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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, 27 Nov 2009 10:28:51 -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/Nov/27/t1259342982yo6amtn43pw46bi.htm/, Retrieved Sun, 28 Apr 2024 22:49:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=61033, Retrieved Sun, 28 Apr 2024 22:49:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114

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       [Variance Reduction Matrix] [Identifying Integ...] [2009-11-22 12:29:54] [b98453cac15ba1066b407e146608df68]
-    D        [Variance Reduction Matrix] [workshop 8.3] [2009-11-25 20:50:47] [35f0fff14d789f48983afb62e692bd0d]
- RMPD            [(Partial) Autocorrelation Function] [] [2009-11-27 17:28:51] [71596e6a53ccce532e52aaf6113616ef] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
252.5
251.1
255.1
258.3
255.3
261.1
253.8
252.9
253.9
255.5
262
262.8
263.3
262.5
269.2
270.8
274.1
273
267.3
267.1
268.2
270.2
271.5
281
280.1
281.5
285.9
289.8
292.9
291.2
291.8
289.8
292.5
290.3
297.5
307.5
304.7
304.6
310.7
310.7
315.7
314.7
312.2
312.8
314.3
319.7
319.9
329.5
326.9
329.7
335.7
337.2
339.7
338.3
339.2
342.5
342.2
338.3
339
345.9

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61033&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]1 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=61033&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61033&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.433259-2.97030.002337
20.1562611.07130.144759
3-0.240954-1.65190.052611
40.1428660.97940.166188
50.1045150.71650.238608
6-0.193502-1.32660.095528
70.2487021.7050.047397
8-0.322436-2.21050.015987
90.1499111.02770.154667
10-0.094728-0.64940.259614
110.3772322.58620.006432
12-0.379667-2.60290.006165
130.0768370.52680.300417
14-0.048731-0.33410.369902
150.1420380.97380.16758
16-0.068152-0.46720.321249
17-0.066574-0.45640.325099
180.1035130.70960.240715
19-0.115447-0.79150.216324
200.2093631.43530.078909
21-0.070996-0.48670.314357
220.153761.05410.148607
23-0.327931-2.24820.014647
240.1885981.2930.101171
25-0.017279-0.11850.453103
260.0241920.16580.434494
27-0.06435-0.44120.33056
28-0.089726-0.61510.270718
290.091220.62540.267376
30-0.094964-0.6510.259095
310.1287720.88280.190914
32-0.085287-0.58470.280775
330.0115820.07940.468525
34-0.159472-1.09330.13992
350.1532971.05090.149328
36-0.060541-0.4150.339999

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.433259 & -2.9703 & 0.002337 \tabularnewline
2 & 0.156261 & 1.0713 & 0.144759 \tabularnewline
3 & -0.240954 & -1.6519 & 0.052611 \tabularnewline
4 & 0.142866 & 0.9794 & 0.166188 \tabularnewline
5 & 0.104515 & 0.7165 & 0.238608 \tabularnewline
6 & -0.193502 & -1.3266 & 0.095528 \tabularnewline
7 & 0.248702 & 1.705 & 0.047397 \tabularnewline
8 & -0.322436 & -2.2105 & 0.015987 \tabularnewline
