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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 computationSat, 29 Nov 2008 07:36:13 -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/2008/Nov/29/t1227969474xceo3a0aiso8xhs.htm/, Retrieved Wed, 05 Nov 2025 13:05:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26297, Retrieved Wed, 05 Nov 2025 13:05:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact52

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD    [(Partial) Autocorrelation Function] [vraag 6] [2008-11-29 14:36:13] [3dc594a6c62226e1e98766c4d385bfaa] [Current]
-   P       [(Partial) Autocorrelation Function] [vraag 6] [2008-11-29 14:44:03] [c45c87b96bbf32ffc2144fc37d767b2e]
-   P       [(Partial) Autocorrelation Function] [vraag 6] [2008-11-29 14:47:29] [c45c87b96bbf32ffc2144fc37d767b2e]
- RMP         [Variance Reduction Matrix] [vraag 6] [2008-11-29 14:53:24] [c45c87b96bbf32ffc2144fc37d767b2e]
- RMP           [Spectral Analysis] [vraag 6] [2008-11-29 14:56:54] [c45c87b96bbf32ffc2144fc37d767b2e]
- RMP           [Spectral Analysis] [vraag 6] [2008-11-29 15:02:46] [c45c87b96bbf32ffc2144fc37d767b2e]
F                 [Spectral Analysis] [Q6 3] [2008-12-01 17:38:11] [5df08e692026406c2f93d5d4ef3e9043]
F RMPD            [Cross Correlation Function] [Q7] [2008-12-01 17:48:05] [5df08e692026406c2f93d5d4ef3e9043]
Feedback Forum
2008-12-08 19:29:29 [Michaël De Kuyer] [reply] 
Ik heb naar mijn mening alle berekeningen van ACF, VRM, raw periodogram correct gemaakt en juist geïnterpreteerd.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
112
118
132
129
121
135
148
148
136
119
104
118
115
126
141
135
125
149
170
170
158
133
114
140
145
150
178
163
172
178
199
199
184
162
146
166
171
180
193
181
183
218
230
242
209
191
172
194
196
196
236
235
229
243
264
272
237
211
180
201
204
188
235
227
234
264
302
293
259
229
203
229
242
233
267
269
270
315
364
347
312
274
237
278
284
277
317
313
318
374
413
405
355
306
271
306
315
301
356
348
355
422
465
467
404
347
305
336
340
318
362
348
363
435
491
505
404
359
310
337
360
342
406
396
420
472
548
559
463
407
362
405
417
391
419
461
472
535
622
606
508
461
390
432

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=26297&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=26297&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26297&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
10.94804711.37660
20.87557510.50690
30.8066819.68020
40.7526259.03150
50.713778.56520
60.6817348.18080
70.6629047.95490
80.655617.86730
90.6709488.05140
100.702728.43260
110.743248.91890
120.7603959.12470
130.7126618.55190
140.6463427.75610
150.5859237.03110
160.5379556.45550
170.4997485.9970
180.4687345.62480
190.4498715.39840
200.4416295.29950
210.4572245.48670
220.4824825.78980
230.5171276.20550
240.532196.38630
250.4939765.92770
260.4377215.25270
270.3876034.65124e-06
