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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, 28 Dec 2009 12:08:44 -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/28/t1262027379gky9zl5gyzp5r09.htm/, Retrieved Sun, 05 May 2024 15:06:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=71038, Retrieved Sun, 05 May 2024 15:06:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96

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] [tijdreeks d2] [2009-12-28 19:08:44] [f47dffd5f5a8c03c3681db4cc9472742] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110,75
110,64
110,46
110,66
110,48
110,5
110,96
111,17
111,07
111,75
111,45
111,24
111,09
111,29
111,15
110,88
111,22
110,62
110,2
109,49
109,32
108,71
107,85
107,44
106,93
106,19
105,71
105,67
105,7
105,28
105,34
105,58
105,23
105,46
104,92
104,68
104,58
104,32
104,36
104,38
104,25
103,93
103,95
103,6
103,23
103,31
102,82
102,76
102,68
102,37
102,54
102,65
102,63
102,22
102,04
101,85
101,88
101,33
100,8

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=71038&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=71038&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=71038&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
10.3220372.18420.017043
20.3257612.20940.016082
30.4242982.87770.003028
40.4008492.71870.004606
5-0.031612-0.21440.41559
60.0525940.35670.361471
70.1320050.89530.187644
8-0.116034-0.7870.217666
9-0.227291-1.54160.065016
10-0.211726-1.4360.078885
11-0.101606-0.68910.247103
12-0.470346-3.190.001281
13-0.252177-1.71030.04697
14-0.117379-0.79610.21503
15-0.125561-0.85160.199426
16-0.249703-1.69360.048555
170.0033620.02280.490954
18-0.00018-0.00120.499515
19-0.074885-0.50790.306976
20-0.029124-0.19750.422143
210.0162910.11050.456251
220.0849370.57610.283688
23-0.012249-0.08310.467076
240.0021020.01430.494343
250.1017930.69040.246708
26-0.049824-0.33790.368479
27-0.098613-0.66880.253474
28-0.021201-0.14380.443145
29-0.04001-0.27140.393663
30-0.101809-0.69050.246674
31-0.058755-0.39850.346054
320.0108990.07390.470697
33-0.015924-0.1080.457231
34-0.040984-0.2780.391142
35-0.017724-0.12020.45242
360.0744410.50490.308027

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.322037 & 2.1842 & 0.017043 \tabularnewline
2 & 0.325761 & 2.2094 & 0.016082 \tabularnewline
3 & 0.424298 & 2.8777 & 0.003028 \tabularnewline
4 & 0.400849 & 2.7187 & 0.004606 \tabularnewline
5 & -0.031612 & -0.2144 & 0.41559 \tabularnewline
6 & 0.052594 & 0.3567 & 0.361471 \tabularnewline
7 & 0.132005 & 0.8953 & 0.187644 \tabularnewline
8 & -0.116034 & -0.787 & 0.217666 \tabularnewline
9 & -0.227291 & -1.5416 & 0.065016 \tabularnewline
10 & -0.211726 & -1.436 & 0.078885 \tabularnewline
11 & -0.101606 & -0.6891 & 0.247103 \tabularnewline
12 & -0.470346 & -3.19 & 0.001281 \tabularnewline
