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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 computationMon, 08 Dec 2008 06:28:31 -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/Dec/08/t1228742940j6b4wc38yi5eeen.htm/, Retrieved Thu, 31 Oct 2024 23:16:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30474, Retrieved Thu, 31 Oct 2024 23:16:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsACF
Estimated Impact223
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]
F RMP   [Standard Deviation-Mean Plot] [q1] [2008-12-08 12:37:39] [3ffd109c9e040b1ae7e5dbe576d4698c]
F RM      [Variance Reduction Matrix] [VRM] [2008-12-08 12:46:37] [3ffd109c9e040b1ae7e5dbe576d4698c]
F RMP       [Spectral Analysis] [spectraal] [2008-12-08 12:59:51] [3ffd109c9e040b1ae7e5dbe576d4698c]
-   P         [Spectral Analysis] [spectraal] [2008-12-08 13:04:01] [3ffd109c9e040b1ae7e5dbe576d4698c]
F RMP             [(Partial) Autocorrelation Function] [ACF] [2008-12-08 13:28:31] [962e6c9020896982bc8283b8971710a9] [Current]
F RM                [Spectral Analysis] [spectraal] [2008-12-08 13:30:33] [3ffd109c9e040b1ae7e5dbe576d4698c]
- RM                [ARIMA Backward Selection] [arima] [2008-12-08 13:33:32] [3ffd109c9e040b1ae7e5dbe576d4698c]
F                     [ARIMA Backward Selection] [arima backward] [2008-12-08 13:36:06] [3ffd109c9e040b1ae7e5dbe576d4698c]
F   PD                  [ARIMA Backward Selection] [step5] [2008-12-09 09:08:43] [3ffd109c9e040b1ae7e5dbe576d4698c]
- R P                     [ARIMA Backward Selection] [arma] [2008-12-18 17:17:40] [3ffd109c9e040b1ae7e5dbe576d4698c]
Feedback Forum
2008-12-15 13:41:20 [Charis Berrevoets] [reply
Step 4:
Om AR-processen te zoeken moet je kijken naar de ACF. Dit kan echter ook negatief zijn. Je moet dus niet enkel naar het positieve gedeelte kijken zoals je hier zegt. Het is inderdaad correct dat er een AR-proces is. Ik denk dat er discussie mogelijk is over de orde van het AR-proces. Heel strikt gezien zijn er slechts 2 staafjes significant (in de PACF), maar het derde ligt slechts een heel klein beetje onder de stippellijn van het betrouwbaarheidsinterval, dus het lijkt me inderdaad correct om te zeggen dat dit een AR(3)-proces maar je had dit nog kunnen verantwoorden in je conclusie.
Er is inderdaad geen seizoenaal verband. Dit kan je zien door in de ACF te kijken naar lags 12,24,36 enz te kijken. We zien hier geen verband waardoor P inderdaad gelijk is aan 0.
Om een MA-proces te ontdekken moet je kijken naar de PACF, en niet gewoon naar het negatieve gedeelte. We zien geen MA-patroon in de PACF, er zijn dus geen MA-processen zoals je zelf ook al concludeerde en q=0.
Om Q te kunnen bepalen moeten we zoeken naar een SMA-proces. Dit doen we door lag 12, 24, 36 enz te bekijken in de ACF. Op lag 12 zien we een significant staafje, Q is dus gelijk aan 1.
Conclusie: je hebt wel de correcte waarden gevonden maar niet altijd op de juiste manier.

