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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, 15 Dec 2009 17:38:03 -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/16/t1260923931eyqbvtiz7mqacnr.htm/, Retrieved Tue, 30 Apr 2024 08:52:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68202, Retrieved Tue, 30 Apr 2024 08:52:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

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 12:19:56] [b98453cac15ba1066b407e146608df68]
-   PD        [(Partial) Autocorrelation Function] [Shwws8_v1] [2009-11-27 19:16:28] [5f89c040fdf1f8599c99d7f78a662321]
-   PD            [(Partial) Autocorrelation Function] [Paper] [2009-12-16 00:38:03] [93b66894f6318f3da4fcda772f2ffa6f] [Current]
-    D              [(Partial) Autocorrelation Function] [Paper] [2009-12-16 00:46:55] [5f89c040fdf1f8599c99d7f78a662321]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
102.1
102.86
102.99
103.73
105.02
104.43
104.63
104.93
105.87
105.66
106.76
106
107.22
107.33
107.11
108.86
107.72
107.88
108.38
107.72
108.41
109.9
111.45
112.18
113.34
113.46
114.06
115.54
116.39
115.94
116.97
115.94
115.91
116.43
116.26
116.35
117.9
117.7
117.53
117.86
117.65
116.51
115.93
115.31
115
115.45
115.83

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=68202&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=68202&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68202&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
10.9022445.33773e-06
20.7941614.69832e-05
30.6593213.90060.000208
40.4986322.94990.002817
50.3399062.01090.026044
60.1943071.14950.129065
70.0533650.31570.377049
8-0.072134-0.42680.336087
9-0.185861-1.09960.139513
10-0.285299-1.68790.050167
11-0.367818-2.1760.018195
12-0.44953-2.65950.005863
13-0.475415-2.81260.004002
14-0.474304-2.8060.004069
15-0.432308-2.55760.007515
16-0.382377-2.26220.015
17-0.321363-1.90120.032767
18-0.252983-1.49670.071722
19-0.181353-1.07290.145329
20-0.130904-0.77440.221935
21-0.072267-0.42750.335804
22-0.023065-0.13650.446122
230.0244390.14460.442935
240.0656640.38850.35001
250.0780720.46190.323514
260.0781250.46220.323401
270.0362370.21440.415747
280.0117350.06940.472523
29-0.001001-0.00590.497654
30-0.020605-0.12190.451838
31-0.041-0.24260.404881
32-0.025207-0.14910.441155
33-0.024674-0.1460.442391
34-0.01674-0.0990.460838
35NANANA
36NANANA

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.902244 & 5.3377 & 3e-06 \tabularnewline
2 & 0.794161 & 4.6983 & 2e-05 \tabularnewline
3 & 0.659321 & 3.9006 & 0.000208 \tabularnewline
4 & 0.498632 & 2.9499 & 0.002817 \tabularnewline
5 & 0.339906 & 2.0109 & 0.026044 \tabularnewline
6 & 0.194307 & 1.1495 & 0.129065 \tabularnewline
7 & 0.053365 & 0.3157 & 0.377049 \tabularnewline
