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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 computationFri, 27 Nov 2009 09:18:28 -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/27/t1259338749f50notcapvhb278.htm/, Retrieved Sun, 28 Apr 2024 23:35:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60949, Retrieved Sun, 28 Apr 2024 23:35:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105

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]
-   PD          [(Partial) Autocorrelation Function] [WS8: acf 1] [2009-11-27 16:18:28] [b090d569c0a4c77894e0b029f4429f19] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13.4
14
14.7
16.1
16.3
16.1
16.5
19
18.7
17.7
17.2
16.6
17.4
15.5
12.4
11.1
11.1
13.7
12.1
11.3
9.9
10.9
13.2
14.2
16.8
17
19.6
18.3
20.6
17.7
19.6
17.4
17.6
19
17.8
17
15.7
16
17.1
17
15.7
15.6
16.1
17.4
16
15
14.3
14.5
14.3
16.2
14.8
16.3
14
15.4
13.4
12.9
14.3
11.9
8.6
5.5
0.8

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=60949&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=60949&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60949&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
10.7272165.67970
20.5404434.2214.1e-05
30.3495042.72970.004137
40.2643162.06440.021621
50.2029141.58480.05909
60.0721940.56390.287459
7-0.057353-0.44790.327892
8-0.114425-0.89370.1875
9-0.173811-1.35750.089811
10-0.176708-1.38010.086291
11-0.201745-1.57570.060136
12-0.180271-1.4080.08211
13-0.153872-1.20180.117047
14-0.12741-0.99510.161809
15-0.112434-0.87810.191658
16-0.101012-0.78890.216605
17-0.088722-0.69290.245488
18-0.034413-0.26880.394506
19-0.016236-0.12680.449756
20-0.020059-0.15670.438012
21-0.020271-0.15830.437363
22-0.042048-0.32840.371866
23-0.02379-0.18580.426605
24-0.074129-0.5790.282371
25-0.09563-0.74690.228998
26-0.126003-0.98410.164473
27-0.150167-1.17280.122709
28-0.15657-1.22290.113045
29-0.168607-1.31690.096405
30-0.187254-1.46250.074368
31-0.15226-1.18920.119487
32-0.175469-1.37050.087783
33-0.129196-1.00910.158467
34-0.09322-0.72810.234677
35-0.009097-0.0710.471797
360.0504530.3940.34746

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.727216 & 5.6797 & 0 \tabularnewline
2 & 0.540443 & 4.221 & 4.1e-05 \tabularnewline
3 & 0.349504 & 2.7297 & 0.004137 \tabularnewline
4 & 0.264316 & 2.0644 & 0.021621 \tabularnewline
5 & 0.202914 & 1.5848 & 0.05909 \tabularnewline
6 & 0.072194 & 0.5639 & 0.287459 \tabularnewline
7 & -0.057353 & -0.4479 & 0.327892 \tabularnewline
8 & -0.114425 & -0.8937 & 0.1875 \tabularnewline
9 & -0.173811 & -1.3575 & 0.089811 \tabularnewline
10 & -0.176708 & -1.3801 & 0.086291 \tabularnewline
11 & -0.201745 & -1.5757 & 0.060136 \tabularnewline
12 & -0.180271 & -1.408 & 0.08211 \tabularnewline
13 & -0.153872 & -1.2018 & 0.117047 \tabularnewline
