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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, 01 Dec 2009 13:18:45 -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/01/t1259698761jnnwgpogaijkkd4.htm/, Retrieved Fri, 19 Apr 2024 05:21:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62240, Retrieved Fri, 19 Apr 2024 05:21:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordslambda=1 d=1 D=0
Estimated Impact176

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   [(Partial) Autocorrelation Function] [] [2009-11-27 14:46:03] [b98453cac15ba1066b407e146608df68]
-    D    [(Partial) Autocorrelation Function] [BBWS9-Autocorella...] [2009-12-01 20:15:32] [408e92805dcb18620260f240a7fb9d53]
-   PD        [(Partial) Autocorrelation Function] [BBWS9-Autocorella...] [2009-12-01 20:18:45] [b32ceebc68d054278e6bda97f3d57f91] [Current]
-   P           [(Partial) Autocorrelation Function] [BBWS9-Autocorella...] [2009-12-01 20:21:05] [408e92805dcb18620260f240a7fb9d53]
-   P           [(Partial) Autocorrelation Function] [workshop 9] [2009-12-08 21:29:07] [28d531aeb5ea2ff1b676cbab66947a19]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
96.8
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

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=62240&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=62240&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62240&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
1-0.336203-2.58240.006156
2-0.240933-1.85060.034615
30.1320681.01440.15726
4-0.204044-1.56730.061197
50.1423941.09380.139255
60.0985040.75660.226144
70.0321090.24660.403025
8-0.068914-0.52930.299279
90.118020.90650.184171
10-0.325682-2.50160.007578
11-0.097652-0.75010.228093
120.6015384.62051.1e-05
13-0.183193-1.40710.082317
14-0.140815-1.08160.141911
15-0.029761-0.22860.409986
16-0.061794-0.47460.318395
170.1327561.01970.156013
18-0.001758-0.01350.494636
190.0743090.57080.28516
20-0.061755-0.47430.318502
210.0249760.19180.424261
22-0.176058-1.35230.090716
23-0.093919-0.72140.236755
240.3965693.04610.001732
25-0.052012-0.39950.345478
26-0.165495-1.27120.104325
27-0.032635-0.25070.401469
280.0086160.06620.473729
290.0586010.45010.327134
300.006230.04790.480998
310.0707390.54340.294466
32-0.078895-0.6060.273418
330.0027690.02130.491552
34-0.033744-0.25920.398195
35-0.148244-1.13870.129719
360.2646242.03260.0233

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.336203 & -2.5824 & 0.006156 \tabularnewline
2 & -0.240933 & -1.8506 & 0.034615 \tabularnewline
3 & 0.132068 & 1.0144 & 0.15726 \tabularnewline
4 & -0.204044 & -1.5673 & 0.061197 \tabularnewline
5 & 0.142394 & 1.0938 & 0.139255 \tabularnewline
6 & 0.098504 & 0.7566 & 0.226144 \tabularnewline
7 & 0.032109 & 0.2466 & 0.403025 \tabularnewline
8 & -0.068914 & -0.5293 & 0.299279 \tabularnewline