8 & -0.072134 & -0.4268 & 0.336087 \tabularnewline
9 & -0.185861 & -1.0996 & 0.139513 \tabularnewline
10 & -0.285299 & -1.6879 & 0.050167 \tabularnewline
11 & -0.367818 & -2.176 & 0.018195 \tabularnewline
12 & -0.44953 & -2.6595 & 0.005863 \tabularnewline
13 & -0.475415 & -2.8126 & 0.004002 \tabularnewline
14 & -0.474304 & -2.806 & 0.004069 \tabularnewline
15 & -0.432308 & -2.5576 & 0.007515 \tabularnewline
16 & -0.382377 & -2.2622 & 0.015 \tabularnewline
17 & -0.321363 & -1.9012 & 0.032767 \tabularnewline
18 & -0.252983 & -1.4967 & 0.071722 \tabularnewline
19 & -0.181353 & -1.0729 & 0.145329 \tabularnewline
20 & -0.130904 & -0.7744 & 0.221935 \tabularnewline
21 & -0.072267 & -0.4275 & 0.335804 \tabularnewline
22 & -0.023065 & -0.1365 & 0.446122 \tabularnewline
23 & 0.024439 & 0.1446 & 0.442935 \tabularnewline
24 & 0.065664 & 0.3885 & 0.35001 \tabularnewline
25 & 0.078072 & 0.4619 & 0.323514 \tabularnewline
26 & 0.078125 & 0.4622 & 0.323401 \tabularnewline
27 & 0.036237 & 0.2144 & 0.415747 \tabularnewline
28 & 0.011735 & 0.0694 & 0.472523 \tabularnewline
29 & -0.001001 & -0.0059 & 0.497654 \tabularnewline
30 & -0.020605 & -0.1219 & 0.451838 \tabularnewline
31 & -0.041 & -0.2426 & 0.404881 \tabularnewline
32 & -0.025207 & -0.1491 & 0.441155 \tabularnewline
33 & -0.024674 & -0.146 & 0.442391 \tabularnewline
34 & -0.01674 & -0.099 & 0.460838 \tabularnewline
35 & NA & NA & NA \tabularnewline
36 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68202&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]0.902244[/C][C]5.3377[/C][C]3e-06[/C][/ROW]
[ROW][C]2[/C][C]0.794161[/C][C]4.6983[/C][C]2e-05[/C][/ROW]
[ROW][C]3[/C][C]0.659321[/C][C]3.9006[/C][C]0.000208[/C][/ROW]
[ROW][C]4[/C][C]0.498632[/C][C]2.9499[/C][C]0.002817[/C][/ROW]
[ROW][C]5[/C][C]0.339906[/C][C]2.0109[/C][C]0.026044[/C][/ROW]
[ROW][C]6[/C][C]0.194307[/C][C]1.1495[/C][C]0.129065[/C][/ROW]
[ROW][C]7[/C][C]0.053365[/C][C]0.3157[/C][C]0.377049[/C][/ROW]
[ROW][C]8[/C][C]-0.072134[/C][C]-0.4268[/C][C]0.336087[/C][/ROW]
[ROW][C]9[/C][C]-0.185861[/C][C]-1.0996[/C][C]0.139513[/C][/ROW]
[ROW][C]10[/C][C]-0.285299[/C][C]-1.6879[/C][C]0.050167[/C][/ROW]
[ROW][C]11[/C][C]-0.367818[/C][C]-2.176[/C][C]0.018195[/C][/ROW]
[ROW][C]12[/C][C]-0.44953[/C][C]-2.6595[/C][C]0.005863[/C][/ROW]
[ROW][C]13[/C][C]-0.475415[/C][C]-2.8126[/C][C]0.004002[/C][/ROW]
[ROW][C]14[/C][C]-0.474304[/C][C]-2.806[/C][C]0.004069[/C][/ROW]
[ROW][C]15[/C][C]-0.432308[/C][C]-2.5576[/C][C]0.007515[/C][/ROW]
[ROW][C]16[/C][C]-0.382377[/C][C]-2.2622[/C][C]0.015[/C][/ROW]
[ROW][C]17[/C][C]-0.321363[/C][C]-1.9012[/C][C]0.032767[/C][/ROW]
[ROW][C]18[/C][C]-0.252983[/C][C]-1.4967[/C][C]0.071722[/C][/ROW]
[ROW][C]19[/C][C]-0.181353[/C][C]-1.0729[/C][C]0.145329[/C][/ROW]
