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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, 23 Nov 2009 08:17:11 -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/23/t1258989510sf9mghbjpnc6uyj.htm/, Retrieved Fri, 03 May 2024 13:26:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58788, Retrieved Fri, 03 May 2024 13:26:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176

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] [] [2009-11-23 15:17:11] [2b679e8ec54382eeb0ec0b6bb527570a] [Current]
-    D            [(Partial) Autocorrelation Function] [] [2009-12-20 07:31:47] [5d885a68c2332cc44f6191ec94766bfa]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100.03
100.25
99.6
100.16
100.49
99.72
100.14
98.48
100.38
101.45
98.42
98.6
100.06
98.62
100.84
100.02
97.95
98.32
98.27
97.22
99.28
100.38
99.02
100.32
99.81
100.6
101.19
100.47
101.77
102.32
102.39
101.16
100.63
101.48
101.44
100.09
100.7
100.78
99.81
98.45
98.49
97.48
97.91
96.94
98.53
96.82
95.76
95.27
97.32
96.68
97.87
97.42
97.94
99.52
100.99
99.92
101.97
101.58
99.54
100.83

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58788&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]2 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=58788&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58788&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.7258075.62210
20.6255474.84555e-06
30.5963484.61931e-05
40.4174453.23350.000995
50.3208932.48560.007868
60.2487091.92650.029392
70.0181080.14030.44446
8-0.043589-0.33760.368407
9-0.176874-1.37010.087887
10-0.347458-2.69140.004603
11-0.360101-2.78930.003534
12-0.388125-3.00640.001928
13-0.469332-3.63540.000289
14-0.448462-3.47380.000479
15-0.447588-3.4670.00049
16-0.432133-3.34730.000707
17-0.397276-3.07730.001573
18-0.3881-3.00620.001929
19-0.265184-2.05410.022165
20-0.168842-1.30780.097957
21-0.116318-0.9010.185597
22-0.036218-0.28050.390014
230.0276560.21420.415549
240.0950760.73650.232163
250.1499651.16160.124994
260.1618841.25390.107362
270.2141291.65860.051205
280.2574861.99450.025324
290.2338821.81160.037524
300.2235731.73180.044225
310.1578611.22280.113096
320.1068340.82750.205607
330.1081580.83780.202737
340.0444930.34460.365784
350.0049450.03830.484785
360.0173770.13460.446689

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.725807 & 5.6221 & 0 \tabularnewline
2 & 0.625547 & 4.8455 & 5e-06 \tabularnewline
3 & 0.596348 & 4.6193 & 1e-05 \tabularnewline
4 & 0.417445 & 3.2335 & 0.000995 \tabularnewline
5 & 0.320893 & 2.4856 & 0.007868 \tabularnewline
6 & 0.248709 & 1.9265 & 0.029392 \tabularnewline
7 & 0.018108 & 0.1403 & 0.44446 \tabularnewline
8 & -0.043589 & -0.3376 & 0.368407 \tabularnewline
9 & -0.176874 & -1.3701 & 0.087887 \tabularnewline
10 & -0.347458 & -2.6914 & 0.004603 \tabularnewline
