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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 computationThu, 26 Nov 2009 07:01:16 -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/2009/Nov/26/t1259244227xlycrs3s4fvdt0c.htm/, Retrieved Mon, 29 Apr 2024 05:19:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60029, Retrieved Mon, 29 Apr 2024 05:19:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188

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 - Meth...] [2009-11-24 15:58:42] [1646a2766cb8c4a6f9d3b2fffef409b3]
-    D            [(Partial) Autocorrelation Function] [Methode 1 ACF ] [2009-11-26 14:01:16] [3ebad5d90a5c8606f133189c73066208] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
91,2
80,8
72,3
99,7
90,1
83,1
71,9
78,6
87,2
90,6
80
73,1
85,6
73,8
70,6
91,8
81,3
85,2
69,6
83,3
89,8
99,5
78,9
83,8
92
80,9
74,6
97,9
88,3
88,1
66,4
92,3
95,6
99,7
78,9
79,4
87,8
80,5
71,8
89,2
96,4
83,5
64,3
85,9
89,2
81,8
79,5
68,7
76,4
73,6
57,7
78,3
75,5
62,4
55,6
62,9
66,7
66,8
59,9
52
61,2

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=60029&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=60029&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60029&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
10.4289993.35060.000694
20.1736231.3560.090043
30.2810132.19480.015998
40.5000373.90540.000119
50.3592512.80580.003363
60.2083851.62750.054389
70.2031951.5870.058841
80.3029742.36630.010579
90.0847810.66220.255182
10-0.068705-0.53660.296747
110.0597920.4670.321085
120.4099343.20170.001086
13-0.003289-0.02570.489796
14-0.209265-1.63440.053662
15-0.131402-1.02630.154405
160.0970580.7580.225671
170.0134860.10530.458231
18-0.104911-0.81940.207879
19-0.077179-0.60280.274442
200.0065020.05080.479832
21-0.127253-0.99390.162104
22-0.202183-1.57910.059743
23-0.105864-0.82680.20578
240.1260170.98420.164446
25-0.109938-0.85860.19695
26-0.30295-2.36610.010584
27-0.237183-1.85250.0344
28-0.001272-0.00990.496054
29-0.056008-0.43740.331669
30-0.135763-1.06030.146585
31-0.098784-0.77150.221687
32-0.076016-0.59370.277452
33-0.128982-1.00740.158866
34-0.141278-1.10340.13709
35-0.113854-0.88920.188687
360.0452440.35340.362515

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.428999 & 3.3506 & 0.000694 \tabularnewline
2 & 0.173623 & 1.356 & 0.090043 \tabularnewline
3 & 0.281013 & 2.1948 & 0.015998 \tabularnewline
4 & 0.500037 & 3.9054 & 0.000119 \tabularnewline
5 & 0.359251 & 2.8058 & 0.003363 \tabularnewline
6 & 0.208385 & 1.6275 & 0.054389 \tabularnewline
7 & 0.203195 & 1.587 & 0.058841 \tabularnewline
8 & 0.302974 & 2.3663 & 0.010579 \tabularnewline
9 & 0.084781 & 0.6622 & 0.255182 \tabularnewline
10 & -0.068705 & -0.5366 & 0.296747 \tabularnewline
11 & 0.059792 & 0.467 & 0.321085 \tabularnewline