13 & -0.252177 & -1.7103 & 0.04697 \tabularnewline
14 & -0.117379 & -0.7961 & 0.21503 \tabularnewline
15 & -0.125561 & -0.8516 & 0.199426 \tabularnewline
16 & -0.249703 & -1.6936 & 0.048555 \tabularnewline
17 & 0.003362 & 0.0228 & 0.490954 \tabularnewline
18 & -0.00018 & -0.0012 & 0.499515 \tabularnewline
19 & -0.074885 & -0.5079 & 0.306976 \tabularnewline
20 & -0.029124 & -0.1975 & 0.422143 \tabularnewline
21 & 0.016291 & 0.1105 & 0.456251 \tabularnewline
22 & 0.084937 & 0.5761 & 0.283688 \tabularnewline
23 & -0.012249 & -0.0831 & 0.467076 \tabularnewline
24 & 0.002102 & 0.0143 & 0.494343 \tabularnewline
25 & 0.101793 & 0.6904 & 0.246708 \tabularnewline
26 & -0.049824 & -0.3379 & 0.368479 \tabularnewline
27 & -0.098613 & -0.6688 & 0.253474 \tabularnewline
28 & -0.021201 & -0.1438 & 0.443145 \tabularnewline
29 & -0.04001 & -0.2714 & 0.393663 \tabularnewline
30 & -0.101809 & -0.6905 & 0.246674 \tabularnewline
31 & -0.058755 & -0.3985 & 0.346054 \tabularnewline
32 & 0.010899 & 0.0739 & 0.470697 \tabularnewline
33 & -0.015924 & -0.108 & 0.457231 \tabularnewline
34 & -0.040984 & -0.278 & 0.391142 \tabularnewline
35 & -0.017724 & -0.1202 & 0.45242 \tabularnewline
36 & 0.074441 & 0.5049 & 0.308027 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=71038&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]0.322037[/C][C]2.1842[/C][C]0.017043[/C][/ROW]
[ROW][C]2[/C][C]0.325761[/C][C]2.2094[/C][C]0.016082[/C][/ROW]
[ROW][C]3[/C][C]0.424298[/C][C]2.8777[/C][C]0.003028[/C][/ROW]
[ROW][C]4[/C][C]0.400849[/C][C]2.7187[/C][C]0.004606[/C][/ROW]
[ROW][C]5[/C][C]-0.031612[/C][C]-0.2144[/C][C]0.41559[/C][/ROW]
[ROW][C]6[/C][C]0.052594[/C][C]0.3567[/C][C]0.361471[/C][/ROW]
[ROW][C]7[/C][C]0.132005[/C][C]0.8953[/C][C]0.187644[/C][/ROW]
[ROW][C]8[/C][C]-0.116034[/C][C]-0.787[/C][C]0.217666[/C][/ROW]
[ROW][C]9[/C][C]-0.227291[/C][C]-1.5416[/C][C]0.065016[/C][/ROW]
[ROW][C]10[/C][C]-0.211726[/C][C]-1.436[/C][C]0.078885[/C][/ROW]
[ROW][C]11[/C][C]-0.101606[/C][C]-0.6891[/C][C]0.247103[/C][/ROW]
[ROW][C]12[/C][C]-0.470346[/C][C]-3.19[/C][C]0.001281[/C][/ROW]
[ROW][C]13[/C][C]-0.252177[/C][C]-1.7103[/C][C]0.04697[/C][/ROW]
[ROW][C]14[/C][C]-0.117379[/C][C]-0.7961[/C][C]0.21503[/C][/ROW]
[ROW][C]15[/C][C]-0.125561[/C][C]-0.8516[/C][C]0.199426[/C][/ROW]
[ROW][C]16[/C][C]-0.249703[/C][C]-1.6936[/C][C]0.048555[/C][/ROW]
[ROW][C]17[/C][C]0.003362[/C][C]0.0228[/C][C]0.490954[/C][/ROW]
[ROW][C]18[/C][C]-0.00018[/C][C]-0.0012[/C][C]0.499515[/C][/ROW]
[ROW][C]19[/C][C]-0.074885[/C][C]-0.5079[/C][C]0.306976[/C][/ROW]
[ROW][C]20[/C][C]-0.029124[/C][C]-0.1975[/C][C]0.422143[/C][/ROW]
[ROW][C]21[/C][C]0.016291[/C][C]0.1105[/C][C]0.456251[/C][/ROW]
[ROW][C]22[/C][C]0.084937[/C][C]0.5761[/C][C]0.283688[/C][/ROW]
[ROW][C]23[/C][C]-0.012249[/C][C]-0.0831[/C][C]0.467076[/C][/ROW]
