.450.077976
80.1527430.90360.186183
9-0.137521-0.81360.210691
100.112550.66590.254935
110.0036510.02160.491445
12-0.152439-0.90180.186652
130.1565530.92620.180348
14-0.088459-0.52330.30202
150.0431220.25510.400065
16-0.014042-0.08310.467133
17-0.001602-0.00950.496245
180.0254070.15030.440691
19-0.083755-0.49550.311672
200.1228490.72680.236095
210.0004970.00290.498834
22-0.228274-1.35050.092764
230.3475092.05590.023656
24-0.235247-1.39170.086392
250.0147460.08720.46549
260.1507870.89210.189224
27-0.126011-0.74550.230476
28-0.041662-0.24650.403377
290.155690.92110.181658
30-0.135158-0.79960.214667
310.031160.18430.427404
320.024830.14690.442029
33-0.039559-0.2340.40816
340.0238750.14120.444243
35NANANA
36NANANA

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.617592 & -3.6537 & 0.00042 \tabularnewline
2 & -0.010299 & -0.0609 & 0.475882 \tabularnewline
3 & 0.379309 & 2.244 & 0.015629 \tabularnewline
4 & -0.310262 & -1.8355 & 0.037468 \tabularnewline
5 & 0.02663 & 0.1575 & 0.437859 \tabularnewline
6 & 0.195076 & 1.1541 & 0.128143 \tabularnewline
7 & -0.245101 & -1.45 & 0.077976 \tabularnewline
8 & 0.152743 & 0.9036 & 0.186183 \tabularnewline
9 & -0.137521 & -0.8136 & 0.210691 \tabularnewline
10 & 0.11255 & 0.6659 & 0.254935 \tabularnewline
11 & 0.003651 & 0.0216 & 0.491445 \tabularnewline
12 & -0.152439 & -0.9018 & 0.186652 \tabularnewline
13 & 0.156553 & 0.9262 & 0.180348 \tabularnewline
14 & -0.088459 & -0.5233 & 0.30202 \tabularnewline
15 & 0.043122 & 0.2551 & 0.400065 \tabularnewline
16 & -0.014042 & -0.0831 & 0.467133 \tabularnewline
17 & -0.001602 & -0.0095 & 0.496245 \tabularnewline
18 & 0.025407 & 0.1503 & 0.440691 \tabularnewline
19 & -0.083755 & -0.4955 & 0.311672 \tabularnewline
20 & 0.122849 & 0.7268 & 0.236095 \tabularnewline
21 & 0.000497 & 0.0029 & 0.498834 \tabularnewline
22 & -0.228274 & -1.3505 & 0.092764 \tabularnewline
23 & 0.347509 & 2.0559 & 0.023656 \tabularnewline
24 & -0.235247 & -1.3917 & 0.086392 \tabularnewline
25 & 0.014746 & 0.0872 & 0.46549 \tabularnewline
26 & 0.150787 & 0.8921 & 0.189224 \tabularnewline
27 & -0.126011 & -0.7455 & 0.230476 \tabularnewline
28 & -0.041662 & -0.2465 & 0.403377 \tabularnewline
29 & 0.15569 & 0.9211 & 0.181658 \tabularnewline
30 & -0.135158 & -0.7996 & 0.214667 \tabularnewline
31 & 0.03116 & 0.1843 & 0.427404 \tabularnewline
32 & 0.02483 & 0.1469 & 0.442029 \tabularnewline
33 & -0.039559 & -0.234 & 0.40816 \tabularnewline
34 & 0.023875 & 0.1412 & 0.444243 \tabularnewline
35 & NA & NA & NA \tabularnewline
36 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62313&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]-0.617592[/C][C]-3.6537[/C][C]0.00042[/C][/ROW]
[ROW][C]2[/C][C]-0.010299[/C][C]-0.0609[/C][C]0.475882[/C][/ROW]
[ROW][C]3[/C][C]0.379309[/C][C]2.244[/C][C]0.015629[/C][/ROW]
[ROW][C]4[/C][C]-0.310262[/C][C]-1.8355[/C][C]0.037468[/C][/ROW]
[ROW][C]5[/C][C]0.02663[/C][C]0.1575[/C][C]0.437859[/C][/ROW]
[ROW][C]6[/C][C]0.195076[/C][C]1.1541[/C][C]0.128143[/C][/ROW]
[ROW][C]7[/C][C]-0.245101[/C][C]-1.45[/C][C]0.077976[/C][/ROW]
[ROW][C]8[/C][C]0.152743[/C][C]0.9036[/C][C]0.186183[/C][/ROW]
