Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationTue, 04 Dec 2012 13:45:44 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/04/t1354646772z82bb8licwo8kln.htm/, Retrieved Fri, 29 Mar 2024 01:50:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196498, Retrieved Fri, 29 Mar 2024 01:50:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [Interest rate of ...] [2012-12-04 18:45:44] [28bea00984b2b9577d411997e7bfd037] [Current]
Feedback Forum

Post a new message
Dataseries X:
4.69
4.88
4.84
4.81
5.22
5.16
5.27
5.35
5.33
5.29
5.28
4.94
4.70
4.73
4.57
4.61
4.69
4.53
4.55
4.31
4.03
3.72
3.64
3.81
3.95
4.26
4.55
4.54
4.55
4.39
4.21
4.00
3.67
3.60
3.53
3.35
3.10
2.89
2.97
3.03
2.71
2.44
2.60
2.93
2.86
2.88
3.00
2.90
2.64
2.75
2.70
2.87
3.03
3.14
3.02
2.86
3.07
2.93
2.83
2.72
2.73
2.72
2.77
2.61
2.47
2.30
2.38
2.43
2.39
2.60
2.84
2.87
2.92
3.08
3.33
3.48
3.57
3.66
3.77
3.75
3.75
3.81
3.82
3.89
4.05
4.10
4.07
4.26
4.40
4.61
4.63
4.48
4.46
4.45
4.32
4.52
4.21
3.97
4.12
4.50
4.73
5.26
5.20
4.94
4.95
4.52
3.85
3.41
2.95
2.68
2.53
2.44
2.16
2.20
2.10
2.29
2.03
2.05
1.94
1.87
1.89
1.94
1.79
1.71
1.66
1.74
1.83
1.64
1.69
1.78
1.89
1.95
2.05
2.24
2.38
2.53
2.36
2.22
2.12
1.75
1.76
1.81
1.71
1.74
1.48
1.24
1.16
1.11
0.98
0.94
0.65
0.42
0.41
0.40




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 12 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196498&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196498&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196498&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.09170.13450.15040.30830.0928-0.0409-0.0728
(p-val)(0.7613 )(0.361 )(0.0632 )(0.2994 )(0.906 )(0.64 )(0.926 )
Estimates ( 2 )0.09210.13460.15010.30830.0202-0.03860
(p-val)(0.7605 )(0.3616 )(0.0635 )(0.3002 )(0.8102 )(0.6539 )(NA )
Estimates ( 3 )0.09140.13630.15020.30850-0.03860
(p-val)(0.7608 )(0.352 )(0.0635 )(0.2963 )(NA )(0.6537 )(NA )
Estimates ( 4 )00.17350.15110.39440-0.0360
(p-val)(NA )(0.0416 )(0.0626 )(0 )(NA )(0.6738 )(NA )
Estimates ( 5 )00.17520.1510.3959000
(p-val)(NA )(0.0392 )(0.0622 )(0 )(NA )(NA )(NA )
Estimates ( 6 )00.191900.432000
(p-val)(NA )(0.0226 )(NA )(0 )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(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 ) & 0.0917 & 0.1345 & 0.1504 & 0.3083 & 0.0928 & -0.0409 & -0.0728 \tabularnewline
(p-val) & (0.7613 ) & (0.361 ) & (0.0632 ) & (0.2994 ) & (0.906 ) & (0.64 ) & (0.926 ) \tabularnewline
Estimates ( 2 ) & 0.0921 & 0.1346 & 0.1501 & 0.3083 & 0.0202 & -0.0386 & 0 \tabularnewline
(p-val) & (0.7605 ) & (0.3616 ) & (0.0635 ) & (0.3002 ) & (0.8102 ) & (0.6539 ) & (NA ) \tabularnewline
Estimates ( 3 ) & 0.0914 & 0.1363 & 0.1502 & 0.3085 & 0 & -0.0386 & 0 \tabularnewline
(p-val) & (0.7608 ) & (0.352 ) & (0.0635 ) & (0.2963 ) & (NA ) & (0.6537 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0 & 0.1735 & 0.1511 & 0.3944 & 0 & -0.036 & 0 \tabularnewline
