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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, 04 Dec 2009 05:11:13 -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/2009/Dec/04/t1259928717ftimj7j9jjoidmc.htm/, Retrieved Sun, 28 Apr 2024 15:57:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63362, Retrieved Sun, 28 Apr 2024 15:57:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105

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]
-   PD        [(Partial) Autocorrelation Function] [] [2009-11-26 19:38:39] [58e1a7a2c10f1de09acf218271f55dfd]
-    D            [(Partial) Autocorrelation Function] [] [2009-12-04 12:11:13] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
89.1
82.6
102.7
91.8
94.1
103.1
93.2
91
94.3
99.4
115.7
116.8
99.8
96
115.9
109.1
117.3
109.8
112.8
110.7
100
113.3
122.4
112.5
104.2
92.5
117.2
109.3
106.1
118.8
105.3
106
102
112.9
116.5
114.8
100.5
85.4
114.6
109.9
100.7
115.5
100.7
99
102.3
108.8
105.9
113.2
95.7
80.9
113.9
98.1
102.8
104.7
95.9
94.6
101.6
103.9
110.3
114.1

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=63362&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=63362&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63362&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
10.5416563.75270.000236
20.6341694.39373.1e-05
30.6889824.77349e-06
40.4114172.85040.003209
50.4136272.86570.003079
60.3865082.67780.005057
70.1736311.20290.117448
80.2164291.49950.070151
90.0812560.5630.288043
100.0496040.34370.3663
110.0727310.50390.308321
12-0.045702-0.31660.376447
130.0094470.06550.474043
140.0061360.04250.483135
15-0.042984-0.29780.383569
16-0.071508-0.49540.311281
17-0.002043-0.01420.494383
18-0.091846-0.63630.263793
19-0.095046-0.65850.256683
20-0.0424-0.29380.385105
21-0.115629-0.80110.21351
22-0.200182-1.38690.08594
23-0.026225-0.18170.428295
24-0.22336-1.54750.064158
25-0.160395-1.11120.135999
26-0.120856-0.83730.203282
27-0.230147-1.59450.058694
28-0.190758-1.32160.096282
29-0.172528-1.19530.118919
30-0.250274-1.73390.044673
31-0.190157-1.31740.096972
32-0.234769-1.62650.055193
33-0.246835-1.71010.046851
34-0.195269-1.35290.091219
35-0.25802-1.78760.040076
36-0.191975-1.330.094895

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.541656 & 3.7527 & 0.000236 \tabularnewline
2 & 0.634169 & 4.3937 & 3.1e-05 \tabularnewline
3 & 0.688982 & 4.7734 & 9e-06 \tabularnewline
4 & 0.411417 & 2.8504 & 0.003209 \tabularnewline
5 & 0.413627 & 2.8657 & 0.003079 \tabularnewline
6 & 0.386508 & 2.6778 & 0.005057 \tabularnewline
7 & 0.173631 & 1.2029 & 0.117448 \tabularnewline
8 & 0.216429 & 1.4995 & 0.070151 \tabularnewline
9 & 0.081256 & 0.563 & 0.288043 \tabularnewline
10 & 0.049604 & 0.3437 & 0.3663 \tabularnewline
11 & 0.072731 & 0.5039 & 0.308321 \tabularnewline
