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Author

*The author of this computation has been verified*

R Software Module

rwasp_arimabackwardselection.wasp

Title produced by software

ARIMA Backward Selection

Date of computation

Sun, 14 Dec 2008 09:30:33 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/14/t1229272578skpad8bunuptaac.htm/, Retrieved Wed, 15 May 2024 15:27:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33470, Retrieved Wed, 15 May 2024 15:27:18 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

190

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-12-13 16:08:49] [d134696a922d84037f02d49ded84b0bd]
-   P   [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-12-14 14:45:58] [d134696a922d84037f02d49ded84b0bd]
- RMP       [ARIMA Backward Selection] [Backward Selectio...] [2008-12-14 16:30:33] [db9a5fd0f9c3e1245d8075d8bb09236d] [Current]
-   P         [ARIMA Backward Selection] [Backward Selectio...] [2008-12-14 16:41:16] [d134696a922d84037f02d49ded84b0bd] 
-   P         [ARIMA Backward Selection] [ARIMA Backward Se...] [2008-12-15 20:23:35] [d134696a922d84037f02d49ded84b0bd] 
-   P           [ARIMA Backward Selection] [ARIMA Backward Se...] [2008-12-15 20:28:38] [d134696a922d84037f02d49ded84b0bd] 
- RMP             [ARIMA Forecasting] [ARIMA Forecasting] [2008-12-16 09:49:01] [d134696a922d84037f02d49ded84b0bd] 
-                   [ARIMA Forecasting] [ARIMA Forecasting] [2008-12-16 09:51:44] [d134696a922d84037f02d49ded84b0bd] 

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	14 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 14 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33470&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33470&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33470&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	14 seconds
R Server	'George Udny Yule' @ 72.249.76.132

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ar3	ma1	sar1	sar2	sma1
Estimates ( 1 )	0.1895	0.0293	-0.0741	-0.438	1.0871	-0.0882	-0.9678
(p-val)	(0.5584 )	(0.7994 )	(0.4327 )	(0.1675 )	(0 )	(0.3817 )	(0 )
Estimates ( 2 )	0.0899	0	-0.0787	-0.3404	1.0783	-0.0824	-0.9397
(p-val)	(0.7427 )	(NA )	(0.3752 )	(0.1977 )	(0 )	(0.4112 )	(0 )
Estimates ( 3 )	0	0	-0.081	-0.2519	1.0729	-0.0755	-0.9523
(p-val)	(NA )	(NA )	(0.3555 )	(0.0032 )	(0 )	(0.4467 )	(0 )
Estimates ( 4 )	0	0	-0.0963	-0.2424	0.9924	0	-0.9137
(p-val)	(NA )	(NA )	(0.255 )	(0.0036 )	(0 )	(NA )	(0 )
Estimates ( 5 )	0	0	0	-0.2354	0.9849	0	-0.8739
(p-val)	(NA )	(NA )	(NA )	(0.007 )	(0 )	(NA )	(0 )
Estimates ( 6 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 10 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 11 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 12 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 13 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & 0.1895 & 0.0293 & -0.0741 & -0.438 & 1.0871 & -0.0882 & -0.9678 \tabularnewline
