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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 computationMon, 23 Nov 2009 14:10:32 -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/23/t1259010703o0wzmneh9zkmhtv.htm/, Retrieved Mon, 29 Apr 2024 01:01:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58908, Retrieved Mon, 29 Apr 2024 01:01:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact240

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       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMPD        [(Partial) Autocorrelation Function] [Autocorrelatie mo...] [2009-11-23 21:04:43] [0df1a6455bedfaf424729b1e006090d0]
-   PD            [(Partial) Autocorrelation Function] [Autocorrelatie mo...] [2009-11-23 21:10:32] [e339dd08bcbfc073ac7494f09a949034] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
25.6
23.7
22
21.3
20.7
20.4
20.3
20.4
19.8
19.5
23.1
23.5
23.5
22.9
21.9
21.5
20.5
20.2
19.4
19.2
18.8
18.8
22.6
23.3
23
21.4
19.9
18.8
18.6
18.4
18.6
19.9
19.2
18.4
21.1
20.5
19.1
18.1
17
17.1
17.4
16.8
15.3
14.3
13.4
15.3
22.1
23.7
22.2
19.5
16.6
17.3
19.8
21.2
21.5
20.6
19.1
19.6
23.5
24

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=58908&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=58908&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58908&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
10.3494282.6840.004713
2-0.198655-1.52590.066189
3-0.372161-2.85860.002936
4-0.36032-2.76770.003765
50.0117170.090.464295
60.2628532.0190.024019
70.1564371.20160.117157
8-0.153333-1.17780.121808
9-0.246534-1.89370.031588
10-0.214874-1.65050.05208
110.1012230.77750.219982
120.5378564.13145.8e-05
130.1351071.03780.151804
14-0.073208-0.56230.288013
15-0.071926-0.55250.291355
16-0.114398-0.87870.191562
170.0040860.03140.487536
180.0321880.24720.40279
19-0.076549-0.5880.279393
20-0.175507-1.34810.091391
21-0.131268-1.00830.158716
22-0.041544-0.31910.375387
230.1670751.28330.102198
240.3952913.03630.001781
250.0334620.2570.399026
26-0.131425-1.00950.15843
27-0.109229-0.8390.202427
28-0.105887-0.81330.209649
290.0186510.14330.443286
300.0512210.39340.347707
31-0.006811-0.05230.479226
32-0.114057-0.87610.192268
33-0.123273-0.94690.173782
34-0.011968-0.09190.463534
350.1372791.05450.147986
360.2825082.170.017023

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.349428 & 2.684 & 0.004713 \tabularnewline
2 & -0.198655 & -1.5259 & 0.066189 \tabularnewline
3 & -0.372161 & -2.8586 & 0.002936 \tabularnewline
4 & -0.36032 & -2.7677 & 0.003765 \tabularnewline
5 & 0.011717 & 0.09 & 0.464295 \tabularnewline
6 & 0.262853 & 2.019 & 0.024019 \tabularnewline
7 & 0.156437 & 1.2016 & 0.117157 \tabularnewline
8 & -0.153333 & -1.1778 & 0.121808 \tabularnewline
9 & -0.246534 & -1.8937 & 0.031588 \tabularnewline
10 & -0.214874 & -1.6505 & 0.05208 \tabularnewline
