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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 computationTue, 02 Dec 2008 13:15:16 -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/02/t1228248951a0n576g294j8p13.htm/, Retrieved Sun, 19 May 2024 12:02:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28348, Retrieved Sun, 19 May 2024 12:02:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [(Partial) Autocorrelation Function] [law of averages q...] [2008-12-02 20:15:16] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-12-06 14:01:03 [Thomas Plasschaert] [reply] 
goede bewerkingen gedaan tot het bekomen van de juiste d en D waarde
2008-12-09 23:07:27 [Peter Van Doninck] [reply] 
berekend door student. Bij textiel zie ik echter geen lange termijntrend! Enkel seizoenaliteit. Bij leer en schoeisel is er daarentegen wel een lange termijntrend en ook seizoenaliteit! De student gaat ook hier te snel van uit om d en D gelijk te stellen aan 1.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
200,7
146,5
143,6
141,5
137,5
138,7
135,5
136,4
112,1
109
123,8
151,2
139,2
115,7
147,6
126,1
122,8
137,3
142
137,4
89,4
108
117,7
127,3
121
104,1
119,5
116,7
96,1
125
118,8
114,9
79,3
90,5
87,8
109,4
88,9
97,4
112
86,8
82,9
105,2
89,1
85,5
87,1
85,2
88,2
104
96,4
82,3
114,1
88,9
93,6
101,8
96,6
93,7
68,4
68,7
81,2
85,1
75,4
71,6
83
72,3
90,2
89
84,9
90,9
46,6
55,4
88,7
76
76,9
72,1
90
92,3
78
93,9
84,5
80,4
60,5
75,3
91,5
105,2
92,7

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=28348&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=28348&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28348&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
10.6948716.40640
20.5985795.51860
30.5965875.50030
40.584865.39210
50.5669495.2271e-06
60.5090074.69285e-06
70.5269884.85863e-06
80.4800994.42631.4e-05
90.4207753.87940.000103
100.4343584.00466.6e-05
110.5196534.7913e-06
120.5548525.11551e-06
130.3871673.56950.000296
140.3520113.24540.000839
150.3118192.87480.002553
160.2950682.72040.003953
170.3069622.830.002903
180.2363812.17930.016037
190.231872.13770.017705
200.1510111.39230.083738
210.1318971.2160.11367
220.1285081.18480.119703
230.1831561.68860.047479
240.1882471.73560.043133
250.0694890.64070.261734
260.0574180.52940.298965
270.0322560.29740.383448
28-0.007378-0.0680.472964
290.0351730.32430.37326
30-0.042621-0.39290.34767
31-0.055072-0.50770.306477
32-0.108871-1.00370.159175
33-0.101697-0.93760.175553
34-0.108837-1.00340.159251
35-0.051951-0.4790.316597
36-0.045697-0.42130.337298

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.694871 & 6.4064 & 0 \tabularnewline
2 & 0.598579 & 5.5186 & 0 \tabularnewline
3 & 0.596587 & 5.5003 & 0 \tabularnewline
4 & 0.58486 & 5.3921 & 0 \tabularnewline
5 & 0.566949 & 5.227 & 1e-06 \tabularnewline