Post a new message
Dataseries X:
235.1
280.7
264.6
240.7
201.4
240.8
241.1
223.8
206.1
174.7
203.3
220.5
299.5
347.4
338.3
327.7
351.6
396.6
438.8
395.6
363.5
378.8
357
369
464.8
479.1
431.3
366.5
326.3
355.1
331.6
261.3
249
205.5
235.6
240.9
264.9
253.8
232.3
193.8
177
213.2
207.2
180.6
188.6
175.4
199
179.6
225.8
234
200.2
183.6
178.2
203.2
208.5
191.8
172.8
148
159.4
154.5
213.2
196.4
182.8
176.4
153.6
173.2
171
151.2
161.9
157.2
201.7
236.4
356.1
398.3
403.7
384.6
365.8
368.1
367.9
347
343.3
292.9
311.5
300.9
366.9
356.9
329.7
316.2
269
289.3
266.2
253.6
233.8
228.4
253.6
260.1
306.6
309.2
309.5
271
279.9
317.9
298.4
246.7
227.3
209.1
259.9
266
320.6
308.5
282.2
262.7
263.5
313.1
284.3
252.6
250.3
246.5
312.7
333.2
446.4
511.6
515.5
506.4
483.2
522.3
509.8
460.7
405.8
375
378.5
406.8
467.8
469.8
429.8
355.8
332.7
378
360.5
334.7
319.5
323.1
363.6
352.1
411.9
388.6
416.4
360.7
338
417.2
388.4
371.1
331.5
353.7
396.7
447
533.5
565.4
542.3
488.7
467.1
531.3
496.1
444
403.4
386.3
394.1
404.1
462.1
448.1
432.3
386.3
395.2
421.9
382.9
384.2
345.5
323.4
372.6
376
462.7
487
444.2
399.3
394.9
455.4
414
375.5
347
339.4
385.8
378.8
451.8
446.1
422.5
383.1
352.8
445.3
367.5
355.1
326.2
319.8
331.8
340.9
394.1
417.2
369.9
349.2
321.4
405.7
342.9
316.5
284.2
270.9
288.8
278.8
324.4
310.9
299
273
279.3
359.2
305
282.1
250.3
246.5
257.9
266.5
315.9
318.4
295.4
266.4
245.8
362.8
324.9
294.2
289.5
295.2
290.3
272
307.4
328.7
292.9
249.1
230.4
361.5
321.7
277.2
260.7
251
257.6
241.8
287.5
292.3
274.7
254.2
230
339
318.2
287
295.8
284
271
262.7
340.6
379.4
373.3
355.2
338.4
466.9
451
422
429.2
425.9
460.7
463.6
541.4
544.2
517.5
469.4
439.4
549
533
506.1
484
457
481.5
469.5
544.7
541.2
521.5
469.7
434.4
542.6
517.3
485.7
465.8
447
426.6
411.6
467.5
484.5
451.2
417.4
379.9
484.7
455
420.8
416.5
376.3
405.6
405.8
500.8
514
475.5
430.1
414.4
538
526
488.5
520.2
504.4
568.5
610.6
818
830.9
835.9
782
762.3
856.9
820.9
769.6
752.2
724.4
723.1
719.5
817.4
803.3
752.5
689
630.4
765.5
757.7
732.2
702.6
683.3
709.5
702.2
784.8
810.9
755.6
656.8
615.1
745.3
694.1
675.7
643.7
622.1
634.6
588
689.7
673.9
647.9
568.8
545.7
632.6
643.8
593.1
579.7
546
562.9
572.5




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=30474&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=30474&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30474&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' @ 72.249.127.135







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.1875523.55360.000215
20.3177946.02130
30.1795633.40220.000372
40.1553032.94260.001733
50.1275962.41760.008061
60.063711.20710.114087
7-0.054701-1.03640.150347
8-0.011735-0.22240.412083
9-0.080833-1.53160.063255
10-0.174708-3.31020.000513
11-0.055263-1.04710.147883
12-0.480698-9.10790
13-0.168514-3.19290.000767
14-0.172913-3.27620.000577
15-0.128287-2.43070.007779
16-0.163167-3.09160.001073