(p-val) & (0.5584 ) & (0.7994 ) & (0.4327 ) & (0.1675 ) & (0 ) & (0.3817 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.0899 & 0 & -0.0787 & -0.3404 & 1.0783 & -0.0824 & -0.9397 \tabularnewline
(p-val) & (0.7427 ) & (NA ) & (0.3752 ) & (0.1977 ) & (0 ) & (0.4112 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0 & 0 & -0.081 & -0.2519 & 1.0729 & -0.0755 & -0.9523 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.3555 ) & (0.0032 ) & (0 ) & (0.4467 ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & -0.0963 & -0.2424 & 0.9924 & 0 & -0.9137 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.255 ) & (0.0036 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0 & -0.2354 & 0.9849 & 0 & -0.8739 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0.007 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33470&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.1895[/C][C]0.0293[/C][C]-0.0741[/C][C]-0.438[/C][C]1.0871[/C][C]-0.0882[/C][C]-0.9678[/C][/ROW]
[ROW][C](p-val)[/C][C](0.5584 )[/C][C](0.7994 )[/C][C](0.4327 )[/C][C](0.1675 )[/C][C](0 )[/C][C](0.3817 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.0899[/C][C]0[/C][C]-0.0787[/C][C]-0.3404[/C][C]1.0783[/C][C]-0.0824[/C][C]-0.9397[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7427 )[/C][C](NA )[/C][C](0.3752 )[/C][C](0.1977 )[/C][C](0 )[/C][C](0.4112 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0[/C][C]-0.081[/C][C]-0.2519[/C][C]1.0729[/C][C]-0.0755[/C][C]-0.9523[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.3555 )[/C][C](0.0032 )[/C][C](0 )[/C][C](0.4467 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]-0.0963[/C][C]-0.2424[/C][C]0.9924[/C][C]0[/C][C]-0.9137[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.255 )[/C][C](0.0036 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.2354[/C][C]0.9849[/C][C]0[/C][C]-0.8739[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.007 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33470&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33470&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ar3	ma1	sar1	sar2	sma1
Estimates ( 1 )	0.1895	0.0293	-0.0741	-0.438	1.0871	-0.0882	-0.9678
(p-val)	(0.5584 )	(0.7994 )	(0.4327 )	(0.1675 )	(0 )	(0.3817 )	(0 )
Estimates ( 2 )	0.0899	0	-0.0787	-0.3404	1.0783	-0.0824	-0.9397
(p-val)	(0.7427 )	(NA )	(0.3752 )	(0.1977 )	(0 )	(0.4112 )	(0 )
Estimates ( 3 )	0	0	-0.081	-0.2519	1.0729	-0.0755	-0.9523
(p-val)	(NA )	(NA )	(0.3555 )	(0.0032 )	(0 )	(0.4467 )	(0 )
Estimates ( 4 )	0	0	-0.0963	-0.2424	0.9924	0	-0.9137
(p-val)	(NA )	(NA )	(0.255 )	(0.0036 )	(0 )	(NA )	(0 )
Estimates ( 5 )	0	0	0	-0.2354	0.9849	0	-0.8739
(p-val)	(NA )	(NA )	(NA )	(0.007 )	(0 )	(NA )	(0 )
Estimates ( 6 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 10 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 11 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 12 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 13 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )

