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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, 04 Dec 2009 05:25:16 -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/Dec/04/t1259929551bgy9pi6zyy6z3ri.htm/, Retrieved Sat, 27 Apr 2024 22:00:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63396, Retrieved Sat, 27 Apr 2024 22:00:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110

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]
-   PD        [(Partial) Autocorrelation Function] [] [2009-11-26 19:41:34] [58e1a7a2c10f1de09acf218271f55dfd]
-    D            [(Partial) Autocorrelation Function] [] [2009-12-04 12:25:16] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
89.1
82.6
102.7
91.8
94.1
103.1
93.2
91
94.3
99.4
115.7
116.8
99.8
96
115.9
109.1
117.3
109.8
112.8
110.7
100
113.3
122.4
112.5
104.2
92.5
117.2
109.3
106.1
118.8
105.3
106
102
112.9
116.5
114.8
100.5
85.4
114.6
109.9
100.7
115.5
100.7
99
102.3
108.8
105.9
113.2
95.7
80.9
113.9
98.1
102.8
104.7
95.9
94.6
101.6
103.9
110.3
114.1

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=63396&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=63396&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63396&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
1-0.629558-4.3164.1e-05
20.041860.2870.387695
30.3598012.46670.00867
4-0.335887-2.30270.012884
50.0851390.58370.281112
60.1656061.13530.130996
7-0.289548-1.9850.026496
80.2440751.67330.050457
9-0.144049-0.98750.164215
10-0.039458-0.27050.393975
110.1945161.33350.094392
12-0.223969-1.53550.065688
130.0905480.62080.268876
140.0355790.24390.404178
15-0.01988-0.13630.446086
16-0.073651-0.50490.307984
170.1133050.77680.220591
18-0.044516-0.30520.380785
19-0.07218-0.49480.31151
200.1198210.82150.207768
210.0200170.13720.445719
22-0.273796-1.87710.033363
230.3907652.67890.005073
24-0.273556-1.87540.033479
250.0370480.2540.400307
260.1528921.04820.14996
27-0.177979-1.22020.114245
280.042760.29310.38535
290.1006330.68990.246822
30-0.150611-1.03250.153552
310.1249680.85670.197969
32-0.060582-0.41530.339895
33-0.057626-0.39510.347291
340.1496011.02560.155162
35-0.174029-1.19310.119413
360.0689420.47260.319327

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.629558 & -4.316 & 4.1e-05 \tabularnewline
2 & 0.04186 & 0.287 & 0.387695 \tabularnewline
3 & 0.359801 & 2.4667 & 0.00867 \tabularnewline
4 & -0.335887 & -2.3027 & 0.012884 \tabularnewline
5 & 0.085139 & 0.5837 & 0.281112 \tabularnewline
6 & 0.165606 & 1.1353 & 0.130996 \tabularnewline
7 & -0.289548 & -1.985 & 0.026496 \tabularnewline
8 & 0.244075 & 1.6733 & 0.050457 \tabularnewline
9 & -0.144049 & -0.9875 & 0.164215 \tabularnewline
10 & -0.039458 & -0.2705 & 0.393975 \tabularnewline
11 & 0.194516 & 1.3335 & 0.094392 \tabularnewline