9 & 0.11802 & 0.9065 & 0.184171 \tabularnewline
10 & -0.325682 & -2.5016 & 0.007578 \tabularnewline
11 & -0.097652 & -0.7501 & 0.228093 \tabularnewline
12 & 0.601538 & 4.6205 & 1.1e-05 \tabularnewline
13 & -0.183193 & -1.4071 & 0.082317 \tabularnewline
14 & -0.140815 & -1.0816 & 0.141911 \tabularnewline
15 & -0.029761 & -0.2286 & 0.409986 \tabularnewline
16 & -0.061794 & -0.4746 & 0.318395 \tabularnewline
17 & 0.132756 & 1.0197 & 0.156013 \tabularnewline
18 & -0.001758 & -0.0135 & 0.494636 \tabularnewline
19 & 0.074309 & 0.5708 & 0.28516 \tabularnewline
20 & -0.061755 & -0.4743 & 0.318502 \tabularnewline
21 & 0.024976 & 0.1918 & 0.424261 \tabularnewline
22 & -0.176058 & -1.3523 & 0.090716 \tabularnewline
23 & -0.093919 & -0.7214 & 0.236755 \tabularnewline
24 & 0.396569 & 3.0461 & 0.001732 \tabularnewline
25 & -0.052012 & -0.3995 & 0.345478 \tabularnewline
26 & -0.165495 & -1.2712 & 0.104325 \tabularnewline
27 & -0.032635 & -0.2507 & 0.401469 \tabularnewline
28 & 0.008616 & 0.0662 & 0.473729 \tabularnewline
29 & 0.058601 & 0.4501 & 0.327134 \tabularnewline
30 & 0.00623 & 0.0479 & 0.480998 \tabularnewline
31 & 0.070739 & 0.5434 & 0.294466 \tabularnewline
32 & -0.078895 & -0.606 & 0.273418 \tabularnewline
33 & 0.002769 & 0.0213 & 0.491552 \tabularnewline
34 & -0.033744 & -0.2592 & 0.398195 \tabularnewline
35 & -0.148244 & -1.1387 & 0.129719 \tabularnewline
36 & 0.264624 & 2.0326 & 0.0233 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62240&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]-0.336203[/C][C]-2.5824[/C][C]0.006156[/C][/ROW]
[ROW][C]2[/C][C]-0.240933[/C][C]-1.8506[/C][C]0.034615[/C][/ROW]
[ROW][C]3[/C][C]0.132068[/C][C]1.0144[/C][C]0.15726[/C][/ROW]
[ROW][C]4[/C][C]-0.204044[/C][C]-1.5673[/C][C]0.061197[/C][/ROW]
[ROW][C]5[/C][C]0.142394[/C][C]1.0938[/C][C]0.139255[/C][/ROW]
[ROW][C]6[/C][C]0.098504[/C][C]0.7566[/C][C]0.226144[/C][/ROW]
[ROW][C]7[/C][C]0.032109[/C][C]0.2466[/C][C]0.403025[/C][/ROW]
[ROW][C]8[/C][C]-0.068914[/C][C]-0.5293[/C][C]0.299279[/C][/ROW]
[ROW][C]9[/C][C]0.11802[/C][C]0.9065[/C][C]0.184171[/C][/ROW]
[ROW][C]10[/C][C]-0.325682[/C][C]-2.5016[/C][C]0.007578[/C][/ROW]
[ROW][C]11[/C][C]-0.097652[/C][C]-0.7501[/C][C]0.228093[/C][/ROW]
[ROW][C]12[/C][C]0.601538[/C][C]4.6205[/C][C]1.1e-05[/C][/ROW]
[ROW][C]13[/C][C]-0.183193[/C][C]-1.4071[/C][C]0.082317[/C][/ROW]
[ROW][C]14[/C][C]-0.140815[/C][C]-1.0816[/C][C]0.141911[/C][/ROW]
[ROW][C]15[/C][C]-0.029761[/C][C]-0.2286[/C][C]0.409986[/C][/ROW]
[ROW][C]16[/C][C]-0.061794[/C][C]-0.4746[/C][C]0.318395[/C][/ROW]
[ROW][C]17[/C][C]0.132756[/C][C]1.0197[/C][C]0.156013[/C][/ROW]
[ROW][C]18[/C][C]-0.001758[/C][C]-0.0135[/C][C]0.494636[/C][/ROW]
[ROW][C]19[/C][C]0.074309[/C][C]0.5708[/C][C]0.28516[/C][/ROW]
[ROW][C]20[/C][C]-0.061755[/C][C]-0.4743[/C][C]0.318502[/C][/ROW]
[ROW][C]21[/C][C]0.024976[/C][C]0.1918[/C][C]0.424261[/C][/ROW]