12 & 0.409934 & 3.2017 & 0.001086 \tabularnewline
13 & -0.003289 & -0.0257 & 0.489796 \tabularnewline
14 & -0.209265 & -1.6344 & 0.053662 \tabularnewline
15 & -0.131402 & -1.0263 & 0.154405 \tabularnewline
16 & 0.097058 & 0.758 & 0.225671 \tabularnewline
17 & 0.013486 & 0.1053 & 0.458231 \tabularnewline
18 & -0.104911 & -0.8194 & 0.207879 \tabularnewline
19 & -0.077179 & -0.6028 & 0.274442 \tabularnewline
20 & 0.006502 & 0.0508 & 0.479832 \tabularnewline
21 & -0.127253 & -0.9939 & 0.162104 \tabularnewline
22 & -0.202183 & -1.5791 & 0.059743 \tabularnewline
23 & -0.105864 & -0.8268 & 0.20578 \tabularnewline
24 & 0.126017 & 0.9842 & 0.164446 \tabularnewline
25 & -0.109938 & -0.8586 & 0.19695 \tabularnewline
26 & -0.30295 & -2.3661 & 0.010584 \tabularnewline
27 & -0.237183 & -1.8525 & 0.0344 \tabularnewline
28 & -0.001272 & -0.0099 & 0.496054 \tabularnewline
29 & -0.056008 & -0.4374 & 0.331669 \tabularnewline
30 & -0.135763 & -1.0603 & 0.146585 \tabularnewline
31 & -0.098784 & -0.7715 & 0.221687 \tabularnewline
32 & -0.076016 & -0.5937 & 0.277452 \tabularnewline
33 & -0.128982 & -1.0074 & 0.158866 \tabularnewline
34 & -0.141278 & -1.1034 & 0.13709 \tabularnewline
35 & -0.113854 & -0.8892 & 0.188687 \tabularnewline
36 & 0.045244 & 0.3534 & 0.362515 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60029&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]0.428999[/C][C]3.3506[/C][C]0.000694[/C][/ROW]
[ROW][C]2[/C][C]0.173623[/C][C]1.356[/C][C]0.090043[/C][/ROW]
[ROW][C]3[/C][C]0.281013[/C][C]2.1948[/C][C]0.015998[/C][/ROW]
[ROW][C]4[/C][C]0.500037[/C][C]3.9054[/C][C]0.000119[/C][/ROW]
[ROW][C]5[/C][C]0.359251[/C][C]2.8058[/C][C]0.003363[/C][/ROW]
[ROW][C]6[/C][C]0.208385[/C][C]1.6275[/C][C]0.054389[/C][/ROW]
[ROW][C]7[/C][C]0.203195[/C][C]1.587[/C][C]0.058841[/C][/ROW]
[ROW][C]8[/C][C]0.302974[/C][C]2.3663[/C][C]0.010579[/C][/ROW]
[ROW][C]9[/C][C]0.084781[/C][C]0.6622[/C][C]0.255182[/C][/ROW]
[ROW][C]10[/C][C]-0.068705[/C][C]-0.5366[/C][C]0.296747[/C][/ROW]
[ROW][C]11[/C][C]0.059792[/C][C]0.467[/C][C]0.321085[/C][/ROW]
[ROW][C]12[/C][C]0.409934[/C][C]3.2017[/C][C]0.001086[/C][/ROW]
[ROW][C]13[/C][C]-0.003289[/C][C]-0.0257[/C][C]0.489796[/C][/ROW]
[ROW][C]14[/C][C]-0.209265[/C][C]-1.6344[/C][C]0.053662[/C][/ROW]
[ROW][C]15[/C][C]-0.131402[/C][C]-1.0263[/C][C]0.154405[/C][/ROW]
[ROW][C]16[/C][C]0.097058[/C][C]0.758[/C][C]0.225671[/C][/ROW]
[ROW][C]17[/C][C]0.013486[/C][C]0.1053[/C][C]0.458231[/C][/ROW]
[ROW][C]18[/C][C]-0.104911[/C][C]-0.8194[/C][C]0.207879[/C][/ROW]
[ROW][C]19[/C][C]-0.077179[/C][C]-0.6028[/C][C]0.274442[/C][/ROW]
[ROW][C]20[/C][C]0.006502[/C][C]0.0508[/C][C]0.479832[/C][/ROW]
[ROW][C]21[/C][C]-0.127253[/C][C]-0.9939[/C][C]0.162104[/C][/ROW]
[ROW][C]22[/C][C]-0.202183[/C][C]-1.5791[/C][C]0.059743[/C][/ROW]