12 & -0.223969 & -1.5355 & 0.065688 \tabularnewline
13 & 0.090548 & 0.6208 & 0.268876 \tabularnewline
14 & 0.035579 & 0.2439 & 0.404178 \tabularnewline
15 & -0.01988 & -0.1363 & 0.446086 \tabularnewline
16 & -0.073651 & -0.5049 & 0.307984 \tabularnewline
17 & 0.113305 & 0.7768 & 0.220591 \tabularnewline
18 & -0.044516 & -0.3052 & 0.380785 \tabularnewline
19 & -0.07218 & -0.4948 & 0.31151 \tabularnewline
20 & 0.119821 & 0.8215 & 0.207768 \tabularnewline
21 & 0.020017 & 0.1372 & 0.445719 \tabularnewline
22 & -0.273796 & -1.8771 & 0.033363 \tabularnewline
23 & 0.390765 & 2.6789 & 0.005073 \tabularnewline
24 & -0.273556 & -1.8754 & 0.033479 \tabularnewline
25 & 0.037048 & 0.254 & 0.400307 \tabularnewline
26 & 0.152892 & 1.0482 & 0.14996 \tabularnewline
27 & -0.177979 & -1.2202 & 0.114245 \tabularnewline
28 & 0.04276 & 0.2931 & 0.38535 \tabularnewline
29 & 0.100633 & 0.6899 & 0.246822 \tabularnewline
30 & -0.150611 & -1.0325 & 0.153552 \tabularnewline
31 & 0.124968 & 0.8567 & 0.197969 \tabularnewline
32 & -0.060582 & -0.4153 & 0.339895 \tabularnewline
33 & -0.057626 & -0.3951 & 0.347291 \tabularnewline
34 & 0.149601 & 1.0256 & 0.155162 \tabularnewline
35 & -0.174029 & -1.1931 & 0.119413 \tabularnewline
36 & 0.068942 & 0.4726 & 0.319327 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63396&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]-0.629558[/C][C]-4.316[/C][C]4.1e-05[/C][/ROW]
[ROW][C]2[/C][C]0.04186[/C][C]0.287[/C][C]0.387695[/C][/ROW]
[ROW][C]3[/C][C]0.359801[/C][C]2.4667[/C][C]0.00867[/C][/ROW]
[ROW][C]4[/C][C]-0.335887[/C][C]-2.3027[/C][C]0.012884[/C][/ROW]
[ROW][C]5[/C][C]0.085139[/C][C]0.5837[/C][C]0.281112[/C][/ROW]
[ROW][C]6[/C][C]0.165606[/C][C]1.1353[/C][C]0.130996[/C][/ROW]
[ROW][C]7[/C][C]-0.289548[/C][C]-1.985[/C][C]0.026496[/C][/ROW]
[ROW][C]8[/C][C]0.244075[/C][C]1.6733[/C][C]0.050457[/C][/ROW]
[ROW][C]9[/C][C]-0.144049[/C][C]-0.9875[/C][C]0.164215[/C][/ROW]
[ROW][C]10[/C][C]-0.039458[/C][C]-0.2705[/C][C]0.393975[/C][/ROW]
[ROW][C]11[/C][C]0.194516[/C][C]1.3335[/C][C]0.094392[/C][/ROW]
[ROW][C]12[/C][C]-0.223969[/C][C]-1.5355[/C][C]0.065688[/C][/ROW]
[ROW][C]13[/C][C]0.090548[/C][C]0.6208[/C][C]0.268876[/C][/ROW]
[ROW][C]14[/C][C]0.035579[/C][C]0.2439[/C][C]0.404178[/C][/ROW]
[ROW][C]15[/C][C]-0.01988[/C][C]-0.1363[/C][C]0.446086[/C][/ROW]
[ROW][C]16[/C][C]-0.073651[/C][C]-0.5049[/C][C]0.307984[/C][/ROW]
[ROW][C]17[/C][C]0.113305[/C][C]0.7768[/C][C]0.220591[/C][/ROW]
[ROW][C]18[/C][C]-0.044516[/C][C]-0.3052[/C][C]0.380785[/C][/ROW]
[ROW][C]19[/C][C]-0.07218[/C][C]-0.4948[/C][C]0.31151[/C][/ROW]
[ROW][C]20[/C][C]0.119821[/C][C]0.8215[/C][C]0.207768[/C][/ROW]
[ROW][C]21[/C][C]0.020017[/C][C]0.1372[/C][C]0.445719[/C][/ROW]
[ROW][C]22[/C][C]-0.273796[/C][C]-1.8771[/C][C]0.033363[/C][/ROW]
[ROW][C]23[/C][C]0.390765[/C][C]2.6789[/C][C]0.005073[/C][/ROW]