11 & 0.101223 & 0.7775 & 0.219982 \tabularnewline
12 & 0.537856 & 4.1314 & 5.8e-05 \tabularnewline
13 & 0.135107 & 1.0378 & 0.151804 \tabularnewline
14 & -0.073208 & -0.5623 & 0.288013 \tabularnewline
15 & -0.071926 & -0.5525 & 0.291355 \tabularnewline
16 & -0.114398 & -0.8787 & 0.191562 \tabularnewline
17 & 0.004086 & 0.0314 & 0.487536 \tabularnewline
18 & 0.032188 & 0.2472 & 0.40279 \tabularnewline
19 & -0.076549 & -0.588 & 0.279393 \tabularnewline
20 & -0.175507 & -1.3481 & 0.091391 \tabularnewline
21 & -0.131268 & -1.0083 & 0.158716 \tabularnewline
22 & -0.041544 & -0.3191 & 0.375387 \tabularnewline
23 & 0.167075 & 1.2833 & 0.102198 \tabularnewline
24 & 0.395291 & 3.0363 & 0.001781 \tabularnewline
25 & 0.033462 & 0.257 & 0.399026 \tabularnewline
26 & -0.131425 & -1.0095 & 0.15843 \tabularnewline
27 & -0.109229 & -0.839 & 0.202427 \tabularnewline
28 & -0.105887 & -0.8133 & 0.209649 \tabularnewline
29 & 0.018651 & 0.1433 & 0.443286 \tabularnewline
30 & 0.051221 & 0.3934 & 0.347707 \tabularnewline
31 & -0.006811 & -0.0523 & 0.479226 \tabularnewline
32 & -0.114057 & -0.8761 & 0.192268 \tabularnewline
33 & -0.123273 & -0.9469 & 0.173782 \tabularnewline
34 & -0.011968 & -0.0919 & 0.463534 \tabularnewline
35 & 0.137279 & 1.0545 & 0.147986 \tabularnewline
36 & 0.282508 & 2.17 & 0.017023 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58908&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]0.349428[/C][C]2.684[/C][C]0.004713[/C][/ROW]
[ROW][C]2[/C][C]-0.198655[/C][C]-1.5259[/C][C]0.066189[/C][/ROW]
[ROW][C]3[/C][C]-0.372161[/C][C]-2.8586[/C][C]0.002936[/C][/ROW]
[ROW][C]4[/C][C]-0.36032[/C][C]-2.7677[/C][C]0.003765[/C][/ROW]
[ROW][C]5[/C][C]0.011717[/C][C]0.09[/C][C]0.464295[/C][/ROW]
[ROW][C]6[/C][C]0.262853[/C][C]2.019[/C][C]0.024019[/C][/ROW]
[ROW][C]7[/C][C]0.156437[/C][C]1.2016[/C][C]0.117157[/C][/ROW]
[ROW][C]8[/C][C]-0.153333[/C][C]-1.1778[/C][C]0.121808[/C][/ROW]
[ROW][C]9[/C][C]-0.246534[/C][C]-1.8937[/C][C]0.031588[/C][/ROW]
[ROW][C]10[/C][C]-0.214874[/C][C]-1.6505[/C][C]0.05208[/C][/ROW]
[ROW][C]11[/C][C]0.101223[/C][C]0.7775[/C][C]0.219982[/C][/ROW]
[ROW][C]12[/C][C]0.537856[/C][C]4.1314[/C][C]5.8e-05[/C][/ROW]
[ROW][C]13[/C][C]0.135107[/C][C]1.0378[/C][C]0.151804[/C][/ROW]
[ROW][C]14[/C][C]-0.073208[/C][C]-0.5623[/C][C]0.288013[/C][/ROW]
[ROW][C]15[/C][C]-0.071926[/C][C]-0.5525[/C][C]0.291355[/C][/ROW]
[ROW][C]16[/C][C]-0.114398[/C][C]-0.8787[/C][C]0.191562[/C][/ROW]
[ROW][C]17[/C][C]0.004086[/C][C]0.0314[/C][C]0.487536[/C][/ROW]
[ROW][C]18[/C][C]0.032188[/C][C]0.2472[/C][C]0.40279[/C][/ROW]
[ROW][C]19[/C][C]-0.076549[/C][C]-0.588[/C][C]0.279393[/C][/ROW]
[ROW][C]20[/C][C]-0.175507[/C][C]-1.3481[/C][C]0.091391[/C][/ROW]
[ROW][C]21[/C][C]-0.131268[/C][C]-1.0083[/C][C]0.158716[/C][/ROW]
[ROW][C]22[/C][C]-0.041544[/C][C]-0.3191[/C][C]0.375387[/C][/ROW]