14 & -0.12741 & -0.9951 & 0.161809 \tabularnewline
15 & -0.112434 & -0.8781 & 0.191658 \tabularnewline
16 & -0.101012 & -0.7889 & 0.216605 \tabularnewline
17 & -0.088722 & -0.6929 & 0.245488 \tabularnewline
18 & -0.034413 & -0.2688 & 0.394506 \tabularnewline
19 & -0.016236 & -0.1268 & 0.449756 \tabularnewline
20 & -0.020059 & -0.1567 & 0.438012 \tabularnewline
21 & -0.020271 & -0.1583 & 0.437363 \tabularnewline
22 & -0.042048 & -0.3284 & 0.371866 \tabularnewline
23 & -0.02379 & -0.1858 & 0.426605 \tabularnewline
24 & -0.074129 & -0.579 & 0.282371 \tabularnewline
25 & -0.09563 & -0.7469 & 0.228998 \tabularnewline
26 & -0.126003 & -0.9841 & 0.164473 \tabularnewline
27 & -0.150167 & -1.1728 & 0.122709 \tabularnewline
28 & -0.15657 & -1.2229 & 0.113045 \tabularnewline
29 & -0.168607 & -1.3169 & 0.096405 \tabularnewline
30 & -0.187254 & -1.4625 & 0.074368 \tabularnewline
31 & -0.15226 & -1.1892 & 0.119487 \tabularnewline
32 & -0.175469 & -1.3705 & 0.087783 \tabularnewline
33 & -0.129196 & -1.0091 & 0.158467 \tabularnewline
34 & -0.09322 & -0.7281 & 0.234677 \tabularnewline
35 & -0.009097 & -0.071 & 0.471797 \tabularnewline
36 & 0.050453 & 0.394 & 0.34746 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60949&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]0.727216[/C][C]5.6797[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.540443[/C][C]4.221[/C][C]4.1e-05[/C][/ROW]
[ROW][C]3[/C][C]0.349504[/C][C]2.7297[/C][C]0.004137[/C][/ROW]
[ROW][C]4[/C][C]0.264316[/C][C]2.0644[/C][C]0.021621[/C][/ROW]
[ROW][C]5[/C][C]0.202914[/C][C]1.5848[/C][C]0.05909[/C][/ROW]
[ROW][C]6[/C][C]0.072194[/C][C]0.5639[/C][C]0.287459[/C][/ROW]
[ROW][C]7[/C][C]-0.057353[/C][C]-0.4479[/C][C]0.327892[/C][/ROW]
[ROW][C]8[/C][C]-0.114425[/C][C]-0.8937[/C][C]0.1875[/C][/ROW]
[ROW][C]9[/C][C]-0.173811[/C][C]-1.3575[/C][C]0.089811[/C][/ROW]
[ROW][C]10[/C][C]-0.176708[/C][C]-1.3801[/C][C]0.086291[/C][/ROW]
[ROW][C]11[/C][C]-0.201745[/C][C]-1.5757[/C][C]0.060136[/C][/ROW]
[ROW][C]12[/C][C]-0.180271[/C][C]-1.408[/C][C]0.08211[/C][/ROW]
[ROW][C]13[/C][C]-0.153872[/C][C]-1.2018[/C][C]0.117047[/C][/ROW]
[ROW][C]14[/C][C]-0.12741[/C][C]-0.9951[/C][C]0.161809[/C][/ROW]
[ROW][C]15[/C][C]-0.112434[/C][C]-0.8781[/C][C]0.191658[/C][/ROW]
[ROW][C]16[/C][C]-0.101012[/C][C]-0.7889[/C][C]0.216605[/C][/ROW]
[ROW][C]17[/C][C]-0.088722[/C][C]-0.6929[/C][C]0.245488[/C][/ROW]
[ROW][C]18[/C][C]-0.034413[/C][C]-0.2688[/C][C]0.394506[/C][/ROW]
[ROW][C]19[/C][C]-0.016236[/C][C]-0.1268[/C][C]0.449756[/C][/ROW]
[ROW][C]20[/C][C]-0.020059[/C][C]-0.1567[/C][C]0.438012[/C][/ROW]
[ROW][C]21[/C][C]-0.020271[/C][C]-0.1583[/C][C]0.437363[/C][/ROW]
[ROW][C]22[/C][C]-0.042048[/C][C]-0.3284[/C][C]0.371866[/C][/ROW]
[ROW][C]23[/C][C]-0.02379[/C][C]-0.1858[/C][C]0.426605[/C][/ROW]