17-0.121097-2.29450.01117
18-0.103099-1.95340.025772
190.0201280.38140.351578
20-0.002775-0.05260.479049
21-0.006426-0.12170.451583
22-0.006358-0.12050.45209
23-0.004364-0.08270.467075
24-0.006343-0.12020.452205
250.0949111.79830.036484

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.187552 & 3.5536 & 0.000215 \tabularnewline
2 & 0.317794 & 6.0213 & 0 \tabularnewline
3 & 0.179563 & 3.4022 & 0.000372 \tabularnewline
4 & 0.155303 & 2.9426 & 0.001733 \tabularnewline
5 & 0.127596 & 2.4176 & 0.008061 \tabularnewline
6 & 0.06371 & 1.2071 & 0.114087 \tabularnewline
7 & -0.054701 & -1.0364 & 0.150347 \tabularnewline
8 & -0.011735 & -0.2224 & 0.412083 \tabularnewline
9 & -0.080833 & -1.5316 & 0.063255 \tabularnewline
10 & -0.174708 & -3.3102 & 0.000513 \tabularnewline
11 & -0.055263 & -1.0471 & 0.147883 \tabularnewline
12 & -0.480698 & -9.1079 & 0 \tabularnewline
13 & -0.168514 & -3.1929 & 0.000767 \tabularnewline
14 & -0.172913 & -3.2762 & 0.000577 \tabularnewline
15 & -0.128287 & -2.4307 & 0.007779 \tabularnewline
16 & -0.163167 & -3.0916 & 0.001073 \tabularnewline
17 & -0.121097 & -2.2945 & 0.01117 \tabularnewline
18 & -0.103099 & -1.9534 & 0.025772 \tabularnewline
19 & 0.020128 & 0.3814 & 0.351578 \tabularnewline
20 & -0.002775 & -0.0526 & 0.479049 \tabularnewline
21 & -0.006426 & -0.1217 & 0.451583 \tabularnewline
22 & -0.006358 & -0.1205 & 0.45209 \tabularnewline
23 & -0.004364 & -0.0827 & 0.467075 \tabularnewline
24 & -0.006343 & -0.1202 & 0.452205 \tabularnewline
25 & 0.094911 & 1.7983 & 0.036484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30474&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]0.187552[/C][C]3.5536[/C][C]0.000215[/C][/ROW]
[ROW][C]2[/C][C]0.317794[/C][C]6.0213[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.179563[/C][C]3.4022[/C][C]0.000372[/C][/ROW]
[ROW][C]4[/C][C]0.155303[/C][C]2.9426[/C][C]0.001733[/C][/ROW]
[ROW][C]5[/C][C]0.127596[/C][C]2.4176[/C][C]0.008061[/C][/ROW]
[ROW][C]6[/C][C]0.06371[/C][C]1.2071[/C][C]0.114087[/C][/ROW]
[ROW][C]7[/C][C]-0.054701[/C][C]-1.0364[/C][C]0.150347[/C][/ROW]
[ROW][C]8[/C][C]-0.011735[/C][C]-0.2224[/C][C]0.412083[/C][/ROW]
[ROW][C]9[/C][C]-0.080833[/C][C]-1.5316[/C][C]0.063255[/C][/ROW]
[ROW][C]10[/C][C]-0.174708[/C][C]-3.3102[/C][C]0.000513[/C][/ROW]
[ROW][C]11[/C][C]-0.055263[/C][C]-1.0471[/C][C]0.147883[/C][/ROW]
[ROW][C]12[/C][C]-0.480698[/C][C]-9.1079[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]-0.168514[/C][C]-3.1929[/C][C]0.000767[/C][/ROW]
[ROW][C]14[/C][C]-0.172913[/C][C]-3.2762[/C][C]0.000577[/C][/ROW]
[ROW][C]15[/C][C]-0.128287[/C][C]-2.4307[/C][C]0.007779[/C][/ROW]
[ROW][C]16[/C][C]-0.163167[/C][C]-3.0916[/C][C]0.001073[/C][/ROW]
[ROW][C]17[/C][C]-0.121097[/C][C]-2.2945[/C][C]0.01117[/C][/ROW]
[ROW][C]18[/C][C]-0.103099[/C][C]-1.9534[/C][C]0.025772[/C][/ROW]
[ROW][C]19[/C][C]0.020128[/C][C]0.3814[/C][C]0.351578[/C][/ROW]