(p-val) & (NA ) & (0.0416 ) & (0.0626 ) & (0 ) & (NA ) & (0.6738 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0.1752 & 0.151 & 0.3959 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (0.0392 ) & (0.0622 ) & (0 ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0.1919 & 0 & 0.432 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (0.0226 ) & (NA ) & (0 ) & (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=196498&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]0.0917[/C][C]0.1345[/C][C]0.1504[/C][C]0.3083[/C][C]0.0928[/C][C]-0.0409[/C][C]-0.0728[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7613 )[/C][C](0.361 )[/C][C](0.0632 )[/C][C](0.2994 )[/C][C](0.906 )[/C][C](0.64 )[/C][C](0.926 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.0921[/C][C]0.1346[/C][C]0.1501[/C][C]0.3083[/C][C]0.0202[/C][C]-0.0386[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7605 )[/C][C](0.3616 )[/C][C](0.0635 )[/C][C](0.3002 )[/C][C](0.8102 )[/C][C](0.6539 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.0914[/C][C]0.1363[/C][C]0.1502[/C][C]0.3085[/C][C]0[/C][C]-0.0386[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7608 )[/C][C](0.352 )[/C][C](0.0635 )[/C][C](0.2963 )[/C][C](NA )[/C][C](0.6537 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.1735[/C][C]0.1511[/C][C]0.3944[/C][C]0[/C][C]-0.036[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0416 )[/C][C](0.0626 )[/C][C](0 )[/C][C](NA )[/C][C](0.6738 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0.1752[/C][C]0.151[/C][C]0.3959[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0392 )[/C][C](0.0622 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0.1919[/C][C]0[/C][C]0.432[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0226 )[/C][C](NA )[/C][C](0 )[/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=196498&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196498&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
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.09170.13450.15040.30830.0928-0.0409-0.0728
(p-val)(0.7613 )(0.361 )(0.0632 )(0.2994 )(0.906 )(0.64 )(0.926 )
Estimates ( 2 )0.09210.13460.15010.30830.0202-0.03860
(p-val)(0.7605 )(0.3616 )(0.0635 )(0.3002 )(0.8102 )(0.6539 )(NA )
Estimates ( 3 )0.09140.13630.15020.30850-0.03860
(p-val)(0.7608 )(0.352 )(0.0635 )(0.2963 )(NA )(0.6537 )(NA )
Estimates ( 4 )00.17350.15110.39440-0.0360
(p-val)(NA )(0.0416 )(0.0626 )(0 )(NA )(0.6738 )(NA )
Estimates ( 5 )00.17520.1510.3959000
(p-val)(NA )(0.0392 )(0.0622 )(0 )(NA )(NA )(NA )
Estimates ( 6 )00.191900.432000
(p-val)(NA )(0.0226 )(NA )(0 )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.00468999706403207
0.169802266154913
-0.119782380592012
-0.0271545468519886
0.398406609909392
-0.206147302008568
0.12426309992944
-0.020583998202967
-0.0220685544157335
-0.0618933396609821
0.00592612713476639
-0.332316173470779
-0.100656587574012
0.130939048295093
-0.118435412023538
0.117866374946737
0.0568504093451992
-0.165354802137876
0.0653980216903398
-0.24992936182677
-0.160407769616404
-0.20746218046511
0.0874343150099515
0.23199357676486
0.108991995051352
0.249142712596343
0.14117010582061
-0.141349302905852
-0.0316758184724719
-0.18949802365028
-0.105227575584839
-0.141815417335265
-0.218157016141096
0.0803409513079806
-0.0122634768985404
-0.113048626805245
-0.182411435983444
-0.0956765384604532
0.188865476222618
0.0597865440045991
-0.325976926844314
-0.163553015094133
0.271762481859521
0.318055431152346
-0.183175111260252
0.0105214566233789
0.0782724182376279
-0.123919958214475
-0.234994208307696
0.202429596208315
-0.0694705884085771