[ROW][C]23[/C][C]0.167075[/C][C]1.2833[/C][C]0.102198[/C][/ROW]
[ROW][C]24[/C][C]0.395291[/C][C]3.0363[/C][C]0.001781[/C][/ROW]
[ROW][C]25[/C][C]0.033462[/C][C]0.257[/C][C]0.399026[/C][/ROW]
[ROW][C]26[/C][C]-0.131425[/C][C]-1.0095[/C][C]0.15843[/C][/ROW]
[ROW][C]27[/C][C]-0.109229[/C][C]-0.839[/C][C]0.202427[/C][/ROW]
[ROW][C]28[/C][C]-0.105887[/C][C]-0.8133[/C][C]0.209649[/C][/ROW]
[ROW][C]29[/C][C]0.018651[/C][C]0.1433[/C][C]0.443286[/C][/ROW]
[ROW][C]30[/C][C]0.051221[/C][C]0.3934[/C][C]0.347707[/C][/ROW]
[ROW][C]31[/C][C]-0.006811[/C][C]-0.0523[/C][C]0.479226[/C][/ROW]
[ROW][C]32[/C][C]-0.114057[/C][C]-0.8761[/C][C]0.192268[/C][/ROW]
[ROW][C]33[/C][C]-0.123273[/C][C]-0.9469[/C][C]0.173782[/C][/ROW]
[ROW][C]34[/C][C]-0.011968[/C][C]-0.0919[/C][C]0.463534[/C][/ROW]
[ROW][C]35[/C][C]0.137279[/C][C]1.0545[/C][C]0.147986[/C][/ROW]
[ROW][C]36[/C][C]0.282508[/C][C]2.17[/C][C]0.017023[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58908&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58908&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
10.3494282.6840.004713
2-0.198655-1.52590.066189
3-0.372161-2.85860.002936
4-0.36032-2.76770.003765
50.0117170.090.464295
60.2628532.0190.024019
70.1564371.20160.117157
8-0.153333-1.17780.121808
9-0.246534-1.89370.031588
10-0.214874-1.65050.05208
110.1012230.77750.219982
120.5378564.13145.8e-05
130.1351071.03780.151804
14-0.073208-0.56230.288013
15-0.071926-0.55250.291355
16-0.114398-0.87870.191562
170.0040860.03140.487536
180.0321880.24720.40279
19-0.076549-0.5880.279393
20-0.175507-1.34810.091391
21-0.131268-1.00830.158716
22-0.041544-0.31910.375387
230.1670751.28330.102198
240.3952913.03630.001781
250.0334620.2570.399026
26-0.131425-1.00950.15843
27-0.109229-0.8390.202427
28-0.105887-0.81330.209649
290.0186510.14330.443286
300.0512210.39340.347707
31-0.006811-0.05230.479226
32-0.114057-0.87610.192268
33-0.123273-0.94690.173782
34-0.011968-0.09190.463534
350.1372791.05450.147986
360.2825082.170.017023






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.3494282.6840.004713
2-0.365367-2.80640.003388
3-0.196808-1.51170.067973
4-0.266236-2.0450.022662
50.1269790.97530.166685
60.0232210.17840.429524
7-0.080958-0.62180.268218
8-0.251994-1.93560.028856
9-0.029184-0.22420.411701
10-0.177991-1.36720.088379
110.1432611.10040.137812
120.3557952.73290.004136
13-0.397976-3.05690.001679
140.3302642.53680.006926
150.1528581.17410.122531
160.116010.89110.18825
17-0.129447-0.99430.162068
18-0.092299-0.7090.240571
19-0.056404-0.43320.333208
200.0312060.23970.405698
21-0.177833-1.3660.088568
220.0097040.07450.470418
230.0437560.33610.368996
240.1341861.03070.153444
250.0068250.05240.479183
26-0.098691-0.75810.225717
27-0.064342-0.49420.311494
280.0234950.18050.4287
29-0.057061-0.43830.331388
30-0.082338-0.63250.264767