9 & 0.149911 & 1.0277 & 0.154667 \tabularnewline
10 & -0.094728 & -0.6494 & 0.259614 \tabularnewline
11 & 0.377232 & 2.5862 & 0.006432 \tabularnewline
12 & -0.379667 & -2.6029 & 0.006165 \tabularnewline
13 & 0.076837 & 0.5268 & 0.300417 \tabularnewline
14 & -0.048731 & -0.3341 & 0.369902 \tabularnewline
15 & 0.142038 & 0.9738 & 0.16758 \tabularnewline
16 & -0.068152 & -0.4672 & 0.321249 \tabularnewline
17 & -0.066574 & -0.4564 & 0.325099 \tabularnewline
18 & 0.103513 & 0.7096 & 0.240715 \tabularnewline
19 & -0.115447 & -0.7915 & 0.216324 \tabularnewline
20 & 0.209363 & 1.4353 & 0.078909 \tabularnewline
21 & -0.070996 & -0.4867 & 0.314357 \tabularnewline
22 & 0.15376 & 1.0541 & 0.148607 \tabularnewline
23 & -0.327931 & -2.2482 & 0.014647 \tabularnewline
24 & 0.188598 & 1.293 & 0.101171 \tabularnewline
25 & -0.017279 & -0.1185 & 0.453103 \tabularnewline
26 & 0.024192 & 0.1658 & 0.434494 \tabularnewline
27 & -0.06435 & -0.4412 & 0.33056 \tabularnewline
28 & -0.089726 & -0.6151 & 0.270718 \tabularnewline
29 & 0.09122 & 0.6254 & 0.267376 \tabularnewline
30 & -0.094964 & -0.651 & 0.259095 \tabularnewline
31 & 0.128772 & 0.8828 & 0.190914 \tabularnewline
32 & -0.085287 & -0.5847 & 0.280775 \tabularnewline
33 & 0.011582 & 0.0794 & 0.468525 \tabularnewline
34 & -0.159472 & -1.0933 & 0.13992 \tabularnewline
35 & 0.153297 & 1.0509 & 0.149328 \tabularnewline
36 & -0.060541 & -0.415 & 0.339999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61033&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]-0.433259[/C][C]-2.9703[/C][C]0.002337[/C][/ROW]
[ROW][C]2[/C][C]0.156261[/C][C]1.0713[/C][C]0.144759[/C][/ROW]
[ROW][C]3[/C][C]-0.240954[/C][C]-1.6519[/C][C]0.052611[/C][/ROW]
[ROW][C]4[/C][C]0.142866[/C][C]0.9794[/C][C]0.166188[/C][/ROW]
[ROW][C]5[/C][C]0.104515[/C][C]0.7165[/C][C]0.238608[/C][/ROW]
[ROW][C]6[/C][C]-0.193502[/C][C]-1.3266[/C][C]0.095528[/C][/ROW]
[ROW][C]7[/C][C]0.248702[/C][C]1.705[/C][C]0.047397[/C][/ROW]
[ROW][C]8[/C][C]-0.322436[/C][C]-2.2105[/C][C]0.015987[/C][/ROW]
[ROW][C]9[/C][C]0.149911[/C][C]1.0277[/C][C]0.154667[/C][/ROW]
[ROW][C]10[/C][C]-0.094728[/C][C]-0.6494[/C][C]0.259614[/C][/ROW]
[ROW][C]11[/C][C]0.377232[/C][C]2.5862[/C][C]0.006432[/C][/ROW]
[ROW][C]12[/C][C]-0.379667[/C][C]-2.6029[/C][C]0.006165[/C][/ROW]
[ROW][C]13[/C][C]0.076837[/C][C]0.5268[/C][C]0.300417[/C][/ROW]
[ROW][C]14[/C][C]-0.048731[/C][C]-0.3341[/C][C]0.369902[/C][/ROW]
[ROW][C]15[/C][C]0.142038[/C][C]0.9738[/C][C]0.16758[/C][/ROW]
[ROW][C]16[/C][C]-0.068152[/C][C]-0.4672[/C][C]0.321249[/C][/ROW]
[ROW][C]17[/C][C]-0.066574[/C][C]-0.4564[/C][C]0.325099[/C][/ROW]
[ROW][C]18[/C][C]0.103513[/C][C]0.7096[/C][C]0.240715[/C][/ROW]
[ROW][C]19[/C][C]-0.115447[/C][C]-0.7915[/C][C]0.216324[/C][/ROW]
[ROW][C]20[/C][C]0.209363[/C][C]1.4353[/C][C]0.078909[/C][/ROW]