[ROW][C]20[/C][C]-0.130904[/C][C]-0.7744[/C][C]0.221935[/C][/ROW]
[ROW][C]21[/C][C]-0.072267[/C][C]-0.4275[/C][C]0.335804[/C][/ROW]
[ROW][C]22[/C][C]-0.023065[/C][C]-0.1365[/C][C]0.446122[/C][/ROW]
[ROW][C]23[/C][C]0.024439[/C][C]0.1446[/C][C]0.442935[/C][/ROW]
[ROW][C]24[/C][C]0.065664[/C][C]0.3885[/C][C]0.35001[/C][/ROW]
[ROW][C]25[/C][C]0.078072[/C][C]0.4619[/C][C]0.323514[/C][/ROW]
[ROW][C]26[/C][C]0.078125[/C][C]0.4622[/C][C]0.323401[/C][/ROW]
[ROW][C]27[/C][C]0.036237[/C][C]0.2144[/C][C]0.415747[/C][/ROW]
[ROW][C]28[/C][C]0.011735[/C][C]0.0694[/C][C]0.472523[/C][/ROW]
[ROW][C]29[/C][C]-0.001001[/C][C]-0.0059[/C][C]0.497654[/C][/ROW]
[ROW][C]30[/C][C]-0.020605[/C][C]-0.1219[/C][C]0.451838[/C][/ROW]
[ROW][C]31[/C][C]-0.041[/C][C]-0.2426[/C][C]0.404881[/C][/ROW]
[ROW][C]32[/C][C]-0.025207[/C][C]-0.1491[/C][C]0.441155[/C][/ROW]
[ROW][C]33[/C][C]-0.024674[/C][C]-0.146[/C][C]0.442391[/C][/ROW]
[ROW][C]34[/C][C]-0.01674[/C][C]-0.099[/C][C]0.460838[/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=68202&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68202&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
10.9022445.33773e-06
20.7941614.69832e-05
30.6593213.90060.000208
40.4986322.94990.002817
50.3399062.01090.026044
60.1943071.14950.129065
70.0533650.31570.377049
8-0.072134-0.42680.336087
9-0.185861-1.09960.139513
10-0.285299-1.68790.050167
11-0.367818-2.1760.018195
12-0.44953-2.65950.005863
13-0.475415-2.81260.004002
14-0.474304-2.8060.004069
15-0.432308-2.55760.007515
16-0.382377-2.26220.015
17-0.321363-1.90120.032767
18-0.252983-1.49670.071722
19-0.181353-1.07290.145329
20-0.130904-0.77440.221935
21-0.072267-0.42750.335804
22-0.023065-0.13650.446122
230.0244390.14460.442935
240.0656640.38850.35001
250.0780720.46190.323514
260.0781250.46220.323401
270.0362370.21440.415747
280.0117350.06940.472523
29-0.001001-0.00590.497654
30-0.020605-0.12190.451838
31-0.041-0.24260.404881
32-0.025207-0.14910.441155
33-0.024674-0.1460.442391
34-0.01674-0.0990.460838
35NANANA
36NANANA






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9022445.33773e-06
2-0.106923-0.63260.265564
3-0.20317-1.2020.118722
4-0.218099-1.29030.102705
5-0.079461-0.47010.3206
6-0.012803-0.07570.470028
7-0.084468-0.49970.310201
8-0.068301-0.40410.344308
9-0.095064-0.56240.288711
10-0.078048-0.46170.323562
11-0.066568-0.39380.34805
12-0.166612-0.98570.165526
130.1531580.90610.185541
140.0443960.26260.39718
150.1114040.65910.257081
16-0.112077-0.66310.255819
17-0.067823-0.40120.345339
18-0.00431-0.02550.489901
190.0156750.09270.463321
20-0.113971-0.67430.252287
210.0276760.16370.435442
22-0.019377-0.11460.454694
230.0391680.23170.409052
24-0.078799-0.46620.321987
25-0.147056-0.870.195116
26-0.061135-0.36170.359884
27-0.162603-0.9620.171331
