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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, 11 Dec 2009 08:01:14 -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/Dec/11/t1260543728lkcigtaqb7z6zc4.htm/, Retrieved Mon, 29 Apr 2024 06:41:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66294, Retrieved Mon, 29 Apr 2024 06:41:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [blog] [2008-12-01 15:44:12] [12d343c4448a5f9e527bb31caeac580b]
-   PD  [Multiple Regression] [blog] [2008-12-01 16:17:50] [12d343c4448a5f9e527bb31caeac580b]
-   PD    [Multiple Regression] [dioxine] [2008-12-01 16:30:23] [7a664918911e34206ce9d0436dd7c1c8]
-    D      [Multiple Regression] [Hypothese 1 en 2 ...] [2008-12-03 15:49:48] [12d343c4448a5f9e527bb31caeac580b]
- RMPD        [(Partial) Autocorrelation Function] [paper:3 ACF (d,D=0)] [2009-12-11 14:59:19] [0f0e461427f61416e46aeda5f4901bed]
-                 [(Partial) Autocorrelation Function] [paper:4 ACF (d=1,...] [2009-12-11 15:01:14] [b090d569c0a4c77894e0b029f4429f19] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
118.7
110.1
110.3
112.9
102.2
99.4
116.1
103.8
101.8
113.7
89.7
99.5
122.9
108.6
114.4
110.5
104.1
103.6
121.6
101.1
116.0
120.1
96.0
105.0
124.7
123.9
123.6
114.8
108.8
106.1
123.2
106.2
115.2
120.6
109.5
114.4
121.4
129.5
124.3
112.6
125.1
117.9
116.4
126.4
93.3
102.9
97.8
97.1
110.7
109.3
103.2
106.2
81.3
84.5
92.7
85.0
79.1
92.6
78.1
76.9
92.5

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=66294&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=66294&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66294&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
1-0.402133-3.11490.00141
2-0.226814-1.75690.042019
30.4008863.10520.001451
4-0.34663-2.6850.004682
5-0.06968-0.53970.295687
60.4033343.12420.001373
7-0.21625-1.67510.049563
8-0.051543-0.39930.345562
90.1545431.19710.117992
10-0.188616-1.4610.074614
11-0.103204-0.79940.213602
120.3597362.78650.003561
13-0.111755-0.86570.195064
14-0.075649-0.5860.280046
150.0849130.65770.256612
16-0.151482-1.17340.122642
170.0257820.19970.421193
180.123850.95930.170619
19-0.032708-0.25340.40043
20-0.132388-1.02550.154628
210.0590080.45710.324634
22-0.009639-0.07470.470365
23-0.094769-0.73410.232881
240.144541.11960.133673
250.1076280.83370.203884
26-0.214699-1.66310.050759
270.0788220.61060.2719
280.005450.04220.483233
29-0.080968-0.62720.266463
300.0727910.56380.287484
310.1267390.98170.165091
32-0.238999-1.85130.034526
330.0329590.25530.399681
340.1075360.8330.204082
35-0.197234-1.52780.065912
360.1482381.14820.127712

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.402133 & -3.1149 & 0.00141 \tabularnewline
2 & -0.226814 & -1.7569 & 0.042019 \tabularnewline
3 & 0.400886 & 3.1052 & 0.001451 \tabularnewline
4 & -0.34663 & -2.685 & 0.004682 \tabularnewline
5 & -0.06968 & -0.5397 & 0.295687 \tabularnewline
