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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 computationThu, 17 Dec 2009 07:49:32 -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/17/t12610614290b8yu0um3absnwh.htm/, Retrieved Tue, 30 Apr 2024 07:28:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68924, Retrieved Tue, 30 Apr 2024 07:28:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

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 12:26:39] [b98453cac15ba1066b407e146608df68]
-   PD        [(Partial) Autocorrelation Function] [ACF2] [2009-11-25 16:43:12] [3b0db66ac8145b1be856a517e2900332]
-   P             [(Partial) Autocorrelation Function] [ACF (2)] [2009-12-17 14:49:32] [2ecea65fec1cd5f6b1ab182881aa2a91] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
21
19
25
21
23
23
19
18
19
19
22
23
20
14
14
14
15
11
17
16
20
24
23
20
21
19
23
23
23
23
27
26
17
24
26
24
27
27
26
24
23
23
24
17
21
19
22
22
18
16
14
12
14
16
8
3
0
5
1
1
3

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=68924&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=68924&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68924&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
1-0.213336-1.65250.051829
2-0.091258-0.70690.241189
30.0058430.04530.482025
4-0.010794-0.08360.466823
5-0.057166-0.44280.329749
60.0760630.58920.278976
70.0189860.14710.441787
80.0695510.53870.29603
9-0.04893-0.3790.35301
100.0055010.04260.483077
110.1009660.78210.218621
12-0.058995-0.4570.32467
130.0389980.30210.381819
14-0.176717-1.36880.088075
150.0195510.15140.440068
160.2086831.61640.055622
17-0.130434-1.01030.158195
180.0259890.20130.420569
190.0927590.71850.237615
20-0.013232-0.10250.459352
21-0.243666-1.88740.031971
22-0.006066-0.0470.48134
230.0238330.18460.427079
240.0604220.4680.32073
25-0.17804-1.37910.086492
260.255071.97580.026393
27-0.070071-0.54280.294649
28-0.079274-0.61410.270749
29-0.011463-0.08880.464772
30-0.149718-1.15970.125381
310.134271.04010.151245
320.0236690.18330.427574
33-0.08366-0.6480.259719
34-0.032649-0.25290.400604
35-0.024755-0.19170.424293
36-5.9e-05-5e-040.499817

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.213336 & -1.6525 & 0.051829 \tabularnewline
2 & -0.091258 & -0.7069 & 0.241189 \tabularnewline
3 & 0.005843 & 0.0453 & 0.482025 \tabularnewline
4 & -0.010794 & -0.0836 & 0.466823 \tabularnewline
5 & -0.057166 & -0.4428 & 0.329749 \tabularnewline
6 & 0.076063 & 0.5892 & 0.278976 \tabularnewline
7 & 0.018986 & 0.1471 & 0.441787 \tabularnewline
8 & 0.069551 & 0.5387 & 0.29603 \tabularnewline
9 & -0.04893 & -0.379 & 0.35301 \tabularnewline
10 & 0.005501 & 0.0426 & 0.483077 \tabularnewline
11 & 0.100966 & 0.7821 & 0.218621 \tabularnewline
12 & -0.058995 & -0.457 & 0.32467 \tabularnewline
