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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 computationThu, 01 Dec 2011 08:55:40 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/01/t132274775279sr7rtvlg98dfg.htm/, Retrieved Thu, 28 Mar 2024 12:00:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=149628, Retrieved Thu, 28 Mar 2024 12:00:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Spectral Analysis] [Unemployment] [2010-11-29 09:21:38] [b98453cac15ba1066b407e146608df68]
-    D    [Spectral Analysis] [Workshop 9, Cumul...] [2010-12-05 17:58:39] [3635fb7041b1998c5a1332cf9de22bce]
- RMPD        [(Partial) Autocorrelation Function] [] [2011-12-01 13:55:40] [d34c5d8ebaf8c35edbecb57bc39ed04e] [Current]
- R             [(Partial) Autocorrelation Function] [] [2011-12-13 16:40:22] [74be16979710d4c4e7c6647856088456]
- RMP           [Spectral Analysis] [] [2011-12-13 16:51:34] [86a47bcc75cd2e0d5b5c9888edc893c2]
- RMP           [Spectral Analysis] [] [2011-12-13 16:52:12] [86a47bcc75cd2e0d5b5c9888edc893c2]
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Dataseries X:
9676
8642
9402
9610
9294
9448
10319
9548
9801
9596
8923
9746
9829
9125
9782
9441
9162
9915
10444
10209
9985
9842
9429
10132
9849
9172
10313
9819
9955
10048
10082
10541
10208
10233
9439
9963
10158
9225
10474
9757
10490
10281
10444
10640
10695
10786
9832
9747
10411
9511
10402
9701
10540
10112
10915
11183
10384
10834
9886
10216
10943
9867
10203
10837
10573
10647
11502
10656
10866
10835
9945
10331




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149628&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149628&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149628&T=0

As an alternative you can also use a 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' @ jenkins.wessa.net







Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.160339-1.2420.109538
2-0.078908-0.61120.271681
30.017230.13350.447137
4-0.097943-0.75870.225511
50.2261311.75160.042476
60.0142670.11050.456186
7-0.311718-2.41460.00941
8-0.092488-0.71640.238259
90.154081.19350.118687
10-0.13747-1.06480.145607
11-0.05245-0.40630.342994
12-0.14879-1.15250.12684
13-0.105796-0.81950.207875
140.1842091.42690.0794
150.0453130.3510.36341
16-0.145938-1.13040.131397
170.1230.95280.172269
180.0800340.61990.268823
190.1008980.78160.218776
200.0228510.1770.430051
210.0529650.41030.341536
22-0.01279-0.09910.460707
230.1590791.23220.111337
24-0.125158-0.96950.168101
25-0.179272-1.38860.085039
260.0296520.22970.40956
270.0745140.57720.282987
28-0.051741-0.40080.345
29-0.127443-0.98720.163762
30-0.133397-1.03330.152807
31-0.064912-0.50280.308471
320.2352081.82190.036727
33-0.053533-0.41470.339932
34-0.109978-0.85190.198832
350.1220730.94560.174078
36-0.053958-0.4180.338734

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.160339 & -1.242 & 0.109538 \tabularnewline
2 & -0.078908 & -0.6112 & 0.271681 \tabularnewline
3 & 0.01723 & 0.1335 & 0.447137 \tabularnewline
4 & -0.097943 & -0.7587 & 0.225511 \tabularnewline
5 & 0.226131 & 1.7516 & 0.042476 \tabularnewline