6 & 0.403334 & 3.1242 & 0.001373 \tabularnewline
7 & -0.21625 & -1.6751 & 0.049563 \tabularnewline
8 & -0.051543 & -0.3993 & 0.345562 \tabularnewline
9 & 0.154543 & 1.1971 & 0.117992 \tabularnewline
10 & -0.188616 & -1.461 & 0.074614 \tabularnewline
11 & -0.103204 & -0.7994 & 0.213602 \tabularnewline
12 & 0.359736 & 2.7865 & 0.003561 \tabularnewline
13 & -0.111755 & -0.8657 & 0.195064 \tabularnewline
14 & -0.075649 & -0.586 & 0.280046 \tabularnewline
15 & 0.084913 & 0.6577 & 0.256612 \tabularnewline
16 & -0.151482 & -1.1734 & 0.122642 \tabularnewline
17 & 0.025782 & 0.1997 & 0.421193 \tabularnewline
18 & 0.12385 & 0.9593 & 0.170619 \tabularnewline
19 & -0.032708 & -0.2534 & 0.40043 \tabularnewline
20 & -0.132388 & -1.0255 & 0.154628 \tabularnewline
21 & 0.059008 & 0.4571 & 0.324634 \tabularnewline
22 & -0.009639 & -0.0747 & 0.470365 \tabularnewline
23 & -0.094769 & -0.7341 & 0.232881 \tabularnewline
24 & 0.14454 & 1.1196 & 0.133673 \tabularnewline
25 & 0.107628 & 0.8337 & 0.203884 \tabularnewline
26 & -0.214699 & -1.6631 & 0.050759 \tabularnewline
27 & 0.078822 & 0.6106 & 0.2719 \tabularnewline
28 & 0.00545 & 0.0422 & 0.483233 \tabularnewline
29 & -0.080968 & -0.6272 & 0.266463 \tabularnewline
30 & 0.072791 & 0.5638 & 0.287484 \tabularnewline
31 & 0.126739 & 0.9817 & 0.165091 \tabularnewline
32 & -0.238999 & -1.8513 & 0.034526 \tabularnewline
33 & 0.032959 & 0.2553 & 0.399681 \tabularnewline
34 & 0.107536 & 0.833 & 0.204082 \tabularnewline
35 & -0.197234 & -1.5278 & 0.065912 \tabularnewline
36 & 0.148238 & 1.1482 & 0.127712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66294&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]-0.402133[/C][C]-3.1149[/C][C]0.00141[/C][/ROW]
[ROW][C]2[/C][C]-0.226814[/C][C]-1.7569[/C][C]0.042019[/C][/ROW]
[ROW][C]3[/C][C]0.400886[/C][C]3.1052[/C][C]0.001451[/C][/ROW]
[ROW][C]4[/C][C]-0.34663[/C][C]-2.685[/C][C]0.004682[/C][/ROW]
[ROW][C]5[/C][C]-0.06968[/C][C]-0.5397[/C][C]0.295687[/C][/ROW]
[ROW][C]6[/C][C]0.403334[/C][C]3.1242[/C][C]0.001373[/C][/ROW]
[ROW][C]7[/C][C]-0.21625[/C][C]-1.6751[/C][C]0.049563[/C][/ROW]
[ROW][C]8[/C][C]-0.051543[/C][C]-0.3993[/C][C]0.345562[/C][/ROW]
[ROW][C]9[/C][C]0.154543[/C][C]1.1971[/C][C]0.117992[/C][/ROW]
[ROW][C]10[/C][C]-0.188616[/C][C]-1.461[/C][C]0.074614[/C][/ROW]
[ROW][C]11[/C][C]-0.103204[/C][C]-0.7994[/C][C]0.213602[/C][/ROW]
[ROW][C]12[/C][C]0.359736[/C][C]2.7865[/C][C]0.003561[/C][/ROW]
[ROW][C]13[/C][C]-0.111755[/C][C]-0.8657[/C][C]0.195064[/C][/ROW]
[ROW][C]14[/C][C]-0.075649[/C][C]-0.586[/C][C]0.280046[/C][/ROW]
[ROW][C]15[/C][C]0.084913[/C][C]0.6577[/C][C]0.256612[/C][/ROW]
[ROW][C]16[/C][C]-0.151482[/C][C]-1.1734[/C][C]0.122642[/C][/ROW]
[ROW][C]17[/C][C]0.025782[/C][C]0.1997[/C][C]0.421193[/C][/ROW]
[ROW][C]18[/C][C]0.12385[/C][C]0.9593[/C][C]0.170619[/C][/ROW]
[ROW][C]19[/C][C]-0.032708[/C][C]-0.2534[/C][C]0.40043[/C][/ROW]