13 & 0.038998 & 0.3021 & 0.381819 \tabularnewline
14 & -0.176717 & -1.3688 & 0.088075 \tabularnewline
15 & 0.019551 & 0.1514 & 0.440068 \tabularnewline
16 & 0.208683 & 1.6164 & 0.055622 \tabularnewline
17 & -0.130434 & -1.0103 & 0.158195 \tabularnewline
18 & 0.025989 & 0.2013 & 0.420569 \tabularnewline
19 & 0.092759 & 0.7185 & 0.237615 \tabularnewline
20 & -0.013232 & -0.1025 & 0.459352 \tabularnewline
21 & -0.243666 & -1.8874 & 0.031971 \tabularnewline
22 & -0.006066 & -0.047 & 0.48134 \tabularnewline
23 & 0.023833 & 0.1846 & 0.427079 \tabularnewline
24 & 0.060422 & 0.468 & 0.32073 \tabularnewline
25 & -0.17804 & -1.3791 & 0.086492 \tabularnewline
26 & 0.25507 & 1.9758 & 0.026393 \tabularnewline
27 & -0.070071 & -0.5428 & 0.294649 \tabularnewline
28 & -0.079274 & -0.6141 & 0.270749 \tabularnewline
29 & -0.011463 & -0.0888 & 0.464772 \tabularnewline
30 & -0.149718 & -1.1597 & 0.125381 \tabularnewline
31 & 0.13427 & 1.0401 & 0.151245 \tabularnewline
32 & 0.023669 & 0.1833 & 0.427574 \tabularnewline
33 & -0.08366 & -0.648 & 0.259719 \tabularnewline
34 & -0.032649 & -0.2529 & 0.400604 \tabularnewline
35 & -0.024755 & -0.1917 & 0.424293 \tabularnewline
36 & -5.9e-05 & -5e-04 & 0.499817 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68924&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]-0.213336[/C][C]-1.6525[/C][C]0.051829[/C][/ROW]
[ROW][C]2[/C][C]-0.091258[/C][C]-0.7069[/C][C]0.241189[/C][/ROW]
[ROW][C]3[/C][C]0.005843[/C][C]0.0453[/C][C]0.482025[/C][/ROW]
[ROW][C]4[/C][C]-0.010794[/C][C]-0.0836[/C][C]0.466823[/C][/ROW]
[ROW][C]5[/C][C]-0.057166[/C][C]-0.4428[/C][C]0.329749[/C][/ROW]
[ROW][C]6[/C][C]0.076063[/C][C]0.5892[/C][C]0.278976[/C][/ROW]
[ROW][C]7[/C][C]0.018986[/C][C]0.1471[/C][C]0.441787[/C][/ROW]
[ROW][C]8[/C][C]0.069551[/C][C]0.5387[/C][C]0.29603[/C][/ROW]
[ROW][C]9[/C][C]-0.04893[/C][C]-0.379[/C][C]0.35301[/C][/ROW]
[ROW][C]10[/C][C]0.005501[/C][C]0.0426[/C][C]0.483077[/C][/ROW]
[ROW][C]11[/C][C]0.100966[/C][C]0.7821[/C][C]0.218621[/C][/ROW]
[ROW][C]12[/C][C]-0.058995[/C][C]-0.457[/C][C]0.32467[/C][/ROW]
[ROW][C]13[/C][C]0.038998[/C][C]0.3021[/C][C]0.381819[/C][/ROW]
[ROW][C]14[/C][C]-0.176717[/C][C]-1.3688[/C][C]0.088075[/C][/ROW]
[ROW][C]15[/C][C]0.019551[/C][C]0.1514[/C][C]0.440068[/C][/ROW]
[ROW][C]16[/C][C]0.208683[/C][C]1.6164[/C][C]0.055622[/C][/ROW]
[ROW][C]17[/C][C]-0.130434[/C][C]-1.0103[/C][C]0.158195[/C][/ROW]
[ROW][C]18[/C][C]0.025989[/C][C]0.2013[/C][C]0.420569[/C][/ROW]
[ROW][C]19[/C][C]0.092759[/C][C]0.7185[/C][C]0.237615[/C][/ROW]
[ROW][C]20[/C][C]-0.013232[/C][C]-0.1025[/C][C]0.459352[/C][/ROW]
[ROW][C]21[/C][C]-0.243666[/C][C]-1.8874[/C][C]0.031971[/C][/ROW]
[ROW][C]22[/C][C]-0.006066[/C][C]-0.047[/C][C]0.48134[/C][/ROW]
[ROW][C]23[/C][C]0.023833[/C][C]0.1846[/C][C]0.427079[/C][/ROW]