[ROW][C]24[/C][C]-0.273556[/C][C]-1.8754[/C][C]0.033479[/C][/ROW]
[ROW][C]25[/C][C]0.037048[/C][C]0.254[/C][C]0.400307[/C][/ROW]
[ROW][C]26[/C][C]0.152892[/C][C]1.0482[/C][C]0.14996[/C][/ROW]
[ROW][C]27[/C][C]-0.177979[/C][C]-1.2202[/C][C]0.114245[/C][/ROW]
[ROW][C]28[/C][C]0.04276[/C][C]0.2931[/C][C]0.38535[/C][/ROW]
[ROW][C]29[/C][C]0.100633[/C][C]0.6899[/C][C]0.246822[/C][/ROW]
[ROW][C]30[/C][C]-0.150611[/C][C]-1.0325[/C][C]0.153552[/C][/ROW]
[ROW][C]31[/C][C]0.124968[/C][C]0.8567[/C][C]0.197969[/C][/ROW]
[ROW][C]32[/C][C]-0.060582[/C][C]-0.4153[/C][C]0.339895[/C][/ROW]
[ROW][C]33[/C][C]-0.057626[/C][C]-0.3951[/C][C]0.347291[/C][/ROW]
[ROW][C]34[/C][C]0.149601[/C][C]1.0256[/C][C]0.155162[/C][/ROW]
[ROW][C]35[/C][C]-0.174029[/C][C]-1.1931[/C][C]0.119413[/C][/ROW]
[ROW][C]36[/C][C]0.068942[/C][C]0.4726[/C][C]0.319327[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63396&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63396&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
1-0.629558-4.3164.1e-05
20.041860.2870.387695
30.3598012.46670.00867
4-0.335887-2.30270.012884
50.0851390.58370.281112
60.1656061.13530.130996
7-0.289548-1.9850.026496
80.2440751.67330.050457
9-0.144049-0.98750.164215
10-0.039458-0.27050.393975
110.1945161.33350.094392
12-0.223969-1.53550.065688
130.0905480.62080.268876
140.0355790.24390.404178
15-0.01988-0.13630.446086
16-0.073651-0.50490.307984
170.1133050.77680.220591
18-0.044516-0.30520.380785
19-0.07218-0.49480.31151
200.1198210.82150.207768
210.0200170.13720.445719
22-0.273796-1.87710.033363
230.3907652.67890.005073
24-0.273556-1.87540.033479
250.0370480.2540.400307
260.1528921.04820.14996
27-0.177979-1.22020.114245
280.042760.29310.38535
290.1006330.68990.246822
30-0.150611-1.03250.153552
310.1249680.85670.197969
32-0.060582-0.41530.339895
33-0.057626-0.39510.347291
340.1496011.02560.155162
35-0.174029-1.19310.119413
360.0689420.47260.319327






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.629558-4.3164.1e-05
2-0.587225-4.02580.000103
30.0807530.55360.291234
40.2001031.37180.088314
50.0541110.3710.356165
60.0689850.47290.319224
7-0.162601-1.11470.135315
8-0.006795-0.04660.481521
9-0.149118-1.02230.155935
10-0.188336-1.29120.10148
110.0524340.35950.360428
120.0679590.46590.321718
130.0274890.18850.425666
14-0.168816-1.15730.12649
150.13960.9570.17172
16-0.00879-0.06030.476101
17-0.058054-0.3980.346218
18-0.009523-0.06530.47411
19-0.096526-0.66170.255682
200.033660.23080.40925
210.2307951.58220.06015
22-0.2446-1.67690.050101
23-0.034738-0.23820.406399
24-0.016554-0.11350.455064
250.1071950.73490.233027
26-0.014322-0.09820.461101
270.0509570.34930.364195
28-0.047533-0.32590.372985
29-0.116392-0.79790.214457
300.039730.27240.393264
31-0.002583-0.01770.492973