[ROW][C]24[/C][C]0.002102[/C][C]0.0143[/C][C]0.494343[/C][/ROW]
[ROW][C]25[/C][C]0.101793[/C][C]0.6904[/C][C]0.246708[/C][/ROW]
[ROW][C]26[/C][C]-0.049824[/C][C]-0.3379[/C][C]0.368479[/C][/ROW]
[ROW][C]27[/C][C]-0.098613[/C][C]-0.6688[/C][C]0.253474[/C][/ROW]
[ROW][C]28[/C][C]-0.021201[/C][C]-0.1438[/C][C]0.443145[/C][/ROW]
[ROW][C]29[/C][C]-0.04001[/C][C]-0.2714[/C][C]0.393663[/C][/ROW]
[ROW][C]30[/C][C]-0.101809[/C][C]-0.6905[/C][C]0.246674[/C][/ROW]
[ROW][C]31[/C][C]-0.058755[/C][C]-0.3985[/C][C]0.346054[/C][/ROW]
[ROW][C]32[/C][C]0.010899[/C][C]0.0739[/C][C]0.470697[/C][/ROW]
[ROW][C]33[/C][C]-0.015924[/C][C]-0.108[/C][C]0.457231[/C][/ROW]
[ROW][C]34[/C][C]-0.040984[/C][C]-0.278[/C][C]0.391142[/C][/ROW]
[ROW][C]35[/C][C]-0.017724[/C][C]-0.1202[/C][C]0.45242[/C][/ROW]
[ROW][C]36[/C][C]0.074441[/C][C]0.5049[/C][C]0.308027[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=71038&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=71038&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
10.3220372.18420.017043
20.3257612.20940.016082
30.4242982.87770.003028
40.4008492.71870.004606
5-0.031612-0.21440.41559
60.0525940.35670.361471
70.1320050.89530.187644
8-0.116034-0.7870.217666
9-0.227291-1.54160.065016
10-0.211726-1.4360.078885
11-0.101606-0.68910.247103
12-0.470346-3.190.001281
13-0.252177-1.71030.04697
14-0.117379-0.79610.21503
15-0.125561-0.85160.199426
16-0.249703-1.69360.048555
170.0033620.02280.490954
18-0.00018-0.00120.499515
19-0.074885-0.50790.306976
20-0.029124-0.19750.422143
210.0162910.11050.456251
220.0849370.57610.283688
23-0.012249-0.08310.467076
240.0021020.01430.494343
250.1017930.69040.246708
26-0.049824-0.33790.368479
27-0.098613-0.66880.253474
28-0.021201-0.14380.443145
29-0.04001-0.27140.393663
30-0.101809-0.69050.246674
31-0.058755-0.39850.346054
320.0108990.07390.470697
33-0.015924-0.1080.457231
34-0.040984-0.2780.391142
35-0.017724-0.12020.45242
360.0744410.50490.308027






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.3220372.18420.017043
20.2477461.68030.04984
30.3157082.14120.018793
40.2294441.55620.063262
5-0.402476-2.72970.004476
6-0.227858-1.54540.06455
70.0491830.33360.370108
8-0.063451-0.43030.334476
9-0.056283-0.38170.352209
10-0.253218-1.71740.046315
110.0564120.38260.351887
12-0.255187-1.73080.045099
130.0876780.59470.277492
140.2436141.65230.052645
150.1575561.06860.145414
160.0001890.00130.499492
17-0.173504-1.17680.122673
18-0.210978-1.43090.079606
190.0008410.00570.497736
200.0463820.31460.377251
21-0.097293-0.65990.256314
22-0.015192-0.1030.459192
230.023730.16090.436422
24-0.186175-1.26270.10653
250.1534421.04070.151728
26-0.092907-0.63010.265865
270.0284690.19310.423871
28-0.03632-0.24630.403259
29-0.119515-0.81060.210888
300.0757220.51360.305005
31-0.030477-0.20670.418576
320.0889480.60330.274644