[ROW][C]9[/C][C]-0.137521[/C][C]-0.8136[/C][C]0.210691[/C][/ROW]
[ROW][C]10[/C][C]0.11255[/C][C]0.6659[/C][C]0.254935[/C][/ROW]
[ROW][C]11[/C][C]0.003651[/C][C]0.0216[/C][C]0.491445[/C][/ROW]
[ROW][C]12[/C][C]-0.152439[/C][C]-0.9018[/C][C]0.186652[/C][/ROW]
[ROW][C]13[/C][C]0.156553[/C][C]0.9262[/C][C]0.180348[/C][/ROW]
[ROW][C]14[/C][C]-0.088459[/C][C]-0.5233[/C][C]0.30202[/C][/ROW]
[ROW][C]15[/C][C]0.043122[/C][C]0.2551[/C][C]0.400065[/C][/ROW]
[ROW][C]16[/C][C]-0.014042[/C][C]-0.0831[/C][C]0.467133[/C][/ROW]
[ROW][C]17[/C][C]-0.001602[/C][C]-0.0095[/C][C]0.496245[/C][/ROW]
[ROW][C]18[/C][C]0.025407[/C][C]0.1503[/C][C]0.440691[/C][/ROW]
[ROW][C]19[/C][C]-0.083755[/C][C]-0.4955[/C][C]0.311672[/C][/ROW]
[ROW][C]20[/C][C]0.122849[/C][C]0.7268[/C][C]0.236095[/C][/ROW]
[ROW][C]21[/C][C]0.000497[/C][C]0.0029[/C][C]0.498834[/C][/ROW]
[ROW][C]22[/C][C]-0.228274[/C][C]-1.3505[/C][C]0.092764[/C][/ROW]
[ROW][C]23[/C][C]0.347509[/C][C]2.0559[/C][C]0.023656[/C][/ROW]
[ROW][C]24[/C][C]-0.235247[/C][C]-1.3917[/C][C]0.086392[/C][/ROW]
[ROW][C]25[/C][C]0.014746[/C][C]0.0872[/C][C]0.46549[/C][/ROW]
[ROW][C]26[/C][C]0.150787[/C][C]0.8921[/C][C]0.189224[/C][/ROW]
[ROW][C]27[/C][C]-0.126011[/C][C]-0.7455[/C][C]0.230476[/C][/ROW]
[ROW][C]28[/C][C]-0.041662[/C][C]-0.2465[/C][C]0.403377[/C][/ROW]
[ROW][C]29[/C][C]0.15569[/C][C]0.9211[/C][C]0.181658[/C][/ROW]
[ROW][C]30[/C][C]-0.135158[/C][C]-0.7996[/C][C]0.214667[/C][/ROW]
[ROW][C]31[/C][C]0.03116[/C][C]0.1843[/C][C]0.427404[/C][/ROW]
[ROW][C]32[/C][C]0.02483[/C][C]0.1469[/C][C]0.442029[/C][/ROW]
[ROW][C]33[/C][C]-0.039559[/C][C]-0.234[/C][C]0.40816[/C][/ROW]
[ROW][C]34[/C][C]0.023875[/C][C]0.1412[/C][C]0.444243[/C][/ROW]
[ROW][C]35[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]36[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62313&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62313&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
1-0.617592-3.65370.00042
2-0.010299-0.06090.475882
30.3793092.2440.015629
4-0.310262-1.83550.037468
50.026630.15750.437859
60.1950761.15410.128143
7-0.245101-1.450.077976
80.1527430.90360.186183
9-0.137521-0.81360.210691
100.112550.66590.254935
110.0036510.02160.491445
12-0.152439-0.90180.186652
130.1565530.92620.180348
14-0.088459-0.52330.30202
150.0431220.25510.400065
16-0.014042-0.08310.467133
17-0.001602-0.00950.496245
180.0254070.15030.440691
19-0.083755-0.49550.311672
200.1228490.72680.236095
210.0004970.00290.498834
22-0.228274-1.35050.092764
230.3475092.05590.023656
24-0.235247-1.39170.086392
250.0147460.08720.46549
260.1507870.89210.189224
27-0.126011-0.74550.230476
28-0.041662-0.24650.403377
290.155690.92110.181658
30-0.135158-0.79960.214667
310.031160.18430.427404
320.024830.14690.442029
33-0.039559-0.2340.40816
340.0238750.14120.444243
35NANANA
36NANANA






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.617592-3.65370.00042
2-0.633255-3.74640.000323
3-0.059841-0.3540.362722
40.1154260.68290.249593
50.0238840.14130.444222
60.0536590.31750.376394
7-0.094967-0.56180.288906
80.0130370.07710.469481
9-0.344796-2.03980.024485
10-0.185048-1.09480.14055
110.0706690.41810.33922
