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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 computationSat, 06 Dec 2008 02:43:18 -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/06/t1228556628kh6z2njnra50jtu.htm/, Retrieved Fri, 17 May 2024 04:41:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29449, Retrieved Fri, 17 May 2024 04:41:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsjulie
Estimated Impact195

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] [eigen data] [2008-12-06 09:43:18] [ff1af8c6f1c2f1c0e8def9bfc9355be9] [Current]
Feedback Forum
2008-12-11 16:11:09 [Katrijn Truyman] [reply] 
Eerst kijken we naar ACF om te onderzoeken of er AR-processen aanwezig zijn. de eerste 3 staafjes zijn significant dus p=3, geen seizonaliteit dus P=0.
Vervolges kijken we naar PACF om te onderzoeken of er MA-processen zijn. Er is geen overeenkomstig patroon te zien met de theoretische voorstellingen, q=0 en Q=0 want er is ook geen seizonaliteit.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97,3
101
113,2
101
105,7
113,9
86,4
96,5
103,3
114,9
105,8
94,2
98,4
99,4
108,8
112,6
104,4
112,2
81,1
97,1
112,6
113,8
107,8
103,2
103,3
101,2
107,7
110,4
101,9
115,9
89,9
88,6
117,2
123,9
100
103,6
94,1
98,7
119,5
112,7
104,4
124,7
89,1
97
121,6
118,8
114
111,5
97,2
102,5
113,4
109,8
104,9
126,1
80
96,8
117,2
112,3
117,3
111,1
102,2
104,3
122,9
107,6
121,3
131,5
89
104,4
128,9
135,9
133,3
121,3
120,5
120,4
137,9
126,1
133,2
146,6
103,4
117,2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29449&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29449&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29449&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.2174131.79280.038723
20.2569572.11890.018876
30.3903863.21920.000986
40.1728071.4250.079365
50.2824462.32910.011416
60.1950321.60830.056204
70.0071110.05860.476706
80.1172890.96720.168439
90.1662731.37110.087422
10-0.033043-0.27250.393041
11-0.052802-0.43540.33232
12-0.189318-1.56120.061564
13-0.016402-0.13530.446404
140.0131820.10870.456881
15-0.092307-0.76120.224589
16-0.152559-1.2580.106341
170.0052780.04350.482707
180.0793020.65390.257678
19-0.061211-0.50480.30768
200.0497770.41050.341377
21-0.112225-0.92540.179008
22-0.079235-0.65340.257855
230.2008781.65650.051116
24-0.145143-1.19690.117755
25-0.079878-0.65870.256159
260.1046330.86280.195632
27-0.026393-0.21760.414181
28-0.017856-0.14720.441687
290.0169870.14010.444507
30-0.118107-0.97390.166771
31-0.010252-0.08450.466437
320.0603750.49790.310093
33-0.109182-0.90030.185559
34-0.025177-0.20760.418074
35-0.068662-0.56620.28656
360.0264480.21810.414005

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.217413 & 1.7928 & 0.038723 \tabularnewline
2 & 0.256957 & 2.1189 & 0.018876 \tabularnewline
3 & 0.390386 & 3.2192 & 0.000986 \tabularnewline
4 & 0.172807 & 1.425 & 0.079365 \tabularnewline
5 & 0.282446 & 2.3291 & 0.011416 \tabularnewline
