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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationThu, 26 Nov 2009 07:12:20 -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/26/t125924481378cxqy00ucmtvth.htm/, Retrieved Mon, 29 Apr 2024 07:55:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60034, Retrieved Mon, 29 Apr 2024 07:55:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact184

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]
-    D        [(Partial) Autocorrelation Function] [Workshop 8 - Meth...] [2009-11-24 15:58:42] [1646a2766cb8c4a6f9d3b2fffef409b3]
-   PD            [(Partial) Autocorrelation Function] [methode 1 ACF d=1...] [2009-11-26 14:12:20] [3ebad5d90a5c8606f133189c73066208] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
91.2
80.8
72.3
99.7
90.1
83.1
71.9
78.6
87.2
90.6
80
73.1
85.6
73.8
70.6
91.8
81.3
85.2
69.6
83.3
89.8
99.5
78.9
83.8
92
80.9
74.6
97.9
88.3
88.1
66.4
92.3
95.6
99.7
78.9
79.4
87.8
80.5
71.8
89.2
96.4
83.5
64.3
85.9
89.2
81.8
79.5
68.7
76.4
73.6
57.7
78.3
75.5
62.4
55.6
62.9
66.7
66.8
59.9
52
61.2

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=60034&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=60034&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60034&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
1-0.596862-4.13527.1e-05
20.0870720.60330.27459
30.1201540.83250.204638
4-0.018344-0.12710.4497
5-0.12923-0.89530.187539
60.2040261.41350.081976
7-0.205941-1.42680.080055
80.1441150.99850.161532
90.0208880.14470.442771
10-0.122386-0.84790.200348
110.1274620.88310.190797
12-0.07807-0.54090.295544
13-0.070199-0.48640.314466
140.1295390.89750.186974
150.015530.10760.457383
16-0.236159-1.63620.054174
170.2799031.93920.029182
18-0.18811-1.30330.09935
190.0846940.58680.280051
20-0.011878-0.08230.467378
210.0233850.1620.435985
22-0.167485-1.16040.125818
230.3559742.46630.008638
24-0.326609-2.26280.014105
250.0581930.40320.344305
260.0834590.57820.28291
27-0.0621-0.43020.334473
28-0.03375-0.23380.408056
290.0418530.290.386546
300.0112990.07830.468965
31-0.053542-0.37090.356154
320.0158930.11010.456392
33-0.022563-0.15630.438217
340.0200810.13910.444966
35-0.023196-0.16070.436499
36-0.034762-0.24080.405354

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.596862 & -4.1352 & 7.1e-05 \tabularnewline
2 & 0.087072 & 0.6033 & 0.27459 \tabularnewline
3 & 0.120154 & 0.8325 & 0.204638 \tabularnewline
4 & -0.018344 & -0.1271 & 0.4497 \tabularnewline
5 & -0.12923 & -0.8953 & 0.187539 \tabularnewline
6 & 0.204026 & 1.4135 & 0.081976 \tabularnewline
7 & -0.205941 & -1.4268 & 0.080055 \tabularnewline
8 & 0.144115 & 0.9985 & 0.161532 \tabularnewline
9 & 0.020888 & 0.1447 & 0.442771 \tabularnewline
10 & -0.122386 & -0.8479 & 0.200348 \tabularnewline
