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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, 11 Dec 2009 08:39:41 -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/11/t1260546034yzriy8eokqp0gez.htm/, Retrieved Sun, 28 Apr 2024 23:05:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66386, Retrieved Sun, 28 Apr 2024 23:05:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [blog] [2008-12-01 15:44:12] [12d343c4448a5f9e527bb31caeac580b]
-   PD  [Multiple Regression] [blog] [2008-12-01 16:17:50] [12d343c4448a5f9e527bb31caeac580b]
-   PD    [Multiple Regression] [dioxine] [2008-12-01 16:30:23] [7a664918911e34206ce9d0436dd7c1c8]
-    D      [Multiple Regression] [Hypothese 1 en 2 ...] [2008-12-03 15:49:48] [12d343c4448a5f9e527bb31caeac580b]
- RMPD        [(Partial) Autocorrelation Function] [paper:3 ACF (d,D=0)] [2009-12-11 14:59:19] [0f0e461427f61416e46aeda5f4901bed]
-                 [(Partial) Autocorrelation Function] [paper:8 ACF (d=1,...] [2009-12-11 15:39:41] [b090d569c0a4c77894e0b029f4429f19] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
118.7
110.1
110.3
112.9
102.2
99.4
116.1
103.8
101.8
113.7
89.7
99.5
122.9
108.6
114.4
110.5
104.1
103.6
121.6
101.1
116.0
120.1
96.0
105.0
124.7
123.9
123.6
114.8
108.8
106.1
123.2
106.2
115.2
120.6
109.5
114.4
121.4
129.5
124.3
112.6
125.1
117.9
116.4
126.4
93.3
102.9
97.8
97.1
110.7
109.3
103.2
106.2
81.3
84.5
92.7
85.0
79.1
92.6
78.1
76.9
92.5

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=66386&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=66386&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66386&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
1-0.522543-3.62030.000354
2-0.077373-0.53610.297198
30.4178312.89480.002847
4-0.447255-3.09870.001623
50.1121190.77680.22055
60.1675841.16110.125681
7-0.294356-2.03940.02347
80.3309622.2930.013136
9-0.138835-0.96190.170467
10-0.043515-0.30150.382174
110.1722111.19310.119345
12-0.299081-2.07210.021826
130.0883370.6120.271707
140.100970.69950.243795
15-0.131818-0.91330.182834
160.0683870.47380.318895
170.0958050.66380.255011
18-0.086115-0.59660.276783
19-0.069418-0.48090.316372
200.1157310.80180.213308
21-0.084837-0.58780.279722
22-0.03875-0.26850.394745
230.2128251.47450.073438
24-0.208725-1.44610.077326
250.0500460.34670.365155
260.088990.61650.270225
27-0.123334-0.85450.198541
280.0118990.08240.46732
290.060110.41650.339466
30-0.119137-0.82540.206613
310.1526321.05750.147796
32-0.097371-0.67460.251581
330.0141990.09840.461024
340.0442080.30630.380356
35-0.083169-0.57620.283582
360.0512870.35530.361949

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.522543 & -3.6203 & 0.000354 \tabularnewline
2 & -0.077373 & -0.5361 & 0.297198 \tabularnewline
3 & 0.417831 & 2.8948 & 0.002847 \tabularnewline
4 & -0.447255 & -3.0987 & 0.001623 \tabularnewline
5 & 0.112119 & 0.7768 & 0.22055 \tabularnewline