[ROW][C]24[/C][C]0.060422[/C][C]0.468[/C][C]0.32073[/C][/ROW]
[ROW][C]25[/C][C]-0.17804[/C][C]-1.3791[/C][C]0.086492[/C][/ROW]
[ROW][C]26[/C][C]0.25507[/C][C]1.9758[/C][C]0.026393[/C][/ROW]
[ROW][C]27[/C][C]-0.070071[/C][C]-0.5428[/C][C]0.294649[/C][/ROW]
[ROW][C]28[/C][C]-0.079274[/C][C]-0.6141[/C][C]0.270749[/C][/ROW]
[ROW][C]29[/C][C]-0.011463[/C][C]-0.0888[/C][C]0.464772[/C][/ROW]
[ROW][C]30[/C][C]-0.149718[/C][C]-1.1597[/C][C]0.125381[/C][/ROW]
[ROW][C]31[/C][C]0.13427[/C][C]1.0401[/C][C]0.151245[/C][/ROW]
[ROW][C]32[/C][C]0.023669[/C][C]0.1833[/C][C]0.427574[/C][/ROW]
[ROW][C]33[/C][C]-0.08366[/C][C]-0.648[/C][C]0.259719[/C][/ROW]
[ROW][C]34[/C][C]-0.032649[/C][C]-0.2529[/C][C]0.400604[/C][/ROW]
[ROW][C]35[/C][C]-0.024755[/C][C]-0.1917[/C][C]0.424293[/C][/ROW]
[ROW][C]36[/C][C]-5.9e-05[/C][C]-5e-04[/C][C]0.499817[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68924&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68924&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
1-0.213336-1.65250.051829
2-0.091258-0.70690.241189
30.0058430.04530.482025
4-0.010794-0.08360.466823
5-0.057166-0.44280.329749
60.0760630.58920.278976
70.0189860.14710.441787
80.0695510.53870.29603
9-0.04893-0.3790.35301
100.0055010.04260.483077
110.1009660.78210.218621
12-0.058995-0.4570.32467
130.0389980.30210.381819
14-0.176717-1.36880.088075
150.0195510.15140.440068
160.2086831.61640.055622
17-0.130434-1.01030.158195
180.0259890.20130.420569
190.0927590.71850.237615
20-0.013232-0.10250.459352
21-0.243666-1.88740.031971
22-0.006066-0.0470.48134
230.0238330.18460.427079
240.0604220.4680.32073
25-0.17804-1.37910.086492
260.255071.97580.026393
27-0.070071-0.54280.294649
28-0.079274-0.61410.270749
29-0.011463-0.08880.464772
30-0.149718-1.15970.125381
310.134271.04010.151245
320.0236690.18330.427574
33-0.08366-0.6480.259719
34-0.032649-0.25290.400604
35-0.024755-0.19170.424293
36-5.9e-05-5e-040.499817






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.213336-1.65250.051829
2-0.143292-1.10990.135728
3-0.050257-0.38930.349221
4-0.036721-0.28440.388526
5-0.077546-0.60070.275162
60.0414680.32120.374584
70.0336890.2610.397509
80.1022610.79210.215709
90.0007030.00540.497837
100.0170840.13230.447583
110.1219670.94470.174288
12-0.00126-0.00980.496123
130.059810.46330.322416
14-0.191443-1.48290.071666
15-0.065246-0.50540.307568
160.1786571.38390.085762
17-0.079861-0.61860.26926
180.0173590.13450.446742
190.0574940.44530.328835
200.0779810.6040.274048
21-0.222194-1.72110.045192
22-0.155115-1.20150.117136
23-0.05956-0.46130.323109
240.0090140.06980.472283
25-0.164115-1.27120.104277
260.1608481.24590.108818
27-0.002008-0.01560.493822
28-0.028961-0.22430.41163
29-0.026325-0.20390.419556
30-0.169413-1.31230.097214
310.1052760.81550.209016
320.0283160.21930.413567