11 & 0.127462 & 0.8831 & 0.190797 \tabularnewline
12 & -0.07807 & -0.5409 & 0.295544 \tabularnewline
13 & -0.070199 & -0.4864 & 0.314466 \tabularnewline
14 & 0.129539 & 0.8975 & 0.186974 \tabularnewline
15 & 0.01553 & 0.1076 & 0.457383 \tabularnewline
16 & -0.236159 & -1.6362 & 0.054174 \tabularnewline
17 & 0.279903 & 1.9392 & 0.029182 \tabularnewline
18 & -0.18811 & -1.3033 & 0.09935 \tabularnewline
19 & 0.084694 & 0.5868 & 0.280051 \tabularnewline
20 & -0.011878 & -0.0823 & 0.467378 \tabularnewline
21 & 0.023385 & 0.162 & 0.435985 \tabularnewline
22 & -0.167485 & -1.1604 & 0.125818 \tabularnewline
23 & 0.355974 & 2.4663 & 0.008638 \tabularnewline
24 & -0.326609 & -2.2628 & 0.014105 \tabularnewline
25 & 0.058193 & 0.4032 & 0.344305 \tabularnewline
26 & 0.083459 & 0.5782 & 0.28291 \tabularnewline
27 & -0.0621 & -0.4302 & 0.334473 \tabularnewline
28 & -0.03375 & -0.2338 & 0.408056 \tabularnewline
29 & 0.041853 & 0.29 & 0.386546 \tabularnewline
30 & 0.011299 & 0.0783 & 0.468965 \tabularnewline
31 & -0.053542 & -0.3709 & 0.356154 \tabularnewline
32 & 0.015893 & 0.1101 & 0.456392 \tabularnewline
33 & -0.022563 & -0.1563 & 0.438217 \tabularnewline
34 & 0.020081 & 0.1391 & 0.444966 \tabularnewline
35 & -0.023196 & -0.1607 & 0.436499 \tabularnewline
36 & -0.034762 & -0.2408 & 0.405354 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60034&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]-0.596862[/C][C]-4.1352[/C][C]7.1e-05[/C][/ROW]
[ROW][C]2[/C][C]0.087072[/C][C]0.6033[/C][C]0.27459[/C][/ROW]
[ROW][C]3[/C][C]0.120154[/C][C]0.8325[/C][C]0.204638[/C][/ROW]
[ROW][C]4[/C][C]-0.018344[/C][C]-0.1271[/C][C]0.4497[/C][/ROW]
[ROW][C]5[/C][C]-0.12923[/C][C]-0.8953[/C][C]0.187539[/C][/ROW]
[ROW][C]6[/C][C]0.204026[/C][C]1.4135[/C][C]0.081976[/C][/ROW]
[ROW][C]7[/C][C]-0.205941[/C][C]-1.4268[/C][C]0.080055[/C][/ROW]
[ROW][C]8[/C][C]0.144115[/C][C]0.9985[/C][C]0.161532[/C][/ROW]
[ROW][C]9[/C][C]0.020888[/C][C]0.1447[/C][C]0.442771[/C][/ROW]
[ROW][C]10[/C][C]-0.122386[/C][C]-0.8479[/C][C]0.200348[/C][/ROW]
[ROW][C]11[/C][C]0.127462[/C][C]0.8831[/C][C]0.190797[/C][/ROW]
[ROW][C]12[/C][C]-0.07807[/C][C]-0.5409[/C][C]0.295544[/C][/ROW]
[ROW][C]13[/C][C]-0.070199[/C][C]-0.4864[/C][C]0.314466[/C][/ROW]
[ROW][C]14[/C][C]0.129539[/C][C]0.8975[/C][C]0.186974[/C][/ROW]
[ROW][C]15[/C][C]0.01553[/C][C]0.1076[/C][C]0.457383[/C][/ROW]
[ROW][C]16[/C][C]-0.236159[/C][C]-1.6362[/C][C]0.054174[/C][/ROW]
[ROW][C]17[/C][C]0.279903[/C][C]1.9392[/C][C]0.029182[/C][/ROW]
[ROW][C]18[/C][C]-0.18811[/C][C]-1.3033[/C][C]0.09935[/C][/ROW]
[ROW][C]19[/C][C]0.084694[/C][C]0.5868[/C][C]0.280051[/C][/ROW]
[ROW][C]20[/C][C]-0.011878[/C][C]-0.0823[/C][C]0.467378[/C][/ROW]
[ROW][C]21[/C][C]0.023385[/C][C]0.162[/C][C]0.435985[/C][/ROW]
[ROW][C]22[/C][C]-0.167485[/C][C]-1.1604[/C][C]0.125818[/C][/ROW]