11 & -0.360101 & -2.7893 & 0.003534 \tabularnewline
12 & -0.388125 & -3.0064 & 0.001928 \tabularnewline
13 & -0.469332 & -3.6354 & 0.000289 \tabularnewline
14 & -0.448462 & -3.4738 & 0.000479 \tabularnewline
15 & -0.447588 & -3.467 & 0.00049 \tabularnewline
16 & -0.432133 & -3.3473 & 0.000707 \tabularnewline
17 & -0.397276 & -3.0773 & 0.001573 \tabularnewline
18 & -0.3881 & -3.0062 & 0.001929 \tabularnewline
19 & -0.265184 & -2.0541 & 0.022165 \tabularnewline
20 & -0.168842 & -1.3078 & 0.097957 \tabularnewline
21 & -0.116318 & -0.901 & 0.185597 \tabularnewline
22 & -0.036218 & -0.2805 & 0.390014 \tabularnewline
23 & 0.027656 & 0.2142 & 0.415549 \tabularnewline
24 & 0.095076 & 0.7365 & 0.232163 \tabularnewline
25 & 0.149965 & 1.1616 & 0.124994 \tabularnewline
26 & 0.161884 & 1.2539 & 0.107362 \tabularnewline
27 & 0.214129 & 1.6586 & 0.051205 \tabularnewline
28 & 0.257486 & 1.9945 & 0.025324 \tabularnewline
29 & 0.233882 & 1.8116 & 0.037524 \tabularnewline
30 & 0.223573 & 1.7318 & 0.044225 \tabularnewline
31 & 0.157861 & 1.2228 & 0.113096 \tabularnewline
32 & 0.106834 & 0.8275 & 0.205607 \tabularnewline
33 & 0.108158 & 0.8378 & 0.202737 \tabularnewline
34 & 0.044493 & 0.3446 & 0.365784 \tabularnewline
35 & 0.004945 & 0.0383 & 0.484785 \tabularnewline
36 & 0.017377 & 0.1346 & 0.446689 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58788&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]0.725807[/C][C]5.6221[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.625547[/C][C]4.8455[/C][C]5e-06[/C][/ROW]
[ROW][C]3[/C][C]0.596348[/C][C]4.6193[/C][C]1e-05[/C][/ROW]
[ROW][C]4[/C][C]0.417445[/C][C]3.2335[/C][C]0.000995[/C][/ROW]
[ROW][C]5[/C][C]0.320893[/C][C]2.4856[/C][C]0.007868[/C][/ROW]
[ROW][C]6[/C][C]0.248709[/C][C]1.9265[/C][C]0.029392[/C][/ROW]
[ROW][C]7[/C][C]0.018108[/C][C]0.1403[/C][C]0.44446[/C][/ROW]
[ROW][C]8[/C][C]-0.043589[/C][C]-0.3376[/C][C]0.368407[/C][/ROW]
[ROW][C]9[/C][C]-0.176874[/C][C]-1.3701[/C][C]0.087887[/C][/ROW]
[ROW][C]10[/C][C]-0.347458[/C][C]-2.6914[/C][C]0.004603[/C][/ROW]
[ROW][C]11[/C][C]-0.360101[/C][C]-2.7893[/C][C]0.003534[/C][/ROW]
[ROW][C]12[/C][C]-0.388125[/C][C]-3.0064[/C][C]0.001928[/C][/ROW]
[ROW][C]13[/C][C]-0.469332[/C][C]-3.6354[/C][C]0.000289[/C][/ROW]
[ROW][C]14[/C][C]-0.448462[/C][C]-3.4738[/C][C]0.000479[/C][/ROW]
[ROW][C]15[/C][C]-0.447588[/C][C]-3.467[/C][C]0.00049[/C][/ROW]
[ROW][C]16[/C][C]-0.432133[/C][C]-3.3473[/C][C]0.000707[/C][/ROW]
[ROW][C]17[/C][C]-0.397276[/C][C]-3.0773[/C][C]0.001573[/C][/ROW]
[ROW][C]18[/C][C]-0.3881[/C][C]-3.0062[/C][C]0.001929[/C][/ROW]
[ROW][C]19[/C][C]-0.265184[/C][C]-2.0541[/C][C]0.022165[/C][/ROW]
[ROW][C]20[/C][C]-0.168842[/C][C]-1.3078[/C][C]0.097957[/C][/ROW]
[ROW][C]21[/C][C]-0.116318[/C][C]-0.901[/C][C]0.185597[/C][/ROW]