12 & -0.045702 & -0.3166 & 0.376447 \tabularnewline
13 & 0.009447 & 0.0655 & 0.474043 \tabularnewline
14 & 0.006136 & 0.0425 & 0.483135 \tabularnewline
15 & -0.042984 & -0.2978 & 0.383569 \tabularnewline
16 & -0.071508 & -0.4954 & 0.311281 \tabularnewline
17 & -0.002043 & -0.0142 & 0.494383 \tabularnewline
18 & -0.091846 & -0.6363 & 0.263793 \tabularnewline
19 & -0.095046 & -0.6585 & 0.256683 \tabularnewline
20 & -0.0424 & -0.2938 & 0.385105 \tabularnewline
21 & -0.115629 & -0.8011 & 0.21351 \tabularnewline
22 & -0.200182 & -1.3869 & 0.08594 \tabularnewline
23 & -0.026225 & -0.1817 & 0.428295 \tabularnewline
24 & -0.22336 & -1.5475 & 0.064158 \tabularnewline
25 & -0.160395 & -1.1112 & 0.135999 \tabularnewline
26 & -0.120856 & -0.8373 & 0.203282 \tabularnewline
27 & -0.230147 & -1.5945 & 0.058694 \tabularnewline
28 & -0.190758 & -1.3216 & 0.096282 \tabularnewline
29 & -0.172528 & -1.1953 & 0.118919 \tabularnewline
30 & -0.250274 & -1.7339 & 0.044673 \tabularnewline
31 & -0.190157 & -1.3174 & 0.096972 \tabularnewline
32 & -0.234769 & -1.6265 & 0.055193 \tabularnewline
33 & -0.246835 & -1.7101 & 0.046851 \tabularnewline
34 & -0.195269 & -1.3529 & 0.091219 \tabularnewline
35 & -0.25802 & -1.7876 & 0.040076 \tabularnewline
36 & -0.191975 & -1.33 & 0.094895 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63362&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]0.541656[/C][C]3.7527[/C][C]0.000236[/C][/ROW]
[ROW][C]2[/C][C]0.634169[/C][C]4.3937[/C][C]3.1e-05[/C][/ROW]
[ROW][C]3[/C][C]0.688982[/C][C]4.7734[/C][C]9e-06[/C][/ROW]
[ROW][C]4[/C][C]0.411417[/C][C]2.8504[/C][C]0.003209[/C][/ROW]
[ROW][C]5[/C][C]0.413627[/C][C]2.8657[/C][C]0.003079[/C][/ROW]
[ROW][C]6[/C][C]0.386508[/C][C]2.6778[/C][C]0.005057[/C][/ROW]
[ROW][C]7[/C][C]0.173631[/C][C]1.2029[/C][C]0.117448[/C][/ROW]
[ROW][C]8[/C][C]0.216429[/C][C]1.4995[/C][C]0.070151[/C][/ROW]
[ROW][C]9[/C][C]0.081256[/C][C]0.563[/C][C]0.288043[/C][/ROW]
[ROW][C]10[/C][C]0.049604[/C][C]0.3437[/C][C]0.3663[/C][/ROW]
[ROW][C]11[/C][C]0.072731[/C][C]0.5039[/C][C]0.308321[/C][/ROW]
[ROW][C]12[/C][C]-0.045702[/C][C]-0.3166[/C][C]0.376447[/C][/ROW]
[ROW][C]13[/C][C]0.009447[/C][C]0.0655[/C][C]0.474043[/C][/ROW]
[ROW][C]14[/C][C]0.006136[/C][C]0.0425[/C][C]0.483135[/C][/ROW]
[ROW][C]15[/C][C]-0.042984[/C][C]-0.2978[/C][C]0.383569[/C][/ROW]
[ROW][C]16[/C][C]-0.071508[/C][C]-0.4954[/C][C]0.311281[/C][/ROW]
[ROW][C]17[/C][C]-0.002043[/C][C]-0.0142[/C][C]0.494383[/C][/ROW]
[ROW][C]18[/C][C]-0.091846[/C][C]-0.6363[/C][C]0.263793[/C][/ROW]
[ROW][C]19[/C][C]-0.095046[/C][C]-0.6585[/C][C]0.256683[/C][/ROW]
[ROW][C]20[/C][C]-0.0424[/C][C]-0.2938[/C][C]0.385105[/C][/ROW]
[ROW][C]21[/C][C]-0.115629[/C][C]-0.8011[/C][C]0.21351[/C][/ROW]
[ROW][C]22[/C][C]-0.200182[/C][C]-1.3869[/C][C]0.08594[/C][/ROW]
[ROW][C]23[/C][C]-0.026225[/C][C]-0.1817[/C][C]0.428295[/C][/ROW]