280.3480254.17632.6e-05
290.3149843.77980.000115
300.2884973.4620.000353
310.2708023.24960.000719
320.264293.17150.000927
330.2767993.32160.000567
340.2985213.58230.000233
350.3255873.9077.2e-05
360.3370244.04434.3e-05

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.948047 & 11.3766 & 0 \tabularnewline
2 & 0.875575 & 10.5069 & 0 \tabularnewline
3 & 0.806681 & 9.6802 & 0 \tabularnewline
4 & 0.752625 & 9.0315 & 0 \tabularnewline
5 & 0.71377 & 8.5652 & 0 \tabularnewline
6 & 0.681734 & 8.1808 & 0 \tabularnewline
7 & 0.662904 & 7.9549 & 0 \tabularnewline
8 & 0.65561 & 7.8673 & 0 \tabularnewline
9 & 0.670948 & 8.0514 & 0 \tabularnewline
10 & 0.70272 & 8.4326 & 0 \tabularnewline
11 & 0.74324 & 8.9189 & 0 \tabularnewline
12 & 0.760395 & 9.1247 & 0 \tabularnewline
13 & 0.712661 & 8.5519 & 0 \tabularnewline
14 & 0.646342 & 7.7561 & 0 \tabularnewline
15 & 0.585923 & 7.0311 & 0 \tabularnewline
16 & 0.537955 & 6.4555 & 0 \tabularnewline
17 & 0.499748 & 5.997 & 0 \tabularnewline
18 & 0.468734 & 5.6248 & 0 \tabularnewline
19 & 0.449871 & 5.3984 & 0 \tabularnewline
20 & 0.441629 & 5.2995 & 0 \tabularnewline
21 & 0.457224 & 5.4867 & 0 \tabularnewline
22 & 0.482482 & 5.7898 & 0 \tabularnewline
23 & 0.517127 & 6.2055 & 0 \tabularnewline
24 & 0.53219 & 6.3863 & 0 \tabularnewline
25 & 0.493976 & 5.9277 & 0 \tabularnewline
26 & 0.437721 & 5.2527 & 0 \tabularnewline
27 & 0.387603 & 4.6512 & 4e-06 \tabularnewline
28 & 0.348025 & 4.1763 & 2.6e-05 \tabularnewline
29 & 0.314984 & 3.7798 & 0.000115 \tabularnewline
30 & 0.288497 & 3.462 & 0.000353 \tabularnewline
31 & 0.270802 & 3.2496 & 0.000719 \tabularnewline
32 & 0.26429 & 3.1715 & 0.000927 \tabularnewline
33 & 0.276799 & 3.3216 & 0.000567 \tabularnewline
34 & 0.298521 & 3.5823 & 0.000233 \tabularnewline
35 & 0.325587 & 3.907 & 7.2e-05 \tabularnewline
36 & 0.337024 & 4.0443 & 4.3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26297&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]0.948047[/C][C]11.3766[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.875575[/C][C]10.5069[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.806681[/C][C]9.6802[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.752625[/C][C]9.0315[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.71377[/C][C]8.5652[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.681734[/C][C]8.1808[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.662904[/C][C]7.9549[/C][C]0[/C][/ROW]
[ROW][C]8[/C][C]0.65561[/C][C]7.8673[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.670948[/C][C]8.0514[/C][C]0[/C][/ROW]
[ROW][C]10[/C][C]0.70272[/C][C]8.4326[/C][C]0[/C][/ROW]
[ROW][C]11[/C][C]0.74324[/C][C]8.9189[/C][C]0[/C][/ROW]
[ROW][C]12[/C][C]0.760395[/C][C]9.1247[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.712661[/C][C]8.5519[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]0.646342[/C][C]7.7561[/C][C]0[/C][/ROW]