[ROW][C]20[/C][C]-0.132388[/C][C]-1.0255[/C][C]0.154628[/C][/ROW]
[ROW][C]21[/C][C]0.059008[/C][C]0.4571[/C][C]0.324634[/C][/ROW]
[ROW][C]22[/C][C]-0.009639[/C][C]-0.0747[/C][C]0.470365[/C][/ROW]
[ROW][C]23[/C][C]-0.094769[/C][C]-0.7341[/C][C]0.232881[/C][/ROW]
[ROW][C]24[/C][C]0.14454[/C][C]1.1196[/C][C]0.133673[/C][/ROW]
[ROW][C]25[/C][C]0.107628[/C][C]0.8337[/C][C]0.203884[/C][/ROW]
[ROW][C]26[/C][C]-0.214699[/C][C]-1.6631[/C][C]0.050759[/C][/ROW]
[ROW][C]27[/C][C]0.078822[/C][C]0.6106[/C][C]0.2719[/C][/ROW]
[ROW][C]28[/C][C]0.00545[/C][C]0.0422[/C][C]0.483233[/C][/ROW]
[ROW][C]29[/C][C]-0.080968[/C][C]-0.6272[/C][C]0.266463[/C][/ROW]
[ROW][C]30[/C][C]0.072791[/C][C]0.5638[/C][C]0.287484[/C][/ROW]
[ROW][C]31[/C][C]0.126739[/C][C]0.9817[/C][C]0.165091[/C][/ROW]
[ROW][C]32[/C][C]-0.238999[/C][C]-1.8513[/C][C]0.034526[/C][/ROW]
[ROW][C]33[/C][C]0.032959[/C][C]0.2553[/C][C]0.399681[/C][/ROW]
[ROW][C]34[/C][C]0.107536[/C][C]0.833[/C][C]0.204082[/C][/ROW]
[ROW][C]35[/C][C]-0.197234[/C][C]-1.5278[/C][C]0.065912[/C][/ROW]
[ROW][C]36[/C][C]0.148238[/C][C]1.1482[/C][C]0.127712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66294&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66294&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
1-0.402133-3.11490.00141
2-0.226814-1.75690.042019
30.4008863.10520.001451
4-0.34663-2.6850.004682
5-0.06968-0.53970.295687
60.4033343.12420.001373
7-0.21625-1.67510.049563
8-0.051543-0.39930.345562
90.1545431.19710.117992
10-0.188616-1.4610.074614
11-0.103204-0.79940.213602
120.3597362.78650.003561
13-0.111755-0.86570.195064
14-0.075649-0.5860.280046
150.0849130.65770.256612
16-0.151482-1.17340.122642
170.0257820.19970.421193
180.123850.95930.170619
19-0.032708-0.25340.40043
20-0.132388-1.02550.154628
210.0590080.45710.324634
22-0.009639-0.07470.470365
23-0.094769-0.73410.232881
240.144541.11960.133673
250.1076280.83370.203884
26-0.214699-1.66310.050759
270.0788220.61060.2719
280.005450.04220.483233
29-0.080968-0.62720.266463
300.0727910.56380.287484
310.1267390.98170.165091
32-0.238999-1.85130.034526
330.0329590.25530.399681
340.1075360.8330.204082
35-0.197234-1.52780.065912
360.1482381.14820.127712






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.402133-3.11490.00141
2-0.463475-3.59010.000333
30.1230960.95350.172081
4-0.266513-2.06440.021655
5-0.258007-1.99850.025099
60.0991720.76820.222696
70.1026780.79530.214776
80.0419430.32490.373197
9-0.059697-0.46240.322729
10-0.048027-0.3720.355597
11-0.241406-1.86990.033188
120.128170.99280.162397
130.1390881.07740.142813
140.1464441.13440.130579
15-0.099158-0.76810.222727
16-0.083711-0.64840.259594
170.060050.46510.321756
18-0.012827-0.09940.460593
190.0361720.28020.390149
20-0.294147-2.27850.013137
21-0.162771-1.26080.106128
22-0.059831-0.46340.32236
230.0268960.20830.417836
24-0.066727-0.51690.303576
250.0846150.65540.257349
26-0.054763-0.42420.336473