Estimated ARIMA Residuals
Value
205.596845413907
-102.691479222268
4661.25214163603
2606.40947620059
4140.47612506907
3369.11221319088
1349.1687210498
2039.99753377768
196.601009395971
871.864037244896
820.701541236408
826.292952398904
1468.93040349527
1088.16236101210
-159.897558490276
-1923.04596083928
407.953043901130
1386.80180147560
-80.2226752036255
314.021729732221
-212.689419220106
1114.17230380485
1463.67415706741
345.984586413716
-1051.29448438652
-404.45571383728
-1070.74674234014
2180.86163808172
-580.329329276605
-1258.95188520314
677.309255623581
1647.06481014820
-1034.52939230692
-713.211148760481
1544.16072090316
-792.546102859134
2902.76903815708
-4287.16664577921
-53.734154260392
1427.27829377512
-150.058708100537
-816.710361010448
785.302430160657
591.717362032966
-502.392149685531
767.961199480116
-265.202749467793
326.298635720684
-2782.37851432238
-4628.12435681823
857.645540217355
-1483.04981193726
263.463630503146
-1694.66000137950
1771.52900023819
431.841120844024
-1053.52205973960
5.39133745707705
1272.39294843253
-1811.07253671823
444.022177553242
-1202.19883951065
-42.2584407235661
-2301.73730850042
1294.69378372644
-2237.29261285837
-418.372359958837
-308.982807138677
-1643.93459984125
377.685824723267
-1132.93474151771
-474.808020013323
-35.1527742244513
-1997.37618921145
-1219.01917063795
4211.86763775479
-495.806218556947
-1355.68588947317
932.534466083714
-973.528948301285
913.232083934714
1053.38153881389
1674.94938214785
-2296.78965247752
-992.881091659165
-893.490725539004
1546.55789770689
-1107.28459912669
3569.11958582642
82.592809043615
-1754.65637518551
-1020.57659450775
-501.501989285411
-94.9759016750937
1725.26507924370
-270.528256831889
2992.32006595204
-4400.62284759098
414.692388429631
-157.535733147410
791.900217737055
-168.102331102327
331.268901376719
-609.089741991739
-105.910274506196
1785.2900186045
973.107756312226
-482.144678179278
-12.552850064332
-1401.94438595181
798.469979874936
-1507.00145940347
2418.17911726117
-1906.46316891971
-963.979052283937
-1165.79432112037
-2342.84980603192
-683.033211994213
1352.69416991687
384.980754519838
881.639282036913
2337.82661167494
1816.93747276481
1398.52712200787
-3631.38813928761
-3243.76126369935
1109.57190456934
-1430.32000692663
5917.00225305534
-166.136515886862
-2578.62453244851
-1773.85990192633
-228.693149644704
-1498.58955836353
3157.34108877441
862.690144732598
-871.103593510166
-3127.90013194825
2723.10856387888
-1090.2996080689
1167.22336441195
-1398.39279731428
-471.781060519955
-3044.22641770293
-4501.16666518776
2837.71815271436
1407.50762840561
-1182.67476934816
1899.46842415070
-2477.76954568838
-2099.05517024044
1972.88042865722
2462.79537011008
-390.211562326524
-3246.70284235479
1237.29451599629
-313.507858610006
417.971522161046
2182.01171617374
-132.754088088350
821.98777776299
-405.036704277358
-4393.87222739648
-1602.55602406286
-788.183873231944
1888.18195565563
-334.953420678941
-1046.76251421895
1268.96801752327
-432.71894316648
-345.710364162375
1534.71590748405
4002.47747117754
-1064.07471134760
1307.43070291376
2919.97605899392
-1912.99380873654
961.419664671588
7811.12433072743
-3893.69859599318
3212.65037081788
1005.71257790290
-979.351022145903
1846.68079540737
-2640.90955080933
16.6881543749549
1205.67268983331
-1027.39222495022
-1363.52068587415
-42.7909876029661
3875.85176096919
13057.2738303126

