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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 12:16:14 -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/t1259349420zzhfbijclpmtzhg.htm/, Retrieved Mon, 29 Apr 2024 05:59:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=61154, Retrieved Mon, 29 Apr 2024 05:59:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact183

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]
- R PD          [(Partial) Autocorrelation Function] [ACF d=2,D=1] [2009-11-27 19:16:14] [18c0746232b29e9668aa6bedcb8dd698] [Current]
-   PD            [(Partial) Autocorrelation Function] [d=2,D=2] [2009-12-19 16:21:26] [fa71ec4c741ffec745cb91dcbd756720]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12,6
15,7
13,2
20,3
12,8
8
0,9
3,6
14,1
21,7
24,5
18,9
13,9
11
5,8
15,5
22,4
31,7
30,3
31,4
20,2
19,7
10,8
13,2
15,1
15,6
15,5
12,7
10,9
10
9,1
10,3
16,9
22
27,6
28,9
31
32,9
38,1
28,8
29
21,8
28,8
25,6
28,2
20,2
17,9
16,3
13,2
8,1
4,5
-0,1
0
2,3
2,8
2,9
0,1
3,5
8,6
13,8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61154&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]0 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=61154&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61154&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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.465276-3.15570.001412
20.4518813.06480.001819
3-0.563486-3.82170.000198
40.0978910.66390.255026
5-0.223877-1.51840.067877
60.1549281.05080.149427
7-0.029764-0.20190.420454
80.137830.93480.177385
90.1024120.69460.245403
10-0.040081-0.27180.393481
110.0598180.40570.343421
12-0.301574-2.04540.023281
130.0822270.55770.289881
14-0.148619-1.0080.159367
150.1981661.3440.092766
16-0.074924-0.50820.306885
170.1684311.14240.12961
18-0.191008-1.29550.100809
190.2528691.7150.046534
20-0.260918-1.76960.041708
210.276661.87640.033477
22-0.324454-2.20060.016414
230.2123881.44050.078251
24-0.196264-1.33110.094854
250.1873391.27060.10513
26-0.071648-0.48590.314659
270.0677110.45920.324114
28-0.029784-0.2020.420402
29-0.005887-0.03990.484162
300.0297290.20160.420546
31-0.111568-0.75670.226547
320.0834410.56590.287099
33-0.166243-1.12750.132686
340.1716281.1640.125205
35-0.063997-0.4340.333141
360.1074940.72910.234831

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.465276 & -3.1557 & 0.001412 \tabularnewline
2 & 0.451881 & 3.0648 & 0.001819 \tabularnewline
3 & -0.563486 & -3.8217 & 0.000198 \tabularnewline
4 & 0.097891 & 0.6639 & 0.255026 \tabularnewline
5 & -0.223877 & -1.5184 & 0.067877 \tabularnewline
6 & 0.154928 & 1.0508 & 0.149427 \tabularnewline
7 & -0.029764 & -0.2019 & 0.420454 \tabularnewline
8 & 0.13783 & 0.9348 & 0.177385 \tabularnewline
9 & 0.102412 & 0.6946 & 0.245403 \tabularnewline
10 & -0.040081 & -0.2718 & 0.393481 \tabularnewline