0.217483550057946
0.0660590744597098
0.0616082139258813
-0.198097184343861
-0.12501774386214
0.263909076761868
-0.198312362729949
-0.0341373321380577
-0.103661941501452
0.0896999930704444
-0.0111319706468356
0.0692641520520939
-0.187176523482705
-0.0731563365511074
-0.120551127035854
0.176415511002926
0.0310953487444023
-0.0406591583974653
0.205253249483667
0.158208066411955
-0.0633897371978978
0.0013251338506157
0.117978362786185
0.190004787416642
0.0391954899763769
0.00651314668085362
0.0233853328609726
0.0623208388732328
-0.0740322742623994
-0.00356027900855249
0.0483043790196161
-0.0061018414643256
0.0619008267041968
0.124683498993136
-0.0131344208805079
-0.0634095978361346
0.182179347507325
0.0655896819817104
0.155269164876945
-0.0946889959153517
-0.170457458511936
0.0122628020397526
0.00841229239382946
-0.107175375450352
0.247198972387701
-0.383564692638402
-0.103580684380659
0.215129483471241
0.38370691025852
0.0880585031563803
0.4058984233763
-0.318365122101769
-0.261580862605992
0.0440348883419828
-0.392808257447564
-0.476995252207654
-0.177331147525602
-0.20745833733321
-0.00959859166597776
0.000851583877323758
0.0264383398272595
-0.223409562906061
0.166860953335315
-0.10339545421285
0.266200156645663
-0.353893793758457
0.14189623859101
-0.149297480082106
0.0248558849752234
0.0264174123768981
0.0684193548148313
-0.170019514651285
-0.0244780949167113
-0.0215733869596959
0.125209440010287
0.0612765912844664
-0.220726605928967
0.10952516017897
0.0663497628683641
0.103662299510155
-0.00435778944181635
0.0688583095891516
0.135617144779686
0.0597301018655756
0.0779588843120199
-0.254084870755631
-0.0868441933460303
-0.0584800881004532
-0.296646003472444
0.166094857288165
0.064189784114954
-0.0712930736433104
0.0479499464003501
-0.269007035975429
-0.123668040103369
0.00998888895647147
0.0273641255487598
-0.0905731097913407
0.0166964877478681
-0.26627779114608
-0.0979513772757262
0.0856359167387129
0.040195942558638

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.00468999706403207 \tabularnewline
0.169802266154913 \tabularnewline
-0.119782380592012 \tabularnewline
-0.0271545468519886 \tabularnewline
0.398406609909392 \tabularnewline
-0.206147302008568 \tabularnewline
0.12426309992944 \tabularnewline
-0.020583998202967 \tabularnewline
-0.0220685544157335 \tabularnewline
-0.0618933396609821 \tabularnewline
0.00592612713476639 \tabularnewline
-0.332316173470779 \tabularnewline
-0.100656587574012 \tabularnewline
0.130939048295093 \tabularnewline
-0.118435412023538 \tabularnewline
0.117866374946737 \tabularnewline
0.0568504093451992 \tabularnewline
-0.165354802137876 \tabularnewline
0.0653980216903398 \tabularnewline
-0.24992936182677 \tabularnewline
-0.160407769616404 \tabularnewline
-0.20746218046511 \tabularnewline
0.0874343150099515 \tabularnewline
0.23199357676486 \tabularnewline
0.108991995051352 \tabularnewline
0.249142712596343 \tabularnewline
0.14117010582061 \tabularnewline
-0.141349302905852 \tabularnewline
-0.0316758184724719 \tabularnewline
-0.18949802365028 \tabularnewline
-0.105227575584839 \tabularnewline
-0.141815417335265 \tabularnewline
-0.218157016141096 \tabularnewline
0.0803409513079806 \tabularnewline
-0.0122634768985404 \tabularnewline
-0.113048626805245 \tabularnewline