6 & 0.509007 & 4.6928 & 5e-06 \tabularnewline
7 & 0.526988 & 4.8586 & 3e-06 \tabularnewline
8 & 0.480099 & 4.4263 & 1.4e-05 \tabularnewline
9 & 0.420775 & 3.8794 & 0.000103 \tabularnewline
10 & 0.434358 & 4.0046 & 6.6e-05 \tabularnewline
11 & 0.519653 & 4.791 & 3e-06 \tabularnewline
12 & 0.554852 & 5.1155 & 1e-06 \tabularnewline
13 & 0.387167 & 3.5695 & 0.000296 \tabularnewline
14 & 0.352011 & 3.2454 & 0.000839 \tabularnewline
15 & 0.311819 & 2.8748 & 0.002553 \tabularnewline
16 & 0.295068 & 2.7204 & 0.003953 \tabularnewline
17 & 0.306962 & 2.83 & 0.002903 \tabularnewline
18 & 0.236381 & 2.1793 & 0.016037 \tabularnewline
19 & 0.23187 & 2.1377 & 0.017705 \tabularnewline
20 & 0.151011 & 1.3923 & 0.083738 \tabularnewline
21 & 0.131897 & 1.216 & 0.11367 \tabularnewline
22 & 0.128508 & 1.1848 & 0.119703 \tabularnewline
23 & 0.183156 & 1.6886 & 0.047479 \tabularnewline
24 & 0.188247 & 1.7356 & 0.043133 \tabularnewline
25 & 0.069489 & 0.6407 & 0.261734 \tabularnewline
26 & 0.057418 & 0.5294 & 0.298965 \tabularnewline
27 & 0.032256 & 0.2974 & 0.383448 \tabularnewline
28 & -0.007378 & -0.068 & 0.472964 \tabularnewline
29 & 0.035173 & 0.3243 & 0.37326 \tabularnewline
30 & -0.042621 & -0.3929 & 0.34767 \tabularnewline
31 & -0.055072 & -0.5077 & 0.306477 \tabularnewline
32 & -0.108871 & -1.0037 & 0.159175 \tabularnewline
33 & -0.101697 & -0.9376 & 0.175553 \tabularnewline
34 & -0.108837 & -1.0034 & 0.159251 \tabularnewline
35 & -0.051951 & -0.479 & 0.316597 \tabularnewline
36 & -0.045697 & -0.4213 & 0.337298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28348&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]0.694871[/C][C]6.4064[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.598579[/C][C]5.5186[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.596587[/C][C]5.5003[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.58486[/C][C]5.3921[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.566949[/C][C]5.227[/C][C]1e-06[/C][/ROW]
[ROW][C]6[/C][C]0.509007[/C][C]4.6928[/C][C]5e-06[/C][/ROW]
[ROW][C]7[/C][C]0.526988[/C][C]4.8586[/C][C]3e-06[/C][/ROW]
[ROW][C]8[/C][C]0.480099[/C][C]4.4263[/C][C]1.4e-05[/C][/ROW]
[ROW][C]9[/C][C]0.420775[/C][C]3.8794[/C][C]0.000103[/C][/ROW]
[ROW][C]10[/C][C]0.434358[/C][C]4.0046[/C][C]6.6e-05[/C][/ROW]
[ROW][C]11[/C][C]0.519653[/C][C]4.791[/C][C]3e-06[/C][/ROW]
[ROW][C]12[/C][C]0.554852[/C][C]5.1155[/C][C]1e-06[/C][/ROW]
[ROW][C]13[/C][C]0.387167[/C][C]3.5695[/C][C]0.000296[/C][/ROW]
[ROW][C]14[/C][C]0.352011[/C][C]3.2454[/C][C]0.000839[/C][/ROW]
[ROW][C]15[/C][C]0.311819[/C][C]2.8748[/C][C]0.002553[/C][/ROW]
[ROW][C]16[/C][C]0.295068[/C][C]2.7204[/C][C]0.003953[/C][/ROW]
[ROW][C]17[/C][C]0.306962[/C][C]2.83[/C][C]0.002903[/C][/ROW]
[ROW][C]18[/C][C]0.236381[/C][C]2.1793[/C][C]0.016037[/C][/ROW]
[ROW][C]19[/C][C]0.23187[/C][C]2.1377[/C][C]0.017705[/C][/ROW]