[ROW][C]24[/C][C]-0.074129[/C][C]-0.579[/C][C]0.282371[/C][/ROW]
[ROW][C]25[/C][C]-0.09563[/C][C]-0.7469[/C][C]0.228998[/C][/ROW]
[ROW][C]26[/C][C]-0.126003[/C][C]-0.9841[/C][C]0.164473[/C][/ROW]
[ROW][C]27[/C][C]-0.150167[/C][C]-1.1728[/C][C]0.122709[/C][/ROW]
[ROW][C]28[/C][C]-0.15657[/C][C]-1.2229[/C][C]0.113045[/C][/ROW]
[ROW][C]29[/C][C]-0.168607[/C][C]-1.3169[/C][C]0.096405[/C][/ROW]
[ROW][C]30[/C][C]-0.187254[/C][C]-1.4625[/C][C]0.074368[/C][/ROW]
[ROW][C]31[/C][C]-0.15226[/C][C]-1.1892[/C][C]0.119487[/C][/ROW]
[ROW][C]32[/C][C]-0.175469[/C][C]-1.3705[/C][C]0.087783[/C][/ROW]
[ROW][C]33[/C][C]-0.129196[/C][C]-1.0091[/C][C]0.158467[/C][/ROW]
[ROW][C]34[/C][C]-0.09322[/C][C]-0.7281[/C][C]0.234677[/C][/ROW]
[ROW][C]35[/C][C]-0.009097[/C][C]-0.071[/C][C]0.471797[/C][/ROW]
[ROW][C]36[/C][C]0.050453[/C][C]0.394[/C][C]0.34746[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60949&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60949&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
10.7272165.67970
20.5404434.2214.1e-05
30.3495042.72970.004137
40.2643162.06440.021621
50.2029141.58480.05909
60.0721940.56390.287459
7-0.057353-0.44790.327892
8-0.114425-0.89370.1875
9-0.173811-1.35750.089811
10-0.176708-1.38010.086291
11-0.201745-1.57570.060136
12-0.180271-1.4080.08211
13-0.153872-1.20180.117047
14-0.12741-0.99510.161809
15-0.112434-0.87810.191658
16-0.101012-0.78890.216605
17-0.088722-0.69290.245488
18-0.034413-0.26880.394506
19-0.016236-0.12680.449756
20-0.020059-0.15670.438012
21-0.020271-0.15830.437363
22-0.042048-0.32840.371866
23-0.02379-0.18580.426605
24-0.074129-0.5790.282371
25-0.09563-0.74690.228998
26-0.126003-0.98410.164473
27-0.150167-1.17280.122709
28-0.15657-1.22290.113045
29-0.168607-1.31690.096405
30-0.187254-1.46250.074368
31-0.15226-1.18920.119487
32-0.175469-1.37050.087783
33-0.129196-1.00910.158467
34-0.09322-0.72810.234677
35-0.009097-0.0710.471797
360.0504530.3940.34746






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.7272165.67970
20.0246180.19230.424085
3-0.109886-0.85820.197062
40.0858680.67060.252488
50.0204430.15970.436838
6-0.200202-1.56360.061539
7-0.118167-0.92290.179844
80.056220.43910.331074
9-0.105094-0.82080.207475
10-0.00758-0.05920.476493
11-0.020492-0.160.436686
120.0401050.31320.377588
13-0.012931-0.1010.459943
14-0.018406-0.14380.443084
15-0.026135-0.20410.419471
16-0.027947-0.21830.413973
17-0.025025-0.19550.422844
180.0595520.46510.321752
19-0.027752-0.21680.414563
20-0.067343-0.5260.300409
210.0277550.21680.414554
22-0.059403-0.4640.322167
230.0086830.06780.473076
24-0.131601-1.02780.154043
250.0012960.01010.495979
26-0.047263-0.36910.356653
27-0.067716-0.52890.299405
28-0.025673-0.20050.420875
29-0.023767-0.18560.426676
30-0.072002-0.56240.287969
310.0281010.21950.413507
32-0.112065-0.87530.192434