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
205.596845413907 \tabularnewline
-102.691479222268 \tabularnewline
4661.25214163603 \tabularnewline
2606.40947620059 \tabularnewline
4140.47612506907 \tabularnewline
3369.11221319088 \tabularnewline
1349.1687210498 \tabularnewline
2039.99753377768 \tabularnewline
196.601009395971 \tabularnewline
871.864037244896 \tabularnewline
820.701541236408 \tabularnewline
826.292952398904 \tabularnewline
1468.93040349527 \tabularnewline
1088.16236101210 \tabularnewline
-159.897558490276 \tabularnewline
-1923.04596083928 \tabularnewline
407.953043901130 \tabularnewline
1386.80180147560 \tabularnewline
-80.2226752036255 \tabularnewline
314.021729732221 \tabularnewline
-212.689419220106 \tabularnewline
1114.17230380485 \tabularnewline
1463.67415706741 \tabularnewline
345.984586413716 \tabularnewline
-1051.29448438652 \tabularnewline
-404.45571383728 \tabularnewline
-1070.74674234014 \tabularnewline
2180.86163808172 \tabularnewline
-580.329329276605 \tabularnewline
-1258.95188520314 \tabularnewline
677.309255623581 \tabularnewline
1647.06481014820 \tabularnewline
-1034.52939230692 \tabularnewline
-713.211148760481 \tabularnewline
1544.16072090316 \tabularnewline
-792.546102859134 \tabularnewline
2902.76903815708 \tabularnewline
-4287.16664577921 \tabularnewline
-53.734154260392 \tabularnewline
1427.27829377512 \tabularnewline
-150.058708100537 \tabularnewline
-816.710361010448 \tabularnewline
785.302430160657 \tabularnewline
591.717362032966 \tabularnewline
-502.392149685531 \tabularnewline
767.961199480116 \tabularnewline
-265.202749467793 \tabularnewline
326.298635720684 \tabularnewline
-2782.37851432238 \tabularnewline
-4628.12435681823 \tabularnewline
857.645540217355 \tabularnewline
-1483.04981193726 \tabularnewline
263.463630503146 \tabularnewline
-1694.66000137950 \tabularnewline
1771.52900023819 \tabularnewline
431.841120844024 \tabularnewline
-1053.52205973960 \tabularnewline
5.39133745707705 \tabularnewline
1272.39294843253 \tabularnewline
-1811.07253671823 \tabularnewline
444.022177553242 \tabularnewline
-1202.19883951065 \tabularnewline
-42.2584407235661 \tabularnewline
-2301.73730850042 \tabularnewline
1294.69378372644 \tabularnewline
-2237.29261285837 \tabularnewline
-418.372359958837 \tabularnewline
-308.982807138677 \tabularnewline
-1643.93459984125 \tabularnewline
377.685824723267 \tabularnewline
-1132.93474151771 \tabularnewline
-474.808020013323 \tabularnewline
-35.1527742244513 \tabularnewline
-1997.37618921145 \tabularnewline
-1219.01917063795 \tabularnewline
4211.86763775479 \tabularnewline
-495.806218556947 \tabularnewline
-1355.68588947317 \tabularnewline
932.534466083714 \tabularnewline
-973.528948301285 \tabularnewline
913.232083934714 \tabularnewline
1053.38153881389 \tabularnewline
1674.94938214785 \tabularnewline
-2296.78965247752 \tabularnewline
-992.881091659165 \tabularnewline
-893.490725539004 \tabularnewline
1546.55789770689 \tabularnewline
-1107.28459912669 \tabularnewline
3569.11958582642 \tabularnewline
82.592809043615 \tabularnewline
-1754.65637518551 \tabularnewline
-1020.57659450775 \tabularnewline
-501.501989285411 \tabularnewline
-94.9759016750937 \tabularnewline
1725.26507924370 \tabularnewline
-270.528256831889 \tabularnewline
2992.32006595204 \tabularnewline
-4400.62284759098 \tabularnewline
414.692388429631 \tabularnewline
-157.535733147410 \tabularnewline