[ROW][C]22[/C][C]-0.036218[/C][C]-0.2805[/C][C]0.390014[/C][/ROW]
[ROW][C]23[/C][C]0.027656[/C][C]0.2142[/C][C]0.415549[/C][/ROW]
[ROW][C]24[/C][C]0.095076[/C][C]0.7365[/C][C]0.232163[/C][/ROW]
[ROW][C]25[/C][C]0.149965[/C][C]1.1616[/C][C]0.124994[/C][/ROW]
[ROW][C]26[/C][C]0.161884[/C][C]1.2539[/C][C]0.107362[/C][/ROW]
[ROW][C]27[/C][C]0.214129[/C][C]1.6586[/C][C]0.051205[/C][/ROW]
[ROW][C]28[/C][C]0.257486[/C][C]1.9945[/C][C]0.025324[/C][/ROW]
[ROW][C]29[/C][C]0.233882[/C][C]1.8116[/C][C]0.037524[/C][/ROW]
[ROW][C]30[/C][C]0.223573[/C][C]1.7318[/C][C]0.044225[/C][/ROW]
[ROW][C]31[/C][C]0.157861[/C][C]1.2228[/C][C]0.113096[/C][/ROW]
[ROW][C]32[/C][C]0.106834[/C][C]0.8275[/C][C]0.205607[/C][/ROW]
[ROW][C]33[/C][C]0.108158[/C][C]0.8378[/C][C]0.202737[/C][/ROW]
[ROW][C]34[/C][C]0.044493[/C][C]0.3446[/C][C]0.365784[/C][/ROW]
[ROW][C]35[/C][C]0.004945[/C][C]0.0383[/C][C]0.484785[/C][/ROW]
[ROW][C]36[/C][C]0.017377[/C][C]0.1346[/C][C]0.446689[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58788&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58788&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
10.7258075.62210
20.6255474.84555e-06
30.5963484.61931e-05
40.4174453.23350.000995
50.3208932.48560.007868
60.2487091.92650.029392
70.0181080.14030.44446
8-0.043589-0.33760.368407
9-0.176874-1.37010.087887
10-0.347458-2.69140.004603
11-0.360101-2.78930.003534
12-0.388125-3.00640.001928
13-0.469332-3.63540.000289
14-0.448462-3.47380.000479
15-0.447588-3.4670.00049
16-0.432133-3.34730.000707
17-0.397276-3.07730.001573
18-0.3881-3.00620.001929
19-0.265184-2.05410.022165
20-0.168842-1.30780.097957
21-0.116318-0.9010.185597
22-0.036218-0.28050.390014
230.0276560.21420.415549
240.0950760.73650.232163
250.1499651.16160.124994
260.1618841.25390.107362
270.2141291.65860.051205
280.2574861.99450.025324
290.2338821.81160.037524
300.2235731.73180.044225
310.1578611.22280.113096
320.1068340.82750.205607
330.1081580.83780.202737
340.0444930.34460.365784
350.0049450.03830.484785
360.0173770.13460.446689






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.7258075.62210
20.2086881.61650.055618
30.1891411.46510.07406
4-0.232337-1.79970.03847
5-0.05252-0.40680.342794
6-0.043183-0.33450.369587
7-0.335534-2.5990.005874
80.0215390.16680.434029
9-0.228031-1.76630.041214
10-0.147491-1.14250.128901
110.02320.17970.428993
120.050380.39020.34887
13-0.033028-0.25580.399476
14-0.075156-0.58220.28132
150.0126050.09760.461272
16-0.01148-0.08890.464719
17-0.147971-1.14620.128136
18-0.120499-0.93340.177183
190.1759351.36280.089021
200.0018790.01460.494217
210.0168430.13050.448317
22-0.029715-0.23020.409371
23-0.02274-0.17610.430389
240.0294320.2280.41022
25-0.11527-0.89290.187744
26-0.026857-0.2080.417955
27-0.020019-0.15510.438643
280.0326040.25250.40074
290.0207640.16080.43638
30-0.021595-0.16730.433858