[ROW][C]24[/C][C]-0.22336[/C][C]-1.5475[/C][C]0.064158[/C][/ROW]
[ROW][C]25[/C][C]-0.160395[/C][C]-1.1112[/C][C]0.135999[/C][/ROW]
[ROW][C]26[/C][C]-0.120856[/C][C]-0.8373[/C][C]0.203282[/C][/ROW]
[ROW][C]27[/C][C]-0.230147[/C][C]-1.5945[/C][C]0.058694[/C][/ROW]
[ROW][C]28[/C][C]-0.190758[/C][C]-1.3216[/C][C]0.096282[/C][/ROW]
[ROW][C]29[/C][C]-0.172528[/C][C]-1.1953[/C][C]0.118919[/C][/ROW]
[ROW][C]30[/C][C]-0.250274[/C][C]-1.7339[/C][C]0.044673[/C][/ROW]
[ROW][C]31[/C][C]-0.190157[/C][C]-1.3174[/C][C]0.096972[/C][/ROW]
[ROW][C]32[/C][C]-0.234769[/C][C]-1.6265[/C][C]0.055193[/C][/ROW]
[ROW][C]33[/C][C]-0.246835[/C][C]-1.7101[/C][C]0.046851[/C][/ROW]
[ROW][C]34[/C][C]-0.195269[/C][C]-1.3529[/C][C]0.091219[/C][/ROW]
[ROW][C]35[/C][C]-0.25802[/C][C]-1.7876[/C][C]0.040076[/C][/ROW]
[ROW][C]36[/C][C]-0.191975[/C][C]-1.33[/C][C]0.094895[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63362&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63362&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
10.5416563.75270.000236
20.6341694.39373.1e-05
30.6889824.77349e-06
40.4114172.85040.003209
50.4136272.86570.003079
60.3865082.67780.005057
70.1736311.20290.117448
80.2164291.49950.070151
90.0812560.5630.288043
100.0496040.34370.3663
110.0727310.50390.308321
12-0.045702-0.31660.376447
130.0094470.06550.474043
140.0061360.04250.483135
15-0.042984-0.29780.383569
16-0.071508-0.49540.311281
17-0.002043-0.01420.494383
18-0.091846-0.63630.263793
19-0.095046-0.65850.256683
20-0.0424-0.29380.385105
21-0.115629-0.80110.21351
22-0.200182-1.38690.08594
23-0.026225-0.18170.428295
24-0.22336-1.54750.064158
25-0.160395-1.11120.135999
26-0.120856-0.83730.203282
27-0.230147-1.59450.058694
28-0.190758-1.32160.096282
29-0.172528-1.19530.118919
30-0.250274-1.73390.044673
31-0.190157-1.31740.096972
32-0.234769-1.62650.055193
33-0.246835-1.71010.046851
34-0.195269-1.35290.091219
35-0.25802-1.78760.040076
36-0.191975-1.330.094895






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.5416563.75270.000236
20.4822733.34130.000811
30.4608793.19310.001243
4-0.239235-1.65750.051974
5-0.319394-2.21280.015849
6-0.073901-0.5120.305498
7-0.073118-0.50660.307385
80.0879430.60930.272604
9-0.106209-0.73580.232704
100.0695720.4820.315995
110.1679381.16350.125187
12-0.019071-0.13210.447717
13-0.024675-0.1710.43249
14-0.031159-0.21590.415
150.0563140.39020.349074
16-0.261906-1.81450.037923
17-0.010366-0.07180.471522
180.0340750.23610.407187
190.0042520.02950.48831
200.059240.41040.34166
21-0.029527-0.20460.419387
22-0.241999-1.67660.05006
230.1893111.31160.097949
24-0.045113-0.31260.377988
25-0.031259-0.21660.414732
26-0.100622-0.69710.244542
27-0.010743-0.07440.470488
28-0.084877-0.5880.279629
29-0.021723-0.15050.4405
300.0310010.21480.415424
31-0.063794-0.4420.330244