[ROW][C]20[/C][C]0.151011[/C][C]1.3923[/C][C]0.083738[/C][/ROW]
[ROW][C]21[/C][C]0.131897[/C][C]1.216[/C][C]0.11367[/C][/ROW]
[ROW][C]22[/C][C]0.128508[/C][C]1.1848[/C][C]0.119703[/C][/ROW]
[ROW][C]23[/C][C]0.183156[/C][C]1.6886[/C][C]0.047479[/C][/ROW]
[ROW][C]24[/C][C]0.188247[/C][C]1.7356[/C][C]0.043133[/C][/ROW]
[ROW][C]25[/C][C]0.069489[/C][C]0.6407[/C][C]0.261734[/C][/ROW]
[ROW][C]26[/C][C]0.057418[/C][C]0.5294[/C][C]0.298965[/C][/ROW]
[ROW][C]27[/C][C]0.032256[/C][C]0.2974[/C][C]0.383448[/C][/ROW]
[ROW][C]28[/C][C]-0.007378[/C][C]-0.068[/C][C]0.472964[/C][/ROW]
[ROW][C]29[/C][C]0.035173[/C][C]0.3243[/C][C]0.37326[/C][/ROW]
[ROW][C]30[/C][C]-0.042621[/C][C]-0.3929[/C][C]0.34767[/C][/ROW]
[ROW][C]31[/C][C]-0.055072[/C][C]-0.5077[/C][C]0.306477[/C][/ROW]
[ROW][C]32[/C][C]-0.108871[/C][C]-1.0037[/C][C]0.159175[/C][/ROW]
[ROW][C]33[/C][C]-0.101697[/C][C]-0.9376[/C][C]0.175553[/C][/ROW]
[ROW][C]34[/C][C]-0.108837[/C][C]-1.0034[/C][C]0.159251[/C][/ROW]
[ROW][C]35[/C][C]-0.051951[/C][C]-0.479[/C][C]0.316597[/C][/ROW]
[ROW][C]36[/C][C]-0.045697[/C][C]-0.4213[/C][C]0.337298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28348&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28348&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
10.6948716.40640
20.5985795.51860
30.5965875.50030
40.584865.39210
50.5669495.2271e-06
60.5090074.69285e-06
70.5269884.85863e-06
80.4800994.42631.4e-05
90.4207753.87940.000103
100.4343584.00466.6e-05
110.5196534.7913e-06
120.5548525.11551e-06
130.3871673.56950.000296
140.3520113.24540.000839
150.3118192.87480.002553
160.2950682.72040.003953
170.3069622.830.002903
180.2363812.17930.016037
190.231872.13770.017705
200.1510111.39230.083738
210.1318971.2160.11367
220.1285081.18480.119703
230.1831561.68860.047479
240.1882471.73560.043133
250.0694890.64070.261734
260.0574180.52940.298965
270.0322560.29740.383448
28-0.007378-0.0680.472964
290.0351730.32430.37326
30-0.042621-0.39290.34767
31-0.055072-0.50770.306477
32-0.108871-1.00370.159175
33-0.101697-0.93760.175553
34-0.108837-1.00340.159251
35-0.051951-0.4790.316597
36-0.045697-0.42130.337298






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.6948716.40640
20.2237882.06320.021071
30.2406692.21890.01458
40.1549781.42880.078359
50.1122941.03530.151732
6-0.010898-0.10050.460101
70.1213821.11910.133127
8-0.039015-0.35970.359982
9-0.060819-0.56070.28823
100.0617580.56940.285299
110.2402922.21540.014704
120.1767831.62990.053416
13-0.270202-2.49110.007339
14-0.101334-0.93420.176411
15-0.177079-1.63260.053127
16-0.041361-0.38130.351955
170.0808080.7450.229161
18-0.088247-0.81360.209076
19-0.01169-0.10780.457212
20-0.093517-0.86220.195507
210.0168930.15570.438303
22-0.089778-0.82770.205076
230.0705840.65080.258481
240.0594370.5480.292571
25-0.074957-0.69110.245703
260.0202720.18690.426094
