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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 computationMon, 30 Nov 2009 12:50:44 -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/30/t1259610768efpw3s7wsh50e0i.htm/, Retrieved Wed, 01 May 2024 23:59:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=61876, Retrieved Wed, 01 May 2024 23:59:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148

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] [2009-11-25 11:31:06] [309ee52d0058ff0a6f7eec15e07b2d9f]
-                 [(Partial) Autocorrelation Function] [review 8] [2009-11-30 19:50:44] [6198946fb53eb5eb18db46bb758f7fde] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.6348
0.634
0.62915
0.62168
0.61328
0.6089
0.60857
0.62672
0.62291
0.62393
0.61838
0.62012
0.61659
0.6116
0.61573
0.61407
0.62823
0.64405
0.6387
0.63633
0.63059
0.62994
0.63709
0.64217
0.65711
0.66977
0.68255
0.68902
0.71322
0.70224
0.70045
0.69919
0.69693
0.69763
0.69278
0.70196
0.69215
0.6769
0.67124
0.66532
0.67157
0.66428
0.66576
0.66942
0.6813
0.69144
0.69862
0.695
0.69867
0.68968
0.69233
0.68293
0.68399
0.66895
0.68756
0.68527
0.6776
0.68137
0.67933
0.67922

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=61876&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=61876&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61876&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
10.9556727.40260
20.9038567.00120
30.8326576.44970
40.7614435.89810
50.6859495.31331e-06
60.6125884.74517e-06
70.5525224.27983.4e-05
80.4968853.84890.000145
90.4420243.42390.000559
100.3830292.96690.002157
110.3144112.43540.008932
120.2463131.90790.030594
130.1813821.4050.082592
140.1339941.03790.151738
150.0959180.7430.230198
160.0658210.50980.306014
170.0429310.33250.370319
180.0189350.14670.441941
19-0.013707-0.10620.4579
20-0.064275-0.49790.310197
21-0.113286-0.87750.191856
22-0.165423-1.28140.102498
23-0.21081-1.63290.053861
24-0.247708-1.91870.029889
25-0.278853-2.160.01739
26-0.282083-2.1850.016402
27-0.287809-2.22940.014773
28-0.289284-2.24080.014377
29-0.286658-2.22040.015088
30-0.293766-2.27550.01323
31-0.298957-2.31570.012005
32-0.306752-2.37610.010353
33-0.305763-2.36840.010551
34-0.306652-2.37530.010373
35-0.302615-2.3440.011203
36-0.291289-2.25630.013854

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.955672 & 7.4026 & 0 \tabularnewline
2 & 0.903856 & 7.0012 & 0 \tabularnewline
3 & 0.832657 & 6.4497 & 0 \tabularnewline
4 & 0.761443 & 5.8981 & 0 \tabularnewline
5 & 0.685949 & 5.3133 & 1e-06 \tabularnewline
6 & 0.612588 & 4.7451 & 7e-06 \tabularnewline
7 & 0.552522 & 4.2798 & 3.4e-05 \tabularnewline
8 & 0.496885 & 3.8489 & 0.000145 \tabularnewline
9 & 0.442024 & 3.4239 & 0.000559 \tabularnewline
10 & 0.383029 & 2.9669 & 0.002157 \tabularnewline