[ROW][C]21[/C][C]-0.070996[/C][C]-0.4867[/C][C]0.314357[/C][/ROW]
[ROW][C]22[/C][C]0.15376[/C][C]1.0541[/C][C]0.148607[/C][/ROW]
[ROW][C]23[/C][C]-0.327931[/C][C]-2.2482[/C][C]0.014647[/C][/ROW]
[ROW][C]24[/C][C]0.188598[/C][C]1.293[/C][C]0.101171[/C][/ROW]
[ROW][C]25[/C][C]-0.017279[/C][C]-0.1185[/C][C]0.453103[/C][/ROW]
[ROW][C]26[/C][C]0.024192[/C][C]0.1658[/C][C]0.434494[/C][/ROW]
[ROW][C]27[/C][C]-0.06435[/C][C]-0.4412[/C][C]0.33056[/C][/ROW]
[ROW][C]28[/C][C]-0.089726[/C][C]-0.6151[/C][C]0.270718[/C][/ROW]
[ROW][C]29[/C][C]0.09122[/C][C]0.6254[/C][C]0.267376[/C][/ROW]
[ROW][C]30[/C][C]-0.094964[/C][C]-0.651[/C][C]0.259095[/C][/ROW]
[ROW][C]31[/C][C]0.128772[/C][C]0.8828[/C][C]0.190914[/C][/ROW]
[ROW][C]32[/C][C]-0.085287[/C][C]-0.5847[/C][C]0.280775[/C][/ROW]
[ROW][C]33[/C][C]0.011582[/C][C]0.0794[/C][C]0.468525[/C][/ROW]
[ROW][C]34[/C][C]-0.159472[/C][C]-1.0933[/C][C]0.13992[/C][/ROW]
[ROW][C]35[/C][C]0.153297[/C][C]1.0509[/C][C]0.149328[/C][/ROW]
[ROW][C]36[/C][C]-0.060541[/C][C]-0.415[/C][C]0.339999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61033&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61033&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
1-0.433259-2.97030.002337
20.1562611.07130.144759
3-0.240954-1.65190.052611
40.1428660.97940.166188
50.1045150.71650.238608
6-0.193502-1.32660.095528
70.2487021.7050.047397
8-0.322436-2.21050.015987
90.1499111.02770.154667
10-0.094728-0.64940.259614
110.3772322.58620.006432
12-0.379667-2.60290.006165
130.0768370.52680.300417
14-0.048731-0.33410.369902
150.1420380.97380.16758
16-0.068152-0.46720.321249
17-0.066574-0.45640.325099
180.1035130.70960.240715
19-0.115447-0.79150.216324
200.2093631.43530.078909
21-0.070996-0.48670.314357
220.153761.05410.148607
23-0.327931-2.24820.014647
240.1885981.2930.101171
25-0.017279-0.11850.453103
260.0241920.16580.434494
27-0.06435-0.44120.33056
28-0.089726-0.61510.270718
290.091220.62540.267376
30-0.094964-0.6510.259095
310.1287720.88280.190914
32-0.085287-0.58470.280775
330.0115820.07940.468525
34-0.159472-1.09330.13992
350.1532971.05090.149328
36-0.060541-0.4150.339999






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.433259-2.97030.002337
2-0.03872-0.26550.39591
3-0.231062-1.58410.059941
4-0.059312-0.40660.343066
50.1964321.34670.092273
6-0.14436-0.98970.163699
70.1861791.27640.104047
8-0.144466-0.99040.163522
9-0.166219-1.13950.130126
100.0060320.04140.483594
110.3735642.5610.006853
12-0.253593-1.73850.044331
13-0.022098-0.15150.440115
140.0213240.14620.442198
150.0055760.03820.484835
16-0.11355-0.77850.2201
170.093010.63760.2634
18-0.103619-0.71040.24049
190.1425320.97720.166749
200.1718311.1780.12236
210.0405990.27830.39099
220.0510380.34990.363989
23-0.010082-0.06910.472595
24-0.09934-0.6810.249594
250.038860.26640.395545
26-0.074432-0.51030.306122
27-0.020546-0.14090.444293
28-0.039675-0.2720.393408