280.1393660.82450.20762
290.1234840.73050.234961
30-0.049621-0.29360.385413
31-0.082073-0.48560.315155
320.1016710.60150.275694
33-0.060239-0.35640.361849
34-0.039746-0.23510.407735
35NANANA
36NANANA

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.902244 & 5.3377 & 3e-06 \tabularnewline
2 & -0.106923 & -0.6326 & 0.265564 \tabularnewline
3 & -0.20317 & -1.202 & 0.118722 \tabularnewline
4 & -0.218099 & -1.2903 & 0.102705 \tabularnewline
5 & -0.079461 & -0.4701 & 0.3206 \tabularnewline
6 & -0.012803 & -0.0757 & 0.470028 \tabularnewline
7 & -0.084468 & -0.4997 & 0.310201 \tabularnewline
8 & -0.068301 & -0.4041 & 0.344308 \tabularnewline
9 & -0.095064 & -0.5624 & 0.288711 \tabularnewline
10 & -0.078048 & -0.4617 & 0.323562 \tabularnewline
11 & -0.066568 & -0.3938 & 0.34805 \tabularnewline
12 & -0.166612 & -0.9857 & 0.165526 \tabularnewline
13 & 0.153158 & 0.9061 & 0.185541 \tabularnewline
14 & 0.044396 & 0.2626 & 0.39718 \tabularnewline
15 & 0.111404 & 0.6591 & 0.257081 \tabularnewline
16 & -0.112077 & -0.6631 & 0.255819 \tabularnewline
17 & -0.067823 & -0.4012 & 0.345339 \tabularnewline
18 & -0.00431 & -0.0255 & 0.489901 \tabularnewline
19 & 0.015675 & 0.0927 & 0.463321 \tabularnewline
20 & -0.113971 & -0.6743 & 0.252287 \tabularnewline
21 & 0.027676 & 0.1637 & 0.435442 \tabularnewline
22 & -0.019377 & -0.1146 & 0.454694 \tabularnewline
23 & 0.039168 & 0.2317 & 0.409052 \tabularnewline
24 & -0.078799 & -0.4662 & 0.321987 \tabularnewline
25 & -0.147056 & -0.87 & 0.195116 \tabularnewline
26 & -0.061135 & -0.3617 & 0.359884 \tabularnewline
27 & -0.162603 & -0.962 & 0.171331 \tabularnewline
28 & 0.139366 & 0.8245 & 0.20762 \tabularnewline
29 & 0.123484 & 0.7305 & 0.234961 \tabularnewline
30 & -0.049621 & -0.2936 & 0.385413 \tabularnewline
31 & -0.082073 & -0.4856 & 0.315155 \tabularnewline
32 & 0.101671 & 0.6015 & 0.275694 \tabularnewline
33 & -0.060239 & -0.3564 & 0.361849 \tabularnewline
34 & -0.039746 & -0.2351 & 0.407735 \tabularnewline
35 & NA & NA & NA \tabularnewline
36 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68202&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]0.902244[/C][C]5.3377[/C][C]3e-06[/C][/ROW]
[ROW][C]2[/C][C]-0.106923[/C][C]-0.6326[/C][C]0.265564[/C][/ROW]
[ROW][C]3[/C][C]-0.20317[/C][C]-1.202[/C][C]0.118722[/C][/ROW]
[ROW][C]4[/C][C]-0.218099[/C][C]-1.2903[/C][C]0.102705[/C][/ROW]
[ROW][C]5[/C][C]-0.079461[/C][C]-0.4701[/C][C]0.3206[/C][/ROW]
[ROW][C]6[/C][C]-0.012803[/C][C]-0.0757[/C][C]0.470028[/C][/ROW]
[ROW][C]7[/C][C]-0.084468[/C][C]-0.4997[/C][C]0.310201[/C][/ROW]
[ROW][C]8[/C][C]-0.068301[/C][C]-0.4041[/C][C]0.344308[/C][/ROW]
[ROW][C]9[/C][C]-0.095064[/C][C]-0.5624[/C][C]0.288711[/C][/ROW]
[ROW][C]10[/C][C]-0.078048[/C][C]-0.4617[/C][C]0.323562[/C][/ROW]