6 & 0.014267 & 0.1105 & 0.456186 \tabularnewline
7 & -0.311718 & -2.4146 & 0.00941 \tabularnewline
8 & -0.092488 & -0.7164 & 0.238259 \tabularnewline
9 & 0.15408 & 1.1935 & 0.118687 \tabularnewline
10 & -0.13747 & -1.0648 & 0.145607 \tabularnewline
11 & -0.05245 & -0.4063 & 0.342994 \tabularnewline
12 & -0.14879 & -1.1525 & 0.12684 \tabularnewline
13 & -0.105796 & -0.8195 & 0.207875 \tabularnewline
14 & 0.184209 & 1.4269 & 0.0794 \tabularnewline
15 & 0.045313 & 0.351 & 0.36341 \tabularnewline
16 & -0.145938 & -1.1304 & 0.131397 \tabularnewline
17 & 0.123 & 0.9528 & 0.172269 \tabularnewline
18 & 0.080034 & 0.6199 & 0.268823 \tabularnewline
19 & 0.100898 & 0.7816 & 0.218776 \tabularnewline
20 & 0.022851 & 0.177 & 0.430051 \tabularnewline
21 & 0.052965 & 0.4103 & 0.341536 \tabularnewline
22 & -0.01279 & -0.0991 & 0.460707 \tabularnewline
23 & 0.159079 & 1.2322 & 0.111337 \tabularnewline
24 & -0.125158 & -0.9695 & 0.168101 \tabularnewline
25 & -0.179272 & -1.3886 & 0.085039 \tabularnewline
26 & 0.029652 & 0.2297 & 0.40956 \tabularnewline
27 & 0.074514 & 0.5772 & 0.282987 \tabularnewline
28 & -0.051741 & -0.4008 & 0.345 \tabularnewline
29 & -0.127443 & -0.9872 & 0.163762 \tabularnewline
30 & -0.133397 & -1.0333 & 0.152807 \tabularnewline
31 & -0.064912 & -0.5028 & 0.308471 \tabularnewline
32 & 0.235208 & 1.8219 & 0.036727 \tabularnewline
33 & -0.053533 & -0.4147 & 0.339932 \tabularnewline
34 & -0.109978 & -0.8519 & 0.198832 \tabularnewline
35 & 0.122073 & 0.9456 & 0.174078 \tabularnewline
36 & -0.053958 & -0.418 & 0.338734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149628&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]-0.160339[/C][C]-1.242[/C][C]0.109538[/C][/ROW]
[ROW][C]2[/C][C]-0.078908[/C][C]-0.6112[/C][C]0.271681[/C][/ROW]
[ROW][C]3[/C][C]0.01723[/C][C]0.1335[/C][C]0.447137[/C][/ROW]
[ROW][C]4[/C][C]-0.097943[/C][C]-0.7587[/C][C]0.225511[/C][/ROW]
[ROW][C]5[/C][C]0.226131[/C][C]1.7516[/C][C]0.042476[/C][/ROW]
[ROW][C]6[/C][C]0.014267[/C][C]0.1105[/C][C]0.456186[/C][/ROW]
[ROW][C]7[/C][C]-0.311718[/C][C]-2.4146[/C][C]0.00941[/C][/ROW]
[ROW][C]8[/C][C]-0.092488[/C][C]-0.7164[/C][C]0.238259[/C][/ROW]
[ROW][C]9[/C][C]0.15408[/C][C]1.1935[/C][C]0.118687[/C][/ROW]
[ROW][C]10[/C][C]-0.13747[/C][C]-1.0648[/C][C]0.145607[/C][/ROW]
[ROW][C]11[/C][C]-0.05245[/C][C]-0.4063[/C][C]0.342994[/C][/ROW]
[ROW][C]12[/C][C]-0.14879[/C][C]-1.1525[/C][C]0.12684[/C][/ROW]
[ROW][C]13[/C][C]-0.105796[/C][C]-0.8195[/C][C]0.207875[/C][/ROW]
[ROW][C]14[/C][C]0.184209[/C][C]1.4269[/C][C]0.0794[/C][/ROW]
[ROW][C]15[/C][C]0.045313[/C][C]0.351[/C][C]0.36341[/C][/ROW]
[ROW][C]16[/C][C]-0.145938[/C][C]-1.1304[/C][C]0.131397[/C][/ROW]
[ROW][C]17[/C][C]0.123[/C][C]0.9528[/C][C]0.172269[/C][/ROW]
[ROW][C]18[/C][C]0.080034[/C][C]0.6199[/C][C]0.268823[/C][/ROW]
[ROW][C]19[/C][C]0.100898[/C][C]0.7816[/C][C]0.218776[/C][/ROW]
[ROW][C]20[/C][C]0.022851[/C][C]0.177[/C][C]0.430051[/C][/ROW]
[ROW][C]21[/C][C]0.052965[/C][C]0.4103[/C][C]0.341536[/C][/ROW]
[ROW][C]22[/C][C]-0.01279[/C][C]-0.0991[/C][C]0.460707[/C][/ROW]
[ROW][C]23[/C][C]0.159079[/C][C]1.2322[/C][C]0.111337[/C][/ROW]
