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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, 25 Nov 2009 11:58:26 -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/Nov/25/t1259175561uhfdjdqih9hmycq.htm/, Retrieved Mon, 12 May 2025 17:28:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59565, Retrieved Mon, 12 May 2025 17:28:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact227

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 12:26:39] [b98453cac15ba1066b407e146608df68]
-    D          [(Partial) Autocorrelation Function] [WS8-ACF3] [2009-11-25 18:58:26] [30970b478e356ce7f8c2e9fca280b230] [Current]
-    D            [(Partial) Autocorrelation Function] [WS8] [2009-11-27 19:26:38] [9f35ad889e41dd0c9322ca60d75b9f47]
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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 time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59565&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]4 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=59565&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59565&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.631799-4.33143.9e-05
20.0390680.26780.394999
30.3540862.42750.009544
4-0.320734-2.19880.016423
50.0726560.49810.310366
60.1662951.14010.130018
7-0.279062-1.91320.030916
80.2385151.63520.054346
9-0.13946-0.95610.171959
10-0.028579-0.19590.422756
110.1865451.27890.103607
12-0.217599-1.49180.071219
130.0872670.59830.276266
140.0355530.24370.404248
15-0.009644-0.06610.473783
16-0.092439-0.63370.264665
170.1289130.88380.190656
18-0.047632-0.32650.372729
19-0.078741-0.53980.295935
200.1213870.83220.204755
210.0256910.17610.430474
22-0.282042-1.93360.0296
230.3968682.72080.004551
24-0.272009-1.86480.03423
250.0338350.2320.408788
260.1491961.02280.15581
27-0.173201-1.18740.120517
280.0416350.28540.388284
290.1022880.70130.243302
30-0.151204-1.03660.152613
310.1209510.82920.205591
32-0.058465-0.40080.345186
33-0.051812-0.35520.362013
340.141840.97240.167913
35-0.172779-1.18450.121082
360.0698730.4790.317072

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.631799 & -4.3314 & 3.9e-05 \tabularnewline
2 & 0.039068 & 0.2678 & 0.394999 \tabularnewline
3 & 0.354086 & 2.4275 & 0.009544 \tabularnewline
4 & -0.320734 & -2.1988 & 0.016423 \tabularnewline
5 & 0.072656 & 0.4981 & 0.310366 \tabularnewline
6 & 0.166295 & 1.1401 & 0.130018 \tabularnewline
7 & -0.279062 & -1.9132 & 0.030916 \tabularnewline
8 & 0.238515 & 1.6352 & 0.054346 \tabularnewline
9 & -0.13946 & -0.9561 & 0.171959 \tabularnewline
10 & -0.028579 & -0.1959 & 0.422756 \tabularnewline
11 & 0.186545 & 1.2789 & 0.103607 \tabularnewline
12 & -0.217599 & -1.4918 & 0.071219 \tabularnewline
13 & 0.087267 & 0.5983 & 0.276266 \tabularnewline