6 & 0.167584 & 1.1611 & 0.125681 \tabularnewline
7 & -0.294356 & -2.0394 & 0.02347 \tabularnewline
8 & 0.330962 & 2.293 & 0.013136 \tabularnewline
9 & -0.138835 & -0.9619 & 0.170467 \tabularnewline
10 & -0.043515 & -0.3015 & 0.382174 \tabularnewline
11 & 0.172211 & 1.1931 & 0.119345 \tabularnewline
12 & -0.299081 & -2.0721 & 0.021826 \tabularnewline
13 & 0.088337 & 0.612 & 0.271707 \tabularnewline
14 & 0.10097 & 0.6995 & 0.243795 \tabularnewline
15 & -0.131818 & -0.9133 & 0.182834 \tabularnewline
16 & 0.068387 & 0.4738 & 0.318895 \tabularnewline
17 & 0.095805 & 0.6638 & 0.255011 \tabularnewline
18 & -0.086115 & -0.5966 & 0.276783 \tabularnewline
19 & -0.069418 & -0.4809 & 0.316372 \tabularnewline
20 & 0.115731 & 0.8018 & 0.213308 \tabularnewline
21 & -0.084837 & -0.5878 & 0.279722 \tabularnewline
22 & -0.03875 & -0.2685 & 0.394745 \tabularnewline
23 & 0.212825 & 1.4745 & 0.073438 \tabularnewline
24 & -0.208725 & -1.4461 & 0.077326 \tabularnewline
25 & 0.050046 & 0.3467 & 0.365155 \tabularnewline
26 & 0.08899 & 0.6165 & 0.270225 \tabularnewline
27 & -0.123334 & -0.8545 & 0.198541 \tabularnewline
28 & 0.011899 & 0.0824 & 0.46732 \tabularnewline
29 & 0.06011 & 0.4165 & 0.339466 \tabularnewline
30 & -0.119137 & -0.8254 & 0.206613 \tabularnewline
31 & 0.152632 & 1.0575 & 0.147796 \tabularnewline
32 & -0.097371 & -0.6746 & 0.251581 \tabularnewline
33 & 0.014199 & 0.0984 & 0.461024 \tabularnewline
34 & 0.044208 & 0.3063 & 0.380356 \tabularnewline
35 & -0.083169 & -0.5762 & 0.283582 \tabularnewline
36 & 0.051287 & 0.3553 & 0.361949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66386&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]-0.522543[/C][C]-3.6203[/C][C]0.000354[/C][/ROW]
[ROW][C]2[/C][C]-0.077373[/C][C]-0.5361[/C][C]0.297198[/C][/ROW]
[ROW][C]3[/C][C]0.417831[/C][C]2.8948[/C][C]0.002847[/C][/ROW]
[ROW][C]4[/C][C]-0.447255[/C][C]-3.0987[/C][C]0.001623[/C][/ROW]
[ROW][C]5[/C][C]0.112119[/C][C]0.7768[/C][C]0.22055[/C][/ROW]
[ROW][C]6[/C][C]0.167584[/C][C]1.1611[/C][C]0.125681[/C][/ROW]
[ROW][C]7[/C][C]-0.294356[/C][C]-2.0394[/C][C]0.02347[/C][/ROW]
[ROW][C]8[/C][C]0.330962[/C][C]2.293[/C][C]0.013136[/C][/ROW]
[ROW][C]9[/C][C]-0.138835[/C][C]-0.9619[/C][C]0.170467[/C][/ROW]
[ROW][C]10[/C][C]-0.043515[/C][C]-0.3015[/C][C]0.382174[/C][/ROW]
[ROW][C]11[/C][C]0.172211[/C][C]1.1931[/C][C]0.119345[/C][/ROW]
[ROW][C]12[/C][C]-0.299081[/C][C]-2.0721[/C][C]0.021826[/C][/ROW]
[ROW][C]13[/C][C]0.088337[/C][C]0.612[/C][C]0.271707[/C][/ROW]
[ROW][C]14[/C][C]0.10097[/C][C]0.6995[/C][C]0.243795[/C][/ROW]
[ROW][C]15[/C][C]-0.131818[/C][C]-0.9133[/C][C]0.182834[/C][/ROW]
[ROW][C]16[/C][C]0.068387[/C][C]0.4738[/C][C]0.318895[/C][/ROW]
[ROW][C]17[/C][C]0.095805[/C][C]0.6638[/C][C]0.255011[/C][/ROW]
[ROW][C]18[/C][C]-0.086115[/C][C]-0.5966[/C][C]0.276783[/C][/ROW]
[ROW][C]19[/C][C]-0.069418[/C][C]-0.4809[/C][C]0.316372[/C][/ROW]