-0.182411435983444 \tabularnewline
-0.0956765384604532 \tabularnewline
0.188865476222618 \tabularnewline
0.0597865440045991 \tabularnewline
-0.325976926844314 \tabularnewline
-0.163553015094133 \tabularnewline
0.271762481859521 \tabularnewline
0.318055431152346 \tabularnewline
-0.183175111260252 \tabularnewline
0.0105214566233789 \tabularnewline
0.0782724182376279 \tabularnewline
-0.123919958214475 \tabularnewline
-0.234994208307696 \tabularnewline
0.202429596208315 \tabularnewline
-0.0694705884085771 \tabularnewline
0.217483550057946 \tabularnewline
0.0660590744597098 \tabularnewline
0.0616082139258813 \tabularnewline
-0.198097184343861 \tabularnewline
-0.12501774386214 \tabularnewline
0.263909076761868 \tabularnewline
-0.198312362729949 \tabularnewline
-0.0341373321380577 \tabularnewline
-0.103661941501452 \tabularnewline
0.0896999930704444 \tabularnewline
-0.0111319706468356 \tabularnewline
0.0692641520520939 \tabularnewline
-0.187176523482705 \tabularnewline
-0.0731563365511074 \tabularnewline
-0.120551127035854 \tabularnewline
0.176415511002926 \tabularnewline
0.0310953487444023 \tabularnewline
-0.0406591583974653 \tabularnewline
0.205253249483667 \tabularnewline
0.158208066411955 \tabularnewline
-0.0633897371978978 \tabularnewline
0.0013251338506157 \tabularnewline
0.117978362786185 \tabularnewline
0.190004787416642 \tabularnewline
0.0391954899763769 \tabularnewline
0.00651314668085362 \tabularnewline
0.0233853328609726 \tabularnewline
0.0623208388732328 \tabularnewline
-0.0740322742623994 \tabularnewline
-0.00356027900855249 \tabularnewline
0.0483043790196161 \tabularnewline
-0.0061018414643256 \tabularnewline
0.0619008267041968 \tabularnewline
0.124683498993136 \tabularnewline
-0.0131344208805079 \tabularnewline
-0.0634095978361346 \tabularnewline
0.182179347507325 \tabularnewline
0.0655896819817104 \tabularnewline
0.155269164876945 \tabularnewline
-0.0946889959153517 \tabularnewline
-0.170457458511936 \tabularnewline
0.0122628020397526 \tabularnewline
0.00841229239382946 \tabularnewline
-0.107175375450352 \tabularnewline
0.247198972387701 \tabularnewline
-0.383564692638402 \tabularnewline
-0.103580684380659 \tabularnewline
0.215129483471241 \tabularnewline
0.38370691025852 \tabularnewline
0.0880585031563803 \tabularnewline
0.4058984233763 \tabularnewline
-0.318365122101769 \tabularnewline
-0.261580862605992 \tabularnewline
0.0440348883419828 \tabularnewline
-0.392808257447564 \tabularnewline
-0.476995252207654 \tabularnewline
-0.177331147525602 \tabularnewline
-0.20745833733321 \tabularnewline
-0.00959859166597776 \tabularnewline
0.000851583877323758 \tabularnewline
0.0264383398272595 \tabularnewline
-0.223409562906061 \tabularnewline
0.166860953335315 \tabularnewline
-0.10339545421285 \tabularnewline
0.266200156645663 \tabularnewline
-0.353893793758457 \tabularnewline
0.14189623859101 \tabularnewline
-0.149297480082106 \tabularnewline
0.0248558849752234 \tabularnewline
0.0264174123768981 \tabularnewline
0.0684193548148313 \tabularnewline
-0.170019514651285 \tabularnewline
-0.0244780949167113 \tabularnewline
-0.0215733869596959 \tabularnewline
0.125209440010287 \tabularnewline
0.0612765912844664 \tabularnewline
-0.220726605928967 \tabularnewline
0.10952516017897 \tabularnewline