14 & 0.035553 & 0.2437 & 0.404248 \tabularnewline
15 & -0.009644 & -0.0661 & 0.473783 \tabularnewline
16 & -0.092439 & -0.6337 & 0.264665 \tabularnewline
17 & 0.128913 & 0.8838 & 0.190656 \tabularnewline
18 & -0.047632 & -0.3265 & 0.372729 \tabularnewline
19 & -0.078741 & -0.5398 & 0.295935 \tabularnewline
20 & 0.121387 & 0.8322 & 0.204755 \tabularnewline
21 & 0.025691 & 0.1761 & 0.430474 \tabularnewline
22 & -0.282042 & -1.9336 & 0.0296 \tabularnewline
23 & 0.396868 & 2.7208 & 0.004551 \tabularnewline
24 & -0.272009 & -1.8648 & 0.03423 \tabularnewline
25 & 0.033835 & 0.232 & 0.408788 \tabularnewline
26 & 0.149196 & 1.0228 & 0.15581 \tabularnewline
27 & -0.173201 & -1.1874 & 0.120517 \tabularnewline
28 & 0.041635 & 0.2854 & 0.388284 \tabularnewline
29 & 0.102288 & 0.7013 & 0.243302 \tabularnewline
30 & -0.151204 & -1.0366 & 0.152613 \tabularnewline
31 & 0.120951 & 0.8292 & 0.205591 \tabularnewline
32 & -0.058465 & -0.4008 & 0.345186 \tabularnewline
33 & -0.051812 & -0.3552 & 0.362013 \tabularnewline
34 & 0.14184 & 0.9724 & 0.167913 \tabularnewline
35 & -0.172779 & -1.1845 & 0.121082 \tabularnewline
36 & 0.069873 & 0.479 & 0.317072 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59565&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]-0.631799[/C][C]-4.3314[/C][C]3.9e-05[/C][/ROW]
[ROW][C]2[/C][C]0.039068[/C][C]0.2678[/C][C]0.394999[/C][/ROW]
[ROW][C]3[/C][C]0.354086[/C][C]2.4275[/C][C]0.009544[/C][/ROW]
[ROW][C]4[/C][C]-0.320734[/C][C]-2.1988[/C][C]0.016423[/C][/ROW]
[ROW][C]5[/C][C]0.072656[/C][C]0.4981[/C][C]0.310366[/C][/ROW]
[ROW][C]6[/C][C]0.166295[/C][C]1.1401[/C][C]0.130018[/C][/ROW]
[ROW][C]7[/C][C]-0.279062[/C][C]-1.9132[/C][C]0.030916[/C][/ROW]
[ROW][C]8[/C][C]0.238515[/C][C]1.6352[/C][C]0.054346[/C][/ROW]
[ROW][C]9[/C][C]-0.13946[/C][C]-0.9561[/C][C]0.171959[/C][/ROW]
[ROW][C]10[/C][C]-0.028579[/C][C]-0.1959[/C][C]0.422756[/C][/ROW]
[ROW][C]11[/C][C]0.186545[/C][C]1.2789[/C][C]0.103607[/C][/ROW]
[ROW][C]12[/C][C]-0.217599[/C][C]-1.4918[/C][C]0.071219[/C][/ROW]
[ROW][C]13[/C][C]0.087267[/C][C]0.5983[/C][C]0.276266[/C][/ROW]
[ROW][C]14[/C][C]0.035553[/C][C]0.2437[/C][C]0.404248[/C][/ROW]
[ROW][C]15[/C][C]-0.009644[/C][C]-0.0661[/C][C]0.473783[/C][/ROW]
[ROW][C]16[/C][C]-0.092439[/C][C]-0.6337[/C][C]0.264665[/C][/ROW]
[ROW][C]17[/C][C]0.128913[/C][C]0.8838[/C][C]0.190656[/C][/ROW]
[ROW][C]18[/C][C]-0.047632[/C][C]-0.3265[/C][C]0.372729[/C][/ROW]
[ROW][C]19[/C][C]-0.078741[/C][C]-0.5398[/C][C]0.295935[/C][/ROW]
[ROW][C]20[/C][C]0.121387[/C][C]0.8322[/C][C]0.204755[/C][/ROW]
[ROW][C]21[/C][C]0.025691[/C][C]0.1761[/C][C]0.430474[/C][/ROW]
[ROW][C]22[/C][C]-0.282042[/C][C]-1.9336[/C][C]0.0296[/C][/ROW]
[ROW][C]23[/C][C]0.396868[/C][C]2.7208[/C][C]0.004551[/C][/ROW]
[ROW][C]24[/C][C]-0.272009[/C][C]-1.8648[/C][C]0.03423[/C][/ROW]