310.0057420.04410.482486
32-0.082033-0.63010.265528
330.1186370.91130.182932
340.1214410.93280.177362
35-0.034591-0.26570.395698
36-0.014159-0.10880.456881

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.349428 & 2.684 & 0.004713 \tabularnewline
2 & -0.365367 & -2.8064 & 0.003388 \tabularnewline
3 & -0.196808 & -1.5117 & 0.067973 \tabularnewline
4 & -0.266236 & -2.045 & 0.022662 \tabularnewline
5 & 0.126979 & 0.9753 & 0.166685 \tabularnewline
6 & 0.023221 & 0.1784 & 0.429524 \tabularnewline
7 & -0.080958 & -0.6218 & 0.268218 \tabularnewline
8 & -0.251994 & -1.9356 & 0.028856 \tabularnewline
9 & -0.029184 & -0.2242 & 0.411701 \tabularnewline
10 & -0.177991 & -1.3672 & 0.088379 \tabularnewline
11 & 0.143261 & 1.1004 & 0.137812 \tabularnewline
12 & 0.355795 & 2.7329 & 0.004136 \tabularnewline
13 & -0.397976 & -3.0569 & 0.001679 \tabularnewline
14 & 0.330264 & 2.5368 & 0.006926 \tabularnewline
15 & 0.152858 & 1.1741 & 0.122531 \tabularnewline
16 & 0.11601 & 0.8911 & 0.18825 \tabularnewline
17 & -0.129447 & -0.9943 & 0.162068 \tabularnewline
18 & -0.092299 & -0.709 & 0.240571 \tabularnewline
19 & -0.056404 & -0.4332 & 0.333208 \tabularnewline
20 & 0.031206 & 0.2397 & 0.405698 \tabularnewline
21 & -0.177833 & -1.366 & 0.088568 \tabularnewline
22 & 0.009704 & 0.0745 & 0.470418 \tabularnewline
23 & 0.043756 & 0.3361 & 0.368996 \tabularnewline
24 & 0.134186 & 1.0307 & 0.153444 \tabularnewline
25 & 0.006825 & 0.0524 & 0.479183 \tabularnewline
26 & -0.098691 & -0.7581 & 0.225717 \tabularnewline
27 & -0.064342 & -0.4942 & 0.311494 \tabularnewline
28 & 0.023495 & 0.1805 & 0.4287 \tabularnewline
29 & -0.057061 & -0.4383 & 0.331388 \tabularnewline
30 & -0.082338 & -0.6325 & 0.264767 \tabularnewline
31 & 0.005742 & 0.0441 & 0.482486 \tabularnewline
32 & -0.082033 & -0.6301 & 0.265528 \tabularnewline
33 & 0.118637 & 0.9113 & 0.182932 \tabularnewline
34 & 0.121441 & 0.9328 & 0.177362 \tabularnewline
35 & -0.034591 & -0.2657 & 0.395698 \tabularnewline
36 & -0.014159 & -0.1088 & 0.456881 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58908&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]0.349428[/C][C]2.684[/C][C]0.004713[/C][/ROW]
[ROW][C]2[/C][C]-0.365367[/C][C]-2.8064[/C][C]0.003388[/C][/ROW]
[ROW][C]3[/C][C]-0.196808[/C][C]-1.5117[/C][C]0.067973[/C][/ROW]
[ROW][C]4[/C][C]-0.266236[/C][C]-2.045[/C][C]0.022662[/C][/ROW]
[ROW][C]5[/C][C]0.126979[/C][C]0.9753[/C][C]0.166685[/C][/ROW]
[ROW][C]6[/C][C]0.023221[/C][C]0.1784[/C][C]0.429524[/C][/ROW]
[ROW][C]7[/C][C]-0.080958[/C][C]-0.6218[/C][C]0.268218[/C][/ROW]
[ROW][C]8[/C][C]-0.251994[/C][C]-1.9356[/C][C]0.028856[/C][/ROW]
[ROW][C]9[/C][C]-0.029184[/C][C]-0.2242[/C][C]0.411701[/C][/ROW]
[ROW][C]10[/C][C]-0.177991[/C][C]-1.3672[/C][C]0.088379[/C][/ROW]
[ROW][C]11[/C][C]0.143261[/C][C]1.1004[/C][C]0.137812[/C][/ROW]