330.0081560.06320.474919
34-0.106356-0.82380.206649
35-0.198124-1.53470.065061
360.0255010.19750.42204

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.213336 & -1.6525 & 0.051829 \tabularnewline
2 & -0.143292 & -1.1099 & 0.135728 \tabularnewline
3 & -0.050257 & -0.3893 & 0.349221 \tabularnewline
4 & -0.036721 & -0.2844 & 0.388526 \tabularnewline
5 & -0.077546 & -0.6007 & 0.275162 \tabularnewline
6 & 0.041468 & 0.3212 & 0.374584 \tabularnewline
7 & 0.033689 & 0.261 & 0.397509 \tabularnewline
8 & 0.102261 & 0.7921 & 0.215709 \tabularnewline
9 & 0.000703 & 0.0054 & 0.497837 \tabularnewline
10 & 0.017084 & 0.1323 & 0.447583 \tabularnewline
11 & 0.121967 & 0.9447 & 0.174288 \tabularnewline
12 & -0.00126 & -0.0098 & 0.496123 \tabularnewline
13 & 0.05981 & 0.4633 & 0.322416 \tabularnewline
14 & -0.191443 & -1.4829 & 0.071666 \tabularnewline
15 & -0.065246 & -0.5054 & 0.307568 \tabularnewline
16 & 0.178657 & 1.3839 & 0.085762 \tabularnewline
17 & -0.079861 & -0.6186 & 0.26926 \tabularnewline
18 & 0.017359 & 0.1345 & 0.446742 \tabularnewline
19 & 0.057494 & 0.4453 & 0.328835 \tabularnewline
20 & 0.077981 & 0.604 & 0.274048 \tabularnewline
21 & -0.222194 & -1.7211 & 0.045192 \tabularnewline
22 & -0.155115 & -1.2015 & 0.117136 \tabularnewline
23 & -0.05956 & -0.4613 & 0.323109 \tabularnewline
24 & 0.009014 & 0.0698 & 0.472283 \tabularnewline
25 & -0.164115 & -1.2712 & 0.104277 \tabularnewline
26 & 0.160848 & 1.2459 & 0.108818 \tabularnewline
27 & -0.002008 & -0.0156 & 0.493822 \tabularnewline
28 & -0.028961 & -0.2243 & 0.41163 \tabularnewline
29 & -0.026325 & -0.2039 & 0.419556 \tabularnewline
30 & -0.169413 & -1.3123 & 0.097214 \tabularnewline
31 & 0.105276 & 0.8155 & 0.209016 \tabularnewline
32 & 0.028316 & 0.2193 & 0.413567 \tabularnewline
33 & 0.008156 & 0.0632 & 0.474919 \tabularnewline
34 & -0.106356 & -0.8238 & 0.206649 \tabularnewline
35 & -0.198124 & -1.5347 & 0.065061 \tabularnewline
36 & 0.025501 & 0.1975 & 0.42204 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68924&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]-0.213336[/C][C]-1.6525[/C][C]0.051829[/C][/ROW]
[ROW][C]2[/C][C]-0.143292[/C][C]-1.1099[/C][C]0.135728[/C][/ROW]
[ROW][C]3[/C][C]-0.050257[/C][C]-0.3893[/C][C]0.349221[/C][/ROW]
[ROW][C]4[/C][C]-0.036721[/C][C]-0.2844[/C][C]0.388526[/C][/ROW]
[ROW][C]5[/C][C]-0.077546[/C][C]-0.6007[/C][C]0.275162[/C][/ROW]
[ROW][C]6[/C][C]0.041468[/C][C]0.3212[/C][C]0.374584[/C][/ROW]
[ROW][C]7[/C][C]0.033689[/C][C]0.261[/C][C]0.397509[/C][/ROW]
[ROW][C]8[/C][C]0.102261[/C][C]0.7921[/C][C]0.215709[/C][/ROW]
[ROW][C]9[/C][C]0.000703[/C][C]0.0054[/C][C]0.497837[/C][/ROW]
[ROW][C]10[/C][C]0.017084[/C][C]0.1323[/C][C]0.447583[/C][/ROW]
[ROW][C]11[/C][C]0.121967[/C][C]0.9447[/C][C]0.174288[/C][/ROW]
[ROW][C]12[/C][C]-0.00126[/C][C]-0.0098[/C][C]0.496123[/C][/ROW]