[ROW][C]15[/C][C]0.585923[/C][C]7.0311[/C][C]0[/C][/ROW]
[ROW][C]16[/C][C]0.537955[/C][C]6.4555[/C][C]0[/C][/ROW]
[ROW][C]17[/C][C]0.499748[/C][C]5.997[/C][C]0[/C][/ROW]
[ROW][C]18[/C][C]0.468734[/C][C]5.6248[/C][C]0[/C][/ROW]
[ROW][C]19[/C][C]0.449871[/C][C]5.3984[/C][C]0[/C][/ROW]
[ROW][C]20[/C][C]0.441629[/C][C]5.2995[/C][C]0[/C][/ROW]
[ROW][C]21[/C][C]0.457224[/C][C]5.4867[/C][C]0[/C][/ROW]
[ROW][C]22[/C][C]0.482482[/C][C]5.7898[/C][C]0[/C][/ROW]
[ROW][C]23[/C][C]0.517127[/C][C]6.2055[/C][C]0[/C][/ROW]
[ROW][C]24[/C][C]0.53219[/C][C]6.3863[/C][C]0[/C][/ROW]
[ROW][C]25[/C][C]0.493976[/C][C]5.9277[/C][C]0[/C][/ROW]
[ROW][C]26[/C][C]0.437721[/C][C]5.2527[/C][C]0[/C][/ROW]
[ROW][C]27[/C][C]0.387603[/C][C]4.6512[/C][C]4e-06[/C][/ROW]
[ROW][C]28[/C][C]0.348025[/C][C]4.1763[/C][C]2.6e-05[/C][/ROW]
[ROW][C]29[/C][C]0.314984[/C][C]3.7798[/C][C]0.000115[/C][/ROW]
[ROW][C]30[/C][C]0.288497[/C][C]3.462[/C][C]0.000353[/C][/ROW]
[ROW][C]31[/C][C]0.270802[/C][C]3.2496[/C][C]0.000719[/C][/ROW]
[ROW][C]32[/C][C]0.26429[/C][C]3.1715[/C][C]0.000927[/C][/ROW]
[ROW][C]33[/C][C]0.276799[/C][C]3.3216[/C][C]0.000567[/C][/ROW]
[ROW][C]34[/C][C]0.298521[/C][C]3.5823[/C][C]0.000233[/C][/ROW]
[ROW][C]35[/C][C]0.325587[/C][C]3.907[/C][C]7.2e-05[/C][/ROW]
[ROW][C]36[/C][C]0.337024[/C][C]4.0443[/C][C]4.3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26297&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26297&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
10.94804711.37660
20.87557510.50690
30.8066819.68020
40.7526259.03150
50.713778.56520
60.6817348.18080
70.6629047.95490
80.655617.86730
90.6709488.05140
100.702728.43260
110.743248.91890
120.7603959.12470
130.7126618.55190
140.6463427.75610
150.5859237.03110
160.5379556.45550
170.4997485.9970
180.4687345.62480
190.4498715.39840
200.4416295.29950
210.4572245.48670
220.4824825.78980
230.5171276.20550
240.532196.38630
250.4939765.92770
260.4377215.25270
270.3876034.65124e-06
280.3480254.17632.6e-05
290.3149843.77980.000115
300.2884973.4620.000353
310.2708023.24960.000719
320.264293.17150.000927
330.2767993.32160.000567
340.2985213.58230.000233
350.3255873.9077.2e-05
360.3370244.04434.3e-05






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.94804711.37660
2-0.229422-2.75310.003332
30.0381480.45780.323903
40.0937851.12540.131141
50.0736070.88330.189279
60.0077280.09270.463123
70.1255971.50720.066979
80.0899511.07940.141103
90.2324892.78990.002994
100.1660511.99260.024097
110.1712742.05530.020829
12-0.135431-1.62520.053156
13-0.539691-6.47630
14-0.02661-0.31930.374973
150.0907651.08920.138947
160.0249560.29950.382508
170.0325160.39020.348487
180.0734330.88120.189841
190.0484420.58130.280972
20-0.045542-0.54650.292784
210.0457530.5490.291916
22-0.100179-1.20210.11564
230.0524350.62920.265101
240.0480140.57620.2827
25-0.162746-1.9530.026382
26-0.036135-0.43360.332607
270.0664240.79710.213357