33-0.032501-0.22040.413256
34-0.065884-0.44680.328542
350.0273860.18570.426732
36-0.066414-0.45040.327255

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.322037 & 2.1842 & 0.017043 \tabularnewline
2 & 0.247746 & 1.6803 & 0.04984 \tabularnewline
3 & 0.315708 & 2.1412 & 0.018793 \tabularnewline
4 & 0.229444 & 1.5562 & 0.063262 \tabularnewline
5 & -0.402476 & -2.7297 & 0.004476 \tabularnewline
6 & -0.227858 & -1.5454 & 0.06455 \tabularnewline
7 & 0.049183 & 0.3336 & 0.370108 \tabularnewline
8 & -0.063451 & -0.4303 & 0.334476 \tabularnewline
9 & -0.056283 & -0.3817 & 0.352209 \tabularnewline
10 & -0.253218 & -1.7174 & 0.046315 \tabularnewline
11 & 0.056412 & 0.3826 & 0.351887 \tabularnewline
12 & -0.255187 & -1.7308 & 0.045099 \tabularnewline
13 & 0.087678 & 0.5947 & 0.277492 \tabularnewline
14 & 0.243614 & 1.6523 & 0.052645 \tabularnewline
15 & 0.157556 & 1.0686 & 0.145414 \tabularnewline
16 & 0.000189 & 0.0013 & 0.499492 \tabularnewline
17 & -0.173504 & -1.1768 & 0.122673 \tabularnewline
18 & -0.210978 & -1.4309 & 0.079606 \tabularnewline
19 & 0.000841 & 0.0057 & 0.497736 \tabularnewline
20 & 0.046382 & 0.3146 & 0.377251 \tabularnewline
21 & -0.097293 & -0.6599 & 0.256314 \tabularnewline
22 & -0.015192 & -0.103 & 0.459192 \tabularnewline
23 & 0.02373 & 0.1609 & 0.436422 \tabularnewline
24 & -0.186175 & -1.2627 & 0.10653 \tabularnewline
25 & 0.153442 & 1.0407 & 0.151728 \tabularnewline
26 & -0.092907 & -0.6301 & 0.265865 \tabularnewline
27 & 0.028469 & 0.1931 & 0.423871 \tabularnewline
28 & -0.03632 & -0.2463 & 0.403259 \tabularnewline
29 & -0.119515 & -0.8106 & 0.210888 \tabularnewline
30 & 0.075722 & 0.5136 & 0.305005 \tabularnewline
31 & -0.030477 & -0.2067 & 0.418576 \tabularnewline
32 & 0.088948 & 0.6033 & 0.274644 \tabularnewline
33 & -0.032501 & -0.2204 & 0.413256 \tabularnewline
34 & -0.065884 & -0.4468 & 0.328542 \tabularnewline
35 & 0.027386 & 0.1857 & 0.426732 \tabularnewline
36 & -0.066414 & -0.4504 & 0.327255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=71038&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]0.322037[/C][C]2.1842[/C][C]0.017043[/C][/ROW]
[ROW][C]2[/C][C]0.247746[/C][C]1.6803[/C][C]0.04984[/C][/ROW]
[ROW][C]3[/C][C]0.315708[/C][C]2.1412[/C][C]0.018793[/C][/ROW]
[ROW][C]4[/C][C]0.229444[/C][C]1.5562[/C][C]0.063262[/C][/ROW]
[ROW][C]5[/C][C]-0.402476[/C][C]-2.7297[/C][C]0.004476[/C][/ROW]
[ROW][C]6[/C][C]-0.227858[/C][C]-1.5454[/C][C]0.06455[/C][/ROW]
[ROW][C]7[/C][C]0.049183[/C][C]0.3336[/C][C]0.370108[/C][/ROW]
[ROW][C]8[/C][C]-0.063451[/C][C]-0.4303[/C][C]0.334476[/C][/ROW]
[ROW][C]9[/C][C]-0.056283[/C][C]-0.3817[/C][C]0.352209[/C][/ROW]
[ROW][C]10[/C][C]-0.253218[/C][C]-1.7174[/C][C]0.046315[/C][/ROW]
[ROW][C]11[/C][C]0.056412[/C][C]0.3826[/C][C]0.351887[/C][/ROW]