11 & 0.059818 & 0.4057 & 0.343421 \tabularnewline
12 & -0.301574 & -2.0454 & 0.023281 \tabularnewline
13 & 0.082227 & 0.5577 & 0.289881 \tabularnewline
14 & -0.148619 & -1.008 & 0.159367 \tabularnewline
15 & 0.198166 & 1.344 & 0.092766 \tabularnewline
16 & -0.074924 & -0.5082 & 0.306885 \tabularnewline
17 & 0.168431 & 1.1424 & 0.12961 \tabularnewline
18 & -0.191008 & -1.2955 & 0.100809 \tabularnewline
19 & 0.252869 & 1.715 & 0.046534 \tabularnewline
20 & -0.260918 & -1.7696 & 0.041708 \tabularnewline
21 & 0.27666 & 1.8764 & 0.033477 \tabularnewline
22 & -0.324454 & -2.2006 & 0.016414 \tabularnewline
23 & 0.212388 & 1.4405 & 0.078251 \tabularnewline
24 & -0.196264 & -1.3311 & 0.094854 \tabularnewline
25 & 0.187339 & 1.2706 & 0.10513 \tabularnewline
26 & -0.071648 & -0.4859 & 0.314659 \tabularnewline
27 & 0.067711 & 0.4592 & 0.324114 \tabularnewline
28 & -0.029784 & -0.202 & 0.420402 \tabularnewline
29 & -0.005887 & -0.0399 & 0.484162 \tabularnewline
30 & 0.029729 & 0.2016 & 0.420546 \tabularnewline
31 & -0.111568 & -0.7567 & 0.226547 \tabularnewline
32 & 0.083441 & 0.5659 & 0.287099 \tabularnewline
33 & -0.166243 & -1.1275 & 0.132686 \tabularnewline
34 & 0.171628 & 1.164 & 0.125205 \tabularnewline
35 & -0.063997 & -0.434 & 0.333141 \tabularnewline
36 & 0.107494 & 0.7291 & 0.234831 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61154&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]-0.465276[/C][C]-3.1557[/C][C]0.001412[/C][/ROW]
[ROW][C]2[/C][C]0.451881[/C][C]3.0648[/C][C]0.001819[/C][/ROW]
[ROW][C]3[/C][C]-0.563486[/C][C]-3.8217[/C][C]0.000198[/C][/ROW]
[ROW][C]4[/C][C]0.097891[/C][C]0.6639[/C][C]0.255026[/C][/ROW]
[ROW][C]5[/C][C]-0.223877[/C][C]-1.5184[/C][C]0.067877[/C][/ROW]
[ROW][C]6[/C][C]0.154928[/C][C]1.0508[/C][C]0.149427[/C][/ROW]
[ROW][C]7[/C][C]-0.029764[/C][C]-0.2019[/C][C]0.420454[/C][/ROW]
[ROW][C]8[/C][C]0.13783[/C][C]0.9348[/C][C]0.177385[/C][/ROW]
[ROW][C]9[/C][C]0.102412[/C][C]0.6946[/C][C]0.245403[/C][/ROW]
[ROW][C]10[/C][C]-0.040081[/C][C]-0.2718[/C][C]0.393481[/C][/ROW]
[ROW][C]11[/C][C]0.059818[/C][C]0.4057[/C][C]0.343421[/C][/ROW]
[ROW][C]12[/C][C]-0.301574[/C][C]-2.0454[/C][C]0.023281[/C][/ROW]
[ROW][C]13[/C][C]0.082227[/C][C]0.5577[/C][C]0.289881[/C][/ROW]
[ROW][C]14[/C][C]-0.148619[/C][C]-1.008[/C][C]0.159367[/C][/ROW]
[ROW][C]15[/C][C]0.198166[/C][C]1.344[/C][C]0.092766[/C][/ROW]
[ROW][C]16[/C][C]-0.074924[/C][C]-0.5082[/C][C]0.306885[/C][/ROW]
[ROW][C]17[/C][C]0.168431[/C][C]1.1424[/C][C]0.12961[/C][/ROW]
[ROW][C]18[/C][C]-0.191008[/C][C]-1.2955[/C][C]0.100809[/C][/ROW]
[ROW][C]19[/C][C]0.252869[/C][C]1.715[/C][C]0.046534[/C][/ROW]
[ROW][C]20[/C][C]-0.260918[/C][C]-1.7696[/C][C]0.041708[/C][/ROW]
[ROW][C]21[/C][C]0.27666[/C][C]1.8764[/C][C]0.033477[/C][/ROW]
[ROW][C]22[/C][C]-0.324454[/C][C]-2.2006[/C][C]0.016414[/C][/ROW]