280.0061760.07410.470511
290.0075370.09040.464029
300.019350.23220.408354
31-0.010251-0.1230.451132
32-0.01831-0.21970.413199
33-0.029001-0.3480.364168
34-0.014805-0.17770.42962
35-0.047725-0.57270.283872
360.0462040.55440.290068

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.948047 & 11.3766 & 0 \tabularnewline
2 & -0.229422 & -2.7531 & 0.003332 \tabularnewline
3 & 0.038148 & 0.4578 & 0.323903 \tabularnewline
4 & 0.093785 & 1.1254 & 0.131141 \tabularnewline
5 & 0.073607 & 0.8833 & 0.189279 \tabularnewline
6 & 0.007728 & 0.0927 & 0.463123 \tabularnewline
7 & 0.125597 & 1.5072 & 0.066979 \tabularnewline
8 & 0.089951 & 1.0794 & 0.141103 \tabularnewline
9 & 0.232489 & 2.7899 & 0.002994 \tabularnewline
10 & 0.166051 & 1.9926 & 0.024097 \tabularnewline
11 & 0.171274 & 2.0553 & 0.020829 \tabularnewline
12 & -0.135431 & -1.6252 & 0.053156 \tabularnewline
13 & -0.539691 & -6.4763 & 0 \tabularnewline
14 & -0.02661 & -0.3193 & 0.374973 \tabularnewline
15 & 0.090765 & 1.0892 & 0.138947 \tabularnewline
16 & 0.024956 & 0.2995 & 0.382508 \tabularnewline
17 & 0.032516 & 0.3902 & 0.348487 \tabularnewline
18 & 0.073433 & 0.8812 & 0.189841 \tabularnewline
19 & 0.048442 & 0.5813 & 0.280972 \tabularnewline
20 & -0.045542 & -0.5465 & 0.292784 \tabularnewline
21 & 0.045753 & 0.549 & 0.291916 \tabularnewline
22 & -0.100179 & -1.2021 & 0.11564 \tabularnewline
23 & 0.052435 & 0.6292 & 0.265101 \tabularnewline
24 & 0.048014 & 0.5762 & 0.2827 \tabularnewline
25 & -0.162746 & -1.953 & 0.026382 \tabularnewline
26 & -0.036135 & -0.4336 & 0.332607 \tabularnewline
27 & 0.066424 & 0.7971 & 0.213357 \tabularnewline
28 & 0.006176 & 0.0741 & 0.470511 \tabularnewline
29 & 0.007537 & 0.0904 & 0.464029 \tabularnewline
30 & 0.01935 & 0.2322 & 0.408354 \tabularnewline
31 & -0.010251 & -0.123 & 0.451132 \tabularnewline
32 & -0.01831 & -0.2197 & 0.413199 \tabularnewline
33 & -0.029001 & -0.348 & 0.364168 \tabularnewline
34 & -0.014805 & -0.1777 & 0.42962 \tabularnewline
35 & -0.047725 & -0.5727 & 0.283872 \tabularnewline
36 & 0.046204 & 0.5544 & 0.290068 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26297&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]0.948047[/C][C]11.3766[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.229422[/C][C]-2.7531[/C][C]0.003332[/C][/ROW]
[ROW][C]3[/C][C]0.038148[/C][C]0.4578[/C][C]0.323903[/C][/ROW]
[ROW][C]4[/C][C]0.093785[/C][C]1.1254[/C][C]0.131141[/C][/ROW]
[ROW][C]5[/C][C]0.073607[/C][C]0.8833[/C][C]0.189279[/C][/ROW]
[ROW][C]6[/C][C]0.007728[/C][C]0.0927[/C][C]0.463123[/C][/ROW]
[ROW][C]7[/C][C]0.125597[/C][C]1.5072[/C][C]0.066979[/C][/ROW]
[ROW][C]8[/C][C]0.089951[/C][C]1.0794[/C][C]0.141103[/C][/ROW]
[ROW][C]9[/C][C]0.232489[/C][C]2.7899[/C][C]0.002994[/C][/ROW]
[ROW][C]10[/C][C]0.166051[/C][C]1.9926[/C][C]0.024097[/C][/ROW]
[ROW][C]11[/C][C]0.171274[/C][C]2.0553[/C][C]0.020829[/C][/ROW]