[ROW][C]23[/C][C]0.355974[/C][C]2.4663[/C][C]0.008638[/C][/ROW]
[ROW][C]24[/C][C]-0.326609[/C][C]-2.2628[/C][C]0.014105[/C][/ROW]
[ROW][C]25[/C][C]0.058193[/C][C]0.4032[/C][C]0.344305[/C][/ROW]
[ROW][C]26[/C][C]0.083459[/C][C]0.5782[/C][C]0.28291[/C][/ROW]
[ROW][C]27[/C][C]-0.0621[/C][C]-0.4302[/C][C]0.334473[/C][/ROW]
[ROW][C]28[/C][C]-0.03375[/C][C]-0.2338[/C][C]0.408056[/C][/ROW]
[ROW][C]29[/C][C]0.041853[/C][C]0.29[/C][C]0.386546[/C][/ROW]
[ROW][C]30[/C][C]0.011299[/C][C]0.0783[/C][C]0.468965[/C][/ROW]
[ROW][C]31[/C][C]-0.053542[/C][C]-0.3709[/C][C]0.356154[/C][/ROW]
[ROW][C]32[/C][C]0.015893[/C][C]0.1101[/C][C]0.456392[/C][/ROW]
[ROW][C]33[/C][C]-0.022563[/C][C]-0.1563[/C][C]0.438217[/C][/ROW]
[ROW][C]34[/C][C]0.020081[/C][C]0.1391[/C][C]0.444966[/C][/ROW]
[ROW][C]35[/C][C]-0.023196[/C][C]-0.1607[/C][C]0.436499[/C][/ROW]
[ROW][C]36[/C][C]-0.034762[/C][C]-0.2408[/C][C]0.405354[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60034&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60034&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
1-0.596862-4.13527.1e-05
20.0870720.60330.27459
30.1201540.83250.204638
4-0.018344-0.12710.4497
5-0.12923-0.89530.187539
60.2040261.41350.081976
7-0.205941-1.42680.080055
80.1441150.99850.161532
90.0208880.14470.442771
10-0.122386-0.84790.200348
110.1274620.88310.190797
12-0.07807-0.54090.295544
13-0.070199-0.48640.314466
140.1295390.89750.186974
150.015530.10760.457383
16-0.236159-1.63620.054174
170.2799031.93920.029182
18-0.18811-1.30330.09935
190.0846940.58680.280051
20-0.011878-0.08230.467378
210.0233850.1620.435985
22-0.167485-1.16040.125818
230.3559742.46630.008638
24-0.326609-2.26280.014105
250.0581930.40320.344305
260.0834590.57820.28291
27-0.0621-0.43020.334473
28-0.03375-0.23380.408056
290.0418530.290.386546
300.0112990.07830.468965
31-0.053542-0.37090.356154
320.0158930.11010.456392
33-0.022563-0.15630.438217
340.0200810.13910.444966
35-0.023196-0.16070.436499
36-0.034762-0.24080.405354






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.596862-4.13527.1e-05
2-0.418127-2.89690.002831
3-0.104873-0.72660.235505
40.133560.92530.179711
5-0.027205-0.18850.425646
60.1108670.76810.223093
7-0.089683-0.62130.268657
80.0057550.03990.484181
90.1530041.060.147214
100.0230.15930.437032
110.0896810.62130.268662
12-0.066341-0.45960.323931
13-0.199048-1.3790.087136
14-0.0933-0.64640.26055
150.1686941.16870.124138
16-0.085987-0.59570.277076
17-0.032786-0.22710.410637
18-0.142723-0.98880.163857
190.0153380.10630.457908
200.0956880.66290.255269
210.1673111.15920.126062
22-0.16551-1.14670.128597
230.115970.80350.212834
240.0758660.52560.300788
25-0.133365-0.9240.180059
26-0.148988-1.03220.153571
27-0.078662-0.5450.294144
28-0.064341-0.44580.328886
29-0.17986-1.24610.109386
300.082570.57210.284975
310.0078250.05420.478496