[ROW][C]23[/C][C]-0.105864[/C][C]-0.8268[/C][C]0.20578[/C][/ROW]
[ROW][C]24[/C][C]0.126017[/C][C]0.9842[/C][C]0.164446[/C][/ROW]
[ROW][C]25[/C][C]-0.109938[/C][C]-0.8586[/C][C]0.19695[/C][/ROW]
[ROW][C]26[/C][C]-0.30295[/C][C]-2.3661[/C][C]0.010584[/C][/ROW]
[ROW][C]27[/C][C]-0.237183[/C][C]-1.8525[/C][C]0.0344[/C][/ROW]
[ROW][C]28[/C][C]-0.001272[/C][C]-0.0099[/C][C]0.496054[/C][/ROW]
[ROW][C]29[/C][C]-0.056008[/C][C]-0.4374[/C][C]0.331669[/C][/ROW]
[ROW][C]30[/C][C]-0.135763[/C][C]-1.0603[/C][C]0.146585[/C][/ROW]
[ROW][C]31[/C][C]-0.098784[/C][C]-0.7715[/C][C]0.221687[/C][/ROW]
[ROW][C]32[/C][C]-0.076016[/C][C]-0.5937[/C][C]0.277452[/C][/ROW]
[ROW][C]33[/C][C]-0.128982[/C][C]-1.0074[/C][C]0.158866[/C][/ROW]
[ROW][C]34[/C][C]-0.141278[/C][C]-1.1034[/C][C]0.13709[/C][/ROW]
[ROW][C]35[/C][C]-0.113854[/C][C]-0.8892[/C][C]0.188687[/C][/ROW]
[ROW][C]36[/C][C]0.045244[/C][C]0.3534[/C][C]0.362515[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60029&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60029&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
10.4289993.35060.000694
20.1736231.3560.090043
30.2810132.19480.015998
40.5000373.90540.000119
50.3592512.80580.003363
60.2083851.62750.054389
70.2031951.5870.058841
80.3029742.36630.010579
90.0847810.66220.255182
10-0.068705-0.53660.296747
110.0597920.4670.321085
120.4099343.20170.001086
13-0.003289-0.02570.489796
14-0.209265-1.63440.053662
15-0.131402-1.02630.154405
160.0970580.7580.225671
170.0134860.10530.458231
18-0.104911-0.81940.207879
19-0.077179-0.60280.274442
200.0065020.05080.479832
21-0.127253-0.99390.162104
22-0.202183-1.57910.059743
23-0.105864-0.82680.20578
240.1260170.98420.164446
25-0.109938-0.85860.19695
26-0.30295-2.36610.010584
27-0.237183-1.85250.0344
28-0.001272-0.00990.496054
29-0.056008-0.43740.331669
30-0.135763-1.06030.146585
31-0.098784-0.77150.221687
32-0.076016-0.59370.277452
33-0.128982-1.00740.158866
34-0.141278-1.10340.13709
35-0.113854-0.88920.188687
360.0452440.35340.362515






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.4289993.35060.000694
2-0.012768-0.09970.460448
30.2587022.02050.023863
40.3780282.95250.002235
50.0502230.39230.34812
60.0350770.2740.392521
7-7.5e-05-6e-040.499767
80.0389480.30420.381008
9-0.277745-2.16930.016985
10-0.223667-1.74690.042845
110.0068130.05320.478868
120.4445823.47230.000477
13-0.32855-2.56610.006379
14-0.036713-0.28670.387643
15-0.099954-0.78070.219009
16-0.029683-0.23180.408722
170.0180760.14120.444098
180.0858540.67050.252521
190.1231460.96180.169972
20-0.102708-0.80220.212782
21-0.033829-0.26420.396253
22-0.030041-0.23460.407642
23-0.085782-0.670.252701
24-0.077884-0.60830.272625
250.0665610.51990.302521
26-0.131536-1.02730.154161
27-0.03071-0.23990.405625
280.0519890.4060.343064
290.0053980.04220.483256
300.0921380.71960.237253