[ROW][C]23[/C][C]0.212388[/C][C]1.4405[/C][C]0.078251[/C][/ROW]
[ROW][C]24[/C][C]-0.196264[/C][C]-1.3311[/C][C]0.094854[/C][/ROW]
[ROW][C]25[/C][C]0.187339[/C][C]1.2706[/C][C]0.10513[/C][/ROW]
[ROW][C]26[/C][C]-0.071648[/C][C]-0.4859[/C][C]0.314659[/C][/ROW]
[ROW][C]27[/C][C]0.067711[/C][C]0.4592[/C][C]0.324114[/C][/ROW]
[ROW][C]28[/C][C]-0.029784[/C][C]-0.202[/C][C]0.420402[/C][/ROW]
[ROW][C]29[/C][C]-0.005887[/C][C]-0.0399[/C][C]0.484162[/C][/ROW]
[ROW][C]30[/C][C]0.029729[/C][C]0.2016[/C][C]0.420546[/C][/ROW]
[ROW][C]31[/C][C]-0.111568[/C][C]-0.7567[/C][C]0.226547[/C][/ROW]
[ROW][C]32[/C][C]0.083441[/C][C]0.5659[/C][C]0.287099[/C][/ROW]
[ROW][C]33[/C][C]-0.166243[/C][C]-1.1275[/C][C]0.132686[/C][/ROW]
[ROW][C]34[/C][C]0.171628[/C][C]1.164[/C][C]0.125205[/C][/ROW]
[ROW][C]35[/C][C]-0.063997[/C][C]-0.434[/C][C]0.333141[/C][/ROW]
[ROW][C]36[/C][C]0.107494[/C][C]0.7291[/C][C]0.234831[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61154&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61154&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
1-0.465276-3.15570.001412
20.4518813.06480.001819
3-0.563486-3.82170.000198
40.0978910.66390.255026
5-0.223877-1.51840.067877
60.1549281.05080.149427
7-0.029764-0.20190.420454
80.137830.93480.177385
90.1024120.69460.245403
10-0.040081-0.27180.393481
110.0598180.40570.343421
12-0.301574-2.04540.023281
130.0822270.55770.289881
14-0.148619-1.0080.159367
150.1981661.3440.092766
16-0.074924-0.50820.306885
170.1684311.14240.12961
18-0.191008-1.29550.100809
190.2528691.7150.046534
20-0.260918-1.76960.041708
210.276661.87640.033477
22-0.324454-2.20060.016414
230.2123881.44050.078251
24-0.196264-1.33110.094854
250.1873391.27060.10513
26-0.071648-0.48590.314659
270.0677110.45920.324114
28-0.029784-0.2020.420402
29-0.005887-0.03990.484162
300.0297290.20160.420546
31-0.111568-0.75670.226547
320.0834410.56590.287099
33-0.166243-1.12750.132686
340.1716281.1640.125205
35-0.063997-0.4340.333141
360.1074940.72910.234831






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.465276-3.15570.001412
20.3004382.03770.023679
3-0.388073-2.6320.005757
4-0.460924-3.12610.001533
5-0.080034-0.54280.294939
6-0.079448-0.53880.296297
7-0.270934-1.83760.036295
8-0.123629-0.83850.203045
90.2841821.92740.030058
10-0.019674-0.13340.447216
11-0.035694-0.24210.404894
12-0.104275-0.70720.241497
13-0.048101-0.32620.372863
140.012170.08250.467287
15-0.108098-0.73320.233591
16-0.243103-1.64880.053001
17-0.017759-0.12040.452326
18-0.216166-1.46610.074711
190.0396760.26910.394531
20-0.02998-0.20330.419885
210.1451580.98450.165008
22-0.01543-0.10470.458553
23-0.007353-0.04990.48022
240.0641710.43520.332716
25-0.005908-0.04010.484106
260.0179630.12180.451781
27-0.077052-0.52260.301884
28-0.066284-0.44960.32757
290.0060590.04110.4837
30-0.070147-0.47580.31825
31-0.079391-0.53850.296429
320.0149750.10160.459773