0.0663497628683641 \tabularnewline
0.103662299510155 \tabularnewline
-0.00435778944181635 \tabularnewline
0.0688583095891516 \tabularnewline
0.135617144779686 \tabularnewline
0.0597301018655756 \tabularnewline
0.0779588843120199 \tabularnewline
-0.254084870755631 \tabularnewline
-0.0868441933460303 \tabularnewline
-0.0584800881004532 \tabularnewline
-0.296646003472444 \tabularnewline
0.166094857288165 \tabularnewline
0.064189784114954 \tabularnewline
-0.0712930736433104 \tabularnewline
0.0479499464003501 \tabularnewline
-0.269007035975429 \tabularnewline
-0.123668040103369 \tabularnewline
0.00998888895647147 \tabularnewline
0.0273641255487598 \tabularnewline
-0.0905731097913407 \tabularnewline
0.0166964877478681 \tabularnewline
-0.26627779114608 \tabularnewline
-0.0979513772757262 \tabularnewline
0.0856359167387129 \tabularnewline
0.040195942558638 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196498&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.00468999706403207[/C][/ROW]
[ROW][C]0.169802266154913[/C][/ROW]
[ROW][C]-0.119782380592012[/C][/ROW]
[ROW][C]-0.0271545468519886[/C][/ROW]
[ROW][C]0.398406609909392[/C][/ROW]
[ROW][C]-0.206147302008568[/C][/ROW]
[ROW][C]0.12426309992944[/C][/ROW]
[ROW][C]-0.020583998202967[/C][/ROW]
[ROW][C]-0.0220685544157335[/C][/ROW]
[ROW][C]-0.0618933396609821[/C][/ROW]
[ROW][C]0.00592612713476639[/C][/ROW]
[ROW][C]-0.332316173470779[/C][/ROW]
[ROW][C]-0.100656587574012[/C][/ROW]
[ROW][C]0.130939048295093[/C][/ROW]
[ROW][C]-0.118435412023538[/C][/ROW]
[ROW][C]0.117866374946737[/C][/ROW]
[ROW][C]0.0568504093451992[/C][/ROW]
[ROW][C]-0.165354802137876[/C][/ROW]
[ROW][C]0.0653980216903398[/C][/ROW]
[ROW][C]-0.24992936182677[/C][/ROW]
[ROW][C]-0.160407769616404[/C][/ROW]
[ROW][C]-0.20746218046511[/C][/ROW]
[ROW][C]0.0874343150099515[/C][/ROW]
[ROW][C]0.23199357676486[/C][/ROW]
[ROW][C]0.108991995051352[/C][/ROW]
[ROW][C]0.249142712596343[/C][/ROW]
[ROW][C]0.14117010582061[/C][/ROW]
[ROW][C]-0.141349302905852[/C][/ROW]
[ROW][C]-0.0316758184724719[/C][/ROW]
[ROW][C]-0.18949802365028[/C][/ROW]
[ROW][C]-0.105227575584839[/C][/ROW]
[ROW][C]-0.141815417335265[/C][/ROW]
[ROW][C]-0.218157016141096[/C][/ROW]
[ROW][C]0.0803409513079806[/C][/ROW]
[ROW][C]-0.0122634768985404[/C][/ROW]
[ROW][C]-0.113048626805245[/C][/ROW]
[ROW][C]-0.182411435983444[/C][/ROW]
[ROW][C]-0.0956765384604532[/C][/ROW]
[ROW][C]0.188865476222618[/C][/ROW]
[ROW][C]0.0597865440045991[/C][/ROW]
[ROW][C]-0.325976926844314[/C][/ROW]
[ROW][C]-0.163553015094133[/C][/ROW]
[ROW][C]0.271762481859521[/C][/ROW]
[ROW][C]0.318055431152346[/C][/ROW]
[ROW][C]-0.183175111260252[/C][/ROW]
[ROW][C]0.0105214566233789[/C][/ROW]
[ROW][C]0.0782724182376279[/C][/ROW]
[ROW][C]-0.123919958214475[/C][/ROW]
[ROW][C]-0.234994208307696[/C][/ROW]
[ROW][C]0.202429596208315[/C][/ROW]
[ROW][C]-0.0694705884085771[/C][/ROW]
[ROW][C]0.217483550057946[/C][/ROW]
[ROW][C]0.0660590744597098[/C][/ROW]
[ROW][C]0.0616082139258813[/C][/ROW]
[ROW][C]-0.198097184343861[/C][/ROW]
[ROW][C]-0.12501774386214[/C][/ROW]
[ROW][C]0.263909076761868[/C][/ROW]
[ROW][C]-0.198312362729949[/C][/ROW]
[ROW][C]-0.0341373321380577[/C][/ROW]
[ROW][C]-0.103661941501452[/C][/ROW]
[ROW][C]0.0896999930704444[/C][/ROW]
[ROW][C]-0.0111319706468356[/C][/ROW]