[ROW][C]24[/C][C]-0.125158[/C][C]-0.9695[/C][C]0.168101[/C][/ROW]
[ROW][C]25[/C][C]-0.179272[/C][C]-1.3886[/C][C]0.085039[/C][/ROW]
[ROW][C]26[/C][C]0.029652[/C][C]0.2297[/C][C]0.40956[/C][/ROW]
[ROW][C]27[/C][C]0.074514[/C][C]0.5772[/C][C]0.282987[/C][/ROW]
[ROW][C]28[/C][C]-0.051741[/C][C]-0.4008[/C][C]0.345[/C][/ROW]
[ROW][C]29[/C][C]-0.127443[/C][C]-0.9872[/C][C]0.163762[/C][/ROW]
[ROW][C]30[/C][C]-0.133397[/C][C]-1.0333[/C][C]0.152807[/C][/ROW]
[ROW][C]31[/C][C]-0.064912[/C][C]-0.5028[/C][C]0.308471[/C][/ROW]
[ROW][C]32[/C][C]0.235208[/C][C]1.8219[/C][C]0.036727[/C][/ROW]
[ROW][C]33[/C][C]-0.053533[/C][C]-0.4147[/C][C]0.339932[/C][/ROW]
[ROW][C]34[/C][C]-0.109978[/C][C]-0.8519[/C][C]0.198832[/C][/ROW]
[ROW][C]35[/C][C]0.122073[/C][C]0.9456[/C][C]0.174078[/C][/ROW]
[ROW][C]36[/C][C]-0.053958[/C][C]-0.418[/C][C]0.338734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149628&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149628&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.160339-1.2420.109538
2-0.078908-0.61120.271681
30.017230.13350.447137
4-0.097943-0.75870.225511
50.2261311.75160.042476
60.0142670.11050.456186
7-0.311718-2.41460.00941
8-0.092488-0.71640.238259
90.154081.19350.118687
10-0.13747-1.06480.145607
11-0.05245-0.40630.342994
12-0.14879-1.15250.12684
13-0.105796-0.81950.207875
140.1842091.42690.0794
150.0453130.3510.36341
16-0.145938-1.13040.131397
170.1230.95280.172269
180.0800340.61990.268823
190.1008980.78160.218776
200.0228510.1770.430051
210.0529650.41030.341536
22-0.01279-0.09910.460707
230.1590791.23220.111337
24-0.125158-0.96950.168101
25-0.179272-1.38860.085039
260.0296520.22970.40956
270.0745140.57720.282987
28-0.051741-0.40080.345
29-0.127443-0.98720.163762
30-0.133397-1.03330.152807
31-0.064912-0.50280.308471
320.2352081.82190.036727
33-0.053533-0.41470.339932
34-0.109978-0.85190.198832
350.1220730.94560.174078
36-0.053958-0.4180.338734







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.160339-1.2420.109538
2-0.107377-0.83170.204426
3-0.014534-0.11260.455368
4-0.109948-0.85170.198896
50.1999591.54890.063335
60.071490.55380.290901
7-0.281799-2.18280.016487
8-0.222813-1.72590.044756
90.1193730.92470.179424
10-0.173298-1.34240.092269
11-0.202679-1.56990.060844
12-0.130346-1.00970.158357
13-0.094139-0.72920.23436
14-0.070146-0.54340.294451
15-0.011152-0.08640.465724
16-0.096524-0.74770.228789
170.0663610.5140.304559
180.0490050.37960.352796
190.042780.33140.370758
20-0.099225-0.76860.222574
210.182531.41390.081283
220.0420670.32580.372837
230.0549550.42570.335933
24-0.149348-1.15680.125961
25-0.106841-0.82760.205591
26-0.067852-0.52560.300558
270.1577151.22170.113308
28-0.066117-0.51210.305218
29-0.084204-0.65220.258367
30-0.045398-0.35170.363165
31-0.064129-0.49670.310592
320.0047580.03690.485363
330.044920.3480.364547
34-0.021364-0.16550.434559
350.1124880.87130.193523
36-0.199805-1.54770.063479

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.160339 & -1.242 & 0.109538 \tabularnewline
2 & -0.107377 & -0.8317 & 0.204426 \tabularnewline