6 & 0.195032 & 1.6083 & 0.056204 \tabularnewline
7 & 0.007111 & 0.0586 & 0.476706 \tabularnewline
8 & 0.117289 & 0.9672 & 0.168439 \tabularnewline
9 & 0.166273 & 1.3711 & 0.087422 \tabularnewline
10 & -0.033043 & -0.2725 & 0.393041 \tabularnewline
11 & -0.052802 & -0.4354 & 0.33232 \tabularnewline
12 & -0.189318 & -1.5612 & 0.061564 \tabularnewline
13 & -0.016402 & -0.1353 & 0.446404 \tabularnewline
14 & 0.013182 & 0.1087 & 0.456881 \tabularnewline
15 & -0.092307 & -0.7612 & 0.224589 \tabularnewline
16 & -0.152559 & -1.258 & 0.106341 \tabularnewline
17 & 0.005278 & 0.0435 & 0.482707 \tabularnewline
18 & 0.079302 & 0.6539 & 0.257678 \tabularnewline
19 & -0.061211 & -0.5048 & 0.30768 \tabularnewline
20 & 0.049777 & 0.4105 & 0.341377 \tabularnewline
21 & -0.112225 & -0.9254 & 0.179008 \tabularnewline
22 & -0.079235 & -0.6534 & 0.257855 \tabularnewline
23 & 0.200878 & 1.6565 & 0.051116 \tabularnewline
24 & -0.145143 & -1.1969 & 0.117755 \tabularnewline
25 & -0.079878 & -0.6587 & 0.256159 \tabularnewline
26 & 0.104633 & 0.8628 & 0.195632 \tabularnewline
27 & -0.026393 & -0.2176 & 0.414181 \tabularnewline
28 & -0.017856 & -0.1472 & 0.441687 \tabularnewline
29 & 0.016987 & 0.1401 & 0.444507 \tabularnewline
30 & -0.118107 & -0.9739 & 0.166771 \tabularnewline
31 & -0.010252 & -0.0845 & 0.466437 \tabularnewline
32 & 0.060375 & 0.4979 & 0.310093 \tabularnewline
33 & -0.109182 & -0.9003 & 0.185559 \tabularnewline
34 & -0.025177 & -0.2076 & 0.418074 \tabularnewline
35 & -0.068662 & -0.5662 & 0.28656 \tabularnewline
36 & 0.026448 & 0.2181 & 0.414005 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29449&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]0.217413[/C][C]1.7928[/C][C]0.038723[/C][/ROW]
[ROW][C]2[/C][C]0.256957[/C][C]2.1189[/C][C]0.018876[/C][/ROW]
[ROW][C]3[/C][C]0.390386[/C][C]3.2192[/C][C]0.000986[/C][/ROW]
[ROW][C]4[/C][C]0.172807[/C][C]1.425[/C][C]0.079365[/C][/ROW]
[ROW][C]5[/C][C]0.282446[/C][C]2.3291[/C][C]0.011416[/C][/ROW]
[ROW][C]6[/C][C]0.195032[/C][C]1.6083[/C][C]0.056204[/C][/ROW]
[ROW][C]7[/C][C]0.007111[/C][C]0.0586[/C][C]0.476706[/C][/ROW]
[ROW][C]8[/C][C]0.117289[/C][C]0.9672[/C][C]0.168439[/C][/ROW]
[ROW][C]9[/C][C]0.166273[/C][C]1.3711[/C][C]0.087422[/C][/ROW]
[ROW][C]10[/C][C]-0.033043[/C][C]-0.2725[/C][C]0.393041[/C][/ROW]
[ROW][C]11[/C][C]-0.052802[/C][C]-0.4354[/C][C]0.33232[/C][/ROW]
[ROW][C]12[/C][C]-0.189318[/C][C]-1.5612[/C][C]0.061564[/C][/ROW]
[ROW][C]13[/C][C]-0.016402[/C][C]-0.1353[/C][C]0.446404[/C][/ROW]
[ROW][C]14[/C][C]0.013182[/C][C]0.1087[/C][C]0.456881[/C][/ROW]
[ROW][C]15[/C][C]-0.092307[/C][C]-0.7612[/C][C]0.224589[/C][/ROW]
[ROW][C]16[/C][C]-0.152559[/C][C]-1.258[/C][C]0.106341[/C][/ROW]
[ROW][C]17[/C][C]0.005278[/C][C]0.0435[/C][C]0.482707[/C][/ROW]
[ROW][C]18[/C][C]0.079302[/C][C]0.6539[/C][C]0.257678[/C][/ROW]
[ROW][C]19[/C][C]-0.061211[/C][C]-0.5048[/C][C]0.30768[/C][/ROW]