11 & 0.314411 & 2.4354 & 0.008932 \tabularnewline
12 & 0.246313 & 1.9079 & 0.030594 \tabularnewline
13 & 0.181382 & 1.405 & 0.082592 \tabularnewline
14 & 0.133994 & 1.0379 & 0.151738 \tabularnewline
15 & 0.095918 & 0.743 & 0.230198 \tabularnewline
16 & 0.065821 & 0.5098 & 0.306014 \tabularnewline
17 & 0.042931 & 0.3325 & 0.370319 \tabularnewline
18 & 0.018935 & 0.1467 & 0.441941 \tabularnewline
19 & -0.013707 & -0.1062 & 0.4579 \tabularnewline
20 & -0.064275 & -0.4979 & 0.310197 \tabularnewline
21 & -0.113286 & -0.8775 & 0.191856 \tabularnewline
22 & -0.165423 & -1.2814 & 0.102498 \tabularnewline
23 & -0.21081 & -1.6329 & 0.053861 \tabularnewline
24 & -0.247708 & -1.9187 & 0.029889 \tabularnewline
25 & -0.278853 & -2.16 & 0.01739 \tabularnewline
26 & -0.282083 & -2.185 & 0.016402 \tabularnewline
27 & -0.287809 & -2.2294 & 0.014773 \tabularnewline
28 & -0.289284 & -2.2408 & 0.014377 \tabularnewline
29 & -0.286658 & -2.2204 & 0.015088 \tabularnewline
30 & -0.293766 & -2.2755 & 0.01323 \tabularnewline
31 & -0.298957 & -2.3157 & 0.012005 \tabularnewline
32 & -0.306752 & -2.3761 & 0.010353 \tabularnewline
33 & -0.305763 & -2.3684 & 0.010551 \tabularnewline
34 & -0.306652 & -2.3753 & 0.010373 \tabularnewline
35 & -0.302615 & -2.344 & 0.011203 \tabularnewline
36 & -0.291289 & -2.2563 & 0.013854 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61876&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]0.955672[/C][C]7.4026[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.903856[/C][C]7.0012[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.832657[/C][C]6.4497[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.761443[/C][C]5.8981[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.685949[/C][C]5.3133[/C][C]1e-06[/C][/ROW]
[ROW][C]6[/C][C]0.612588[/C][C]4.7451[/C][C]7e-06[/C][/ROW]
[ROW][C]7[/C][C]0.552522[/C][C]4.2798[/C][C]3.4e-05[/C][/ROW]
[ROW][C]8[/C][C]0.496885[/C][C]3.8489[/C][C]0.000145[/C][/ROW]
[ROW][C]9[/C][C]0.442024[/C][C]3.4239[/C][C]0.000559[/C][/ROW]
[ROW][C]10[/C][C]0.383029[/C][C]2.9669[/C][C]0.002157[/C][/ROW]
[ROW][C]11[/C][C]0.314411[/C][C]2.4354[/C][C]0.008932[/C][/ROW]
[ROW][C]12[/C][C]0.246313[/C][C]1.9079[/C][C]0.030594[/C][/ROW]
[ROW][C]13[/C][C]0.181382[/C][C]1.405[/C][C]0.082592[/C][/ROW]
[ROW][C]14[/C][C]0.133994[/C][C]1.0379[/C][C]0.151738[/C][/ROW]
[ROW][C]15[/C][C]0.095918[/C][C]0.743[/C][C]0.230198[/C][/ROW]
[ROW][C]16[/C][C]0.065821[/C][C]0.5098[/C][C]0.306014[/C][/ROW]
[ROW][C]17[/C][C]0.042931[/C][C]0.3325[/C][C]0.370319[/C][/ROW]
[ROW][C]18[/C][C]0.018935[/C][C]0.1467[/C][C]0.441941[/C][/ROW]
[ROW][C]19[/C][C]-0.013707[/C][C]-0.1062[/C][C]0.4579[/C][/ROW]
[ROW][C]20[/C][C]-0.064275[/C][C]-0.4979[/C][C]0.310197[/C][/ROW]
[ROW][C]21[/C][C]-0.113286[/C][C]-0.8775[/C][C]0.191856[/C][/ROW]