[ROW][C]22[/C][C]-0.176058[/C][C]-1.3523[/C][C]0.090716[/C][/ROW]
[ROW][C]23[/C][C]-0.093919[/C][C]-0.7214[/C][C]0.236755[/C][/ROW]
[ROW][C]24[/C][C]0.396569[/C][C]3.0461[/C][C]0.001732[/C][/ROW]
[ROW][C]25[/C][C]-0.052012[/C][C]-0.3995[/C][C]0.345478[/C][/ROW]
[ROW][C]26[/C][C]-0.165495[/C][C]-1.2712[/C][C]0.104325[/C][/ROW]
[ROW][C]27[/C][C]-0.032635[/C][C]-0.2507[/C][C]0.401469[/C][/ROW]
[ROW][C]28[/C][C]0.008616[/C][C]0.0662[/C][C]0.473729[/C][/ROW]
[ROW][C]29[/C][C]0.058601[/C][C]0.4501[/C][C]0.327134[/C][/ROW]
[ROW][C]30[/C][C]0.00623[/C][C]0.0479[/C][C]0.480998[/C][/ROW]
[ROW][C]31[/C][C]0.070739[/C][C]0.5434[/C][C]0.294466[/C][/ROW]
[ROW][C]32[/C][C]-0.078895[/C][C]-0.606[/C][C]0.273418[/C][/ROW]
[ROW][C]33[/C][C]0.002769[/C][C]0.0213[/C][C]0.491552[/C][/ROW]
[ROW][C]34[/C][C]-0.033744[/C][C]-0.2592[/C][C]0.398195[/C][/ROW]
[ROW][C]35[/C][C]-0.148244[/C][C]-1.1387[/C][C]0.129719[/C][/ROW]
[ROW][C]36[/C][C]0.264624[/C][C]2.0326[/C][C]0.0233[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62240&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62240&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
1-0.336203-2.58240.006156
2-0.240933-1.85060.034615
30.1320681.01440.15726
4-0.204044-1.56730.061197
50.1423941.09380.139255
60.0985040.75660.226144
70.0321090.24660.403025
8-0.068914-0.52930.299279
90.118020.90650.184171
10-0.325682-2.50160.007578
11-0.097652-0.75010.228093
120.6015384.62051.1e-05
13-0.183193-1.40710.082317
14-0.140815-1.08160.141911
15-0.029761-0.22860.409986
16-0.061794-0.47460.318395
170.1327561.01970.156013
18-0.001758-0.01350.494636
190.0743090.57080.28516
20-0.061755-0.47430.318502
210.0249760.19180.424261
22-0.176058-1.35230.090716
23-0.093919-0.72140.236755
240.3965693.04610.001732
25-0.052012-0.39950.345478
26-0.165495-1.27120.104325
27-0.032635-0.25070.401469
280.0086160.06620.473729
290.0586010.45010.327134
300.006230.04790.480998
310.0707390.54340.294466
32-0.078895-0.6060.273418
330.0027690.02130.491552
34-0.033744-0.25920.398195
35-0.148244-1.13870.129719
360.2646242.03260.0233






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.336203-2.58240.006156
2-0.399073-3.06530.001639
3-0.154792-1.1890.119605
4-0.411488-3.16070.001242
5-0.197839-1.51960.066972
6-0.143465-1.1020.137473
70.113320.87040.193797
80.0633760.48680.314102
90.4394073.37510.000655
10-0.076782-0.58980.278797
11-0.340083-2.61220.005696
120.2237531.71870.045458
130.2387181.83360.035877
140.081340.62480.26726
15-0.171152-1.31460.096858
160.0409680.31470.377057
170.1163960.89410.187463
18-0.066113-0.50780.306736
190.0484540.37220.355546
20-0.004904-0.03770.485041
21-0.117252-0.90060.185723
22-0.015717-0.12070.452159
23-0.050831-0.39040.34881
24-0.054391-0.41780.338809
25-0.092127-0.70760.240976
26-0.082642-0.63480.264011
270.0223420.17160.432165
280.0337160.2590.398277