330.0250740.19580.422695
340.030.23430.407767
350.0728940.56930.285615
36-0.009346-0.0730.471026

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.727216 & 5.6797 & 0 \tabularnewline
2 & 0.024618 & 0.1923 & 0.424085 \tabularnewline
3 & -0.109886 & -0.8582 & 0.197062 \tabularnewline
4 & 0.085868 & 0.6706 & 0.252488 \tabularnewline
5 & 0.020443 & 0.1597 & 0.436838 \tabularnewline
6 & -0.200202 & -1.5636 & 0.061539 \tabularnewline
7 & -0.118167 & -0.9229 & 0.179844 \tabularnewline
8 & 0.05622 & 0.4391 & 0.331074 \tabularnewline
9 & -0.105094 & -0.8208 & 0.207475 \tabularnewline
10 & -0.00758 & -0.0592 & 0.476493 \tabularnewline
11 & -0.020492 & -0.16 & 0.436686 \tabularnewline
12 & 0.040105 & 0.3132 & 0.377588 \tabularnewline
13 & -0.012931 & -0.101 & 0.459943 \tabularnewline
14 & -0.018406 & -0.1438 & 0.443084 \tabularnewline
15 & -0.026135 & -0.2041 & 0.419471 \tabularnewline
16 & -0.027947 & -0.2183 & 0.413973 \tabularnewline
17 & -0.025025 & -0.1955 & 0.422844 \tabularnewline
18 & 0.059552 & 0.4651 & 0.321752 \tabularnewline
19 & -0.027752 & -0.2168 & 0.414563 \tabularnewline
20 & -0.067343 & -0.526 & 0.300409 \tabularnewline
21 & 0.027755 & 0.2168 & 0.414554 \tabularnewline
22 & -0.059403 & -0.464 & 0.322167 \tabularnewline
23 & 0.008683 & 0.0678 & 0.473076 \tabularnewline
24 & -0.131601 & -1.0278 & 0.154043 \tabularnewline
25 & 0.001296 & 0.0101 & 0.495979 \tabularnewline
26 & -0.047263 & -0.3691 & 0.356653 \tabularnewline
27 & -0.067716 & -0.5289 & 0.299405 \tabularnewline
28 & -0.025673 & -0.2005 & 0.420875 \tabularnewline
29 & -0.023767 & -0.1856 & 0.426676 \tabularnewline
30 & -0.072002 & -0.5624 & 0.287969 \tabularnewline
31 & 0.028101 & 0.2195 & 0.413507 \tabularnewline
32 & -0.112065 & -0.8753 & 0.192434 \tabularnewline
33 & 0.025074 & 0.1958 & 0.422695 \tabularnewline
34 & 0.03 & 0.2343 & 0.407767 \tabularnewline
35 & 0.072894 & 0.5693 & 0.285615 \tabularnewline
36 & -0.009346 & -0.073 & 0.471026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60949&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]0.727216[/C][C]5.6797[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.024618[/C][C]0.1923[/C][C]0.424085[/C][/ROW]
[ROW][C]3[/C][C]-0.109886[/C][C]-0.8582[/C][C]0.197062[/C][/ROW]
[ROW][C]4[/C][C]0.085868[/C][C]0.6706[/C][C]0.252488[/C][/ROW]
[ROW][C]5[/C][C]0.020443[/C][C]0.1597[/C][C]0.436838[/C][/ROW]
[ROW][C]6[/C][C]-0.200202[/C][C]-1.5636[/C][C]0.061539[/C][/ROW]
[ROW][C]7[/C][C]-0.118167[/C][C]-0.9229[/C][C]0.179844[/C][/ROW]
[ROW][C]8[/C][C]0.05622[/C][C]0.4391[/C][C]0.331074[/C][/ROW]
[ROW][C]9[/C][C]-0.105094[/C][C]-0.8208[/C][C]0.207475[/C][/ROW]
[ROW][C]10[/C][C]-0.00758[/C][C]-0.0592[/C][C]0.476493[/C][/ROW]
[ROW][C]11[/C][C]-0.020492[/C][C]-0.16[/C][C]0.436686[/C][/ROW]
[ROW][C]12[/C][C]0.040105[/C][C]0.3132[/C][C]0.377588[/C][/ROW]