[ROW][C]11[/C][C]-0.066568[/C][C]-0.3938[/C][C]0.34805[/C][/ROW]
[ROW][C]12[/C][C]-0.166612[/C][C]-0.9857[/C][C]0.165526[/C][/ROW]
[ROW][C]13[/C][C]0.153158[/C][C]0.9061[/C][C]0.185541[/C][/ROW]
[ROW][C]14[/C][C]0.044396[/C][C]0.2626[/C][C]0.39718[/C][/ROW]
[ROW][C]15[/C][C]0.111404[/C][C]0.6591[/C][C]0.257081[/C][/ROW]
[ROW][C]16[/C][C]-0.112077[/C][C]-0.6631[/C][C]0.255819[/C][/ROW]
[ROW][C]17[/C][C]-0.067823[/C][C]-0.4012[/C][C]0.345339[/C][/ROW]
[ROW][C]18[/C][C]-0.00431[/C][C]-0.0255[/C][C]0.489901[/C][/ROW]
[ROW][C]19[/C][C]0.015675[/C][C]0.0927[/C][C]0.463321[/C][/ROW]
[ROW][C]20[/C][C]-0.113971[/C][C]-0.6743[/C][C]0.252287[/C][/ROW]
[ROW][C]21[/C][C]0.027676[/C][C]0.1637[/C][C]0.435442[/C][/ROW]
[ROW][C]22[/C][C]-0.019377[/C][C]-0.1146[/C][C]0.454694[/C][/ROW]
[ROW][C]23[/C][C]0.039168[/C][C]0.2317[/C][C]0.409052[/C][/ROW]
[ROW][C]24[/C][C]-0.078799[/C][C]-0.4662[/C][C]0.321987[/C][/ROW]
[ROW][C]25[/C][C]-0.147056[/C][C]-0.87[/C][C]0.195116[/C][/ROW]
[ROW][C]26[/C][C]-0.061135[/C][C]-0.3617[/C][C]0.359884[/C][/ROW]
[ROW][C]27[/C][C]-0.162603[/C][C]-0.962[/C][C]0.171331[/C][/ROW]
[ROW][C]28[/C][C]0.139366[/C][C]0.8245[/C][C]0.20762[/C][/ROW]
[ROW][C]29[/C][C]0.123484[/C][C]0.7305[/C][C]0.234961[/C][/ROW]
[ROW][C]30[/C][C]-0.049621[/C][C]-0.2936[/C][C]0.385413[/C][/ROW]
[ROW][C]31[/C][C]-0.082073[/C][C]-0.4856[/C][C]0.315155[/C][/ROW]
[ROW][C]32[/C][C]0.101671[/C][C]0.6015[/C][C]0.275694[/C][/ROW]
[ROW][C]33[/C][C]-0.060239[/C][C]-0.3564[/C][C]0.361849[/C][/ROW]
[ROW][C]34[/C][C]-0.039746[/C][C]-0.2351[/C][C]0.407735[/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=68202&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68202&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
10.9022445.33773e-06
2-0.106923-0.63260.265564
3-0.20317-1.2020.118722
4-0.218099-1.29030.102705
5-0.079461-0.47010.3206
6-0.012803-0.07570.470028
7-0.084468-0.49970.310201
8-0.068301-0.40410.344308
9-0.095064-0.56240.288711
10-0.078048-0.46170.323562
11-0.066568-0.39380.34805
12-0.166612-0.98570.165526
130.1531580.90610.185541
140.0443960.26260.39718
150.1114040.65910.257081
16-0.112077-0.66310.255819
17-0.067823-0.40120.345339
18-0.00431-0.02550.489901
190.0156750.09270.463321
20-0.113971-0.67430.252287
210.0276760.16370.435442
22-0.019377-0.11460.454694
230.0391680.23170.409052
24-0.078799-0.46620.321987
25-0.147056-0.870.195116
26-0.061135-0.36170.359884
27-0.162603-0.9620.171331
280.1393660.82450.20762
290.1234840.73050.234961
30-0.049621-0.29360.385413
31-0.082073-0.48560.315155
320.1016710.60150.275694
33-0.060239-0.35640.361849
34-0.039746-0.23510.407735
35NANANA
36NANANA


Figures (Output of Computation)

Input Parameters & R Code

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