[ROW][C]25[/C][C]0.033835[/C][C]0.232[/C][C]0.408788[/C][/ROW]
[ROW][C]26[/C][C]0.149196[/C][C]1.0228[/C][C]0.15581[/C][/ROW]
[ROW][C]27[/C][C]-0.173201[/C][C]-1.1874[/C][C]0.120517[/C][/ROW]
[ROW][C]28[/C][C]0.041635[/C][C]0.2854[/C][C]0.388284[/C][/ROW]
[ROW][C]29[/C][C]0.102288[/C][C]0.7013[/C][C]0.243302[/C][/ROW]
[ROW][C]30[/C][C]-0.151204[/C][C]-1.0366[/C][C]0.152613[/C][/ROW]
[ROW][C]31[/C][C]0.120951[/C][C]0.8292[/C][C]0.205591[/C][/ROW]
[ROW][C]32[/C][C]-0.058465[/C][C]-0.4008[/C][C]0.345186[/C][/ROW]
[ROW][C]33[/C][C]-0.051812[/C][C]-0.3552[/C][C]0.362013[/C][/ROW]
[ROW][C]34[/C][C]0.14184[/C][C]0.9724[/C][C]0.167913[/C][/ROW]
[ROW][C]35[/C][C]-0.172779[/C][C]-1.1845[/C][C]0.121082[/C][/ROW]
[ROW][C]36[/C][C]0.069873[/C][C]0.479[/C][C]0.317072[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59565&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59565&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
1-0.631799-4.33143.9e-05
20.0390680.26780.394999
30.3540862.42750.009544
4-0.320734-2.19880.016423
50.0726560.49810.310366
60.1662951.14010.130018
7-0.279062-1.91320.030916
80.2385151.63520.054346
9-0.13946-0.95610.171959
10-0.028579-0.19590.422756
110.1865451.27890.103607
12-0.217599-1.49180.071219
130.0872670.59830.276266
140.0355530.24370.404248
15-0.009644-0.06610.473783
16-0.092439-0.63370.264665
170.1289130.88380.190656
18-0.047632-0.32650.372729
19-0.078741-0.53980.295935
200.1213870.83220.204755
210.0256910.17610.430474
22-0.282042-1.93360.0296
230.3968682.72080.004551
24-0.272009-1.86480.03423
250.0338350.2320.408788
260.1491961.02280.15581
27-0.173201-1.18740.120517
280.0416350.28540.388284
290.1022880.70130.243302
30-0.151204-1.03660.152613
310.1209510.82920.205591
32-0.058465-0.40080.345186
33-0.051812-0.35520.362013
340.141840.97240.167913
35-0.172779-1.18450.121082
360.0698730.4790.317072






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.631799-4.33143.9e-05
2-0.599342-4.10897.9e-05
30.0386980.26530.39597
40.1955671.34070.093225
50.0748130.51290.305214
60.08770.60120.275284
7-0.158428-1.08610.141481
8-0.000779-0.00530.49788
9-0.126595-0.86790.194931
10-0.150284-1.03030.154073
110.0903920.61970.269226
120.0914070.62670.26696
130.023060.15810.43753
14-0.192093-1.31690.097125
150.1355330.92920.178776
16-0.001671-0.01150.495455
17-0.026549-0.1820.428178
180.018420.12630.450025
19-0.079315-0.54380.294589
200.0156990.10760.457374
210.2137351.46530.074749
22-0.238357-1.63410.054461
23-0.021455-0.14710.441845
24-0.000715-0.00490.498055
250.1092870.74920.228725
26-0.008935-0.06130.475707
270.042860.29380.385089
28-0.069428-0.4760.318149
29-0.118307-0.81110.210707
300.0552530.37880.353274
310.0248990.17070.432596
320.0047960.03290.486956
33-0.023823-0.16330.435482