[ROW][C]20[/C][C]0.115731[/C][C]0.8018[/C][C]0.213308[/C][/ROW]
[ROW][C]21[/C][C]-0.084837[/C][C]-0.5878[/C][C]0.279722[/C][/ROW]
[ROW][C]22[/C][C]-0.03875[/C][C]-0.2685[/C][C]0.394745[/C][/ROW]
[ROW][C]23[/C][C]0.212825[/C][C]1.4745[/C][C]0.073438[/C][/ROW]
[ROW][C]24[/C][C]-0.208725[/C][C]-1.4461[/C][C]0.077326[/C][/ROW]
[ROW][C]25[/C][C]0.050046[/C][C]0.3467[/C][C]0.365155[/C][/ROW]
[ROW][C]26[/C][C]0.08899[/C][C]0.6165[/C][C]0.270225[/C][/ROW]
[ROW][C]27[/C][C]-0.123334[/C][C]-0.8545[/C][C]0.198541[/C][/ROW]
[ROW][C]28[/C][C]0.011899[/C][C]0.0824[/C][C]0.46732[/C][/ROW]
[ROW][C]29[/C][C]0.06011[/C][C]0.4165[/C][C]0.339466[/C][/ROW]
[ROW][C]30[/C][C]-0.119137[/C][C]-0.8254[/C][C]0.206613[/C][/ROW]
[ROW][C]31[/C][C]0.152632[/C][C]1.0575[/C][C]0.147796[/C][/ROW]
[ROW][C]32[/C][C]-0.097371[/C][C]-0.6746[/C][C]0.251581[/C][/ROW]
[ROW][C]33[/C][C]0.014199[/C][C]0.0984[/C][C]0.461024[/C][/ROW]
[ROW][C]34[/C][C]0.044208[/C][C]0.3063[/C][C]0.380356[/C][/ROW]
[ROW][C]35[/C][C]-0.083169[/C][C]-0.5762[/C][C]0.283582[/C][/ROW]
[ROW][C]36[/C][C]0.051287[/C][C]0.3553[/C][C]0.361949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66386&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66386&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
1-0.522543-3.62030.000354
2-0.077373-0.53610.297198
30.4178312.89480.002847
4-0.447255-3.09870.001623
50.1121190.77680.22055
60.1675841.16110.125681
7-0.294356-2.03940.02347
80.3309622.2930.013136
9-0.138835-0.96190.170467
10-0.043515-0.30150.382174
110.1722111.19310.119345
12-0.299081-2.07210.021826
130.0883370.6120.271707
140.100970.69950.243795
15-0.131818-0.91330.182834
160.0683870.47380.318895
170.0958050.66380.255011
18-0.086115-0.59660.276783
19-0.069418-0.48090.316372
200.1157310.80180.213308
21-0.084837-0.58780.279722
22-0.03875-0.26850.394745
230.2128251.47450.073438
24-0.208725-1.44610.077326
250.0500460.34670.365155
260.088990.61650.270225
27-0.123334-0.85450.198541
280.0118990.08240.46732
290.060110.41650.339466
30-0.119137-0.82540.206613
310.1526321.05750.147796
32-0.097371-0.67460.251581
330.0141990.09840.461024
340.0442080.30630.380356
35-0.083169-0.57620.283582
360.0512870.35530.361949






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.522543-3.62030.000354
2-0.482047-3.33970.000814
30.1899921.31630.097161
4-0.16467-1.14090.129793
5-0.184144-1.27580.104085
6-0.088362-0.61220.271651
7-0.123388-0.85490.198438
80.2048291.41910.081166
90.0235460.16310.43555
100.1071560.74240.230733
110.0443070.3070.380099
12-0.179285-1.24210.110113
13-0.237292-1.6440.053355
14-0.161785-1.12090.133958
150.078930.54680.29351
16-0.134561-0.93230.177932
170.0162850.11280.455321
180.0831130.57580.283711
19-0.122751-0.85040.199651
200.0430740.29840.383333
210.0194780.13490.446609
220.0374830.25970.398106
230.1634371.13230.131562
24-0.112163-0.77710.220461
25-0.181758-1.25930.107013