290.0277280.2130.416036
300.0152810.11740.453481
310.0479720.36850.356917
320.0266550.20470.419241
33-0.08892-0.6830.248638
340.1230850.94540.174147
35-0.003026-0.02320.490767
36-0.135128-1.03790.151768

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.336203 & -2.5824 & 0.006156 \tabularnewline
2 & -0.399073 & -3.0653 & 0.001639 \tabularnewline
3 & -0.154792 & -1.189 & 0.119605 \tabularnewline
4 & -0.411488 & -3.1607 & 0.001242 \tabularnewline
5 & -0.197839 & -1.5196 & 0.066972 \tabularnewline
6 & -0.143465 & -1.102 & 0.137473 \tabularnewline
7 & 0.11332 & 0.8704 & 0.193797 \tabularnewline
8 & 0.063376 & 0.4868 & 0.314102 \tabularnewline
9 & 0.439407 & 3.3751 & 0.000655 \tabularnewline
10 & -0.076782 & -0.5898 & 0.278797 \tabularnewline
11 & -0.340083 & -2.6122 & 0.005696 \tabularnewline
12 & 0.223753 & 1.7187 & 0.045458 \tabularnewline
13 & 0.238718 & 1.8336 & 0.035877 \tabularnewline
14 & 0.08134 & 0.6248 & 0.26726 \tabularnewline
15 & -0.171152 & -1.3146 & 0.096858 \tabularnewline
16 & 0.040968 & 0.3147 & 0.377057 \tabularnewline
17 & 0.116396 & 0.8941 & 0.187463 \tabularnewline
18 & -0.066113 & -0.5078 & 0.306736 \tabularnewline
19 & 0.048454 & 0.3722 & 0.355546 \tabularnewline
20 & -0.004904 & -0.0377 & 0.485041 \tabularnewline
21 & -0.117252 & -0.9006 & 0.185723 \tabularnewline
22 & -0.015717 & -0.1207 & 0.452159 \tabularnewline
23 & -0.050831 & -0.3904 & 0.34881 \tabularnewline
24 & -0.054391 & -0.4178 & 0.338809 \tabularnewline
25 & -0.092127 & -0.7076 & 0.240976 \tabularnewline
26 & -0.082642 & -0.6348 & 0.264011 \tabularnewline
27 & 0.022342 & 0.1716 & 0.432165 \tabularnewline
28 & 0.033716 & 0.259 & 0.398277 \tabularnewline
29 & 0.027728 & 0.213 & 0.416036 \tabularnewline
30 & 0.015281 & 0.1174 & 0.453481 \tabularnewline
31 & 0.047972 & 0.3685 & 0.356917 \tabularnewline
32 & 0.026655 & 0.2047 & 0.419241 \tabularnewline
33 & -0.08892 & -0.683 & 0.248638 \tabularnewline
34 & 0.123085 & 0.9454 & 0.174147 \tabularnewline
35 & -0.003026 & -0.0232 & 0.490767 \tabularnewline
36 & -0.135128 & -1.0379 & 0.151768 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62240&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]-0.336203[/C][C]-2.5824[/C][C]0.006156[/C][/ROW]
[ROW][C]2[/C][C]-0.399073[/C][C]-3.0653[/C][C]0.001639[/C][/ROW]
[ROW][C]3[/C][C]-0.154792[/C][C]-1.189[/C][C]0.119605[/C][/ROW]
[ROW][C]4[/C][C]-0.411488[/C][C]-3.1607[/C][C]0.001242[/C][/ROW]
[ROW][C]5[/C][C]-0.197839[/C][C]-1.5196[/C][C]0.066972[/C][/ROW]
[ROW][C]6[/C][C]-0.143465[/C][C]-1.102[/C][C]0.137473[/C][/ROW]
[ROW][C]7[/C][C]0.11332[/C][C]0.8704[/C][C]0.193797[/C][/ROW]
[ROW][C]8[/C][C]0.063376[/C][C]0.4868[/C][C]0.314102[/C][/ROW]
[ROW][C]9[/C][C]0.439407[/C][C]3.3751[/C][C]0.000655[/C][/ROW]
[ROW][C]10[/C][C]-0.076782[/C][C]-0.5898[/C][C]0.278797[/C][/ROW]
[ROW][C]11[/C][C]-0.340083[/C][C]-2.6122[/C][C]0.005696[/C][/ROW]