[ROW][C]12[/C][C]0.355795[/C][C]2.7329[/C][C]0.004136[/C][/ROW]
[ROW][C]13[/C][C]-0.397976[/C][C]-3.0569[/C][C]0.001679[/C][/ROW]
[ROW][C]14[/C][C]0.330264[/C][C]2.5368[/C][C]0.006926[/C][/ROW]
[ROW][C]15[/C][C]0.152858[/C][C]1.1741[/C][C]0.122531[/C][/ROW]
[ROW][C]16[/C][C]0.11601[/C][C]0.8911[/C][C]0.18825[/C][/ROW]
[ROW][C]17[/C][C]-0.129447[/C][C]-0.9943[/C][C]0.162068[/C][/ROW]
[ROW][C]18[/C][C]-0.092299[/C][C]-0.709[/C][C]0.240571[/C][/ROW]
[ROW][C]19[/C][C]-0.056404[/C][C]-0.4332[/C][C]0.333208[/C][/ROW]
[ROW][C]20[/C][C]0.031206[/C][C]0.2397[/C][C]0.405698[/C][/ROW]
[ROW][C]21[/C][C]-0.177833[/C][C]-1.366[/C][C]0.088568[/C][/ROW]
[ROW][C]22[/C][C]0.009704[/C][C]0.0745[/C][C]0.470418[/C][/ROW]
[ROW][C]23[/C][C]0.043756[/C][C]0.3361[/C][C]0.368996[/C][/ROW]
[ROW][C]24[/C][C]0.134186[/C][C]1.0307[/C][C]0.153444[/C][/ROW]
[ROW][C]25[/C][C]0.006825[/C][C]0.0524[/C][C]0.479183[/C][/ROW]
[ROW][C]26[/C][C]-0.098691[/C][C]-0.7581[/C][C]0.225717[/C][/ROW]
[ROW][C]27[/C][C]-0.064342[/C][C]-0.4942[/C][C]0.311494[/C][/ROW]
[ROW][C]28[/C][C]0.023495[/C][C]0.1805[/C][C]0.4287[/C][/ROW]
[ROW][C]29[/C][C]-0.057061[/C][C]-0.4383[/C][C]0.331388[/C][/ROW]
[ROW][C]30[/C][C]-0.082338[/C][C]-0.6325[/C][C]0.264767[/C][/ROW]
[ROW][C]31[/C][C]0.005742[/C][C]0.0441[/C][C]0.482486[/C][/ROW]
[ROW][C]32[/C][C]-0.082033[/C][C]-0.6301[/C][C]0.265528[/C][/ROW]
[ROW][C]33[/C][C]0.118637[/C][C]0.9113[/C][C]0.182932[/C][/ROW]
[ROW][C]34[/C][C]0.121441[/C][C]0.9328[/C][C]0.177362[/C][/ROW]
[ROW][C]35[/C][C]-0.034591[/C][C]-0.2657[/C][C]0.395698[/C][/ROW]
[ROW][C]36[/C][C]-0.014159[/C][C]-0.1088[/C][C]0.456881[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58908&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58908&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
10.3494282.6840.004713
2-0.365367-2.80640.003388
3-0.196808-1.51170.067973
4-0.266236-2.0450.022662
50.1269790.97530.166685
60.0232210.17840.429524
7-0.080958-0.62180.268218
8-0.251994-1.93560.028856
9-0.029184-0.22420.411701
10-0.177991-1.36720.088379
110.1432611.10040.137812
120.3557952.73290.004136
13-0.397976-3.05690.001679
140.3302642.53680.006926
150.1528581.17410.122531
160.116010.89110.18825
17-0.129447-0.99430.162068
18-0.092299-0.7090.240571
19-0.056404-0.43320.333208
200.0312060.23970.405698
21-0.177833-1.3660.088568
220.0097040.07450.470418
230.0437560.33610.368996
240.1341861.03070.153444
250.0068250.05240.479183
26-0.098691-0.75810.225717
27-0.064342-0.49420.311494
280.0234950.18050.4287
29-0.057061-0.43830.331388
30-0.082338-0.63250.264767
310.0057420.04410.482486
32-0.082033-0.63010.265528
330.1186370.91130.182932
340.1214410.93280.177362
35-0.034591-0.26570.395698
36-0.014159-0.10880.456881


Figures (Output of Computation)

Input Parameters & R Code

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