29-0.098579-0.67580.251233
30-0.063028-0.43210.333823
31-0.019328-0.13250.447575
32-0.048968-0.33570.369292
330.0127940.08770.465238
34-0.088222-0.60480.274104
35-0.077106-0.52860.299782
36-0.054246-0.37190.355822

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.433259 & -2.9703 & 0.002337 \tabularnewline
2 & -0.03872 & -0.2655 & 0.39591 \tabularnewline
3 & -0.231062 & -1.5841 & 0.059941 \tabularnewline
4 & -0.059312 & -0.4066 & 0.343066 \tabularnewline
5 & 0.196432 & 1.3467 & 0.092273 \tabularnewline
6 & -0.14436 & -0.9897 & 0.163699 \tabularnewline
7 & 0.186179 & 1.2764 & 0.104047 \tabularnewline
8 & -0.144466 & -0.9904 & 0.163522 \tabularnewline
9 & -0.166219 & -1.1395 & 0.130126 \tabularnewline
10 & 0.006032 & 0.0414 & 0.483594 \tabularnewline
11 & 0.373564 & 2.561 & 0.006853 \tabularnewline
12 & -0.253593 & -1.7385 & 0.044331 \tabularnewline
13 & -0.022098 & -0.1515 & 0.440115 \tabularnewline
14 & 0.021324 & 0.1462 & 0.442198 \tabularnewline
15 & 0.005576 & 0.0382 & 0.484835 \tabularnewline
16 & -0.11355 & -0.7785 & 0.2201 \tabularnewline
17 & 0.09301 & 0.6376 & 0.2634 \tabularnewline
18 & -0.103619 & -0.7104 & 0.24049 \tabularnewline
19 & 0.142532 & 0.9772 & 0.166749 \tabularnewline
20 & 0.171831 & 1.178 & 0.12236 \tabularnewline
21 & 0.040599 & 0.2783 & 0.39099 \tabularnewline
22 & 0.051038 & 0.3499 & 0.363989 \tabularnewline
23 & -0.010082 & -0.0691 & 0.472595 \tabularnewline
24 & -0.09934 & -0.681 & 0.249594 \tabularnewline
25 & 0.03886 & 0.2664 & 0.395545 \tabularnewline
26 & -0.074432 & -0.5103 & 0.306122 \tabularnewline
27 & -0.020546 & -0.1409 & 0.444293 \tabularnewline
28 & -0.039675 & -0.272 & 0.393408 \tabularnewline
29 & -0.098579 & -0.6758 & 0.251233 \tabularnewline
30 & -0.063028 & -0.4321 & 0.333823 \tabularnewline
31 & -0.019328 & -0.1325 & 0.447575 \tabularnewline
32 & -0.048968 & -0.3357 & 0.369292 \tabularnewline
33 & 0.012794 & 0.0877 & 0.465238 \tabularnewline
34 & -0.088222 & -0.6048 & 0.274104 \tabularnewline
35 & -0.077106 & -0.5286 & 0.299782 \tabularnewline
36 & -0.054246 & -0.3719 & 0.355822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61033&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]-0.433259[/C][C]-2.9703[/C][C]0.002337[/C][/ROW]
[ROW][C]2[/C][C]-0.03872[/C][C]-0.2655[/C][C]0.39591[/C][/ROW]
[ROW][C]3[/C][C]-0.231062[/C][C]-1.5841[/C][C]0.059941[/C][/ROW]
[ROW][C]4[/C][C]-0.059312[/C][C]-0.4066[/C][C]0.343066[/C][/ROW]
[ROW][C]5[/C][C]0.196432[/C][C]1.3467[/C][C]0.092273[/C][/ROW]
[ROW][C]6[/C][C]-0.14436[/C][C]-0.9897[/C][C]0.163699[/C][/ROW]
[ROW][C]7[/C][C]0.186179[/C][C]1.2764[/C][C]0.104047[/C][/ROW]
[ROW][C]8[/C][C]-0.144466[/C][C]-0.9904[/C][C]0.163522[/C][/ROW]
[ROW][C]9[/C][C]-0.166219[/C][C]-1.1395[/C][C]0.130126[/C][/ROW]
[ROW][C]10[/C][C]0.006032[/C][C]0.0414[/C][C]0.483594[/C][/ROW]
[ROW][C]11[/C][C]0.373564[/C][C]2.561[/C][C]0.006853[/C][/ROW]