791.900217737055 \tabularnewline
-168.102331102327 \tabularnewline
331.268901376719 \tabularnewline
-609.089741991739 \tabularnewline
-105.910274506196 \tabularnewline
1785.2900186045 \tabularnewline
973.107756312226 \tabularnewline
-482.144678179278 \tabularnewline
-12.552850064332 \tabularnewline
-1401.94438595181 \tabularnewline
798.469979874936 \tabularnewline
-1507.00145940347 \tabularnewline
2418.17911726117 \tabularnewline
-1906.46316891971 \tabularnewline
-963.979052283937 \tabularnewline
-1165.79432112037 \tabularnewline
-2342.84980603192 \tabularnewline
-683.033211994213 \tabularnewline
1352.69416991687 \tabularnewline
384.980754519838 \tabularnewline
881.639282036913 \tabularnewline
2337.82661167494 \tabularnewline
1816.93747276481 \tabularnewline
1398.52712200787 \tabularnewline
-3631.38813928761 \tabularnewline
-3243.76126369935 \tabularnewline
1109.57190456934 \tabularnewline
-1430.32000692663 \tabularnewline
5917.00225305534 \tabularnewline
-166.136515886862 \tabularnewline
-2578.62453244851 \tabularnewline
-1773.85990192633 \tabularnewline
-228.693149644704 \tabularnewline
-1498.58955836353 \tabularnewline
3157.34108877441 \tabularnewline
862.690144732598 \tabularnewline
-871.103593510166 \tabularnewline
-3127.90013194825 \tabularnewline
2723.10856387888 \tabularnewline
-1090.2996080689 \tabularnewline
1167.22336441195 \tabularnewline
-1398.39279731428 \tabularnewline
-471.781060519955 \tabularnewline
-3044.22641770293 \tabularnewline
-4501.16666518776 \tabularnewline
2837.71815271436 \tabularnewline
1407.50762840561 \tabularnewline
-1182.67476934816 \tabularnewline
1899.46842415070 \tabularnewline
-2477.76954568838 \tabularnewline
-2099.05517024044 \tabularnewline
1972.88042865722 \tabularnewline
2462.79537011008 \tabularnewline
-390.211562326524 \tabularnewline
-3246.70284235479 \tabularnewline
1237.29451599629 \tabularnewline
-313.507858610006 \tabularnewline
417.971522161046 \tabularnewline
2182.01171617374 \tabularnewline
-132.754088088350 \tabularnewline
821.98777776299 \tabularnewline
-405.036704277358 \tabularnewline
-4393.87222739648 \tabularnewline
-1602.55602406286 \tabularnewline
-788.183873231944 \tabularnewline
1888.18195565563 \tabularnewline
-334.953420678941 \tabularnewline
-1046.76251421895 \tabularnewline
1268.96801752327 \tabularnewline
-432.71894316648 \tabularnewline
-345.710364162375 \tabularnewline
1534.71590748405 \tabularnewline
4002.47747117754 \tabularnewline
-1064.07471134760 \tabularnewline
1307.43070291376 \tabularnewline
2919.97605899392 \tabularnewline
-1912.99380873654 \tabularnewline
961.419664671588 \tabularnewline
7811.12433072743 \tabularnewline
-3893.69859599318 \tabularnewline
3212.65037081788 \tabularnewline
1005.71257790290 \tabularnewline
-979.351022145903 \tabularnewline
1846.68079540737 \tabularnewline
-2640.90955080933 \tabularnewline
16.6881543749549 \tabularnewline
1205.67268983331 \tabularnewline
-1027.39222495022 \tabularnewline
-1363.52068587415 \tabularnewline
-42.7909876029661 \tabularnewline
3875.85176096919 \tabularnewline
13057.2738303126 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33470&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]205.596845413907[/C][/ROW]
[ROW][C]-102.691479222268[/C][/ROW]
[ROW][C]4661.25214163603[/C][/ROW]
[ROW][C]2606.40947620059[/C][/ROW]
[ROW][C]4140.47612506907[/C][/ROW]
[ROW][C]3369.11221319088[/C][/ROW]