[ROW][C]20[/C][C]0.049777[/C][C]0.4105[/C][C]0.341377[/C][/ROW]
[ROW][C]21[/C][C]-0.112225[/C][C]-0.9254[/C][C]0.179008[/C][/ROW]
[ROW][C]22[/C][C]-0.079235[/C][C]-0.6534[/C][C]0.257855[/C][/ROW]
[ROW][C]23[/C][C]0.200878[/C][C]1.6565[/C][C]0.051116[/C][/ROW]
[ROW][C]24[/C][C]-0.145143[/C][C]-1.1969[/C][C]0.117755[/C][/ROW]
[ROW][C]25[/C][C]-0.079878[/C][C]-0.6587[/C][C]0.256159[/C][/ROW]
[ROW][C]26[/C][C]0.104633[/C][C]0.8628[/C][C]0.195632[/C][/ROW]
[ROW][C]27[/C][C]-0.026393[/C][C]-0.2176[/C][C]0.414181[/C][/ROW]
[ROW][C]28[/C][C]-0.017856[/C][C]-0.1472[/C][C]0.441687[/C][/ROW]
[ROW][C]29[/C][C]0.016987[/C][C]0.1401[/C][C]0.444507[/C][/ROW]
[ROW][C]30[/C][C]-0.118107[/C][C]-0.9739[/C][C]0.166771[/C][/ROW]
[ROW][C]31[/C][C]-0.010252[/C][C]-0.0845[/C][C]0.466437[/C][/ROW]
[ROW][C]32[/C][C]0.060375[/C][C]0.4979[/C][C]0.310093[/C][/ROW]
[ROW][C]33[/C][C]-0.109182[/C][C]-0.9003[/C][C]0.185559[/C][/ROW]
[ROW][C]34[/C][C]-0.025177[/C][C]-0.2076[/C][C]0.418074[/C][/ROW]
[ROW][C]35[/C][C]-0.068662[/C][C]-0.5662[/C][C]0.28656[/C][/ROW]
[ROW][C]36[/C][C]0.026448[/C][C]0.2181[/C][C]0.414005[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29449&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29449&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
10.2174131.79280.038723
20.2569572.11890.018876
30.3903863.21920.000986
40.1728071.4250.079365
50.2824462.32910.011416
60.1950321.60830.056204
70.0071110.05860.476706
80.1172890.96720.168439
90.1662731.37110.087422
10-0.033043-0.27250.393041
11-0.052802-0.43540.33232
12-0.189318-1.56120.061564
13-0.016402-0.13530.446404
140.0131820.10870.456881
15-0.092307-0.76120.224589
16-0.152559-1.2580.106341
170.0052780.04350.482707
180.0793020.65390.257678
19-0.061211-0.50480.30768
200.0497770.41050.341377
21-0.112225-0.92540.179008
22-0.079235-0.65340.257855
230.2008781.65650.051116
24-0.145143-1.19690.117755
25-0.079878-0.65870.256159
260.1046330.86280.195632
27-0.026393-0.21760.414181
28-0.017856-0.14720.441687
290.0169870.14010.444507
30-0.118107-0.97390.166771
31-0.010252-0.08450.466437
320.0603750.49790.310093
33-0.109182-0.90030.185559
34-0.025177-0.20760.418074
35-0.068662-0.56620.28656
360.0264480.21810.414005






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.2174131.79280.038723
20.2200921.81490.036973
30.3297732.71940.004147
40.0260690.2150.415216
50.142421.17440.12216
6-0.002213-0.01820.492748
7-0.171574-1.41480.080839
8-0.048968-0.40380.343812
90.1186160.97810.165739
10-0.08017-0.66110.255391
11-0.148969-1.22840.111759
12-0.272228-2.24480.014017
130.1009980.83290.203921
140.1341271.1060.136304
150.0880110.72580.23524
16-0.121261-0.99990.160441
170.1071160.88330.190094
180.1812831.49490.069784
19-0.063706-0.52530.300531
200.0433530.35750.360914
21-0.120507-0.99370.16194
22-0.196404-1.61960.054975
230.1257151.03670.15178
24-0.169484-1.39760.083388
250.0167080.13780.445411
260.1033930.85260.198437
270.0613970.50630.307144