[ROW][C]12[/C][C]-0.255187[/C][C]-1.7308[/C][C]0.045099[/C][/ROW]
[ROW][C]13[/C][C]0.087678[/C][C]0.5947[/C][C]0.277492[/C][/ROW]
[ROW][C]14[/C][C]0.243614[/C][C]1.6523[/C][C]0.052645[/C][/ROW]
[ROW][C]15[/C][C]0.157556[/C][C]1.0686[/C][C]0.145414[/C][/ROW]
[ROW][C]16[/C][C]0.000189[/C][C]0.0013[/C][C]0.499492[/C][/ROW]
[ROW][C]17[/C][C]-0.173504[/C][C]-1.1768[/C][C]0.122673[/C][/ROW]
[ROW][C]18[/C][C]-0.210978[/C][C]-1.4309[/C][C]0.079606[/C][/ROW]
[ROW][C]19[/C][C]0.000841[/C][C]0.0057[/C][C]0.497736[/C][/ROW]
[ROW][C]20[/C][C]0.046382[/C][C]0.3146[/C][C]0.377251[/C][/ROW]
[ROW][C]21[/C][C]-0.097293[/C][C]-0.6599[/C][C]0.256314[/C][/ROW]
[ROW][C]22[/C][C]-0.015192[/C][C]-0.103[/C][C]0.459192[/C][/ROW]
[ROW][C]23[/C][C]0.02373[/C][C]0.1609[/C][C]0.436422[/C][/ROW]
[ROW][C]24[/C][C]-0.186175[/C][C]-1.2627[/C][C]0.10653[/C][/ROW]
[ROW][C]25[/C][C]0.153442[/C][C]1.0407[/C][C]0.151728[/C][/ROW]
[ROW][C]26[/C][C]-0.092907[/C][C]-0.6301[/C][C]0.265865[/C][/ROW]
[ROW][C]27[/C][C]0.028469[/C][C]0.1931[/C][C]0.423871[/C][/ROW]
[ROW][C]28[/C][C]-0.03632[/C][C]-0.2463[/C][C]0.403259[/C][/ROW]
[ROW][C]29[/C][C]-0.119515[/C][C]-0.8106[/C][C]0.210888[/C][/ROW]
[ROW][C]30[/C][C]0.075722[/C][C]0.5136[/C][C]0.305005[/C][/ROW]
[ROW][C]31[/C][C]-0.030477[/C][C]-0.2067[/C][C]0.418576[/C][/ROW]
[ROW][C]32[/C][C]0.088948[/C][C]0.6033[/C][C]0.274644[/C][/ROW]
[ROW][C]33[/C][C]-0.032501[/C][C]-0.2204[/C][C]0.413256[/C][/ROW]
[ROW][C]34[/C][C]-0.065884[/C][C]-0.4468[/C][C]0.328542[/C][/ROW]
[ROW][C]35[/C][C]0.027386[/C][C]0.1857[/C][C]0.426732[/C][/ROW]
[ROW][C]36[/C][C]-0.066414[/C][C]-0.4504[/C][C]0.327255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=71038&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=71038&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
10.3220372.18420.017043
20.2477461.68030.04984
30.3157082.14120.018793
40.2294441.55620.063262
5-0.402476-2.72970.004476
6-0.227858-1.54540.06455
70.0491830.33360.370108
8-0.063451-0.43030.334476
9-0.056283-0.38170.352209
10-0.253218-1.71740.046315
110.0564120.38260.351887
12-0.255187-1.73080.045099
130.0876780.59470.277492
140.2436141.65230.052645
150.1575561.06860.145414
160.0001890.00130.499492
17-0.173504-1.17680.122673
18-0.210978-1.43090.079606
190.0008410.00570.497736
200.0463820.31460.377251
21-0.097293-0.65990.256314
22-0.015192-0.1030.459192
230.023730.16090.436422
24-0.186175-1.26270.10653
250.1534421.04070.151728
26-0.092907-0.63010.265865
270.0284690.19310.423871
28-0.03632-0.24630.403259
29-0.119515-0.81060.210888
300.0757220.51360.305005
31-0.030477-0.20670.418576
320.0889480.60330.274644
33-0.032501-0.22040.413256
34-0.065884-0.44680.328542
350.0273860.18570.426732
36-0.066414-0.45040.327255


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 = 1 ; 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')