32-0.021613-0.14970.440799
33-0.044622-0.30920.379272
34-0.041871-0.29010.386497
350.0626470.4340.333106
360.0884710.61290.271403

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.541656 & 3.7527 & 0.000236 \tabularnewline
2 & 0.482273 & 3.3413 & 0.000811 \tabularnewline
3 & 0.460879 & 3.1931 & 0.001243 \tabularnewline
4 & -0.239235 & -1.6575 & 0.051974 \tabularnewline
5 & -0.319394 & -2.2128 & 0.015849 \tabularnewline
6 & -0.073901 & -0.512 & 0.305498 \tabularnewline
7 & -0.073118 & -0.5066 & 0.307385 \tabularnewline
8 & 0.087943 & 0.6093 & 0.272604 \tabularnewline
9 & -0.106209 & -0.7358 & 0.232704 \tabularnewline
10 & 0.069572 & 0.482 & 0.315995 \tabularnewline
11 & 0.167938 & 1.1635 & 0.125187 \tabularnewline
12 & -0.019071 & -0.1321 & 0.447717 \tabularnewline
13 & -0.024675 & -0.171 & 0.43249 \tabularnewline
14 & -0.031159 & -0.2159 & 0.415 \tabularnewline
15 & 0.056314 & 0.3902 & 0.349074 \tabularnewline
16 & -0.261906 & -1.8145 & 0.037923 \tabularnewline
17 & -0.010366 & -0.0718 & 0.471522 \tabularnewline
18 & 0.034075 & 0.2361 & 0.407187 \tabularnewline
19 & 0.004252 & 0.0295 & 0.48831 \tabularnewline
20 & 0.05924 & 0.4104 & 0.34166 \tabularnewline
21 & -0.029527 & -0.2046 & 0.419387 \tabularnewline
22 & -0.241999 & -1.6766 & 0.05006 \tabularnewline
23 & 0.189311 & 1.3116 & 0.097949 \tabularnewline
24 & -0.045113 & -0.3126 & 0.377988 \tabularnewline
25 & -0.031259 & -0.2166 & 0.414732 \tabularnewline
26 & -0.100622 & -0.6971 & 0.244542 \tabularnewline
27 & -0.010743 & -0.0744 & 0.470488 \tabularnewline
28 & -0.084877 & -0.588 & 0.279629 \tabularnewline
29 & -0.021723 & -0.1505 & 0.4405 \tabularnewline
30 & 0.031001 & 0.2148 & 0.415424 \tabularnewline
31 & -0.063794 & -0.442 & 0.330244 \tabularnewline
32 & -0.021613 & -0.1497 & 0.440799 \tabularnewline
33 & -0.044622 & -0.3092 & 0.379272 \tabularnewline
34 & -0.041871 & -0.2901 & 0.386497 \tabularnewline
35 & 0.062647 & 0.434 & 0.333106 \tabularnewline
36 & 0.088471 & 0.6129 & 0.271403 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63362&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]0.541656[/C][C]3.7527[/C][C]0.000236[/C][/ROW]
[ROW][C]2[/C][C]0.482273[/C][C]3.3413[/C][C]0.000811[/C][/ROW]
[ROW][C]3[/C][C]0.460879[/C][C]3.1931[/C][C]0.001243[/C][/ROW]
[ROW][C]4[/C][C]-0.239235[/C][C]-1.6575[/C][C]0.051974[/C][/ROW]
[ROW][C]5[/C][C]-0.319394[/C][C]-2.2128[/C][C]0.015849[/C][/ROW]
[ROW][C]6[/C][C]-0.073901[/C][C]-0.512[/C][C]0.305498[/C][/ROW]
[ROW][C]7[/C][C]-0.073118[/C][C]-0.5066[/C][C]0.307385[/C][/ROW]
[ROW][C]8[/C][C]0.087943[/C][C]0.6093[/C][C]0.272604[/C][/ROW]
[ROW][C]9[/C][C]-0.106209[/C][C]-0.7358[/C][C]0.232704[/C][/ROW]
[ROW][C]10[/C][C]0.069572[/C][C]0.482[/C][C]0.315995[/C][/ROW]
[ROW][C]11[/C][C]0.167938[/C][C]1.1635[/C][C]0.125187[/C][/ROW]