28-0.119554-0.98590.163846
290.0758290.62530.266933
300.1158560.95540.171388
31-0.057609-0.47510.318136
32-0.027268-0.22490.411384
33-0.098327-0.81080.210147
34-0.024043-0.19830.421717
35-0.035021-0.28880.386812
360.0054370.04480.482185

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.217413 & 1.7928 & 0.038723 \tabularnewline
2 & 0.220092 & 1.8149 & 0.036973 \tabularnewline
3 & 0.329773 & 2.7194 & 0.004147 \tabularnewline
4 & 0.026069 & 0.215 & 0.415216 \tabularnewline
5 & 0.14242 & 1.1744 & 0.12216 \tabularnewline
6 & -0.002213 & -0.0182 & 0.492748 \tabularnewline
7 & -0.171574 & -1.4148 & 0.080839 \tabularnewline
8 & -0.048968 & -0.4038 & 0.343812 \tabularnewline
9 & 0.118616 & 0.9781 & 0.165739 \tabularnewline
10 & -0.08017 & -0.6611 & 0.255391 \tabularnewline
11 & -0.148969 & -1.2284 & 0.111759 \tabularnewline
12 & -0.272228 & -2.2448 & 0.014017 \tabularnewline
13 & 0.100998 & 0.8329 & 0.203921 \tabularnewline
14 & 0.134127 & 1.106 & 0.136304 \tabularnewline
15 & 0.088011 & 0.7258 & 0.23524 \tabularnewline
16 & -0.121261 & -0.9999 & 0.160441 \tabularnewline
17 & 0.107116 & 0.8833 & 0.190094 \tabularnewline
18 & 0.181283 & 1.4949 & 0.069784 \tabularnewline
19 & -0.063706 & -0.5253 & 0.300531 \tabularnewline
20 & 0.043353 & 0.3575 & 0.360914 \tabularnewline
21 & -0.120507 & -0.9937 & 0.16194 \tabularnewline
22 & -0.196404 & -1.6196 & 0.054975 \tabularnewline
23 & 0.125715 & 1.0367 & 0.15178 \tabularnewline
24 & -0.169484 & -1.3976 & 0.083388 \tabularnewline
25 & 0.016708 & 0.1378 & 0.445411 \tabularnewline
26 & 0.103393 & 0.8526 & 0.198437 \tabularnewline
27 & 0.061397 & 0.5063 & 0.307144 \tabularnewline
28 & -0.119554 & -0.9859 & 0.163846 \tabularnewline
29 & 0.075829 & 0.6253 & 0.266933 \tabularnewline
30 & 0.115856 & 0.9554 & 0.171388 \tabularnewline
31 & -0.057609 & -0.4751 & 0.318136 \tabularnewline
32 & -0.027268 & -0.2249 & 0.411384 \tabularnewline
33 & -0.098327 & -0.8108 & 0.210147 \tabularnewline
34 & -0.024043 & -0.1983 & 0.421717 \tabularnewline
35 & -0.035021 & -0.2888 & 0.386812 \tabularnewline
36 & 0.005437 & 0.0448 & 0.482185 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29449&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]0.217413[/C][C]1.7928[/C][C]0.038723[/C][/ROW]
[ROW][C]2[/C][C]0.220092[/C][C]1.8149[/C][C]0.036973[/C][/ROW]
[ROW][C]3[/C][C]0.329773[/C][C]2.7194[/C][C]0.004147[/C][/ROW]
[ROW][C]4[/C][C]0.026069[/C][C]0.215[/C][C]0.415216[/C][/ROW]
[ROW][C]5[/C][C]0.14242[/C][C]1.1744[/C][C]0.12216[/C][/ROW]
[ROW][C]6[/C][C]-0.002213[/C][C]-0.0182[/C][C]0.492748[/C][/ROW]
[ROW][C]7[/C][C]-0.171574[/C][C]-1.4148[/C][C]0.080839[/C][/ROW]
[ROW][C]8[/C][C]-0.048968[/C][C]-0.4038[/C][C]0.343812[/C][/ROW]
[ROW][C]9[/C][C]0.118616[/C][C]0.9781[/C][C]0.165739[/C][/ROW]
[ROW][C]10[/C][C]-0.08017[/C][C]-0.6611[/C][C]0.255391[/C][/ROW]
[ROW][C]11[/C][C]-0.148969[/C][C]-1.2284[/C][C]0.111759[/C][/ROW]