31-0.195622-1.51530.067476
32-0.0555-0.42990.334405
330.0788070.61040.271939
34-0.04069-0.31520.376858
35-0.0202-0.15650.438095
360.05280.4090.342001

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.725807 & 5.6221 & 0 \tabularnewline
2 & 0.208688 & 1.6165 & 0.055618 \tabularnewline
3 & 0.189141 & 1.4651 & 0.07406 \tabularnewline
4 & -0.232337 & -1.7997 & 0.03847 \tabularnewline
5 & -0.05252 & -0.4068 & 0.342794 \tabularnewline
6 & -0.043183 & -0.3345 & 0.369587 \tabularnewline
7 & -0.335534 & -2.599 & 0.005874 \tabularnewline
8 & 0.021539 & 0.1668 & 0.434029 \tabularnewline
9 & -0.228031 & -1.7663 & 0.041214 \tabularnewline
10 & -0.147491 & -1.1425 & 0.128901 \tabularnewline
11 & 0.0232 & 0.1797 & 0.428993 \tabularnewline
12 & 0.05038 & 0.3902 & 0.34887 \tabularnewline
13 & -0.033028 & -0.2558 & 0.399476 \tabularnewline
14 & -0.075156 & -0.5822 & 0.28132 \tabularnewline
15 & 0.012605 & 0.0976 & 0.461272 \tabularnewline
16 & -0.01148 & -0.0889 & 0.464719 \tabularnewline
17 & -0.147971 & -1.1462 & 0.128136 \tabularnewline
18 & -0.120499 & -0.9334 & 0.177183 \tabularnewline
19 & 0.175935 & 1.3628 & 0.089021 \tabularnewline
20 & 0.001879 & 0.0146 & 0.494217 \tabularnewline
21 & 0.016843 & 0.1305 & 0.448317 \tabularnewline
22 & -0.029715 & -0.2302 & 0.409371 \tabularnewline
23 & -0.02274 & -0.1761 & 0.430389 \tabularnewline
24 & 0.029432 & 0.228 & 0.41022 \tabularnewline
25 & -0.11527 & -0.8929 & 0.187744 \tabularnewline
26 & -0.026857 & -0.208 & 0.417955 \tabularnewline
27 & -0.020019 & -0.1551 & 0.438643 \tabularnewline
28 & 0.032604 & 0.2525 & 0.40074 \tabularnewline
29 & 0.020764 & 0.1608 & 0.43638 \tabularnewline
30 & -0.021595 & -0.1673 & 0.433858 \tabularnewline
31 & -0.195622 & -1.5153 & 0.067476 \tabularnewline
32 & -0.0555 & -0.4299 & 0.334405 \tabularnewline
33 & 0.078807 & 0.6104 & 0.271939 \tabularnewline
34 & -0.04069 & -0.3152 & 0.376858 \tabularnewline
35 & -0.0202 & -0.1565 & 0.438095 \tabularnewline
36 & 0.0528 & 0.409 & 0.342001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58788&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]0.725807[/C][C]5.6221[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.208688[/C][C]1.6165[/C][C]0.055618[/C][/ROW]
[ROW][C]3[/C][C]0.189141[/C][C]1.4651[/C][C]0.07406[/C][/ROW]
[ROW][C]4[/C][C]-0.232337[/C][C]-1.7997[/C][C]0.03847[/C][/ROW]
[ROW][C]5[/C][C]-0.05252[/C][C]-0.4068[/C][C]0.342794[/C][/ROW]
[ROW][C]6[/C][C]-0.043183[/C][C]-0.3345[/C][C]0.369587[/C][/ROW]
[ROW][C]7[/C][C]-0.335534[/C][C]-2.599[/C][C]0.005874[/C][/ROW]
[ROW][C]8[/C][C]0.021539[/C][C]0.1668[/C][C]0.434029[/C][/ROW]
[ROW][C]9[/C][C]-0.228031[/C][C]-1.7663[/C][C]0.041214[/C][/ROW]
[ROW][C]10[/C][C]-0.147491[/C][C]-1.1425[/C][C]0.128901[/C][/ROW]
[ROW][C]11[/C][C]0.0232[/C][C]0.1797[/C][C]0.428993[/C][/ROW]