[ROW][C]0.0692641520520939[/C][/ROW]
[ROW][C]-0.187176523482705[/C][/ROW]
[ROW][C]-0.0731563365511074[/C][/ROW]
[ROW][C]-0.120551127035854[/C][/ROW]
[ROW][C]0.176415511002926[/C][/ROW]
[ROW][C]0.0310953487444023[/C][/ROW]
[ROW][C]-0.0406591583974653[/C][/ROW]
[ROW][C]0.205253249483667[/C][/ROW]
[ROW][C]0.158208066411955[/C][/ROW]
[ROW][C]-0.0633897371978978[/C][/ROW]
[ROW][C]0.0013251338506157[/C][/ROW]
[ROW][C]0.117978362786185[/C][/ROW]
[ROW][C]0.190004787416642[/C][/ROW]
[ROW][C]0.0391954899763769[/C][/ROW]
[ROW][C]0.00651314668085362[/C][/ROW]
[ROW][C]0.0233853328609726[/C][/ROW]
[ROW][C]0.0623208388732328[/C][/ROW]
[ROW][C]-0.0740322742623994[/C][/ROW]
[ROW][C]-0.00356027900855249[/C][/ROW]
[ROW][C]0.0483043790196161[/C][/ROW]
[ROW][C]-0.0061018414643256[/C][/ROW]
[ROW][C]0.0619008267041968[/C][/ROW]
[ROW][C]0.124683498993136[/C][/ROW]
[ROW][C]-0.0131344208805079[/C][/ROW]
[ROW][C]-0.0634095978361346[/C][/ROW]
[ROW][C]0.182179347507325[/C][/ROW]
[ROW][C]0.0655896819817104[/C][/ROW]
[ROW][C]0.155269164876945[/C][/ROW]
[ROW][C]-0.0946889959153517[/C][/ROW]
[ROW][C]-0.170457458511936[/C][/ROW]
[ROW][C]0.0122628020397526[/C][/ROW]
[ROW][C]0.00841229239382946[/C][/ROW]
[ROW][C]-0.107175375450352[/C][/ROW]
[ROW][C]0.247198972387701[/C][/ROW]
[ROW][C]-0.383564692638402[/C][/ROW]
[ROW][C]-0.103580684380659[/C][/ROW]
[ROW][C]0.215129483471241[/C][/ROW]
[ROW][C]0.38370691025852[/C][/ROW]
[ROW][C]0.0880585031563803[/C][/ROW]
[ROW][C]0.4058984233763[/C][/ROW]
[ROW][C]-0.318365122101769[/C][/ROW]
[ROW][C]-0.261580862605992[/C][/ROW]
[ROW][C]0.0440348883419828[/C][/ROW]
[ROW][C]-0.392808257447564[/C][/ROW]
[ROW][C]-0.476995252207654[/C][/ROW]
[ROW][C]-0.177331147525602[/C][/ROW]
[ROW][C]-0.20745833733321[/C][/ROW]
[ROW][C]-0.00959859166597776[/C][/ROW]
[ROW][C]0.000851583877323758[/C][/ROW]
[ROW][C]0.0264383398272595[/C][/ROW]
[ROW][C]-0.223409562906061[/C][/ROW]
[ROW][C]0.166860953335315[/C][/ROW]
[ROW][C]-0.10339545421285[/C][/ROW]
[ROW][C]0.266200156645663[/C][/ROW]
[ROW][C]-0.353893793758457[/C][/ROW]
[ROW][C]0.14189623859101[/C][/ROW]
[ROW][C]-0.149297480082106[/C][/ROW]
[ROW][C]0.0248558849752234[/C][/ROW]
[ROW][C]0.0264174123768981[/C][/ROW]
[ROW][C]0.0684193548148313[/C][/ROW]
[ROW][C]-0.170019514651285[/C][/ROW]
[ROW][C]-0.0244780949167113[/C][/ROW]
[ROW][C]-0.0215733869596959[/C][/ROW]
[ROW][C]0.125209440010287[/C][/ROW]
[ROW][C]0.0612765912844664[/C][/ROW]
[ROW][C]-0.220726605928967[/C][/ROW]
[ROW][C]0.10952516017897[/C][/ROW]
[ROW][C]0.0663497628683641[/C][/ROW]
[ROW][C]0.103662299510155[/C][/ROW]
[ROW][C]-0.00435778944181635[/C][/ROW]
[ROW][C]0.0688583095891516[/C][/ROW]
[ROW][C]0.135617144779686[/C][/ROW]
[ROW][C]0.0597301018655756[/C][/ROW]
[ROW][C]0.0779588843120199[/C][/ROW]
[ROW][C]-0.254084870755631[/C][/ROW]
[ROW][C]-0.0868441933460303[/C][/ROW]
[ROW][C]-0.0584800881004532[/C][/ROW]
[ROW][C]-0.296646003472444[/C][/ROW]
[ROW][C]0.166094857288165[/C][/ROW]
[ROW][C]0.064189784114954[/C][/ROW]
[ROW][C]-0.0712930736433104[/C][/ROW]
[ROW][C]0.0479499464003501[/C][/ROW]
[ROW][C]-0.269007035975429[/C][/ROW]
[ROW][C]-0.123668040103369[/C][/ROW]
[ROW][C]0.00998888895647147[/C][/ROW]
[ROW][C]0.0273641255487598[/C][/ROW]
[ROW][C]-0.0905731097913407[/C][/ROW]
[ROW][C]0.0166964877478681[/C][/ROW]
[ROW][C]-0.26627779114608[/C][/ROW]