[ROW][C]13[/C][C]0.05981[/C][C]0.4633[/C][C]0.322416[/C][/ROW]
[ROW][C]14[/C][C]-0.191443[/C][C]-1.4829[/C][C]0.071666[/C][/ROW]
[ROW][C]15[/C][C]-0.065246[/C][C]-0.5054[/C][C]0.307568[/C][/ROW]
[ROW][C]16[/C][C]0.178657[/C][C]1.3839[/C][C]0.085762[/C][/ROW]
[ROW][C]17[/C][C]-0.079861[/C][C]-0.6186[/C][C]0.26926[/C][/ROW]
[ROW][C]18[/C][C]0.017359[/C][C]0.1345[/C][C]0.446742[/C][/ROW]
[ROW][C]19[/C][C]0.057494[/C][C]0.4453[/C][C]0.328835[/C][/ROW]
[ROW][C]20[/C][C]0.077981[/C][C]0.604[/C][C]0.274048[/C][/ROW]
[ROW][C]21[/C][C]-0.222194[/C][C]-1.7211[/C][C]0.045192[/C][/ROW]
[ROW][C]22[/C][C]-0.155115[/C][C]-1.2015[/C][C]0.117136[/C][/ROW]
[ROW][C]23[/C][C]-0.05956[/C][C]-0.4613[/C][C]0.323109[/C][/ROW]
[ROW][C]24[/C][C]0.009014[/C][C]0.0698[/C][C]0.472283[/C][/ROW]
[ROW][C]25[/C][C]-0.164115[/C][C]-1.2712[/C][C]0.104277[/C][/ROW]
[ROW][C]26[/C][C]0.160848[/C][C]1.2459[/C][C]0.108818[/C][/ROW]
[ROW][C]27[/C][C]-0.002008[/C][C]-0.0156[/C][C]0.493822[/C][/ROW]
[ROW][C]28[/C][C]-0.028961[/C][C]-0.2243[/C][C]0.41163[/C][/ROW]
[ROW][C]29[/C][C]-0.026325[/C][C]-0.2039[/C][C]0.419556[/C][/ROW]
[ROW][C]30[/C][C]-0.169413[/C][C]-1.3123[/C][C]0.097214[/C][/ROW]
[ROW][C]31[/C][C]0.105276[/C][C]0.8155[/C][C]0.209016[/C][/ROW]
[ROW][C]32[/C][C]0.028316[/C][C]0.2193[/C][C]0.413567[/C][/ROW]
[ROW][C]33[/C][C]0.008156[/C][C]0.0632[/C][C]0.474919[/C][/ROW]
[ROW][C]34[/C][C]-0.106356[/C][C]-0.8238[/C][C]0.206649[/C][/ROW]
[ROW][C]35[/C][C]-0.198124[/C][C]-1.5347[/C][C]0.065061[/C][/ROW]
[ROW][C]36[/C][C]0.025501[/C][C]0.1975[/C][C]0.42204[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68924&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68924&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
1-0.213336-1.65250.051829
2-0.143292-1.10990.135728
3-0.050257-0.38930.349221
4-0.036721-0.28440.388526
5-0.077546-0.60070.275162
60.0414680.32120.374584
70.0336890.2610.397509
80.1022610.79210.215709
90.0007030.00540.497837
100.0170840.13230.447583
110.1219670.94470.174288
12-0.00126-0.00980.496123
130.059810.46330.322416
14-0.191443-1.48290.071666
15-0.065246-0.50540.307568
160.1786571.38390.085762
17-0.079861-0.61860.26926
180.0173590.13450.446742
190.0574940.44530.328835
200.0779810.6040.274048
21-0.222194-1.72110.045192
22-0.155115-1.20150.117136
23-0.05956-0.46130.323109
240.0090140.06980.472283
25-0.164115-1.27120.104277
260.1608481.24590.108818
27-0.002008-0.01560.493822
28-0.028961-0.22430.41163
29-0.026325-0.20390.419556
30-0.169413-1.31230.097214
310.1052760.81550.209016
320.0283160.21930.413567
330.0081560.06320.474919
34-0.106356-0.82380.206649
35-0.198124-1.53470.065061
360.0255010.19750.42204


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 1 ; 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')