[ROW][C]12[/C][C]0.05038[/C][C]0.3902[/C][C]0.34887[/C][/ROW]
[ROW][C]13[/C][C]-0.033028[/C][C]-0.2558[/C][C]0.399476[/C][/ROW]
[ROW][C]14[/C][C]-0.075156[/C][C]-0.5822[/C][C]0.28132[/C][/ROW]
[ROW][C]15[/C][C]0.012605[/C][C]0.0976[/C][C]0.461272[/C][/ROW]
[ROW][C]16[/C][C]-0.01148[/C][C]-0.0889[/C][C]0.464719[/C][/ROW]
[ROW][C]17[/C][C]-0.147971[/C][C]-1.1462[/C][C]0.128136[/C][/ROW]
[ROW][C]18[/C][C]-0.120499[/C][C]-0.9334[/C][C]0.177183[/C][/ROW]
[ROW][C]19[/C][C]0.175935[/C][C]1.3628[/C][C]0.089021[/C][/ROW]
[ROW][C]20[/C][C]0.001879[/C][C]0.0146[/C][C]0.494217[/C][/ROW]
[ROW][C]21[/C][C]0.016843[/C][C]0.1305[/C][C]0.448317[/C][/ROW]
[ROW][C]22[/C][C]-0.029715[/C][C]-0.2302[/C][C]0.409371[/C][/ROW]
[ROW][C]23[/C][C]-0.02274[/C][C]-0.1761[/C][C]0.430389[/C][/ROW]
[ROW][C]24[/C][C]0.029432[/C][C]0.228[/C][C]0.41022[/C][/ROW]
[ROW][C]25[/C][C]-0.11527[/C][C]-0.8929[/C][C]0.187744[/C][/ROW]
[ROW][C]26[/C][C]-0.026857[/C][C]-0.208[/C][C]0.417955[/C][/ROW]
[ROW][C]27[/C][C]-0.020019[/C][C]-0.1551[/C][C]0.438643[/C][/ROW]
[ROW][C]28[/C][C]0.032604[/C][C]0.2525[/C][C]0.40074[/C][/ROW]
[ROW][C]29[/C][C]0.020764[/C][C]0.1608[/C][C]0.43638[/C][/ROW]
[ROW][C]30[/C][C]-0.021595[/C][C]-0.1673[/C][C]0.433858[/C][/ROW]
[ROW][C]31[/C][C]-0.195622[/C][C]-1.5153[/C][C]0.067476[/C][/ROW]
[ROW][C]32[/C][C]-0.0555[/C][C]-0.4299[/C][C]0.334405[/C][/ROW]
[ROW][C]33[/C][C]0.078807[/C][C]0.6104[/C][C]0.271939[/C][/ROW]
[ROW][C]34[/C][C]-0.04069[/C][C]-0.3152[/C][C]0.376858[/C][/ROW]
[ROW][C]35[/C][C]-0.0202[/C][C]-0.1565[/C][C]0.438095[/C][/ROW]
[ROW][C]36[/C][C]0.0528[/C][C]0.409[/C][C]0.342001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58788&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58788&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
10.7258075.62210
20.2086881.61650.055618
30.1891411.46510.07406
4-0.232337-1.79970.03847
5-0.05252-0.40680.342794
6-0.043183-0.33450.369587
7-0.335534-2.5990.005874
80.0215390.16680.434029
9-0.228031-1.76630.041214
10-0.147491-1.14250.128901
110.02320.17970.428993
120.050380.39020.34887
13-0.033028-0.25580.399476
14-0.075156-0.58220.28132
150.0126050.09760.461272
16-0.01148-0.08890.464719
17-0.147971-1.14620.128136
18-0.120499-0.93340.177183
190.1759351.36280.089021
200.0018790.01460.494217
210.0168430.13050.448317
22-0.029715-0.23020.409371
23-0.02274-0.17610.430389
240.0294320.2280.41022
25-0.11527-0.89290.187744
26-0.026857-0.2080.417955
27-0.020019-0.15510.438643
280.0326040.25250.40074
290.0207640.16080.43638
30-0.021595-0.16730.433858
31-0.195622-1.51530.067476
32-0.0555-0.42990.334405
330.0788070.61040.271939
34-0.04069-0.31520.376858
35-0.0202-0.15650.438095
360.05280.4090.342001


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 ;
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')