3 & -0.014534 & -0.1126 & 0.455368 \tabularnewline
4 & -0.109948 & -0.8517 & 0.198896 \tabularnewline
5 & 0.199959 & 1.5489 & 0.063335 \tabularnewline
6 & 0.07149 & 0.5538 & 0.290901 \tabularnewline
7 & -0.281799 & -2.1828 & 0.016487 \tabularnewline
8 & -0.222813 & -1.7259 & 0.044756 \tabularnewline
9 & 0.119373 & 0.9247 & 0.179424 \tabularnewline
10 & -0.173298 & -1.3424 & 0.092269 \tabularnewline
11 & -0.202679 & -1.5699 & 0.060844 \tabularnewline
12 & -0.130346 & -1.0097 & 0.158357 \tabularnewline
13 & -0.094139 & -0.7292 & 0.23436 \tabularnewline
14 & -0.070146 & -0.5434 & 0.294451 \tabularnewline
15 & -0.011152 & -0.0864 & 0.465724 \tabularnewline
16 & -0.096524 & -0.7477 & 0.228789 \tabularnewline
17 & 0.066361 & 0.514 & 0.304559 \tabularnewline
18 & 0.049005 & 0.3796 & 0.352796 \tabularnewline
19 & 0.04278 & 0.3314 & 0.370758 \tabularnewline
20 & -0.099225 & -0.7686 & 0.222574 \tabularnewline
21 & 0.18253 & 1.4139 & 0.081283 \tabularnewline
22 & 0.042067 & 0.3258 & 0.372837 \tabularnewline
23 & 0.054955 & 0.4257 & 0.335933 \tabularnewline
24 & -0.149348 & -1.1568 & 0.125961 \tabularnewline
25 & -0.106841 & -0.8276 & 0.205591 \tabularnewline
26 & -0.067852 & -0.5256 & 0.300558 \tabularnewline
27 & 0.157715 & 1.2217 & 0.113308 \tabularnewline
28 & -0.066117 & -0.5121 & 0.305218 \tabularnewline
29 & -0.084204 & -0.6522 & 0.258367 \tabularnewline
30 & -0.045398 & -0.3517 & 0.363165 \tabularnewline
31 & -0.064129 & -0.4967 & 0.310592 \tabularnewline
32 & 0.004758 & 0.0369 & 0.485363 \tabularnewline
33 & 0.04492 & 0.348 & 0.364547 \tabularnewline
34 & -0.021364 & -0.1655 & 0.434559 \tabularnewline
35 & 0.112488 & 0.8713 & 0.193523 \tabularnewline
36 & -0.199805 & -1.5477 & 0.063479 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149628&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]-0.160339[/C][C]-1.242[/C][C]0.109538[/C][/ROW]
[ROW][C]2[/C][C]-0.107377[/C][C]-0.8317[/C][C]0.204426[/C][/ROW]
[ROW][C]3[/C][C]-0.014534[/C][C]-0.1126[/C][C]0.455368[/C][/ROW]
[ROW][C]4[/C][C]-0.109948[/C][C]-0.8517[/C][C]0.198896[/C][/ROW]
[ROW][C]5[/C][C]0.199959[/C][C]1.5489[/C][C]0.063335[/C][/ROW]
[ROW][C]6[/C][C]0.07149[/C][C]0.5538[/C][C]0.290901[/C][/ROW]
[ROW][C]7[/C][C]-0.281799[/C][C]-2.1828[/C][C]0.016487[/C][/ROW]
[ROW][C]8[/C][C]-0.222813[/C][C]-1.7259[/C][C]0.044756[/C][/ROW]
[ROW][C]9[/C][C]0.119373[/C][C]0.9247[/C][C]0.179424[/C][/ROW]
[ROW][C]10[/C][C]-0.173298[/C][C]-1.3424[/C][C]0.092269[/C][/ROW]
[ROW][C]11[/C][C]-0.202679[/C][C]-1.5699[/C][C]0.060844[/C][/ROW]
[ROW][C]12[/C][C]-0.130346[/C][C]-1.0097[/C][C]0.158357[/C][/ROW]
[ROW][C]13[/C][C]-0.094139[/C][C]-0.7292[/C][C]0.23436[/C][/ROW]
[ROW][C]14[/C][C]-0.070146[/C][C]-0.5434[/C][C]0.294451[/C][/ROW]
[ROW][C]15[/C][C]-0.011152[/C][C]-0.0864[/C][C]0.465724[/C][/ROW]
[ROW][C]16[/C][C]-0.096524[/C][C]-0.7477[/C][C]0.228789[/C][/ROW]
[ROW][C]17[/C][C]0.066361[/C][C]0.514[/C][C]0.304559[/C][/ROW]
[ROW][C]18[/C][C]0.049005[/C][C]0.3796[/C][C]0.352796[/C][/ROW]
[ROW][C]19[/C][C]0.04278[/C][C]0.3314[/C][C]0.370758[/C][/ROW]
[ROW][C]20[/C][C]-0.099225[/C][C]-0.7686[/C][C]0.222574[/C][/ROW]