310.0695640.54330.294449
32-0.07079-0.55290.291182
33-0.016801-0.13120.448016
34-0.020075-0.15680.437965
35-0.157585-1.23080.111565
36-0.03507-0.27390.392543

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.428999 & 3.3506 & 0.000694 \tabularnewline
2 & -0.012768 & -0.0997 & 0.460448 \tabularnewline
3 & 0.258702 & 2.0205 & 0.023863 \tabularnewline
4 & 0.378028 & 2.9525 & 0.002235 \tabularnewline
5 & 0.050223 & 0.3923 & 0.34812 \tabularnewline
6 & 0.035077 & 0.274 & 0.392521 \tabularnewline
7 & -7.5e-05 & -6e-04 & 0.499767 \tabularnewline
8 & 0.038948 & 0.3042 & 0.381008 \tabularnewline
9 & -0.277745 & -2.1693 & 0.016985 \tabularnewline
10 & -0.223667 & -1.7469 & 0.042845 \tabularnewline
11 & 0.006813 & 0.0532 & 0.478868 \tabularnewline
12 & 0.444582 & 3.4723 & 0.000477 \tabularnewline
13 & -0.32855 & -2.5661 & 0.006379 \tabularnewline
14 & -0.036713 & -0.2867 & 0.387643 \tabularnewline
15 & -0.099954 & -0.7807 & 0.219009 \tabularnewline
16 & -0.029683 & -0.2318 & 0.408722 \tabularnewline
17 & 0.018076 & 0.1412 & 0.444098 \tabularnewline
18 & 0.085854 & 0.6705 & 0.252521 \tabularnewline
19 & 0.123146 & 0.9618 & 0.169972 \tabularnewline
20 & -0.102708 & -0.8022 & 0.212782 \tabularnewline
21 & -0.033829 & -0.2642 & 0.396253 \tabularnewline
22 & -0.030041 & -0.2346 & 0.407642 \tabularnewline
23 & -0.085782 & -0.67 & 0.252701 \tabularnewline
24 & -0.077884 & -0.6083 & 0.272625 \tabularnewline
25 & 0.066561 & 0.5199 & 0.302521 \tabularnewline
26 & -0.131536 & -1.0273 & 0.154161 \tabularnewline
27 & -0.03071 & -0.2399 & 0.405625 \tabularnewline
28 & 0.051989 & 0.406 & 0.343064 \tabularnewline
29 & 0.005398 & 0.0422 & 0.483256 \tabularnewline
30 & 0.092138 & 0.7196 & 0.237253 \tabularnewline
31 & 0.069564 & 0.5433 & 0.294449 \tabularnewline
32 & -0.07079 & -0.5529 & 0.291182 \tabularnewline
33 & -0.016801 & -0.1312 & 0.448016 \tabularnewline
34 & -0.020075 & -0.1568 & 0.437965 \tabularnewline
35 & -0.157585 & -1.2308 & 0.111565 \tabularnewline
36 & -0.03507 & -0.2739 & 0.392543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60029&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]0.428999[/C][C]3.3506[/C][C]0.000694[/C][/ROW]
[ROW][C]2[/C][C]-0.012768[/C][C]-0.0997[/C][C]0.460448[/C][/ROW]
[ROW][C]3[/C][C]0.258702[/C][C]2.0205[/C][C]0.023863[/C][/ROW]
[ROW][C]4[/C][C]0.378028[/C][C]2.9525[/C][C]0.002235[/C][/ROW]
[ROW][C]5[/C][C]0.050223[/C][C]0.3923[/C][C]0.34812[/C][/ROW]
[ROW][C]6[/C][C]0.035077[/C][C]0.274[/C][C]0.392521[/C][/ROW]
[ROW][C]7[/C][C]-7.5e-05[/C][C]-6e-04[/C][C]0.499767[/C][/ROW]
[ROW][C]8[/C][C]0.038948[/C][C]0.3042[/C][C]0.381008[/C][/ROW]
[ROW][C]9[/C][C]-0.277745[/C][C]-2.1693[/C][C]0.016985[/C][/ROW]
[ROW][C]10[/C][C]-0.223667[/C][C]-1.7469[/C][C]0.042845[/C][/ROW]
[ROW][C]11[/C][C]0.006813[/C][C]0.0532[/C][C]0.478868[/C][/ROW]