27-0.019815-0.18270.42774
28-0.150001-1.38290.085152
290.0993860.91630.181052
30-0.092287-0.85080.198622
31-0.004602-0.04240.48313
320.0064940.05990.476199
330.0561520.51770.303006
34-0.087797-0.80940.21026
350.0903560.8330.203579
360.0451470.41620.339142

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.694871 & 6.4064 & 0 \tabularnewline
2 & 0.223788 & 2.0632 & 0.021071 \tabularnewline
3 & 0.240669 & 2.2189 & 0.01458 \tabularnewline
4 & 0.154978 & 1.4288 & 0.078359 \tabularnewline
5 & 0.112294 & 1.0353 & 0.151732 \tabularnewline
6 & -0.010898 & -0.1005 & 0.460101 \tabularnewline
7 & 0.121382 & 1.1191 & 0.133127 \tabularnewline
8 & -0.039015 & -0.3597 & 0.359982 \tabularnewline
9 & -0.060819 & -0.5607 & 0.28823 \tabularnewline
10 & 0.061758 & 0.5694 & 0.285299 \tabularnewline
11 & 0.240292 & 2.2154 & 0.014704 \tabularnewline
12 & 0.176783 & 1.6299 & 0.053416 \tabularnewline
13 & -0.270202 & -2.4911 & 0.007339 \tabularnewline
14 & -0.101334 & -0.9342 & 0.176411 \tabularnewline
15 & -0.177079 & -1.6326 & 0.053127 \tabularnewline
16 & -0.041361 & -0.3813 & 0.351955 \tabularnewline
17 & 0.080808 & 0.745 & 0.229161 \tabularnewline
18 & -0.088247 & -0.8136 & 0.209076 \tabularnewline
19 & -0.01169 & -0.1078 & 0.457212 \tabularnewline
20 & -0.093517 & -0.8622 & 0.195507 \tabularnewline
21 & 0.016893 & 0.1557 & 0.438303 \tabularnewline
22 & -0.089778 & -0.8277 & 0.205076 \tabularnewline
23 & 0.070584 & 0.6508 & 0.258481 \tabularnewline
24 & 0.059437 & 0.548 & 0.292571 \tabularnewline
25 & -0.074957 & -0.6911 & 0.245703 \tabularnewline
26 & 0.020272 & 0.1869 & 0.426094 \tabularnewline
27 & -0.019815 & -0.1827 & 0.42774 \tabularnewline
28 & -0.150001 & -1.3829 & 0.085152 \tabularnewline
29 & 0.099386 & 0.9163 & 0.181052 \tabularnewline
30 & -0.092287 & -0.8508 & 0.198622 \tabularnewline
31 & -0.004602 & -0.0424 & 0.48313 \tabularnewline
32 & 0.006494 & 0.0599 & 0.476199 \tabularnewline
33 & 0.056152 & 0.5177 & 0.303006 \tabularnewline
34 & -0.087797 & -0.8094 & 0.21026 \tabularnewline
35 & 0.090356 & 0.833 & 0.203579 \tabularnewline
36 & 0.045147 & 0.4162 & 0.339142 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28348&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]0.694871[/C][C]6.4064[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.223788[/C][C]2.0632[/C][C]0.021071[/C][/ROW]
[ROW][C]3[/C][C]0.240669[/C][C]2.2189[/C][C]0.01458[/C][/ROW]
[ROW][C]4[/C][C]0.154978[/C][C]1.4288[/C][C]0.078359[/C][/ROW]
[ROW][C]5[/C][C]0.112294[/C][C]1.0353[/C][C]0.151732[/C][/ROW]
[ROW][C]6[/C][C]-0.010898[/C][C]-0.1005[/C][C]0.460101[/C][/ROW]
[ROW][C]7[/C][C]0.121382[/C][C]1.1191[/C][C]0.133127[/C][/ROW]
[ROW][C]8[/C][C]-0.039015[/C][C]-0.3597[/C][C]0.359982[/C][/ROW]
[ROW][C]9[/C][C]-0.060819[/C][C]-0.5607[/C][C]0.28823[/C][/ROW]
[ROW][C]10[/C][C]0.061758[/C][C]0.5694[/C][C]0.285299[/C][/ROW]