[ROW][C]21[/C][C]0.18253[/C][C]1.4139[/C][C]0.081283[/C][/ROW]
[ROW][C]22[/C][C]0.042067[/C][C]0.3258[/C][C]0.372837[/C][/ROW]
[ROW][C]23[/C][C]0.054955[/C][C]0.4257[/C][C]0.335933[/C][/ROW]
[ROW][C]24[/C][C]-0.149348[/C][C]-1.1568[/C][C]0.125961[/C][/ROW]
[ROW][C]25[/C][C]-0.106841[/C][C]-0.8276[/C][C]0.205591[/C][/ROW]
[ROW][C]26[/C][C]-0.067852[/C][C]-0.5256[/C][C]0.300558[/C][/ROW]
[ROW][C]27[/C][C]0.157715[/C][C]1.2217[/C][C]0.113308[/C][/ROW]
[ROW][C]28[/C][C]-0.066117[/C][C]-0.5121[/C][C]0.305218[/C][/ROW]
[ROW][C]29[/C][C]-0.084204[/C][C]-0.6522[/C][C]0.258367[/C][/ROW]
[ROW][C]30[/C][C]-0.045398[/C][C]-0.3517[/C][C]0.363165[/C][/ROW]
[ROW][C]31[/C][C]-0.064129[/C][C]-0.4967[/C][C]0.310592[/C][/ROW]
[ROW][C]32[/C][C]0.004758[/C][C]0.0369[/C][C]0.485363[/C][/ROW]
[ROW][C]33[/C][C]0.04492[/C][C]0.348[/C][C]0.364547[/C][/ROW]
[ROW][C]34[/C][C]-0.021364[/C][C]-0.1655[/C][C]0.434559[/C][/ROW]
[ROW][C]35[/C][C]0.112488[/C][C]0.8713[/C][C]0.193523[/C][/ROW]
[ROW][C]36[/C][C]-0.199805[/C][C]-1.5477[/C][C]0.063479[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149628&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149628&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.160339-1.2420.109538
2-0.107377-0.83170.204426
3-0.014534-0.11260.455368
4-0.109948-0.85170.198896
50.1999591.54890.063335
60.071490.55380.290901
7-0.281799-2.18280.016487
8-0.222813-1.72590.044756
90.1193730.92470.179424
10-0.173298-1.34240.092269
11-0.202679-1.56990.060844
12-0.130346-1.00970.158357
13-0.094139-0.72920.23436
14-0.070146-0.54340.294451
15-0.011152-0.08640.465724
16-0.096524-0.74770.228789
170.0663610.5140.304559
180.0490050.37960.352796
190.042780.33140.370758
20-0.099225-0.76860.222574
210.182531.41390.081283
220.0420670.32580.372837
230.0549550.42570.335933
24-0.149348-1.15680.125961
25-0.106841-0.82760.205591
26-0.067852-0.52560.300558
270.1577151.22170.113308
28-0.066117-0.51210.305218
29-0.084204-0.65220.258367
30-0.045398-0.35170.363165
31-0.064129-0.49670.310592
320.0047580.03690.485363
330.044920.3480.364547
34-0.021364-0.16550.434559
350.1124880.87130.193523
36-0.199805-1.54770.063479



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 ; par8 = ;
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 (par8 != '') par8 <- as.numeric(par8)
ox <- x
if (par8 == '') {
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
} else {
x <- log(x,base=par8)
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='picts.png')
op <- par(mfrow=c(2,1))
plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value')
if (par8=='') {
mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='')
mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
} else {
mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='')
mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
}
plot(x,type='l', main=mytitle,xlab='time',ylab='value')
par(op)
dev.off()
bitmap(file='pic1.png')
racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub)
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub)
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')