[ROW][C]20[/C][C]-0.002775[/C][C]-0.0526[/C][C]0.479049[/C][/ROW]
[ROW][C]21[/C][C]-0.006426[/C][C]-0.1217[/C][C]0.451583[/C][/ROW]
[ROW][C]22[/C][C]-0.006358[/C][C]-0.1205[/C][C]0.45209[/C][/ROW]
[ROW][C]23[/C][C]-0.004364[/C][C]-0.0827[/C][C]0.467075[/C][/ROW]
[ROW][C]24[/C][C]-0.006343[/C][C]-0.1202[/C][C]0.452205[/C][/ROW]
[ROW][C]25[/C][C]0.094911[/C][C]1.7983[/C][C]0.036484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30474&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30474&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
10.1875523.55360.000215
20.3177946.02130
30.1795633.40220.000372
40.1553032.94260.001733
50.1275962.41760.008061
60.063711.20710.114087
7-0.054701-1.03640.150347
8-0.011735-0.22240.412083
9-0.080833-1.53160.063255
10-0.174708-3.31020.000513
11-0.055263-1.04710.147883
12-0.480698-9.10790
13-0.168514-3.19290.000767
14-0.172913-3.27620.000577
15-0.128287-2.43070.007779
16-0.163167-3.09160.001073
17-0.121097-2.29450.01117
18-0.103099-1.95340.025772
190.0201280.38140.351578
20-0.002775-0.05260.479049
21-0.006426-0.12170.451583
22-0.006358-0.12050.45209
23-0.004364-0.08270.467075
24-0.006343-0.12020.452205
250.0949111.79830.036484







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.1875523.55360.000215
20.2929225.55010
30.0935121.77180.038638
40.0339950.64410.259959
50.0322140.61040.271005
6-0.024285-0.46010.322846
7-0.139405-2.64140.004309
8-0.030697-0.58160.280592
9-0.045877-0.86930.192645
10-0.156693-2.96890.001595
110.0375910.71220.238389
12-0.427945-8.10840
13-0.036401-0.68970.245414
140.1233192.33660.010006
150.0471120.89260.186322
16-0.060609-1.14840.125789
17-0.017722-0.33580.368615
180.0078180.14810.44116
190.0233680.44280.329106
200.0479830.90910.181941
21-0.03962-0.75070.226664
22-0.157258-2.97960.001541
230.0101950.19320.42347
24-0.263321-4.98920
250.0569861.07970.140495

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.187552 & 3.5536 & 0.000215 \tabularnewline
2 & 0.292922 & 5.5501 & 0 \tabularnewline
3 & 0.093512 & 1.7718 & 0.038638 \tabularnewline
4 & 0.033995 & 0.6441 & 0.259959 \tabularnewline
5 & 0.032214 & 0.6104 & 0.271005 \tabularnewline
6 & -0.024285 & -0.4601 & 0.322846 \tabularnewline
7 & -0.139405 & -2.6414 & 0.004309 \tabularnewline
8 & -0.030697 & -0.5816 & 0.280592 \tabularnewline
9 & -0.045877 & -0.8693 & 0.192645 \tabularnewline
10 & -0.156693 & -2.9689 & 0.001595 \tabularnewline
11 & 0.037591 & 0.7122 & 0.238389 \tabularnewline
12 & -0.427945 & -8.1084 & 0 \tabularnewline
13 & -0.036401 & -0.6897 & 0.245414 \tabularnewline
14 & 0.123319 & 2.3366 & 0.010006 \tabularnewline
15 & 0.047112 & 0.8926 & 0.186322 \tabularnewline
16 & -0.060609 & -1.1484 & 0.125789 \tabularnewline
17 & -0.017722 & -0.3358 & 0.368615 \tabularnewline
18 & 0.007818 & 0.1481 & 0.44116 \tabularnewline
19 & 0.023368 & 0.4428 & 0.329106 \tabularnewline
20 & 0.047983 & 0.9091 & 0.181941 \tabularnewline