320.0053310.03650.485502
33-0.021146-0.1450.442678
34-0.07591-0.52040.302609
35-0.116109-0.7960.215017
36-0.106951-0.73320.233533

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.629558 & -4.316 & 4.1e-05 \tabularnewline
2 & -0.587225 & -4.0258 & 0.000103 \tabularnewline
3 & 0.080753 & 0.5536 & 0.291234 \tabularnewline
4 & 0.200103 & 1.3718 & 0.088314 \tabularnewline
5 & 0.054111 & 0.371 & 0.356165 \tabularnewline
6 & 0.068985 & 0.4729 & 0.319224 \tabularnewline
7 & -0.162601 & -1.1147 & 0.135315 \tabularnewline
8 & -0.006795 & -0.0466 & 0.481521 \tabularnewline
9 & -0.149118 & -1.0223 & 0.155935 \tabularnewline
10 & -0.188336 & -1.2912 & 0.10148 \tabularnewline
11 & 0.052434 & 0.3595 & 0.360428 \tabularnewline
12 & 0.067959 & 0.4659 & 0.321718 \tabularnewline
13 & 0.027489 & 0.1885 & 0.425666 \tabularnewline
14 & -0.168816 & -1.1573 & 0.12649 \tabularnewline
15 & 0.1396 & 0.957 & 0.17172 \tabularnewline
16 & -0.00879 & -0.0603 & 0.476101 \tabularnewline
17 & -0.058054 & -0.398 & 0.346218 \tabularnewline
18 & -0.009523 & -0.0653 & 0.47411 \tabularnewline
19 & -0.096526 & -0.6617 & 0.255682 \tabularnewline
20 & 0.03366 & 0.2308 & 0.40925 \tabularnewline
21 & 0.230795 & 1.5822 & 0.06015 \tabularnewline
22 & -0.2446 & -1.6769 & 0.050101 \tabularnewline
23 & -0.034738 & -0.2382 & 0.406399 \tabularnewline
24 & -0.016554 & -0.1135 & 0.455064 \tabularnewline
25 & 0.107195 & 0.7349 & 0.233027 \tabularnewline
26 & -0.014322 & -0.0982 & 0.461101 \tabularnewline
27 & 0.050957 & 0.3493 & 0.364195 \tabularnewline
28 & -0.047533 & -0.3259 & 0.372985 \tabularnewline
29 & -0.116392 & -0.7979 & 0.214457 \tabularnewline
30 & 0.03973 & 0.2724 & 0.393264 \tabularnewline
31 & -0.002583 & -0.0177 & 0.492973 \tabularnewline
32 & 0.005331 & 0.0365 & 0.485502 \tabularnewline
33 & -0.021146 & -0.145 & 0.442678 \tabularnewline
34 & -0.07591 & -0.5204 & 0.302609 \tabularnewline
35 & -0.116109 & -0.796 & 0.215017 \tabularnewline
36 & -0.106951 & -0.7332 & 0.233533 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63396&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]-0.629558[/C][C]-4.316[/C][C]4.1e-05[/C][/ROW]
[ROW][C]2[/C][C]-0.587225[/C][C]-4.0258[/C][C]0.000103[/C][/ROW]
[ROW][C]3[/C][C]0.080753[/C][C]0.5536[/C][C]0.291234[/C][/ROW]
[ROW][C]4[/C][C]0.200103[/C][C]1.3718[/C][C]0.088314[/C][/ROW]
[ROW][C]5[/C][C]0.054111[/C][C]0.371[/C][C]0.356165[/C][/ROW]
[ROW][C]6[/C][C]0.068985[/C][C]0.4729[/C][C]0.319224[/C][/ROW]
[ROW][C]7[/C][C]-0.162601[/C][C]-1.1147[/C][C]0.135315[/C][/ROW]
[ROW][C]8[/C][C]-0.006795[/C][C]-0.0466[/C][C]0.481521[/C][/ROW]
[ROW][C]9[/C][C]-0.149118[/C][C]-1.0223[/C][C]0.155935[/C][/ROW]
[ROW][C]10[/C][C]-0.188336[/C][C]-1.2912[/C][C]0.10148[/C][/ROW]
[ROW][C]11[/C][C]0.052434[/C][C]0.3595[/C][C]0.360428[/C][/ROW]