[ROW][C]22[/C][C]-0.165423[/C][C]-1.2814[/C][C]0.102498[/C][/ROW]
[ROW][C]23[/C][C]-0.21081[/C][C]-1.6329[/C][C]0.053861[/C][/ROW]
[ROW][C]24[/C][C]-0.247708[/C][C]-1.9187[/C][C]0.029889[/C][/ROW]
[ROW][C]25[/C][C]-0.278853[/C][C]-2.16[/C][C]0.01739[/C][/ROW]
[ROW][C]26[/C][C]-0.282083[/C][C]-2.185[/C][C]0.016402[/C][/ROW]
[ROW][C]27[/C][C]-0.287809[/C][C]-2.2294[/C][C]0.014773[/C][/ROW]
[ROW][C]28[/C][C]-0.289284[/C][C]-2.2408[/C][C]0.014377[/C][/ROW]
[ROW][C]29[/C][C]-0.286658[/C][C]-2.2204[/C][C]0.015088[/C][/ROW]
[ROW][C]30[/C][C]-0.293766[/C][C]-2.2755[/C][C]0.01323[/C][/ROW]
[ROW][C]31[/C][C]-0.298957[/C][C]-2.3157[/C][C]0.012005[/C][/ROW]
[ROW][C]32[/C][C]-0.306752[/C][C]-2.3761[/C][C]0.010353[/C][/ROW]
[ROW][C]33[/C][C]-0.305763[/C][C]-2.3684[/C][C]0.010551[/C][/ROW]
[ROW][C]34[/C][C]-0.306652[/C][C]-2.3753[/C][C]0.010373[/C][/ROW]
[ROW][C]35[/C][C]-0.302615[/C][C]-2.344[/C][C]0.011203[/C][/ROW]
[ROW][C]36[/C][C]-0.291289[/C][C]-2.2563[/C][C]0.013854[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61876&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61876&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
10.9556727.40260
20.9038567.00120
30.8326576.44970
40.7614435.89810
50.6859495.31331e-06
60.6125884.74517e-06
70.5525224.27983.4e-05
80.4968853.84890.000145
90.4420243.42390.000559
100.3830292.96690.002157
110.3144112.43540.008932
120.2463131.90790.030594
130.1813821.4050.082592
140.1339941.03790.151738
150.0959180.7430.230198
160.0658210.50980.306014
170.0429310.33250.370319
180.0189350.14670.441941
19-0.013707-0.10620.4579
20-0.064275-0.49790.310197
21-0.113286-0.87750.191856
22-0.165423-1.28140.102498
23-0.21081-1.63290.053861
24-0.247708-1.91870.029889
25-0.278853-2.160.01739
26-0.282083-2.1850.016402
27-0.287809-2.22940.014773
28-0.289284-2.24080.014377
29-0.286658-2.22040.015088
30-0.293766-2.27550.01323
31-0.298957-2.31570.012005
32-0.306752-2.37610.010353
33-0.305763-2.36840.010551
34-0.306652-2.37530.010373
35-0.302615-2.3440.011203
36-0.291289-2.25630.013854






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9556727.40260
2-0.109041-0.84460.200836
3-0.246497-1.90940.030501
4-0.008749-0.06780.473097
5-0.047771-0.370.356329
6-0.023008-0.17820.429575
70.1288590.99810.16111
8-0.015276-0.11830.453103
9-0.103006-0.79790.214042
10-0.101104-0.78310.21831
11-0.164031-1.27060.104391
12-0.010907-0.08450.466476
130.0690960.53520.297239
140.1804631.39790.083651
150.041530.32170.374401
16-0.071113-0.55080.291894
17-0.060485-0.46850.320557
18-0.120726-0.93510.176733
19-0.157048-1.21650.114282
20-0.169846-1.31560.096653
210.1064350.82440.206478
220.0297010.23010.409412
230.0187170.1450.442606
240.0080980.06270.475095
25-0.125867-0.9750.166748
260.20581.59410.058082
27-0.065544-0.50770.306761
28-0.052185-0.40420.343743
290.1632861.26480.105415