34-0.080256-0.55020.292392
35-0.14519-0.99540.162326
36-0.133794-0.91720.181849

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.631799 & -4.3314 & 3.9e-05 \tabularnewline
2 & -0.599342 & -4.1089 & 7.9e-05 \tabularnewline
3 & 0.038698 & 0.2653 & 0.39597 \tabularnewline
4 & 0.195567 & 1.3407 & 0.093225 \tabularnewline
5 & 0.074813 & 0.5129 & 0.305214 \tabularnewline
6 & 0.0877 & 0.6012 & 0.275284 \tabularnewline
7 & -0.158428 & -1.0861 & 0.141481 \tabularnewline
8 & -0.000779 & -0.0053 & 0.49788 \tabularnewline
9 & -0.126595 & -0.8679 & 0.194931 \tabularnewline
10 & -0.150284 & -1.0303 & 0.154073 \tabularnewline
11 & 0.090392 & 0.6197 & 0.269226 \tabularnewline
12 & 0.091407 & 0.6267 & 0.26696 \tabularnewline
13 & 0.02306 & 0.1581 & 0.43753 \tabularnewline
14 & -0.192093 & -1.3169 & 0.097125 \tabularnewline
15 & 0.135533 & 0.9292 & 0.178776 \tabularnewline
16 & -0.001671 & -0.0115 & 0.495455 \tabularnewline
17 & -0.026549 & -0.182 & 0.428178 \tabularnewline
18 & 0.01842 & 0.1263 & 0.450025 \tabularnewline
19 & -0.079315 & -0.5438 & 0.294589 \tabularnewline
20 & 0.015699 & 0.1076 & 0.457374 \tabularnewline
21 & 0.213735 & 1.4653 & 0.074749 \tabularnewline
22 & -0.238357 & -1.6341 & 0.054461 \tabularnewline
23 & -0.021455 & -0.1471 & 0.441845 \tabularnewline
24 & -0.000715 & -0.0049 & 0.498055 \tabularnewline
25 & 0.109287 & 0.7492 & 0.228725 \tabularnewline
26 & -0.008935 & -0.0613 & 0.475707 \tabularnewline
27 & 0.04286 & 0.2938 & 0.385089 \tabularnewline
28 & -0.069428 & -0.476 & 0.318149 \tabularnewline
29 & -0.118307 & -0.8111 & 0.210707 \tabularnewline
30 & 0.055253 & 0.3788 & 0.353274 \tabularnewline
31 & 0.024899 & 0.1707 & 0.432596 \tabularnewline
32 & 0.004796 & 0.0329 & 0.486956 \tabularnewline
33 & -0.023823 & -0.1633 & 0.435482 \tabularnewline
34 & -0.080256 & -0.5502 & 0.292392 \tabularnewline
35 & -0.14519 & -0.9954 & 0.162326 \tabularnewline
36 & -0.133794 & -0.9172 & 0.181849 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59565&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]-0.631799[/C][C]-4.3314[/C][C]3.9e-05[/C][/ROW]
[ROW][C]2[/C][C]-0.599342[/C][C]-4.1089[/C][C]7.9e-05[/C][/ROW]
[ROW][C]3[/C][C]0.038698[/C][C]0.2653[/C][C]0.39597[/C][/ROW]
[ROW][C]4[/C][C]0.195567[/C][C]1.3407[/C][C]0.093225[/C][/ROW]
[ROW][C]5[/C][C]0.074813[/C][C]0.5129[/C][C]0.305214[/C][/ROW]
[ROW][C]6[/C][C]0.0877[/C][C]0.6012[/C][C]0.275284[/C][/ROW]
[ROW][C]7[/C][C]-0.158428[/C][C]-1.0861[/C][C]0.141481[/C][/ROW]
[ROW][C]8[/C][C]-0.000779[/C][C]-0.0053[/C][C]0.49788[/C][/ROW]
[ROW][C]9[/C][C]-0.126595[/C][C]-0.8679[/C][C]0.194931[/C][/ROW]
[ROW][C]10[/C][C]-0.150284[/C][C]-1.0303[/C][C]0.154073[/C][/ROW]
[ROW][C]11[/C][C]0.090392[/C][C]0.6197[/C][C]0.269226[/C][/ROW]
[ROW][C]12[/C][C]0.091407[/C][C]0.6267[/C][C]0.26696[/C][/ROW]