[ROW][C]12[/C][C]-0.272228[/C][C]-2.2448[/C][C]0.014017[/C][/ROW]
[ROW][C]13[/C][C]0.100998[/C][C]0.8329[/C][C]0.203921[/C][/ROW]
[ROW][C]14[/C][C]0.134127[/C][C]1.106[/C][C]0.136304[/C][/ROW]
[ROW][C]15[/C][C]0.088011[/C][C]0.7258[/C][C]0.23524[/C][/ROW]
[ROW][C]16[/C][C]-0.121261[/C][C]-0.9999[/C][C]0.160441[/C][/ROW]
[ROW][C]17[/C][C]0.107116[/C][C]0.8833[/C][C]0.190094[/C][/ROW]
[ROW][C]18[/C][C]0.181283[/C][C]1.4949[/C][C]0.069784[/C][/ROW]
[ROW][C]19[/C][C]-0.063706[/C][C]-0.5253[/C][C]0.300531[/C][/ROW]
[ROW][C]20[/C][C]0.043353[/C][C]0.3575[/C][C]0.360914[/C][/ROW]
[ROW][C]21[/C][C]-0.120507[/C][C]-0.9937[/C][C]0.16194[/C][/ROW]
[ROW][C]22[/C][C]-0.196404[/C][C]-1.6196[/C][C]0.054975[/C][/ROW]
[ROW][C]23[/C][C]0.125715[/C][C]1.0367[/C][C]0.15178[/C][/ROW]
[ROW][C]24[/C][C]-0.169484[/C][C]-1.3976[/C][C]0.083388[/C][/ROW]
[ROW][C]25[/C][C]0.016708[/C][C]0.1378[/C][C]0.445411[/C][/ROW]
[ROW][C]26[/C][C]0.103393[/C][C]0.8526[/C][C]0.198437[/C][/ROW]
[ROW][C]27[/C][C]0.061397[/C][C]0.5063[/C][C]0.307144[/C][/ROW]
[ROW][C]28[/C][C]-0.119554[/C][C]-0.9859[/C][C]0.163846[/C][/ROW]
[ROW][C]29[/C][C]0.075829[/C][C]0.6253[/C][C]0.266933[/C][/ROW]
[ROW][C]30[/C][C]0.115856[/C][C]0.9554[/C][C]0.171388[/C][/ROW]
[ROW][C]31[/C][C]-0.057609[/C][C]-0.4751[/C][C]0.318136[/C][/ROW]
[ROW][C]32[/C][C]-0.027268[/C][C]-0.2249[/C][C]0.411384[/C][/ROW]
[ROW][C]33[/C][C]-0.098327[/C][C]-0.8108[/C][C]0.210147[/C][/ROW]
[ROW][C]34[/C][C]-0.024043[/C][C]-0.1983[/C][C]0.421717[/C][/ROW]
[ROW][C]35[/C][C]-0.035021[/C][C]-0.2888[/C][C]0.386812[/C][/ROW]
[ROW][C]36[/C][C]0.005437[/C][C]0.0448[/C][C]0.482185[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29449&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29449&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
10.2174131.79280.038723
20.2200921.81490.036973
30.3297732.71940.004147
40.0260690.2150.415216
50.142421.17440.12216
6-0.002213-0.01820.492748
7-0.171574-1.41480.080839
8-0.048968-0.40380.343812
90.1186160.97810.165739
10-0.08017-0.66110.255391
11-0.148969-1.22840.111759
12-0.272228-2.24480.014017
130.1009980.83290.203921
140.1341271.1060.136304
150.0880110.72580.23524
16-0.121261-0.99990.160441
170.1071160.88330.190094
180.1812831.49490.069784
19-0.063706-0.52530.300531
200.0433530.35750.360914
21-0.120507-0.99370.16194
22-0.196404-1.61960.054975
230.1257151.03670.15178
24-0.169484-1.39760.083388
250.0167080.13780.445411
260.1033930.85260.198437
270.0613970.50630.307144
28-0.119554-0.98590.163846
290.0758290.62530.266933
300.1158560.95540.171388
31-0.057609-0.47510.318136
32-0.027268-0.22490.411384
33-0.098327-0.81080.210147
34-0.024043-0.19830.421717
35-0.035021-0.28880.386812
360.0054370.04480.482185


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = -2.0 ; par3 = 0 ; par4 = 1 ; par5 = 12 ;
Parameters (R input):
par1 = 36 ; par2 = -2.0 ; par3 = 0 ; par4 = 1 ; 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')