[ROW][C]1349.1687210498[/C][/ROW]
[ROW][C]2039.99753377768[/C][/ROW]
[ROW][C]196.601009395971[/C][/ROW]
[ROW][C]871.864037244896[/C][/ROW]
[ROW][C]820.701541236408[/C][/ROW]
[ROW][C]826.292952398904[/C][/ROW]
[ROW][C]1468.93040349527[/C][/ROW]
[ROW][C]1088.16236101210[/C][/ROW]
[ROW][C]-159.897558490276[/C][/ROW]
[ROW][C]-1923.04596083928[/C][/ROW]
[ROW][C]407.953043901130[/C][/ROW]
[ROW][C]1386.80180147560[/C][/ROW]
[ROW][C]-80.2226752036255[/C][/ROW]
[ROW][C]314.021729732221[/C][/ROW]
[ROW][C]-212.689419220106[/C][/ROW]
[ROW][C]1114.17230380485[/C][/ROW]
[ROW][C]1463.67415706741[/C][/ROW]
[ROW][C]345.984586413716[/C][/ROW]
[ROW][C]-1051.29448438652[/C][/ROW]
[ROW][C]-404.45571383728[/C][/ROW]
[ROW][C]-1070.74674234014[/C][/ROW]
[ROW][C]2180.86163808172[/C][/ROW]
[ROW][C]-580.329329276605[/C][/ROW]
[ROW][C]-1258.95188520314[/C][/ROW]
[ROW][C]677.309255623581[/C][/ROW]
[ROW][C]1647.06481014820[/C][/ROW]
[ROW][C]-1034.52939230692[/C][/ROW]
[ROW][C]-713.211148760481[/C][/ROW]
[ROW][C]1544.16072090316[/C][/ROW]
[ROW][C]-792.546102859134[/C][/ROW]
[ROW][C]2902.76903815708[/C][/ROW]
[ROW][C]-4287.16664577921[/C][/ROW]
[ROW][C]-53.734154260392[/C][/ROW]
[ROW][C]1427.27829377512[/C][/ROW]
[ROW][C]-150.058708100537[/C][/ROW]
[ROW][C]-816.710361010448[/C][/ROW]
[ROW][C]785.302430160657[/C][/ROW]
[ROW][C]591.717362032966[/C][/ROW]
[ROW][C]-502.392149685531[/C][/ROW]
[ROW][C]767.961199480116[/C][/ROW]
[ROW][C]-265.202749467793[/C][/ROW]
[ROW][C]326.298635720684[/C][/ROW]
[ROW][C]-2782.37851432238[/C][/ROW]
[ROW][C]-4628.12435681823[/C][/ROW]
[ROW][C]857.645540217355[/C][/ROW]
[ROW][C]-1483.04981193726[/C][/ROW]
[ROW][C]263.463630503146[/C][/ROW]
[ROW][C]-1694.66000137950[/C][/ROW]
[ROW][C]1771.52900023819[/C][/ROW]
[ROW][C]431.841120844024[/C][/ROW]
[ROW][C]-1053.52205973960[/C][/ROW]
[ROW][C]5.39133745707705[/C][/ROW]
[ROW][C]1272.39294843253[/C][/ROW]
[ROW][C]-1811.07253671823[/C][/ROW]
[ROW][C]444.022177553242[/C][/ROW]
[ROW][C]-1202.19883951065[/C][/ROW]
[ROW][C]-42.2584407235661[/C][/ROW]
[ROW][C]-2301.73730850042[/C][/ROW]
[ROW][C]1294.69378372644[/C][/ROW]
[ROW][C]-2237.29261285837[/C][/ROW]
[ROW][C]-418.372359958837[/C][/ROW]
[ROW][C]-308.982807138677[/C][/ROW]
[ROW][C]-1643.93459984125[/C][/ROW]
[ROW][C]377.685824723267[/C][/ROW]
[ROW][C]-1132.93474151771[/C][/ROW]
[ROW][C]-474.808020013323[/C][/ROW]
[ROW][C]-35.1527742244513[/C][/ROW]
[ROW][C]-1997.37618921145[/C][/ROW]
[ROW][C]-1219.01917063795[/C][/ROW]
[ROW][C]4211.86763775479[/C][/ROW]
[ROW][C]-495.806218556947[/C][/ROW]
[ROW][C]-1355.68588947317[/C][/ROW]
[ROW][C]932.534466083714[/C][/ROW]
[ROW][C]-973.528948301285[/C][/ROW]
[ROW][C]913.232083934714[/C][/ROW]
[ROW][C]1053.38153881389[/C][/ROW]
[ROW][C]1674.94938214785[/C][/ROW]
[ROW][C]-2296.78965247752[/C][/ROW]
[ROW][C]-992.881091659165[/C][/ROW]
[ROW][C]-893.490725539004[/C][/ROW]
[ROW][C]1546.55789770689[/C][/ROW]
[ROW][C]-1107.28459912669[/C][/ROW]
[ROW][C]3569.11958582642[/C][/ROW]
[ROW][C]82.592809043615[/C][/ROW]
[ROW][C]-1754.65637518551[/C][/ROW]
[ROW][C]-1020.57659450775[/C][/ROW]
[ROW][C]-501.501989285411[/C][/ROW]
[ROW][C]-94.9759016750937[/C][/ROW]
[ROW][C]1725.26507924370[/C][/ROW]
[ROW][C]-270.528256831889[/C][/ROW]
[ROW][C]2992.32006595204[/C][/ROW]
[ROW][C]-4400.62284759098[/C][/ROW]
[ROW][C]414.692388429631[/C][/ROW]
[ROW][C]-157.535733147410[/C][/ROW]
[ROW][C]791.900217737055[/C][/ROW]