32-0.140574-0.97390.167487
33-0.046231-0.32030.375068
34-0.085947-0.59550.277167
350.0968240.67080.252776
360.0078520.05440.478421

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.596862 & -4.1352 & 7.1e-05 \tabularnewline
2 & -0.418127 & -2.8969 & 0.002831 \tabularnewline
3 & -0.104873 & -0.7266 & 0.235505 \tabularnewline
4 & 0.13356 & 0.9253 & 0.179711 \tabularnewline
5 & -0.027205 & -0.1885 & 0.425646 \tabularnewline
6 & 0.110867 & 0.7681 & 0.223093 \tabularnewline
7 & -0.089683 & -0.6213 & 0.268657 \tabularnewline
8 & 0.005755 & 0.0399 & 0.484181 \tabularnewline
9 & 0.153004 & 1.06 & 0.147214 \tabularnewline
10 & 0.023 & 0.1593 & 0.437032 \tabularnewline
11 & 0.089681 & 0.6213 & 0.268662 \tabularnewline
12 & -0.066341 & -0.4596 & 0.323931 \tabularnewline
13 & -0.199048 & -1.379 & 0.087136 \tabularnewline
14 & -0.0933 & -0.6464 & 0.26055 \tabularnewline
15 & 0.168694 & 1.1687 & 0.124138 \tabularnewline
16 & -0.085987 & -0.5957 & 0.277076 \tabularnewline
17 & -0.032786 & -0.2271 & 0.410637 \tabularnewline
18 & -0.142723 & -0.9888 & 0.163857 \tabularnewline
19 & 0.015338 & 0.1063 & 0.457908 \tabularnewline
20 & 0.095688 & 0.6629 & 0.255269 \tabularnewline
21 & 0.167311 & 1.1592 & 0.126062 \tabularnewline
22 & -0.16551 & -1.1467 & 0.128597 \tabularnewline
23 & 0.11597 & 0.8035 & 0.212834 \tabularnewline
24 & 0.075866 & 0.5256 & 0.300788 \tabularnewline
25 & -0.133365 & -0.924 & 0.180059 \tabularnewline
26 & -0.148988 & -1.0322 & 0.153571 \tabularnewline
27 & -0.078662 & -0.545 & 0.294144 \tabularnewline
28 & -0.064341 & -0.4458 & 0.328886 \tabularnewline
29 & -0.17986 & -1.2461 & 0.109386 \tabularnewline
30 & 0.08257 & 0.5721 & 0.284975 \tabularnewline
31 & 0.007825 & 0.0542 & 0.478496 \tabularnewline
32 & -0.140574 & -0.9739 & 0.167487 \tabularnewline
33 & -0.046231 & -0.3203 & 0.375068 \tabularnewline
34 & -0.085947 & -0.5955 & 0.277167 \tabularnewline
35 & 0.096824 & 0.6708 & 0.252776 \tabularnewline
36 & 0.007852 & 0.0544 & 0.478421 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60034&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]-0.596862[/C][C]-4.1352[/C][C]7.1e-05[/C][/ROW]
[ROW][C]2[/C][C]-0.418127[/C][C]-2.8969[/C][C]0.002831[/C][/ROW]
[ROW][C]3[/C][C]-0.104873[/C][C]-0.7266[/C][C]0.235505[/C][/ROW]
[ROW][C]4[/C][C]0.13356[/C][C]0.9253[/C][C]0.179711[/C][/ROW]
[ROW][C]5[/C][C]-0.027205[/C][C]-0.1885[/C][C]0.425646[/C][/ROW]
[ROW][C]6[/C][C]0.110867[/C][C]0.7681[/C][C]0.223093[/C][/ROW]
[ROW][C]7[/C][C]-0.089683[/C][C]-0.6213[/C][C]0.268657[/C][/ROW]
[ROW][C]8[/C][C]0.005755[/C][C]0.0399[/C][C]0.484181[/C][/ROW]
[ROW][C]9[/C][C]0.153004[/C][C]1.06[/C][C]0.147214[/C][/ROW]
[ROW][C]10[/C][C]0.023[/C][C]0.1593[/C][C]0.437032[/C][/ROW]
[ROW][C]11[/C][C]0.089681[/C][C]0.6213[/C][C]0.268662[/C][/ROW]