[ROW][C]13[/C][C]-0.012931[/C][C]-0.101[/C][C]0.459943[/C][/ROW]
[ROW][C]14[/C][C]-0.018406[/C][C]-0.1438[/C][C]0.443084[/C][/ROW]
[ROW][C]15[/C][C]-0.026135[/C][C]-0.2041[/C][C]0.419471[/C][/ROW]
[ROW][C]16[/C][C]-0.027947[/C][C]-0.2183[/C][C]0.413973[/C][/ROW]
[ROW][C]17[/C][C]-0.025025[/C][C]-0.1955[/C][C]0.422844[/C][/ROW]
[ROW][C]18[/C][C]0.059552[/C][C]0.4651[/C][C]0.321752[/C][/ROW]
[ROW][C]19[/C][C]-0.027752[/C][C]-0.2168[/C][C]0.414563[/C][/ROW]
[ROW][C]20[/C][C]-0.067343[/C][C]-0.526[/C][C]0.300409[/C][/ROW]
[ROW][C]21[/C][C]0.027755[/C][C]0.2168[/C][C]0.414554[/C][/ROW]
[ROW][C]22[/C][C]-0.059403[/C][C]-0.464[/C][C]0.322167[/C][/ROW]
[ROW][C]23[/C][C]0.008683[/C][C]0.0678[/C][C]0.473076[/C][/ROW]
[ROW][C]24[/C][C]-0.131601[/C][C]-1.0278[/C][C]0.154043[/C][/ROW]
[ROW][C]25[/C][C]0.001296[/C][C]0.0101[/C][C]0.495979[/C][/ROW]
[ROW][C]26[/C][C]-0.047263[/C][C]-0.3691[/C][C]0.356653[/C][/ROW]
[ROW][C]27[/C][C]-0.067716[/C][C]-0.5289[/C][C]0.299405[/C][/ROW]
[ROW][C]28[/C][C]-0.025673[/C][C]-0.2005[/C][C]0.420875[/C][/ROW]
[ROW][C]29[/C][C]-0.023767[/C][C]-0.1856[/C][C]0.426676[/C][/ROW]
[ROW][C]30[/C][C]-0.072002[/C][C]-0.5624[/C][C]0.287969[/C][/ROW]
[ROW][C]31[/C][C]0.028101[/C][C]0.2195[/C][C]0.413507[/C][/ROW]
[ROW][C]32[/C][C]-0.112065[/C][C]-0.8753[/C][C]0.192434[/C][/ROW]
[ROW][C]33[/C][C]0.025074[/C][C]0.1958[/C][C]0.422695[/C][/ROW]
[ROW][C]34[/C][C]0.03[/C][C]0.2343[/C][C]0.407767[/C][/ROW]
[ROW][C]35[/C][C]0.072894[/C][C]0.5693[/C][C]0.285615[/C][/ROW]
[ROW][C]36[/C][C]-0.009346[/C][C]-0.073[/C][C]0.471026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60949&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60949&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
10.7272165.67970
20.0246180.19230.424085
3-0.109886-0.85820.197062
40.0858680.67060.252488
50.0204430.15970.436838
6-0.200202-1.56360.061539
7-0.118167-0.92290.179844
80.056220.43910.331074
9-0.105094-0.82080.207475
10-0.00758-0.05920.476493
11-0.020492-0.160.436686
120.0401050.31320.377588
13-0.012931-0.1010.459943
14-0.018406-0.14380.443084
15-0.026135-0.20410.419471
16-0.027947-0.21830.413973
17-0.025025-0.19550.422844
180.0595520.46510.321752
19-0.027752-0.21680.414563
20-0.067343-0.5260.300409
210.0277550.21680.414554
22-0.059403-0.4640.322167
230.0086830.06780.473076
24-0.131601-1.02780.154043
250.0012960.01010.495979
26-0.047263-0.36910.356653
27-0.067716-0.52890.299405
28-0.025673-0.20050.420875
29-0.023767-0.18560.426676
30-0.072002-0.56240.287969
310.0281010.21950.413507
32-0.112065-0.87530.192434
330.0250740.19580.422695
340.030.23430.407767
350.0728940.56930.285615
36-0.009346-0.0730.471026


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 = 0 ; 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')