26-0.204323-1.41560.081676
270.1114990.77250.221807
28-0.122468-0.84850.200189
29-0.038728-0.26830.394803
30-0.105989-0.73430.233166
31-0.073563-0.50970.306312
32-0.027897-0.19330.42378
330.0269850.1870.42624
340.0328110.22730.410569
350.0252080.17460.431046
36-0.007293-0.05050.479956

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.522543 & -3.6203 & 0.000354 \tabularnewline
2 & -0.482047 & -3.3397 & 0.000814 \tabularnewline
3 & 0.189992 & 1.3163 & 0.097161 \tabularnewline
4 & -0.16467 & -1.1409 & 0.129793 \tabularnewline
5 & -0.184144 & -1.2758 & 0.104085 \tabularnewline
6 & -0.088362 & -0.6122 & 0.271651 \tabularnewline
7 & -0.123388 & -0.8549 & 0.198438 \tabularnewline
8 & 0.204829 & 1.4191 & 0.081166 \tabularnewline
9 & 0.023546 & 0.1631 & 0.43555 \tabularnewline
10 & 0.107156 & 0.7424 & 0.230733 \tabularnewline
11 & 0.044307 & 0.307 & 0.380099 \tabularnewline
12 & -0.179285 & -1.2421 & 0.110113 \tabularnewline
13 & -0.237292 & -1.644 & 0.053355 \tabularnewline
14 & -0.161785 & -1.1209 & 0.133958 \tabularnewline
15 & 0.07893 & 0.5468 & 0.29351 \tabularnewline
16 & -0.134561 & -0.9323 & 0.177932 \tabularnewline
17 & 0.016285 & 0.1128 & 0.455321 \tabularnewline
18 & 0.083113 & 0.5758 & 0.283711 \tabularnewline
19 & -0.122751 & -0.8504 & 0.199651 \tabularnewline
20 & 0.043074 & 0.2984 & 0.383333 \tabularnewline
21 & 0.019478 & 0.1349 & 0.446609 \tabularnewline
22 & 0.037483 & 0.2597 & 0.398106 \tabularnewline
23 & 0.163437 & 1.1323 & 0.131562 \tabularnewline
24 & -0.112163 & -0.7771 & 0.220461 \tabularnewline
25 & -0.181758 & -1.2593 & 0.107013 \tabularnewline
26 & -0.204323 & -1.4156 & 0.081676 \tabularnewline
27 & 0.111499 & 0.7725 & 0.221807 \tabularnewline
28 & -0.122468 & -0.8485 & 0.200189 \tabularnewline
29 & -0.038728 & -0.2683 & 0.394803 \tabularnewline
30 & -0.105989 & -0.7343 & 0.233166 \tabularnewline
31 & -0.073563 & -0.5097 & 0.306312 \tabularnewline
32 & -0.027897 & -0.1933 & 0.42378 \tabularnewline
33 & 0.026985 & 0.187 & 0.42624 \tabularnewline
34 & 0.032811 & 0.2273 & 0.410569 \tabularnewline
35 & 0.025208 & 0.1746 & 0.431046 \tabularnewline
36 & -0.007293 & -0.0505 & 0.479956 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66386&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]-0.522543[/C][C]-3.6203[/C][C]0.000354[/C][/ROW]
[ROW][C]2[/C][C]-0.482047[/C][C]-3.3397[/C][C]0.000814[/C][/ROW]
[ROW][C]3[/C][C]0.189992[/C][C]1.3163[/C][C]0.097161[/C][/ROW]
[ROW][C]4[/C][C]-0.16467[/C][C]-1.1409[/C][C]0.129793[/C][/ROW]
[ROW][C]5[/C][C]-0.184144[/C][C]-1.2758[/C][C]0.104085[/C][/ROW]
[ROW][C]6[/C][C]-0.088362[/C][C]-0.6122[/C][C]0.271651[/C][/ROW]
[ROW][C]7[/C][C]-0.123388[/C][C]-0.8549[/C][C]0.198438[/C][/ROW]
[ROW][C]8[/C][C]0.204829[/C][C]1.4191[/C][C]0.081166[/C][/ROW]
[ROW][C]9[/C][C]0.023546[/C][C]0.1631[/C][C]0.43555[/C][/ROW]
[ROW][C]10[/C][C]0.107156[/C][C]0.7424[/C][C]0.230733[/C][/ROW]