33-0.029382-0.19930.42146
34-0.099749-0.67650.251046
350.1068810.72490.236092
36-0.028338-0.19220.424218

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.465276 & -3.1557 & 0.001412 \tabularnewline
2 & 0.300438 & 2.0377 & 0.023679 \tabularnewline
3 & -0.388073 & -2.632 & 0.005757 \tabularnewline
4 & -0.460924 & -3.1261 & 0.001533 \tabularnewline
5 & -0.080034 & -0.5428 & 0.294939 \tabularnewline
6 & -0.079448 & -0.5388 & 0.296297 \tabularnewline
7 & -0.270934 & -1.8376 & 0.036295 \tabularnewline
8 & -0.123629 & -0.8385 & 0.203045 \tabularnewline
9 & 0.284182 & 1.9274 & 0.030058 \tabularnewline
10 & -0.019674 & -0.1334 & 0.447216 \tabularnewline
11 & -0.035694 & -0.2421 & 0.404894 \tabularnewline
12 & -0.104275 & -0.7072 & 0.241497 \tabularnewline
13 & -0.048101 & -0.3262 & 0.372863 \tabularnewline
14 & 0.01217 & 0.0825 & 0.467287 \tabularnewline
15 & -0.108098 & -0.7332 & 0.233591 \tabularnewline
16 & -0.243103 & -1.6488 & 0.053001 \tabularnewline
17 & -0.017759 & -0.1204 & 0.452326 \tabularnewline
18 & -0.216166 & -1.4661 & 0.074711 \tabularnewline
19 & 0.039676 & 0.2691 & 0.394531 \tabularnewline
20 & -0.02998 & -0.2033 & 0.419885 \tabularnewline
21 & 0.145158 & 0.9845 & 0.165008 \tabularnewline
22 & -0.01543 & -0.1047 & 0.458553 \tabularnewline
23 & -0.007353 & -0.0499 & 0.48022 \tabularnewline
24 & 0.064171 & 0.4352 & 0.332716 \tabularnewline
25 & -0.005908 & -0.0401 & 0.484106 \tabularnewline
26 & 0.017963 & 0.1218 & 0.451781 \tabularnewline
27 & -0.077052 & -0.5226 & 0.301884 \tabularnewline
28 & -0.066284 & -0.4496 & 0.32757 \tabularnewline
29 & 0.006059 & 0.0411 & 0.4837 \tabularnewline
30 & -0.070147 & -0.4758 & 0.31825 \tabularnewline
31 & -0.079391 & -0.5385 & 0.296429 \tabularnewline
32 & 0.014975 & 0.1016 & 0.459773 \tabularnewline
33 & -0.029382 & -0.1993 & 0.42146 \tabularnewline
34 & -0.099749 & -0.6765 & 0.251046 \tabularnewline
35 & 0.106881 & 0.7249 & 0.236092 \tabularnewline
36 & -0.028338 & -0.1922 & 0.424218 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61154&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]-0.465276[/C][C]-3.1557[/C][C]0.001412[/C][/ROW]
[ROW][C]2[/C][C]0.300438[/C][C]2.0377[/C][C]0.023679[/C][/ROW]
[ROW][C]3[/C][C]-0.388073[/C][C]-2.632[/C][C]0.005757[/C][/ROW]
[ROW][C]4[/C][C]-0.460924[/C][C]-3.1261[/C][C]0.001533[/C][/ROW]
[ROW][C]5[/C][C]-0.080034[/C][C]-0.5428[/C][C]0.294939[/C][/ROW]
[ROW][C]6[/C][C]-0.079448[/C][C]-0.5388[/C][C]0.296297[/C][/ROW]
[ROW][C]7[/C][C]-0.270934[/C][C]-1.8376[/C][C]0.036295[/C][/ROW]
[ROW][C]8[/C][C]-0.123629[/C][C]-0.8385[/C][C]0.203045[/C][/ROW]
[ROW][C]9[/C][C]0.284182[/C][C]1.9274[/C][C]0.030058[/C][/ROW]
[ROW][C]10[/C][C]-0.019674[/C][C]-0.1334[/C][C]0.447216[/C][/ROW]
[ROW][C]11[/C][C]-0.035694[/C][C]-0.2421[/C][C]0.404894[/C][/ROW]
[ROW][C]12[/C][C]-0.104275[/C][C]-0.7072[/C][C]0.241497[/C][/ROW]