[ROW][C]12[/C][C]0.223753[/C][C]1.7187[/C][C]0.045458[/C][/ROW]
[ROW][C]13[/C][C]0.238718[/C][C]1.8336[/C][C]0.035877[/C][/ROW]
[ROW][C]14[/C][C]0.08134[/C][C]0.6248[/C][C]0.26726[/C][/ROW]
[ROW][C]15[/C][C]-0.171152[/C][C]-1.3146[/C][C]0.096858[/C][/ROW]
[ROW][C]16[/C][C]0.040968[/C][C]0.3147[/C][C]0.377057[/C][/ROW]
[ROW][C]17[/C][C]0.116396[/C][C]0.8941[/C][C]0.187463[/C][/ROW]
[ROW][C]18[/C][C]-0.066113[/C][C]-0.5078[/C][C]0.306736[/C][/ROW]
[ROW][C]19[/C][C]0.048454[/C][C]0.3722[/C][C]0.355546[/C][/ROW]
[ROW][C]20[/C][C]-0.004904[/C][C]-0.0377[/C][C]0.485041[/C][/ROW]
[ROW][C]21[/C][C]-0.117252[/C][C]-0.9006[/C][C]0.185723[/C][/ROW]
[ROW][C]22[/C][C]-0.015717[/C][C]-0.1207[/C][C]0.452159[/C][/ROW]
[ROW][C]23[/C][C]-0.050831[/C][C]-0.3904[/C][C]0.34881[/C][/ROW]
[ROW][C]24[/C][C]-0.054391[/C][C]-0.4178[/C][C]0.338809[/C][/ROW]
[ROW][C]25[/C][C]-0.092127[/C][C]-0.7076[/C][C]0.240976[/C][/ROW]
[ROW][C]26[/C][C]-0.082642[/C][C]-0.6348[/C][C]0.264011[/C][/ROW]
[ROW][C]27[/C][C]0.022342[/C][C]0.1716[/C][C]0.432165[/C][/ROW]
[ROW][C]28[/C][C]0.033716[/C][C]0.259[/C][C]0.398277[/C][/ROW]
[ROW][C]29[/C][C]0.027728[/C][C]0.213[/C][C]0.416036[/C][/ROW]
[ROW][C]30[/C][C]0.015281[/C][C]0.1174[/C][C]0.453481[/C][/ROW]
[ROW][C]31[/C][C]0.047972[/C][C]0.3685[/C][C]0.356917[/C][/ROW]
[ROW][C]32[/C][C]0.026655[/C][C]0.2047[/C][C]0.419241[/C][/ROW]
[ROW][C]33[/C][C]-0.08892[/C][C]-0.683[/C][C]0.248638[/C][/ROW]
[ROW][C]34[/C][C]0.123085[/C][C]0.9454[/C][C]0.174147[/C][/ROW]
[ROW][C]35[/C][C]-0.003026[/C][C]-0.0232[/C][C]0.490767[/C][/ROW]
[ROW][C]36[/C][C]-0.135128[/C][C]-1.0379[/C][C]0.151768[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62240&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62240&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
1-0.336203-2.58240.006156
2-0.399073-3.06530.001639
3-0.154792-1.1890.119605
4-0.411488-3.16070.001242
5-0.197839-1.51960.066972
6-0.143465-1.1020.137473
70.113320.87040.193797
80.0633760.48680.314102
90.4394073.37510.000655
10-0.076782-0.58980.278797
11-0.340083-2.61220.005696
120.2237531.71870.045458
130.2387181.83360.035877
140.081340.62480.26726
15-0.171152-1.31460.096858
160.0409680.31470.377057
170.1163960.89410.187463
18-0.066113-0.50780.306736
190.0484540.37220.355546
20-0.004904-0.03770.485041
21-0.117252-0.90060.185723
22-0.015717-0.12070.452159
23-0.050831-0.39040.34881
24-0.054391-0.41780.338809
25-0.092127-0.70760.240976
26-0.082642-0.63480.264011
270.0223420.17160.432165
280.0337160.2590.398277
290.0277280.2130.416036
300.0152810.11740.453481
310.0479720.36850.356917
320.0266550.20470.419241
33-0.08892-0.6830.248638
340.1230850.94540.174147
35-0.003026-0.02320.490767
36-0.135128-1.03790.151768


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; 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')