21 & -0.03962 & -0.7507 & 0.226664 \tabularnewline
22 & -0.157258 & -2.9796 & 0.001541 \tabularnewline
23 & 0.010195 & 0.1932 & 0.42347 \tabularnewline
24 & -0.263321 & -4.9892 & 0 \tabularnewline
25 & 0.056986 & 1.0797 & 0.140495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30474&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]0.187552[/C][C]3.5536[/C][C]0.000215[/C][/ROW]
[ROW][C]2[/C][C]0.292922[/C][C]5.5501[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.093512[/C][C]1.7718[/C][C]0.038638[/C][/ROW]
[ROW][C]4[/C][C]0.033995[/C][C]0.6441[/C][C]0.259959[/C][/ROW]
[ROW][C]5[/C][C]0.032214[/C][C]0.6104[/C][C]0.271005[/C][/ROW]
[ROW][C]6[/C][C]-0.024285[/C][C]-0.4601[/C][C]0.322846[/C][/ROW]
[ROW][C]7[/C][C]-0.139405[/C][C]-2.6414[/C][C]0.004309[/C][/ROW]
[ROW][C]8[/C][C]-0.030697[/C][C]-0.5816[/C][C]0.280592[/C][/ROW]
[ROW][C]9[/C][C]-0.045877[/C][C]-0.8693[/C][C]0.192645[/C][/ROW]
[ROW][C]10[/C][C]-0.156693[/C][C]-2.9689[/C][C]0.001595[/C][/ROW]
[ROW][C]11[/C][C]0.037591[/C][C]0.7122[/C][C]0.238389[/C][/ROW]
[ROW][C]12[/C][C]-0.427945[/C][C]-8.1084[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]-0.036401[/C][C]-0.6897[/C][C]0.245414[/C][/ROW]
[ROW][C]14[/C][C]0.123319[/C][C]2.3366[/C][C]0.010006[/C][/ROW]
[ROW][C]15[/C][C]0.047112[/C][C]0.8926[/C][C]0.186322[/C][/ROW]
[ROW][C]16[/C][C]-0.060609[/C][C]-1.1484[/C][C]0.125789[/C][/ROW]
[ROW][C]17[/C][C]-0.017722[/C][C]-0.3358[/C][C]0.368615[/C][/ROW]
[ROW][C]18[/C][C]0.007818[/C][C]0.1481[/C][C]0.44116[/C][/ROW]
[ROW][C]19[/C][C]0.023368[/C][C]0.4428[/C][C]0.329106[/C][/ROW]
[ROW][C]20[/C][C]0.047983[/C][C]0.9091[/C][C]0.181941[/C][/ROW]
[ROW][C]21[/C][C]-0.03962[/C][C]-0.7507[/C][C]0.226664[/C][/ROW]
[ROW][C]22[/C][C]-0.157258[/C][C]-2.9796[/C][C]0.001541[/C][/ROW]
[ROW][C]23[/C][C]0.010195[/C][C]0.1932[/C][C]0.42347[/C][/ROW]
[ROW][C]24[/C][C]-0.263321[/C][C]-4.9892[/C][C]0[/C][/ROW]
[ROW][C]25[/C][C]0.056986[/C][C]1.0797[/C][C]0.140495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30474&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30474&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
10.1875523.55360.000215
20.2929225.55010
30.0935121.77180.038638
40.0339950.64410.259959
50.0322140.61040.271005
6-0.024285-0.46010.322846
7-0.139405-2.64140.004309
8-0.030697-0.58160.280592
9-0.045877-0.86930.192645
10-0.156693-2.96890.001595
110.0375910.71220.238389
12-0.427945-8.10840
13-0.036401-0.68970.245414
140.1233192.33660.010006
150.0471120.89260.186322
16-0.060609-1.14840.125789
17-0.017722-0.33580.368615
180.0078180.14810.44116
190.0233680.44280.329106
200.0479830.90910.181941
21-0.03962-0.75070.226664
22-0.157258-2.97960.001541
230.0101950.19320.42347
24-0.263321-4.98920
250.0569861.07970.140495



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = Default ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; 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')