[ROW][C]12[/C][C]0.444582[/C][C]3.4723[/C][C]0.000477[/C][/ROW]
[ROW][C]13[/C][C]-0.32855[/C][C]-2.5661[/C][C]0.006379[/C][/ROW]
[ROW][C]14[/C][C]-0.036713[/C][C]-0.2867[/C][C]0.387643[/C][/ROW]
[ROW][C]15[/C][C]-0.099954[/C][C]-0.7807[/C][C]0.219009[/C][/ROW]
[ROW][C]16[/C][C]-0.029683[/C][C]-0.2318[/C][C]0.408722[/C][/ROW]
[ROW][C]17[/C][C]0.018076[/C][C]0.1412[/C][C]0.444098[/C][/ROW]
[ROW][C]18[/C][C]0.085854[/C][C]0.6705[/C][C]0.252521[/C][/ROW]
[ROW][C]19[/C][C]0.123146[/C][C]0.9618[/C][C]0.169972[/C][/ROW]
[ROW][C]20[/C][C]-0.102708[/C][C]-0.8022[/C][C]0.212782[/C][/ROW]
[ROW][C]21[/C][C]-0.033829[/C][C]-0.2642[/C][C]0.396253[/C][/ROW]
[ROW][C]22[/C][C]-0.030041[/C][C]-0.2346[/C][C]0.407642[/C][/ROW]
[ROW][C]23[/C][C]-0.085782[/C][C]-0.67[/C][C]0.252701[/C][/ROW]
[ROW][C]24[/C][C]-0.077884[/C][C]-0.6083[/C][C]0.272625[/C][/ROW]
[ROW][C]25[/C][C]0.066561[/C][C]0.5199[/C][C]0.302521[/C][/ROW]
[ROW][C]26[/C][C]-0.131536[/C][C]-1.0273[/C][C]0.154161[/C][/ROW]
[ROW][C]27[/C][C]-0.03071[/C][C]-0.2399[/C][C]0.405625[/C][/ROW]
[ROW][C]28[/C][C]0.051989[/C][C]0.406[/C][C]0.343064[/C][/ROW]
[ROW][C]29[/C][C]0.005398[/C][C]0.0422[/C][C]0.483256[/C][/ROW]
[ROW][C]30[/C][C]0.092138[/C][C]0.7196[/C][C]0.237253[/C][/ROW]
[ROW][C]31[/C][C]0.069564[/C][C]0.5433[/C][C]0.294449[/C][/ROW]
[ROW][C]32[/C][C]-0.07079[/C][C]-0.5529[/C][C]0.291182[/C][/ROW]
[ROW][C]33[/C][C]-0.016801[/C][C]-0.1312[/C][C]0.448016[/C][/ROW]
[ROW][C]34[/C][C]-0.020075[/C][C]-0.1568[/C][C]0.437965[/C][/ROW]
[ROW][C]35[/C][C]-0.157585[/C][C]-1.2308[/C][C]0.111565[/C][/ROW]
[ROW][C]36[/C][C]-0.03507[/C][C]-0.2739[/C][C]0.392543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60029&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60029&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
10.4289993.35060.000694
2-0.012768-0.09970.460448
30.2587022.02050.023863
40.3780282.95250.002235
50.0502230.39230.34812
60.0350770.2740.392521
7-7.5e-05-6e-040.499767
80.0389480.30420.381008
9-0.277745-2.16930.016985
10-0.223667-1.74690.042845
110.0068130.05320.478868
120.4445823.47230.000477
13-0.32855-2.56610.006379
14-0.036713-0.28670.387643
15-0.099954-0.78070.219009
16-0.029683-0.23180.408722
170.0180760.14120.444098
180.0858540.67050.252521
190.1231460.96180.169972
20-0.102708-0.80220.212782
21-0.033829-0.26420.396253
22-0.030041-0.23460.407642
23-0.085782-0.670.252701
24-0.077884-0.60830.272625
250.0665610.51990.302521
26-0.131536-1.02730.154161
27-0.03071-0.23990.405625
280.0519890.4060.343064
290.0053980.04220.483256
300.0921380.71960.237253
310.0695640.54330.294449
32-0.07079-0.55290.291182
33-0.016801-0.13120.448016
34-0.020075-0.15680.437965
35-0.157585-1.23080.111565
36-0.03507-0.27390.392543


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')