[ROW][C]-168.102331102327[/C][/ROW]
[ROW][C]331.268901376719[/C][/ROW]
[ROW][C]-609.089741991739[/C][/ROW]
[ROW][C]-105.910274506196[/C][/ROW]
[ROW][C]1785.2900186045[/C][/ROW]
[ROW][C]973.107756312226[/C][/ROW]
[ROW][C]-482.144678179278[/C][/ROW]
[ROW][C]-12.552850064332[/C][/ROW]
[ROW][C]-1401.94438595181[/C][/ROW]
[ROW][C]798.469979874936[/C][/ROW]
[ROW][C]-1507.00145940347[/C][/ROW]
[ROW][C]2418.17911726117[/C][/ROW]
[ROW][C]-1906.46316891971[/C][/ROW]
[ROW][C]-963.979052283937[/C][/ROW]
[ROW][C]-1165.79432112037[/C][/ROW]
[ROW][C]-2342.84980603192[/C][/ROW]
[ROW][C]-683.033211994213[/C][/ROW]
[ROW][C]1352.69416991687[/C][/ROW]
[ROW][C]384.980754519838[/C][/ROW]
[ROW][C]881.639282036913[/C][/ROW]
[ROW][C]2337.82661167494[/C][/ROW]
[ROW][C]1816.93747276481[/C][/ROW]
[ROW][C]1398.52712200787[/C][/ROW]
[ROW][C]-3631.38813928761[/C][/ROW]
[ROW][C]-3243.76126369935[/C][/ROW]
[ROW][C]1109.57190456934[/C][/ROW]
[ROW][C]-1430.32000692663[/C][/ROW]
[ROW][C]5917.00225305534[/C][/ROW]
[ROW][C]-166.136515886862[/C][/ROW]
[ROW][C]-2578.62453244851[/C][/ROW]
[ROW][C]-1773.85990192633[/C][/ROW]
[ROW][C]-228.693149644704[/C][/ROW]
[ROW][C]-1498.58955836353[/C][/ROW]
[ROW][C]3157.34108877441[/C][/ROW]
[ROW][C]862.690144732598[/C][/ROW]
[ROW][C]-871.103593510166[/C][/ROW]
[ROW][C]-3127.90013194825[/C][/ROW]
[ROW][C]2723.10856387888[/C][/ROW]
[ROW][C]-1090.2996080689[/C][/ROW]
[ROW][C]1167.22336441195[/C][/ROW]
[ROW][C]-1398.39279731428[/C][/ROW]
[ROW][C]-471.781060519955[/C][/ROW]
[ROW][C]-3044.22641770293[/C][/ROW]
[ROW][C]-4501.16666518776[/C][/ROW]
[ROW][C]2837.71815271436[/C][/ROW]
[ROW][C]1407.50762840561[/C][/ROW]
[ROW][C]-1182.67476934816[/C][/ROW]
[ROW][C]1899.46842415070[/C][/ROW]
[ROW][C]-2477.76954568838[/C][/ROW]
[ROW][C]-2099.05517024044[/C][/ROW]
[ROW][C]1972.88042865722[/C][/ROW]
[ROW][C]2462.79537011008[/C][/ROW]
[ROW][C]-390.211562326524[/C][/ROW]
[ROW][C]-3246.70284235479[/C][/ROW]
[ROW][C]1237.29451599629[/C][/ROW]
[ROW][C]-313.507858610006[/C][/ROW]
[ROW][C]417.971522161046[/C][/ROW]
[ROW][C]2182.01171617374[/C][/ROW]
[ROW][C]-132.754088088350[/C][/ROW]
[ROW][C]821.98777776299[/C][/ROW]
[ROW][C]-405.036704277358[/C][/ROW]
[ROW][C]-4393.87222739648[/C][/ROW]
[ROW][C]-1602.55602406286[/C][/ROW]
[ROW][C]-788.183873231944[/C][/ROW]
[ROW][C]1888.18195565563[/C][/ROW]
[ROW][C]-334.953420678941[/C][/ROW]
[ROW][C]-1046.76251421895[/C][/ROW]
[ROW][C]1268.96801752327[/C][/ROW]
[ROW][C]-432.71894316648[/C][/ROW]
[ROW][C]-345.710364162375[/C][/ROW]
[ROW][C]1534.71590748405[/C][/ROW]
[ROW][C]4002.47747117754[/C][/ROW]
[ROW][C]-1064.07471134760[/C][/ROW]
[ROW][C]1307.43070291376[/C][/ROW]
[ROW][C]2919.97605899392[/C][/ROW]
[ROW][C]-1912.99380873654[/C][/ROW]
[ROW][C]961.419664671588[/C][/ROW]
[ROW][C]7811.12433072743[/C][/ROW]
[ROW][C]-3893.69859599318[/C][/ROW]
[ROW][C]3212.65037081788[/C][/ROW]
[ROW][C]1005.71257790290[/C][/ROW]
[ROW][C]-979.351022145903[/C][/ROW]
[ROW][C]1846.68079540737[/C][/ROW]
[ROW][C]-2640.90955080933[/C][/ROW]
[ROW][C]16.6881543749549[/C][/ROW]
[ROW][C]1205.67268983331[/C][/ROW]
[ROW][C]-1027.39222495022[/C][/ROW]
[ROW][C]-1363.52068587415[/C][/ROW]
[ROW][C]-42.7909876029661[/C][/ROW]
[ROW][C]3875.85176096919[/C][/ROW]
[ROW][C]13057.2738303126[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33470&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33470&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