[ROW][C]11[/C][C]0.240292[/C][C]2.2154[/C][C]0.014704[/C][/ROW]
[ROW][C]12[/C][C]0.176783[/C][C]1.6299[/C][C]0.053416[/C][/ROW]
[ROW][C]13[/C][C]-0.270202[/C][C]-2.4911[/C][C]0.007339[/C][/ROW]
[ROW][C]14[/C][C]-0.101334[/C][C]-0.9342[/C][C]0.176411[/C][/ROW]
[ROW][C]15[/C][C]-0.177079[/C][C]-1.6326[/C][C]0.053127[/C][/ROW]
[ROW][C]16[/C][C]-0.041361[/C][C]-0.3813[/C][C]0.351955[/C][/ROW]
[ROW][C]17[/C][C]0.080808[/C][C]0.745[/C][C]0.229161[/C][/ROW]
[ROW][C]18[/C][C]-0.088247[/C][C]-0.8136[/C][C]0.209076[/C][/ROW]
[ROW][C]19[/C][C]-0.01169[/C][C]-0.1078[/C][C]0.457212[/C][/ROW]
[ROW][C]20[/C][C]-0.093517[/C][C]-0.8622[/C][C]0.195507[/C][/ROW]
[ROW][C]21[/C][C]0.016893[/C][C]0.1557[/C][C]0.438303[/C][/ROW]
[ROW][C]22[/C][C]-0.089778[/C][C]-0.8277[/C][C]0.205076[/C][/ROW]
[ROW][C]23[/C][C]0.070584[/C][C]0.6508[/C][C]0.258481[/C][/ROW]
[ROW][C]24[/C][C]0.059437[/C][C]0.548[/C][C]0.292571[/C][/ROW]
[ROW][C]25[/C][C]-0.074957[/C][C]-0.6911[/C][C]0.245703[/C][/ROW]
[ROW][C]26[/C][C]0.020272[/C][C]0.1869[/C][C]0.426094[/C][/ROW]
[ROW][C]27[/C][C]-0.019815[/C][C]-0.1827[/C][C]0.42774[/C][/ROW]
[ROW][C]28[/C][C]-0.150001[/C][C]-1.3829[/C][C]0.085152[/C][/ROW]
[ROW][C]29[/C][C]0.099386[/C][C]0.9163[/C][C]0.181052[/C][/ROW]
[ROW][C]30[/C][C]-0.092287[/C][C]-0.8508[/C][C]0.198622[/C][/ROW]
[ROW][C]31[/C][C]-0.004602[/C][C]-0.0424[/C][C]0.48313[/C][/ROW]
[ROW][C]32[/C][C]0.006494[/C][C]0.0599[/C][C]0.476199[/C][/ROW]
[ROW][C]33[/C][C]0.056152[/C][C]0.5177[/C][C]0.303006[/C][/ROW]
[ROW][C]34[/C][C]-0.087797[/C][C]-0.8094[/C][C]0.21026[/C][/ROW]
[ROW][C]35[/C][C]0.090356[/C][C]0.833[/C][C]0.203579[/C][/ROW]
[ROW][C]36[/C][C]0.045147[/C][C]0.4162[/C][C]0.339142[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28348&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28348&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
10.6948716.40640
20.2237882.06320.021071
30.2406692.21890.01458
40.1549781.42880.078359
50.1122941.03530.151732
6-0.010898-0.10050.460101
70.1213821.11910.133127
8-0.039015-0.35970.359982
9-0.060819-0.56070.28823
100.0617580.56940.285299
110.2402922.21540.014704
120.1767831.62990.053416
13-0.270202-2.49110.007339
14-0.101334-0.93420.176411
15-0.177079-1.63260.053127
16-0.041361-0.38130.351955
170.0808080.7450.229161
18-0.088247-0.81360.209076
19-0.01169-0.10780.457212
20-0.093517-0.86220.195507
210.0168930.15570.438303
22-0.089778-0.82770.205076
230.0705840.65080.258481
240.0594370.5480.292571
25-0.074957-0.69110.245703
260.0202720.18690.426094
27-0.019815-0.18270.42774
28-0.150001-1.38290.085152
290.0993860.91630.181052
30-0.092287-0.85080.198622
31-0.004602-0.04240.48313
320.0064940.05990.476199
330.0561520.51770.303006
34-0.087797-0.80940.21026
350.0903560.8330.203579
360.0451470.41620.339142


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')