120.0412680.24410.404272
13-0.065435-0.38710.350507
14-0.297922-1.76250.043355
150.0356980.21120.41698
16-0.013405-0.07930.468622
17-0.006479-0.03830.484822
18-0.030986-0.18330.427805
19-0.156735-0.92730.180071
20-0.012589-0.07450.470526
210.06850.40520.34388
22-0.190764-1.12860.133377
230.0182350.10790.457354
240.034910.20650.418786
250.0811520.48010.317072
26-0.111324-0.65860.25723
270.0692390.40960.34229
280.0146520.08670.46571
29-0.039394-0.23310.408537
30-0.090464-0.53520.297951
31-0.170301-1.00750.160301
320.0859860.50870.307078
33-0.014113-0.08350.466968
34-0.050397-0.29820.383675
35NANANA
36NANANA

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.617592 & -3.6537 & 0.00042 \tabularnewline
2 & -0.633255 & -3.7464 & 0.000323 \tabularnewline
3 & -0.059841 & -0.354 & 0.362722 \tabularnewline
4 & 0.115426 & 0.6829 & 0.249593 \tabularnewline
5 & 0.023884 & 0.1413 & 0.444222 \tabularnewline
6 & 0.053659 & 0.3175 & 0.376394 \tabularnewline
7 & -0.094967 & -0.5618 & 0.288906 \tabularnewline
8 & 0.013037 & 0.0771 & 0.469481 \tabularnewline
9 & -0.344796 & -2.0398 & 0.024485 \tabularnewline
10 & -0.185048 & -1.0948 & 0.14055 \tabularnewline
11 & 0.070669 & 0.4181 & 0.33922 \tabularnewline
12 & 0.041268 & 0.2441 & 0.404272 \tabularnewline
13 & -0.065435 & -0.3871 & 0.350507 \tabularnewline
14 & -0.297922 & -1.7625 & 0.043355 \tabularnewline
15 & 0.035698 & 0.2112 & 0.41698 \tabularnewline
16 & -0.013405 & -0.0793 & 0.468622 \tabularnewline
17 & -0.006479 & -0.0383 & 0.484822 \tabularnewline
18 & -0.030986 & -0.1833 & 0.427805 \tabularnewline
19 & -0.156735 & -0.9273 & 0.180071 \tabularnewline
20 & -0.012589 & -0.0745 & 0.470526 \tabularnewline
21 & 0.0685 & 0.4052 & 0.34388 \tabularnewline
22 & -0.190764 & -1.1286 & 0.133377 \tabularnewline
23 & 0.018235 & 0.1079 & 0.457354 \tabularnewline
24 & 0.03491 & 0.2065 & 0.418786 \tabularnewline
25 & 0.081152 & 0.4801 & 0.317072 \tabularnewline
26 & -0.111324 & -0.6586 & 0.25723 \tabularnewline
27 & 0.069239 & 0.4096 & 0.34229 \tabularnewline
28 & 0.014652 & 0.0867 & 0.46571 \tabularnewline
29 & -0.039394 & -0.2331 & 0.408537 \tabularnewline
30 & -0.090464 & -0.5352 & 0.297951 \tabularnewline
31 & -0.170301 & -1.0075 & 0.160301 \tabularnewline
32 & 0.085986 & 0.5087 & 0.307078 \tabularnewline
33 & -0.014113 & -0.0835 & 0.466968 \tabularnewline
34 & -0.050397 & -0.2982 & 0.383675 \tabularnewline
35 & NA & NA & NA \tabularnewline
36 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62313&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]-0.617592[/C][C]-3.6537[/C][C]0.00042[/C][/ROW]
[ROW][C]2[/C][C]-0.633255[/C][C]-3.7464[/C][C]0.000323[/C][/ROW]
[ROW][C]3[/C][C]-0.059841[/C][C]-0.354[/C][C]0.362722[/C][/ROW]
[ROW][C]4[/C][C]0.115426[/C][C]0.6829[/C][C]0.249593[/C][/ROW]
[ROW][C]5[/C][C]0.023884[/C][C]0.1413[/C][C]0.444222[/C][/ROW]
[ROW][C]6[/C][C]0.053659[/C][C]0.3175[/C][C]0.376394[/C][/ROW]
[ROW][C]7[/C][C]-0.094967[/C][C]-0.5618[/C][C]0.288906[/C][/ROW]
[ROW][C]8[/C][C]0.013037[/C][C]0.0771[/C][C]0.469481[/C][/ROW]
[ROW][C]9[/C][C]-0.344796[/C][C]-2.0398[/C][C]0.024485[/C][/ROW]