[ROW][C]12[/C][C]-0.135431[/C][C]-1.6252[/C][C]0.053156[/C][/ROW]
[ROW][C]13[/C][C]-0.539691[/C][C]-6.4763[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]-0.02661[/C][C]-0.3193[/C][C]0.374973[/C][/ROW]
[ROW][C]15[/C][C]0.090765[/C][C]1.0892[/C][C]0.138947[/C][/ROW]
[ROW][C]16[/C][C]0.024956[/C][C]0.2995[/C][C]0.382508[/C][/ROW]
[ROW][C]17[/C][C]0.032516[/C][C]0.3902[/C][C]0.348487[/C][/ROW]
[ROW][C]18[/C][C]0.073433[/C][C]0.8812[/C][C]0.189841[/C][/ROW]
[ROW][C]19[/C][C]0.048442[/C][C]0.5813[/C][C]0.280972[/C][/ROW]
[ROW][C]20[/C][C]-0.045542[/C][C]-0.5465[/C][C]0.292784[/C][/ROW]
[ROW][C]21[/C][C]0.045753[/C][C]0.549[/C][C]0.291916[/C][/ROW]
[ROW][C]22[/C][C]-0.100179[/C][C]-1.2021[/C][C]0.11564[/C][/ROW]
[ROW][C]23[/C][C]0.052435[/C][C]0.6292[/C][C]0.265101[/C][/ROW]
[ROW][C]24[/C][C]0.048014[/C][C]0.5762[/C][C]0.2827[/C][/ROW]
[ROW][C]25[/C][C]-0.162746[/C][C]-1.953[/C][C]0.026382[/C][/ROW]
[ROW][C]26[/C][C]-0.036135[/C][C]-0.4336[/C][C]0.332607[/C][/ROW]
[ROW][C]27[/C][C]0.066424[/C][C]0.7971[/C][C]0.213357[/C][/ROW]
[ROW][C]28[/C][C]0.006176[/C][C]0.0741[/C][C]0.470511[/C][/ROW]
[ROW][C]29[/C][C]0.007537[/C][C]0.0904[/C][C]0.464029[/C][/ROW]
[ROW][C]30[/C][C]0.01935[/C][C]0.2322[/C][C]0.408354[/C][/ROW]
[ROW][C]31[/C][C]-0.010251[/C][C]-0.123[/C][C]0.451132[/C][/ROW]
[ROW][C]32[/C][C]-0.01831[/C][C]-0.2197[/C][C]0.413199[/C][/ROW]
[ROW][C]33[/C][C]-0.029001[/C][C]-0.348[/C][C]0.364168[/C][/ROW]
[ROW][C]34[/C][C]-0.014805[/C][C]-0.1777[/C][C]0.42962[/C][/ROW]
[ROW][C]35[/C][C]-0.047725[/C][C]-0.5727[/C][C]0.283872[/C][/ROW]
[ROW][C]36[/C][C]0.046204[/C][C]0.5544[/C][C]0.290068[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26297&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26297&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
10.94804711.37660
2-0.229422-2.75310.003332
30.0381480.45780.323903
40.0937851.12540.131141
50.0736070.88330.189279
60.0077280.09270.463123
70.1255971.50720.066979
80.0899511.07940.141103
90.2324892.78990.002994
100.1660511.99260.024097
110.1712742.05530.020829
12-0.135431-1.62520.053156
13-0.539691-6.47630
14-0.02661-0.31930.374973
150.0907651.08920.138947
160.0249560.29950.382508
170.0325160.39020.348487
180.0734330.88120.189841
190.0484420.58130.280972
20-0.045542-0.54650.292784
210.0457530.5490.291916
22-0.100179-1.20210.11564
230.0524350.62920.265101
240.0480140.57620.2827
25-0.162746-1.9530.026382
26-0.036135-0.43360.332607
270.0664240.79710.213357
280.0061760.07410.470511
290.0075370.09040.464029
300.019350.23220.408354
31-0.010251-0.1230.451132
32-0.01831-0.21970.413199
33-0.029001-0.3480.364168
34-0.014805-0.17770.42962
35-0.047725-0.57270.283872
360.0462040.55440.290068


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
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 (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='lags',ylab='ACF')
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')