205.596845413907
-102.691479222268
4661.25214163603
2606.40947620059
4140.47612506907
3369.11221319088
1349.1687210498
2039.99753377768
196.601009395971
871.864037244896
820.701541236408
826.292952398904
1468.93040349527
1088.16236101210
-159.897558490276
-1923.04596083928
407.953043901130
1386.80180147560
-80.2226752036255
314.021729732221
-212.689419220106
1114.17230380485
1463.67415706741
345.984586413716
-1051.29448438652
-404.45571383728
-1070.74674234014
2180.86163808172
-580.329329276605
-1258.95188520314
677.309255623581
1647.06481014820
-1034.52939230692
-713.211148760481
1544.16072090316
-792.546102859134
2902.76903815708
-4287.16664577921
-53.734154260392
1427.27829377512
-150.058708100537
-816.710361010448
785.302430160657
591.717362032966
-502.392149685531
767.961199480116
-265.202749467793
326.298635720684
-2782.37851432238
-4628.12435681823
857.645540217355
-1483.04981193726
263.463630503146
-1694.66000137950
1771.52900023819
431.841120844024
-1053.52205973960
5.39133745707705
1272.39294843253
-1811.07253671823
444.022177553242
-1202.19883951065
-42.2584407235661
-2301.73730850042
1294.69378372644
-2237.29261285837
-418.372359958837
-308.982807138677
-1643.93459984125
377.685824723267
-1132.93474151771
-474.808020013323
-35.1527742244513
-1997.37618921145
-1219.01917063795
4211.86763775479
-495.806218556947
-1355.68588947317
932.534466083714
-973.528948301285
913.232083934714
1053.38153881389
1674.94938214785
-2296.78965247752
-992.881091659165
-893.490725539004
1546.55789770689
-1107.28459912669
3569.11958582642
82.592809043615
-1754.65637518551
-1020.57659450775
-501.501989285411
-94.9759016750937
1725.26507924370
-270.528256831889
2992.32006595204
-4400.62284759098
414.692388429631
-157.535733147410
791.900217737055
-168.102331102327
331.268901376719
-609.089741991739
-105.910274506196
1785.2900186045
973.107756312226
-482.144678179278
-12.552850064332
-1401.94438595181
798.469979874936
-1507.00145940347
2418.17911726117
-1906.46316891971
-963.979052283937
-1165.79432112037
-2342.84980603192
-683.033211994213
1352.69416991687
384.980754519838
881.639282036913
2337.82661167494
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Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Parameters (Session):

par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

R code (references can be found in the software module):

library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame  <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names  <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval  <- arimaSelect.out[[2]][1:k,]
aic   <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft  = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop   = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01)                            symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1)  & (pval[j,i]>=.05)) symb = 'red'
else                                         symb = 'black'
polygon(c(i+.5   ,i+.2   ,i+.5   ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix)  {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv  <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i   ,1+2*i   ,1+2*i-.5   ,1+2*i),
c(.4      ,.7      , .4        , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
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,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code