30-0.156307-1.21080.115369
31-0.075084-0.58160.281508
32-0.011645-0.09020.464215
33-0.049158-0.38080.352357
34-0.063923-0.49510.311152
350.0312520.24210.404773
360.0317580.2460.403261

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.955672 & 7.4026 & 0 \tabularnewline
2 & -0.109041 & -0.8446 & 0.200836 \tabularnewline
3 & -0.246497 & -1.9094 & 0.030501 \tabularnewline
4 & -0.008749 & -0.0678 & 0.473097 \tabularnewline
5 & -0.047771 & -0.37 & 0.356329 \tabularnewline
6 & -0.023008 & -0.1782 & 0.429575 \tabularnewline
7 & 0.128859 & 0.9981 & 0.16111 \tabularnewline
8 & -0.015276 & -0.1183 & 0.453103 \tabularnewline
9 & -0.103006 & -0.7979 & 0.214042 \tabularnewline
10 & -0.101104 & -0.7831 & 0.21831 \tabularnewline
11 & -0.164031 & -1.2706 & 0.104391 \tabularnewline
12 & -0.010907 & -0.0845 & 0.466476 \tabularnewline
13 & 0.069096 & 0.5352 & 0.297239 \tabularnewline
14 & 0.180463 & 1.3979 & 0.083651 \tabularnewline
15 & 0.04153 & 0.3217 & 0.374401 \tabularnewline
16 & -0.071113 & -0.5508 & 0.291894 \tabularnewline
17 & -0.060485 & -0.4685 & 0.320557 \tabularnewline
18 & -0.120726 & -0.9351 & 0.176733 \tabularnewline
19 & -0.157048 & -1.2165 & 0.114282 \tabularnewline
20 & -0.169846 & -1.3156 & 0.096653 \tabularnewline
21 & 0.106435 & 0.8244 & 0.206478 \tabularnewline
22 & 0.029701 & 0.2301 & 0.409412 \tabularnewline
23 & 0.018717 & 0.145 & 0.442606 \tabularnewline
24 & 0.008098 & 0.0627 & 0.475095 \tabularnewline
25 & -0.125867 & -0.975 & 0.166748 \tabularnewline
26 & 0.2058 & 1.5941 & 0.058082 \tabularnewline
27 & -0.065544 & -0.5077 & 0.306761 \tabularnewline
28 & -0.052185 & -0.4042 & 0.343743 \tabularnewline
29 & 0.163286 & 1.2648 & 0.105415 \tabularnewline
30 & -0.156307 & -1.2108 & 0.115369 \tabularnewline
31 & -0.075084 & -0.5816 & 0.281508 \tabularnewline
32 & -0.011645 & -0.0902 & 0.464215 \tabularnewline
33 & -0.049158 & -0.3808 & 0.352357 \tabularnewline
34 & -0.063923 & -0.4951 & 0.311152 \tabularnewline
35 & 0.031252 & 0.2421 & 0.404773 \tabularnewline
36 & 0.031758 & 0.246 & 0.403261 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61876&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]0.955672[/C][C]7.4026[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.109041[/C][C]-0.8446[/C][C]0.200836[/C][/ROW]
[ROW][C]3[/C][C]-0.246497[/C][C]-1.9094[/C][C]0.030501[/C][/ROW]
[ROW][C]4[/C][C]-0.008749[/C][C]-0.0678[/C][C]0.473097[/C][/ROW]
[ROW][C]5[/C][C]-0.047771[/C][C]-0.37[/C][C]0.356329[/C][/ROW]
[ROW][C]6[/C][C]-0.023008[/C][C]-0.1782[/C][C]0.429575[/C][/ROW]
[ROW][C]7[/C][C]0.128859[/C][C]0.9981[/C][C]0.16111[/C][/ROW]
[ROW][C]8[/C][C]-0.015276[/C][C]-0.1183[/C][C]0.453103[/C][/ROW]
[ROW][C]9[/C][C]-0.103006[/C][C]-0.7979[/C][C]0.214042[/C][/ROW]
[ROW][C]10[/C][C]-0.101104[/C][C]-0.7831[/C][C]0.21831[/C][/ROW]