[ROW][C]12[/C][C]-0.253593[/C][C]-1.7385[/C][C]0.044331[/C][/ROW]
[ROW][C]13[/C][C]-0.022098[/C][C]-0.1515[/C][C]0.440115[/C][/ROW]
[ROW][C]14[/C][C]0.021324[/C][C]0.1462[/C][C]0.442198[/C][/ROW]
[ROW][C]15[/C][C]0.005576[/C][C]0.0382[/C][C]0.484835[/C][/ROW]
[ROW][C]16[/C][C]-0.11355[/C][C]-0.7785[/C][C]0.2201[/C][/ROW]
[ROW][C]17[/C][C]0.09301[/C][C]0.6376[/C][C]0.2634[/C][/ROW]
[ROW][C]18[/C][C]-0.103619[/C][C]-0.7104[/C][C]0.24049[/C][/ROW]
[ROW][C]19[/C][C]0.142532[/C][C]0.9772[/C][C]0.166749[/C][/ROW]
[ROW][C]20[/C][C]0.171831[/C][C]1.178[/C][C]0.12236[/C][/ROW]
[ROW][C]21[/C][C]0.040599[/C][C]0.2783[/C][C]0.39099[/C][/ROW]
[ROW][C]22[/C][C]0.051038[/C][C]0.3499[/C][C]0.363989[/C][/ROW]
[ROW][C]23[/C][C]-0.010082[/C][C]-0.0691[/C][C]0.472595[/C][/ROW]
[ROW][C]24[/C][C]-0.09934[/C][C]-0.681[/C][C]0.249594[/C][/ROW]
[ROW][C]25[/C][C]0.03886[/C][C]0.2664[/C][C]0.395545[/C][/ROW]
[ROW][C]26[/C][C]-0.074432[/C][C]-0.5103[/C][C]0.306122[/C][/ROW]
[ROW][C]27[/C][C]-0.020546[/C][C]-0.1409[/C][C]0.444293[/C][/ROW]
[ROW][C]28[/C][C]-0.039675[/C][C]-0.272[/C][C]0.393408[/C][/ROW]
[ROW][C]29[/C][C]-0.098579[/C][C]-0.6758[/C][C]0.251233[/C][/ROW]
[ROW][C]30[/C][C]-0.063028[/C][C]-0.4321[/C][C]0.333823[/C][/ROW]
[ROW][C]31[/C][C]-0.019328[/C][C]-0.1325[/C][C]0.447575[/C][/ROW]
[ROW][C]32[/C][C]-0.048968[/C][C]-0.3357[/C][C]0.369292[/C][/ROW]
[ROW][C]33[/C][C]0.012794[/C][C]0.0877[/C][C]0.465238[/C][/ROW]
[ROW][C]34[/C][C]-0.088222[/C][C]-0.6048[/C][C]0.274104[/C][/ROW]
[ROW][C]35[/C][C]-0.077106[/C][C]-0.5286[/C][C]0.299782[/C][/ROW]
[ROW][C]36[/C][C]-0.054246[/C][C]-0.3719[/C][C]0.355822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61033&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61033&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
1-0.433259-2.97030.002337
2-0.03872-0.26550.39591
3-0.231062-1.58410.059941
4-0.059312-0.40660.343066
50.1964321.34670.092273
6-0.14436-0.98970.163699
70.1861791.27640.104047
8-0.144466-0.99040.163522
9-0.166219-1.13950.130126
100.0060320.04140.483594
110.3735642.5610.006853
12-0.253593-1.73850.044331
13-0.022098-0.15150.440115
140.0213240.14620.442198
150.0055760.03820.484835
16-0.11355-0.77850.2201
170.093010.63760.2634
18-0.103619-0.71040.24049
190.1425320.97720.166749
200.1718311.1780.12236
210.0405990.27830.39099
220.0510380.34990.363989
23-0.010082-0.06910.472595
24-0.09934-0.6810.249594
250.038860.26640.395545
26-0.074432-0.51030.306122
27-0.020546-0.14090.444293
28-0.039675-0.2720.393408
29-0.098579-0.67580.251233
30-0.063028-0.43210.333823
31-0.019328-0.13250.447575
32-0.048968-0.33570.369292
330.0127940.08770.465238
34-0.088222-0.60480.274104
35-0.077106-0.52860.299782
36-0.054246-0.37190.355822


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
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')