[ROW][C]12[/C][C]-0.066341[/C][C]-0.4596[/C][C]0.323931[/C][/ROW]
[ROW][C]13[/C][C]-0.199048[/C][C]-1.379[/C][C]0.087136[/C][/ROW]
[ROW][C]14[/C][C]-0.0933[/C][C]-0.6464[/C][C]0.26055[/C][/ROW]
[ROW][C]15[/C][C]0.168694[/C][C]1.1687[/C][C]0.124138[/C][/ROW]
[ROW][C]16[/C][C]-0.085987[/C][C]-0.5957[/C][C]0.277076[/C][/ROW]
[ROW][C]17[/C][C]-0.032786[/C][C]-0.2271[/C][C]0.410637[/C][/ROW]
[ROW][C]18[/C][C]-0.142723[/C][C]-0.9888[/C][C]0.163857[/C][/ROW]
[ROW][C]19[/C][C]0.015338[/C][C]0.1063[/C][C]0.457908[/C][/ROW]
[ROW][C]20[/C][C]0.095688[/C][C]0.6629[/C][C]0.255269[/C][/ROW]
[ROW][C]21[/C][C]0.167311[/C][C]1.1592[/C][C]0.126062[/C][/ROW]
[ROW][C]22[/C][C]-0.16551[/C][C]-1.1467[/C][C]0.128597[/C][/ROW]
[ROW][C]23[/C][C]0.11597[/C][C]0.8035[/C][C]0.212834[/C][/ROW]
[ROW][C]24[/C][C]0.075866[/C][C]0.5256[/C][C]0.300788[/C][/ROW]
[ROW][C]25[/C][C]-0.133365[/C][C]-0.924[/C][C]0.180059[/C][/ROW]
[ROW][C]26[/C][C]-0.148988[/C][C]-1.0322[/C][C]0.153571[/C][/ROW]
[ROW][C]27[/C][C]-0.078662[/C][C]-0.545[/C][C]0.294144[/C][/ROW]
[ROW][C]28[/C][C]-0.064341[/C][C]-0.4458[/C][C]0.328886[/C][/ROW]
[ROW][C]29[/C][C]-0.17986[/C][C]-1.2461[/C][C]0.109386[/C][/ROW]
[ROW][C]30[/C][C]0.08257[/C][C]0.5721[/C][C]0.284975[/C][/ROW]
[ROW][C]31[/C][C]0.007825[/C][C]0.0542[/C][C]0.478496[/C][/ROW]
[ROW][C]32[/C][C]-0.140574[/C][C]-0.9739[/C][C]0.167487[/C][/ROW]
[ROW][C]33[/C][C]-0.046231[/C][C]-0.3203[/C][C]0.375068[/C][/ROW]
[ROW][C]34[/C][C]-0.085947[/C][C]-0.5955[/C][C]0.277167[/C][/ROW]
[ROW][C]35[/C][C]0.096824[/C][C]0.6708[/C][C]0.252776[/C][/ROW]
[ROW][C]36[/C][C]0.007852[/C][C]0.0544[/C][C]0.478421[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60034&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60034&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
1-0.596862-4.13527.1e-05
2-0.418127-2.89690.002831
3-0.104873-0.72660.235505
40.133560.92530.179711
5-0.027205-0.18850.425646
60.1108670.76810.223093
7-0.089683-0.62130.268657
80.0057550.03990.484181
90.1530041.060.147214
100.0230.15930.437032
110.0896810.62130.268662
12-0.066341-0.45960.323931
13-0.199048-1.3790.087136
14-0.0933-0.64640.26055
150.1686941.16870.124138
16-0.085987-0.59570.277076
17-0.032786-0.22710.410637
18-0.142723-0.98880.163857
190.0153380.10630.457908
200.0956880.66290.255269
210.1673111.15920.126062
22-0.16551-1.14670.128597
230.115970.80350.212834
240.0758660.52560.300788
25-0.133365-0.9240.180059
26-0.148988-1.03220.153571
27-0.078662-0.5450.294144
28-0.064341-0.44580.328886
29-0.17986-1.24610.109386
300.082570.57210.284975
310.0078250.05420.478496
32-0.140574-0.97390.167487
33-0.046231-0.32030.375068
34-0.085947-0.59550.277167
350.0968240.67080.252776
360.0078520.05440.478421


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 = 1 ; 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')