[ROW][C]12[/C][C]0.067959[/C][C]0.4659[/C][C]0.321718[/C][/ROW]
[ROW][C]13[/C][C]0.027489[/C][C]0.1885[/C][C]0.425666[/C][/ROW]
[ROW][C]14[/C][C]-0.168816[/C][C]-1.1573[/C][C]0.12649[/C][/ROW]
[ROW][C]15[/C][C]0.1396[/C][C]0.957[/C][C]0.17172[/C][/ROW]
[ROW][C]16[/C][C]-0.00879[/C][C]-0.0603[/C][C]0.476101[/C][/ROW]
[ROW][C]17[/C][C]-0.058054[/C][C]-0.398[/C][C]0.346218[/C][/ROW]
[ROW][C]18[/C][C]-0.009523[/C][C]-0.0653[/C][C]0.47411[/C][/ROW]
[ROW][C]19[/C][C]-0.096526[/C][C]-0.6617[/C][C]0.255682[/C][/ROW]
[ROW][C]20[/C][C]0.03366[/C][C]0.2308[/C][C]0.40925[/C][/ROW]
[ROW][C]21[/C][C]0.230795[/C][C]1.5822[/C][C]0.06015[/C][/ROW]
[ROW][C]22[/C][C]-0.2446[/C][C]-1.6769[/C][C]0.050101[/C][/ROW]
[ROW][C]23[/C][C]-0.034738[/C][C]-0.2382[/C][C]0.406399[/C][/ROW]
[ROW][C]24[/C][C]-0.016554[/C][C]-0.1135[/C][C]0.455064[/C][/ROW]
[ROW][C]25[/C][C]0.107195[/C][C]0.7349[/C][C]0.233027[/C][/ROW]
[ROW][C]26[/C][C]-0.014322[/C][C]-0.0982[/C][C]0.461101[/C][/ROW]
[ROW][C]27[/C][C]0.050957[/C][C]0.3493[/C][C]0.364195[/C][/ROW]
[ROW][C]28[/C][C]-0.047533[/C][C]-0.3259[/C][C]0.372985[/C][/ROW]
[ROW][C]29[/C][C]-0.116392[/C][C]-0.7979[/C][C]0.214457[/C][/ROW]
[ROW][C]30[/C][C]0.03973[/C][C]0.2724[/C][C]0.393264[/C][/ROW]
[ROW][C]31[/C][C]-0.002583[/C][C]-0.0177[/C][C]0.492973[/C][/ROW]
[ROW][C]32[/C][C]0.005331[/C][C]0.0365[/C][C]0.485502[/C][/ROW]
[ROW][C]33[/C][C]-0.021146[/C][C]-0.145[/C][C]0.442678[/C][/ROW]
[ROW][C]34[/C][C]-0.07591[/C][C]-0.5204[/C][C]0.302609[/C][/ROW]
[ROW][C]35[/C][C]-0.116109[/C][C]-0.796[/C][C]0.215017[/C][/ROW]
[ROW][C]36[/C][C]-0.106951[/C][C]-0.7332[/C][C]0.233533[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63396&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63396&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
1-0.629558-4.3164.1e-05
2-0.587225-4.02580.000103
30.0807530.55360.291234
40.2001031.37180.088314
50.0541110.3710.356165
60.0689850.47290.319224
7-0.162601-1.11470.135315
8-0.006795-0.04660.481521
9-0.149118-1.02230.155935
10-0.188336-1.29120.10148
110.0524340.35950.360428
120.0679590.46590.321718
130.0274890.18850.425666
14-0.168816-1.15730.12649
150.13960.9570.17172
16-0.00879-0.06030.476101
17-0.058054-0.3980.346218
18-0.009523-0.06530.47411
19-0.096526-0.66170.255682
200.033660.23080.40925
210.2307951.58220.06015
22-0.2446-1.67690.050101
23-0.034738-0.23820.406399
24-0.016554-0.11350.455064
250.1071950.73490.233027
26-0.014322-0.09820.461101
270.0509570.34930.364195
28-0.047533-0.32590.372985
29-0.116392-0.79790.214457
300.039730.27240.393264
31-0.002583-0.01770.492973
320.0053310.03650.485502
33-0.021146-0.1450.442678
34-0.07591-0.52040.302609
35-0.116109-0.7960.215017
36-0.106951-0.73320.233533


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; 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')