[ROW][C]13[/C][C]-0.048101[/C][C]-0.3262[/C][C]0.372863[/C][/ROW]
[ROW][C]14[/C][C]0.01217[/C][C]0.0825[/C][C]0.467287[/C][/ROW]
[ROW][C]15[/C][C]-0.108098[/C][C]-0.7332[/C][C]0.233591[/C][/ROW]
[ROW][C]16[/C][C]-0.243103[/C][C]-1.6488[/C][C]0.053001[/C][/ROW]
[ROW][C]17[/C][C]-0.017759[/C][C]-0.1204[/C][C]0.452326[/C][/ROW]
[ROW][C]18[/C][C]-0.216166[/C][C]-1.4661[/C][C]0.074711[/C][/ROW]
[ROW][C]19[/C][C]0.039676[/C][C]0.2691[/C][C]0.394531[/C][/ROW]
[ROW][C]20[/C][C]-0.02998[/C][C]-0.2033[/C][C]0.419885[/C][/ROW]
[ROW][C]21[/C][C]0.145158[/C][C]0.9845[/C][C]0.165008[/C][/ROW]
[ROW][C]22[/C][C]-0.01543[/C][C]-0.1047[/C][C]0.458553[/C][/ROW]
[ROW][C]23[/C][C]-0.007353[/C][C]-0.0499[/C][C]0.48022[/C][/ROW]
[ROW][C]24[/C][C]0.064171[/C][C]0.4352[/C][C]0.332716[/C][/ROW]
[ROW][C]25[/C][C]-0.005908[/C][C]-0.0401[/C][C]0.484106[/C][/ROW]
[ROW][C]26[/C][C]0.017963[/C][C]0.1218[/C][C]0.451781[/C][/ROW]
[ROW][C]27[/C][C]-0.077052[/C][C]-0.5226[/C][C]0.301884[/C][/ROW]
[ROW][C]28[/C][C]-0.066284[/C][C]-0.4496[/C][C]0.32757[/C][/ROW]
[ROW][C]29[/C][C]0.006059[/C][C]0.0411[/C][C]0.4837[/C][/ROW]
[ROW][C]30[/C][C]-0.070147[/C][C]-0.4758[/C][C]0.31825[/C][/ROW]
[ROW][C]31[/C][C]-0.079391[/C][C]-0.5385[/C][C]0.296429[/C][/ROW]
[ROW][C]32[/C][C]0.014975[/C][C]0.1016[/C][C]0.459773[/C][/ROW]
[ROW][C]33[/C][C]-0.029382[/C][C]-0.1993[/C][C]0.42146[/C][/ROW]
[ROW][C]34[/C][C]-0.099749[/C][C]-0.6765[/C][C]0.251046[/C][/ROW]
[ROW][C]35[/C][C]0.106881[/C][C]0.7249[/C][C]0.236092[/C][/ROW]
[ROW][C]36[/C][C]-0.028338[/C][C]-0.1922[/C][C]0.424218[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61154&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61154&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
1-0.465276-3.15570.001412
20.3004382.03770.023679
3-0.388073-2.6320.005757
4-0.460924-3.12610.001533
5-0.080034-0.54280.294939
6-0.079448-0.53880.296297
7-0.270934-1.83760.036295
8-0.123629-0.83850.203045
90.2841821.92740.030058
10-0.019674-0.13340.447216
11-0.035694-0.24210.404894
12-0.104275-0.70720.241497
13-0.048101-0.32620.372863
140.012170.08250.467287
15-0.108098-0.73320.233591
16-0.243103-1.64880.053001
17-0.017759-0.12040.452326
18-0.216166-1.46610.074711
190.0396760.26910.394531
20-0.02998-0.20330.419885
210.1451580.98450.165008
22-0.01543-0.10470.458553
23-0.007353-0.04990.48022
240.0641710.43520.332716
25-0.005908-0.04010.484106
260.0179630.12180.451781
27-0.077052-0.52260.301884
28-0.066284-0.44960.32757
290.0060590.04110.4837
30-0.070147-0.47580.31825
31-0.079391-0.53850.296429
320.0149750.10160.459773
33-0.029382-0.19930.42146
34-0.099749-0.67650.251046
350.1068810.72490.236092
36-0.028338-0.19220.424218


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 2 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 2 ; 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')