[ROW][C]11[/C][C]0.044307[/C][C]0.307[/C][C]0.380099[/C][/ROW]
[ROW][C]12[/C][C]-0.179285[/C][C]-1.2421[/C][C]0.110113[/C][/ROW]
[ROW][C]13[/C][C]-0.237292[/C][C]-1.644[/C][C]0.053355[/C][/ROW]
[ROW][C]14[/C][C]-0.161785[/C][C]-1.1209[/C][C]0.133958[/C][/ROW]
[ROW][C]15[/C][C]0.07893[/C][C]0.5468[/C][C]0.29351[/C][/ROW]
[ROW][C]16[/C][C]-0.134561[/C][C]-0.9323[/C][C]0.177932[/C][/ROW]
[ROW][C]17[/C][C]0.016285[/C][C]0.1128[/C][C]0.455321[/C][/ROW]
[ROW][C]18[/C][C]0.083113[/C][C]0.5758[/C][C]0.283711[/C][/ROW]
[ROW][C]19[/C][C]-0.122751[/C][C]-0.8504[/C][C]0.199651[/C][/ROW]
[ROW][C]20[/C][C]0.043074[/C][C]0.2984[/C][C]0.383333[/C][/ROW]
[ROW][C]21[/C][C]0.019478[/C][C]0.1349[/C][C]0.446609[/C][/ROW]
[ROW][C]22[/C][C]0.037483[/C][C]0.2597[/C][C]0.398106[/C][/ROW]
[ROW][C]23[/C][C]0.163437[/C][C]1.1323[/C][C]0.131562[/C][/ROW]
[ROW][C]24[/C][C]-0.112163[/C][C]-0.7771[/C][C]0.220461[/C][/ROW]
[ROW][C]25[/C][C]-0.181758[/C][C]-1.2593[/C][C]0.107013[/C][/ROW]
[ROW][C]26[/C][C]-0.204323[/C][C]-1.4156[/C][C]0.081676[/C][/ROW]
[ROW][C]27[/C][C]0.111499[/C][C]0.7725[/C][C]0.221807[/C][/ROW]
[ROW][C]28[/C][C]-0.122468[/C][C]-0.8485[/C][C]0.200189[/C][/ROW]
[ROW][C]29[/C][C]-0.038728[/C][C]-0.2683[/C][C]0.394803[/C][/ROW]
[ROW][C]30[/C][C]-0.105989[/C][C]-0.7343[/C][C]0.233166[/C][/ROW]
[ROW][C]31[/C][C]-0.073563[/C][C]-0.5097[/C][C]0.306312[/C][/ROW]
[ROW][C]32[/C][C]-0.027897[/C][C]-0.1933[/C][C]0.42378[/C][/ROW]
[ROW][C]33[/C][C]0.026985[/C][C]0.187[/C][C]0.42624[/C][/ROW]
[ROW][C]34[/C][C]0.032811[/C][C]0.2273[/C][C]0.410569[/C][/ROW]
[ROW][C]35[/C][C]0.025208[/C][C]0.1746[/C][C]0.431046[/C][/ROW]
[ROW][C]36[/C][C]-0.007293[/C][C]-0.0505[/C][C]0.479956[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66386&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66386&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
1-0.522543-3.62030.000354
2-0.482047-3.33970.000814
30.1899921.31630.097161
4-0.16467-1.14090.129793
5-0.184144-1.27580.104085
6-0.088362-0.61220.271651
7-0.123388-0.85490.198438
80.2048291.41910.081166
90.0235460.16310.43555
100.1071560.74240.230733
110.0443070.3070.380099
12-0.179285-1.24210.110113
13-0.237292-1.6440.053355
14-0.161785-1.12090.133958
150.078930.54680.29351
16-0.134561-0.93230.177932
170.0162850.11280.455321
180.0831130.57580.283711
19-0.122751-0.85040.199651
200.0430740.29840.383333
210.0194780.13490.446609
220.0374830.25970.398106
230.1634371.13230.131562
24-0.112163-0.77710.220461
25-0.181758-1.25930.107013
26-0.204323-1.41560.081676
270.1114990.77250.221807
28-0.122468-0.84850.200189
29-0.038728-0.26830.394803
30-0.105989-0.73430.233166
31-0.073563-0.50970.306312
32-0.027897-0.19330.42378
330.0269850.1870.42624
340.0328110.22730.410569
350.0252080.17460.431046
36-0.007293-0.05050.479956


Figures (Output of Computation)

Input Parameters & R Code

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