[ROW][C]-0.0979513772757262[/C][/ROW]
[ROW][C]0.0856359167387129[/C][/ROW]
[ROW][C]0.040195942558638[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196498&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196498&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
0.00468999706403207
0.169802266154913
-0.119782380592012
-0.0271545468519886
0.398406609909392
-0.206147302008568
0.12426309992944
-0.020583998202967
-0.0220685544157335
-0.0618933396609821
0.00592612713476639
-0.332316173470779
-0.100656587574012
0.130939048295093
-0.118435412023538
0.117866374946737
0.0568504093451992
-0.165354802137876
0.0653980216903398
-0.24992936182677
-0.160407769616404
-0.20746218046511
0.0874343150099515
0.23199357676486
0.108991995051352
0.249142712596343
0.14117010582061
-0.141349302905852
-0.0316758184724719
-0.18949802365028
-0.105227575584839
-0.141815417335265
-0.218157016141096
0.0803409513079806
-0.0122634768985404
-0.113048626805245
-0.182411435983444
-0.0956765384604532
0.188865476222618
0.0597865440045991
-0.325976926844314
-0.163553015094133
0.271762481859521
0.318055431152346
-0.183175111260252
0.0105214566233789
0.0782724182376279
-0.123919958214475
-0.234994208307696
0.202429596208315
-0.0694705884085771
0.217483550057946
0.0660590744597098
0.0616082139258813
-0.198097184343861
-0.12501774386214
0.263909076761868
-0.198312362729949
-0.0341373321380577
-0.103661941501452
0.0896999930704444
-0.0111319706468356
0.0692641520520939
-0.187176523482705
-0.0731563365511074
-0.120551127035854
0.176415511002926
0.0310953487444023
-0.0406591583974653
0.205253249483667
0.158208066411955
-0.0633897371978978
0.0013251338506157
0.117978362786185
0.190004787416642
0.0391954899763769
0.00651314668085362
0.0233853328609726
0.0623208388732328
-0.0740322742623994
-0.00356027900855249
0.0483043790196161
-0.0061018414643256
0.0619008267041968
0.124683498993136
-0.0131344208805079
-0.0634095978361346
0.182179347507325
0.0655896819817104
0.155269164876945
-0.0946889959153517
-0.170457458511936
0.0122628020397526
0.00841229239382946
-0.107175375450352
0.247198972387701
-0.383564692638402
-0.103580684380659
0.215129483471241
0.38370691025852
0.0880585031563803
0.4058984233763
-0.318365122101769
-0.261580862605992
0.0440348883419828
-0.392808257447564
-0.476995252207654
-0.177331147525602
-0.20745833733321
-0.00959859166597776
0.000851583877323758
0.0264383398272595
-0.223409562906061
0.166860953335315
-0.10339545421285
0.266200156645663
-0.353893793758457
0.14189623859101
-0.149297480082106
0.0248558849752234
0.0264174123768981
0.0684193548148313
-0.170019514651285
-0.0244780949167113
-0.0215733869596959
0.125209440010287
0.0612765912844664
-0.220726605928967
0.10952516017897
0.0663497628683641
0.103662299510155
-0.00435778944181635
0.0688583095891516
0.135617144779686
0.0597301018655756
0.0779588843120199
-0.254084870755631
-0.0868441933460303
-0.0584800881004532
-0.296646003472444
0.166094857288165
0.064189784114954
-0.0712930736433104
0.0479499464003501
-0.269007035975429
-0.123668040103369
0.00998888895647147
0.0273641255487598
-0.0905731097913407
0.0166964877478681
-0.26627779114608
-0.0979513772757262
0.0856359167387129
0.040195942558638



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')