[ROW][C]11[/C][C]-0.164031[/C][C]-1.2706[/C][C]0.104391[/C][/ROW]
[ROW][C]12[/C][C]-0.010907[/C][C]-0.0845[/C][C]0.466476[/C][/ROW]
[ROW][C]13[/C][C]0.069096[/C][C]0.5352[/C][C]0.297239[/C][/ROW]
[ROW][C]14[/C][C]0.180463[/C][C]1.3979[/C][C]0.083651[/C][/ROW]
[ROW][C]15[/C][C]0.04153[/C][C]0.3217[/C][C]0.374401[/C][/ROW]
[ROW][C]16[/C][C]-0.071113[/C][C]-0.5508[/C][C]0.291894[/C][/ROW]
[ROW][C]17[/C][C]-0.060485[/C][C]-0.4685[/C][C]0.320557[/C][/ROW]
[ROW][C]18[/C][C]-0.120726[/C][C]-0.9351[/C][C]0.176733[/C][/ROW]
[ROW][C]19[/C][C]-0.157048[/C][C]-1.2165[/C][C]0.114282[/C][/ROW]
[ROW][C]20[/C][C]-0.169846[/C][C]-1.3156[/C][C]0.096653[/C][/ROW]
[ROW][C]21[/C][C]0.106435[/C][C]0.8244[/C][C]0.206478[/C][/ROW]
[ROW][C]22[/C][C]0.029701[/C][C]0.2301[/C][C]0.409412[/C][/ROW]
[ROW][C]23[/C][C]0.018717[/C][C]0.145[/C][C]0.442606[/C][/ROW]
[ROW][C]24[/C][C]0.008098[/C][C]0.0627[/C][C]0.475095[/C][/ROW]
[ROW][C]25[/C][C]-0.125867[/C][C]-0.975[/C][C]0.166748[/C][/ROW]
[ROW][C]26[/C][C]0.2058[/C][C]1.5941[/C][C]0.058082[/C][/ROW]
[ROW][C]27[/C][C]-0.065544[/C][C]-0.5077[/C][C]0.306761[/C][/ROW]
[ROW][C]28[/C][C]-0.052185[/C][C]-0.4042[/C][C]0.343743[/C][/ROW]
[ROW][C]29[/C][C]0.163286[/C][C]1.2648[/C][C]0.105415[/C][/ROW]
[ROW][C]30[/C][C]-0.156307[/C][C]-1.2108[/C][C]0.115369[/C][/ROW]
[ROW][C]31[/C][C]-0.075084[/C][C]-0.5816[/C][C]0.281508[/C][/ROW]
[ROW][C]32[/C][C]-0.011645[/C][C]-0.0902[/C][C]0.464215[/C][/ROW]
[ROW][C]33[/C][C]-0.049158[/C][C]-0.3808[/C][C]0.352357[/C][/ROW]
[ROW][C]34[/C][C]-0.063923[/C][C]-0.4951[/C][C]0.311152[/C][/ROW]
[ROW][C]35[/C][C]0.031252[/C][C]0.2421[/C][C]0.404773[/C][/ROW]
[ROW][C]36[/C][C]0.031758[/C][C]0.246[/C][C]0.403261[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61876&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61876&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
10.9556727.40260
2-0.109041-0.84460.200836
3-0.246497-1.90940.030501
4-0.008749-0.06780.473097
5-0.047771-0.370.356329
6-0.023008-0.17820.429575
70.1288590.99810.16111
8-0.015276-0.11830.453103
9-0.103006-0.79790.214042
10-0.101104-0.78310.21831
11-0.164031-1.27060.104391
12-0.010907-0.08450.466476
130.0690960.53520.297239
140.1804631.39790.083651
150.041530.32170.374401
16-0.071113-0.55080.291894
17-0.060485-0.46850.320557
18-0.120726-0.93510.176733
19-0.157048-1.21650.114282
20-0.169846-1.31560.096653
210.1064350.82440.206478
220.0297010.23010.409412
230.0187170.1450.442606
240.0080980.06270.475095
25-0.125867-0.9750.166748
260.20581.59410.058082
27-0.065544-0.50770.306761
28-0.052185-0.40420.343743
290.1632861.26480.105415
30-0.156307-1.21080.115369
31-0.075084-0.58160.281508
32-0.011645-0.09020.464215
33-0.049158-0.38080.352357
34-0.063923-0.49510.311152
350.0312520.24210.404773
360.0317580.2460.403261


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')