270.0169130.1310.448104
280.036510.28280.389151
290.065040.50380.308125
30-0.125809-0.97450.166858
31-0.003262-0.02530.489964
32-0.059615-0.46180.322957
33-0.058249-0.45120.326737
34-0.020199-0.15650.438097
35-0.179141-1.38760.085193
36-0.067941-0.52630.30032

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.402133 & -3.1149 & 0.00141 \tabularnewline
2 & -0.463475 & -3.5901 & 0.000333 \tabularnewline
3 & 0.123096 & 0.9535 & 0.172081 \tabularnewline
4 & -0.266513 & -2.0644 & 0.021655 \tabularnewline
5 & -0.258007 & -1.9985 & 0.025099 \tabularnewline
6 & 0.099172 & 0.7682 & 0.222696 \tabularnewline
7 & 0.102678 & 0.7953 & 0.214776 \tabularnewline
8 & 0.041943 & 0.3249 & 0.373197 \tabularnewline
9 & -0.059697 & -0.4624 & 0.322729 \tabularnewline
10 & -0.048027 & -0.372 & 0.355597 \tabularnewline
11 & -0.241406 & -1.8699 & 0.033188 \tabularnewline
12 & 0.12817 & 0.9928 & 0.162397 \tabularnewline
13 & 0.139088 & 1.0774 & 0.142813 \tabularnewline
14 & 0.146444 & 1.1344 & 0.130579 \tabularnewline
15 & -0.099158 & -0.7681 & 0.222727 \tabularnewline
16 & -0.083711 & -0.6484 & 0.259594 \tabularnewline
17 & 0.06005 & 0.4651 & 0.321756 \tabularnewline
18 & -0.012827 & -0.0994 & 0.460593 \tabularnewline
19 & 0.036172 & 0.2802 & 0.390149 \tabularnewline
20 & -0.294147 & -2.2785 & 0.013137 \tabularnewline
21 & -0.162771 & -1.2608 & 0.106128 \tabularnewline
22 & -0.059831 & -0.4634 & 0.32236 \tabularnewline
23 & 0.026896 & 0.2083 & 0.417836 \tabularnewline
24 & -0.066727 & -0.5169 & 0.303576 \tabularnewline
25 & 0.084615 & 0.6554 & 0.257349 \tabularnewline
26 & -0.054763 & -0.4242 & 0.336473 \tabularnewline
27 & 0.016913 & 0.131 & 0.448104 \tabularnewline
28 & 0.03651 & 0.2828 & 0.389151 \tabularnewline
29 & 0.06504 & 0.5038 & 0.308125 \tabularnewline
30 & -0.125809 & -0.9745 & 0.166858 \tabularnewline
31 & -0.003262 & -0.0253 & 0.489964 \tabularnewline
32 & -0.059615 & -0.4618 & 0.322957 \tabularnewline
33 & -0.058249 & -0.4512 & 0.326737 \tabularnewline
34 & -0.020199 & -0.1565 & 0.438097 \tabularnewline
35 & -0.179141 & -1.3876 & 0.085193 \tabularnewline
36 & -0.067941 & -0.5263 & 0.30032 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66294&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]-0.402133[/C][C]-3.1149[/C][C]0.00141[/C][/ROW]
[ROW][C]2[/C][C]-0.463475[/C][C]-3.5901[/C][C]0.000333[/C][/ROW]
[ROW][C]3[/C][C]0.123096[/C][C]0.9535[/C][C]0.172081[/C][/ROW]
[ROW][C]4[/C][C]-0.266513[/C][C]-2.0644[/C][C]0.021655[/C][/ROW]
[ROW][C]5[/C][C]-0.258007[/C][C]-1.9985[/C][C]0.025099[/C][/ROW]
[ROW][C]6[/C][C]0.099172[/C][C]0.7682[/C][C]0.222696[/C][/ROW]
[ROW][C]7[/C][C]0.102678[/C][C]0.7953[/C][C]0.214776[/C][/ROW]
[ROW][C]8[/C][C]0.041943[/C][C]0.3249[/C][C]0.373197[/C][/ROW]
[ROW][C]9[/C][C]-0.059697[/C][C]-0.4624[/C][C]0.322729[/C][/ROW]
[ROW][C]10[/C][C]-0.048027[/C][C]-0.372[/C][C]0.355597[/C][/ROW]