[ROW][C]12[/C][C]-0.019071[/C][C]-0.1321[/C][C]0.447717[/C][/ROW]
[ROW][C]13[/C][C]-0.024675[/C][C]-0.171[/C][C]0.43249[/C][/ROW]
[ROW][C]14[/C][C]-0.031159[/C][C]-0.2159[/C][C]0.415[/C][/ROW]
[ROW][C]15[/C][C]0.056314[/C][C]0.3902[/C][C]0.349074[/C][/ROW]
[ROW][C]16[/C][C]-0.261906[/C][C]-1.8145[/C][C]0.037923[/C][/ROW]
[ROW][C]17[/C][C]-0.010366[/C][C]-0.0718[/C][C]0.471522[/C][/ROW]
[ROW][C]18[/C][C]0.034075[/C][C]0.2361[/C][C]0.407187[/C][/ROW]
[ROW][C]19[/C][C]0.004252[/C][C]0.0295[/C][C]0.48831[/C][/ROW]
[ROW][C]20[/C][C]0.05924[/C][C]0.4104[/C][C]0.34166[/C][/ROW]
[ROW][C]21[/C][C]-0.029527[/C][C]-0.2046[/C][C]0.419387[/C][/ROW]
[ROW][C]22[/C][C]-0.241999[/C][C]-1.6766[/C][C]0.05006[/C][/ROW]
[ROW][C]23[/C][C]0.189311[/C][C]1.3116[/C][C]0.097949[/C][/ROW]
[ROW][C]24[/C][C]-0.045113[/C][C]-0.3126[/C][C]0.377988[/C][/ROW]
[ROW][C]25[/C][C]-0.031259[/C][C]-0.2166[/C][C]0.414732[/C][/ROW]
[ROW][C]26[/C][C]-0.100622[/C][C]-0.6971[/C][C]0.244542[/C][/ROW]
[ROW][C]27[/C][C]-0.010743[/C][C]-0.0744[/C][C]0.470488[/C][/ROW]
[ROW][C]28[/C][C]-0.084877[/C][C]-0.588[/C][C]0.279629[/C][/ROW]
[ROW][C]29[/C][C]-0.021723[/C][C]-0.1505[/C][C]0.4405[/C][/ROW]
[ROW][C]30[/C][C]0.031001[/C][C]0.2148[/C][C]0.415424[/C][/ROW]
[ROW][C]31[/C][C]-0.063794[/C][C]-0.442[/C][C]0.330244[/C][/ROW]
[ROW][C]32[/C][C]-0.021613[/C][C]-0.1497[/C][C]0.440799[/C][/ROW]
[ROW][C]33[/C][C]-0.044622[/C][C]-0.3092[/C][C]0.379272[/C][/ROW]
[ROW][C]34[/C][C]-0.041871[/C][C]-0.2901[/C][C]0.386497[/C][/ROW]
[ROW][C]35[/C][C]0.062647[/C][C]0.434[/C][C]0.333106[/C][/ROW]
[ROW][C]36[/C][C]0.088471[/C][C]0.6129[/C][C]0.271403[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63362&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63362&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
10.5416563.75270.000236
20.4822733.34130.000811
30.4608793.19310.001243
4-0.239235-1.65750.051974
5-0.319394-2.21280.015849
6-0.073901-0.5120.305498
7-0.073118-0.50660.307385
80.0879430.60930.272604
9-0.106209-0.73580.232704
100.0695720.4820.315995
110.1679381.16350.125187
12-0.019071-0.13210.447717
13-0.024675-0.1710.43249
14-0.031159-0.21590.415
150.0563140.39020.349074
16-0.261906-1.81450.037923
17-0.010366-0.07180.471522
180.0340750.23610.407187
190.0042520.02950.48831
200.059240.41040.34166
21-0.029527-0.20460.419387
22-0.241999-1.67660.05006
230.1893111.31160.097949
24-0.045113-0.31260.377988
25-0.031259-0.21660.414732
26-0.100622-0.69710.244542
27-0.010743-0.07440.470488
28-0.084877-0.5880.279629
29-0.021723-0.15050.4405
300.0310010.21480.415424
31-0.063794-0.4420.330244
32-0.021613-0.14970.440799
33-0.044622-0.30920.379272
34-0.041871-0.29010.386497
350.0626470.4340.333106
360.0884710.61290.271403


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = MA ; 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')