[ROW][C]13[/C][C]0.02306[/C][C]0.1581[/C][C]0.43753[/C][/ROW]
[ROW][C]14[/C][C]-0.192093[/C][C]-1.3169[/C][C]0.097125[/C][/ROW]
[ROW][C]15[/C][C]0.135533[/C][C]0.9292[/C][C]0.178776[/C][/ROW]
[ROW][C]16[/C][C]-0.001671[/C][C]-0.0115[/C][C]0.495455[/C][/ROW]
[ROW][C]17[/C][C]-0.026549[/C][C]-0.182[/C][C]0.428178[/C][/ROW]
[ROW][C]18[/C][C]0.01842[/C][C]0.1263[/C][C]0.450025[/C][/ROW]
[ROW][C]19[/C][C]-0.079315[/C][C]-0.5438[/C][C]0.294589[/C][/ROW]
[ROW][C]20[/C][C]0.015699[/C][C]0.1076[/C][C]0.457374[/C][/ROW]
[ROW][C]21[/C][C]0.213735[/C][C]1.4653[/C][C]0.074749[/C][/ROW]
[ROW][C]22[/C][C]-0.238357[/C][C]-1.6341[/C][C]0.054461[/C][/ROW]
[ROW][C]23[/C][C]-0.021455[/C][C]-0.1471[/C][C]0.441845[/C][/ROW]
[ROW][C]24[/C][C]-0.000715[/C][C]-0.0049[/C][C]0.498055[/C][/ROW]
[ROW][C]25[/C][C]0.109287[/C][C]0.7492[/C][C]0.228725[/C][/ROW]
[ROW][C]26[/C][C]-0.008935[/C][C]-0.0613[/C][C]0.475707[/C][/ROW]
[ROW][C]27[/C][C]0.04286[/C][C]0.2938[/C][C]0.385089[/C][/ROW]
[ROW][C]28[/C][C]-0.069428[/C][C]-0.476[/C][C]0.318149[/C][/ROW]
[ROW][C]29[/C][C]-0.118307[/C][C]-0.8111[/C][C]0.210707[/C][/ROW]
[ROW][C]30[/C][C]0.055253[/C][C]0.3788[/C][C]0.353274[/C][/ROW]
[ROW][C]31[/C][C]0.024899[/C][C]0.1707[/C][C]0.432596[/C][/ROW]
[ROW][C]32[/C][C]0.004796[/C][C]0.0329[/C][C]0.486956[/C][/ROW]
[ROW][C]33[/C][C]-0.023823[/C][C]-0.1633[/C][C]0.435482[/C][/ROW]
[ROW][C]34[/C][C]-0.080256[/C][C]-0.5502[/C][C]0.292392[/C][/ROW]
[ROW][C]35[/C][C]-0.14519[/C][C]-0.9954[/C][C]0.162326[/C][/ROW]
[ROW][C]36[/C][C]-0.133794[/C][C]-0.9172[/C][C]0.181849[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59565&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59565&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
1-0.631799-4.33143.9e-05
2-0.599342-4.10897.9e-05
30.0386980.26530.39597
40.1955671.34070.093225
50.0748130.51290.305214
60.08770.60120.275284
7-0.158428-1.08610.141481
8-0.000779-0.00530.49788
9-0.126595-0.86790.194931
10-0.150284-1.03030.154073
110.0903920.61970.269226
120.0914070.62670.26696
130.023060.15810.43753
14-0.192093-1.31690.097125
150.1355330.92920.178776
16-0.001671-0.01150.495455
17-0.026549-0.1820.428178
180.018420.12630.450025
19-0.079315-0.54380.294589
200.0156990.10760.457374
210.2137351.46530.074749
22-0.238357-1.63410.054461
23-0.021455-0.14710.441845
24-0.000715-0.00490.498055
250.1092870.74920.228725
26-0.008935-0.06130.475707
270.042860.29380.385089
28-0.069428-0.4760.318149
29-0.118307-0.81110.210707
300.0552530.37880.353274
310.0248990.17070.432596
320.0047960.03290.486956
33-0.023823-0.16330.435482
34-0.080256-0.55020.292392
35-0.14519-0.99540.162326
36-0.133794-0.91720.181849


Figures (Output of Computation)

Input Parameters & R Code

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