[ROW][C]11[/C][C]-0.241406[/C][C]-1.8699[/C][C]0.033188[/C][/ROW]
[ROW][C]12[/C][C]0.12817[/C][C]0.9928[/C][C]0.162397[/C][/ROW]
[ROW][C]13[/C][C]0.139088[/C][C]1.0774[/C][C]0.142813[/C][/ROW]
[ROW][C]14[/C][C]0.146444[/C][C]1.1344[/C][C]0.130579[/C][/ROW]
[ROW][C]15[/C][C]-0.099158[/C][C]-0.7681[/C][C]0.222727[/C][/ROW]
[ROW][C]16[/C][C]-0.083711[/C][C]-0.6484[/C][C]0.259594[/C][/ROW]
[ROW][C]17[/C][C]0.06005[/C][C]0.4651[/C][C]0.321756[/C][/ROW]
[ROW][C]18[/C][C]-0.012827[/C][C]-0.0994[/C][C]0.460593[/C][/ROW]
[ROW][C]19[/C][C]0.036172[/C][C]0.2802[/C][C]0.390149[/C][/ROW]
[ROW][C]20[/C][C]-0.294147[/C][C]-2.2785[/C][C]0.013137[/C][/ROW]
[ROW][C]21[/C][C]-0.162771[/C][C]-1.2608[/C][C]0.106128[/C][/ROW]
[ROW][C]22[/C][C]-0.059831[/C][C]-0.4634[/C][C]0.32236[/C][/ROW]
[ROW][C]23[/C][C]0.026896[/C][C]0.2083[/C][C]0.417836[/C][/ROW]
[ROW][C]24[/C][C]-0.066727[/C][C]-0.5169[/C][C]0.303576[/C][/ROW]
[ROW][C]25[/C][C]0.084615[/C][C]0.6554[/C][C]0.257349[/C][/ROW]
[ROW][C]26[/C][C]-0.054763[/C][C]-0.4242[/C][C]0.336473[/C][/ROW]
[ROW][C]27[/C][C]0.016913[/C][C]0.131[/C][C]0.448104[/C][/ROW]
[ROW][C]28[/C][C]0.03651[/C][C]0.2828[/C][C]0.389151[/C][/ROW]
[ROW][C]29[/C][C]0.06504[/C][C]0.5038[/C][C]0.308125[/C][/ROW]
[ROW][C]30[/C][C]-0.125809[/C][C]-0.9745[/C][C]0.166858[/C][/ROW]
[ROW][C]31[/C][C]-0.003262[/C][C]-0.0253[/C][C]0.489964[/C][/ROW]
[ROW][C]32[/C][C]-0.059615[/C][C]-0.4618[/C][C]0.322957[/C][/ROW]
[ROW][C]33[/C][C]-0.058249[/C][C]-0.4512[/C][C]0.326737[/C][/ROW]
[ROW][C]34[/C][C]-0.020199[/C][C]-0.1565[/C][C]0.438097[/C][/ROW]
[ROW][C]35[/C][C]-0.179141[/C][C]-1.3876[/C][C]0.085193[/C][/ROW]
[ROW][C]36[/C][C]-0.067941[/C][C]-0.5263[/C][C]0.30032[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66294&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66294&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
1-0.402133-3.11490.00141
2-0.463475-3.59010.000333
30.1230960.95350.172081
4-0.266513-2.06440.021655
5-0.258007-1.99850.025099
60.0991720.76820.222696
70.1026780.79530.214776
80.0419430.32490.373197
9-0.059697-0.46240.322729
10-0.048027-0.3720.355597
11-0.241406-1.86990.033188
120.128170.99280.162397
130.1390881.07740.142813
140.1464441.13440.130579
15-0.099158-0.76810.222727
16-0.083711-0.64840.259594
170.060050.46510.321756
18-0.012827-0.09940.460593
190.0361720.28020.390149
20-0.294147-2.27850.013137
21-0.162771-1.26080.106128
22-0.059831-0.46340.32236
230.0268960.20830.417836
24-0.066727-0.51690.303576
250.0846150.65540.257349
26-0.054763-0.42420.336473
270.0169130.1310.448104
280.036510.28280.389151
290.065040.50380.308125
30-0.125809-0.97450.166858
31-0.003262-0.02530.489964
32-0.059615-0.46180.322957
33-0.058249-0.45120.326737
34-0.020199-0.15650.438097
35-0.179141-1.38760.085193
36-0.067941-0.52630.30032


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ;
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')