[ROW][C]10[/C][C]-0.185048[/C][C]-1.0948[/C][C]0.14055[/C][/ROW]
[ROW][C]11[/C][C]0.070669[/C][C]0.4181[/C][C]0.33922[/C][/ROW]
[ROW][C]12[/C][C]0.041268[/C][C]0.2441[/C][C]0.404272[/C][/ROW]
[ROW][C]13[/C][C]-0.065435[/C][C]-0.3871[/C][C]0.350507[/C][/ROW]
[ROW][C]14[/C][C]-0.297922[/C][C]-1.7625[/C][C]0.043355[/C][/ROW]
[ROW][C]15[/C][C]0.035698[/C][C]0.2112[/C][C]0.41698[/C][/ROW]
[ROW][C]16[/C][C]-0.013405[/C][C]-0.0793[/C][C]0.468622[/C][/ROW]
[ROW][C]17[/C][C]-0.006479[/C][C]-0.0383[/C][C]0.484822[/C][/ROW]
[ROW][C]18[/C][C]-0.030986[/C][C]-0.1833[/C][C]0.427805[/C][/ROW]
[ROW][C]19[/C][C]-0.156735[/C][C]-0.9273[/C][C]0.180071[/C][/ROW]
[ROW][C]20[/C][C]-0.012589[/C][C]-0.0745[/C][C]0.470526[/C][/ROW]
[ROW][C]21[/C][C]0.0685[/C][C]0.4052[/C][C]0.34388[/C][/ROW]
[ROW][C]22[/C][C]-0.190764[/C][C]-1.1286[/C][C]0.133377[/C][/ROW]
[ROW][C]23[/C][C]0.018235[/C][C]0.1079[/C][C]0.457354[/C][/ROW]
[ROW][C]24[/C][C]0.03491[/C][C]0.2065[/C][C]0.418786[/C][/ROW]
[ROW][C]25[/C][C]0.081152[/C][C]0.4801[/C][C]0.317072[/C][/ROW]
[ROW][C]26[/C][C]-0.111324[/C][C]-0.6586[/C][C]0.25723[/C][/ROW]
[ROW][C]27[/C][C]0.069239[/C][C]0.4096[/C][C]0.34229[/C][/ROW]
[ROW][C]28[/C][C]0.014652[/C][C]0.0867[/C][C]0.46571[/C][/ROW]
[ROW][C]29[/C][C]-0.039394[/C][C]-0.2331[/C][C]0.408537[/C][/ROW]
[ROW][C]30[/C][C]-0.090464[/C][C]-0.5352[/C][C]0.297951[/C][/ROW]
[ROW][C]31[/C][C]-0.170301[/C][C]-1.0075[/C][C]0.160301[/C][/ROW]
[ROW][C]32[/C][C]0.085986[/C][C]0.5087[/C][C]0.307078[/C][/ROW]
[ROW][C]33[/C][C]-0.014113[/C][C]-0.0835[/C][C]0.466968[/C][/ROW]
[ROW][C]34[/C][C]-0.050397[/C][C]-0.2982[/C][C]0.383675[/C][/ROW]
[ROW][C]35[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]36[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62313&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62313&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
1-0.617592-3.65370.00042
2-0.633255-3.74640.000323
3-0.059841-0.3540.362722
40.1154260.68290.249593
50.0238840.14130.444222
60.0536590.31750.376394
7-0.094967-0.56180.288906
80.0130370.07710.469481
9-0.344796-2.03980.024485
10-0.185048-1.09480.14055
110.0706690.41810.33922
120.0412680.24410.404272
13-0.065435-0.38710.350507
14-0.297922-1.76250.043355
150.0356980.21120.41698
16-0.013405-0.07930.468622
17-0.006479-0.03830.484822
18-0.030986-0.18330.427805
19-0.156735-0.92730.180071
20-0.012589-0.07450.470526
210.06850.40520.34388
22-0.190764-1.12860.133377
230.0182350.10790.457354
240.034910.20650.418786
250.0811520.48010.317072
26-0.111324-0.65860.25723
270.0692390.40960.34229
280.0146520.08670.46571
29-0.039394-0.23310.408537
30-0.090464-0.53520.297951
31-0.170301-1.00750.160301
320.0859860.50870.307078
33-0.014113-0.08350.466968
34-0.050397-0.29820.383675
35NANANA
36NANANA


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 2 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 2 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
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 (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='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))
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')