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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationMon, 05 Jul 2021 04:21:04 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2021/Jul/05/t1625451935qwieer6jvi2rqza.htm/, Retrieved Sat, 04 May 2024 13:14:49 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 13:14:49 +0200
QR Codes:

Original text written by user:since 2014,all wkly
IsPrivate?This computation is private
User-defined keywordsmeal,basis
Estimated Impact0

Tree of Dependent Computations

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
609.95
609.62
608.17
459.88
671.17
873.34
894.90
884.64
771.35
755.46
399.05
806.63
801.54
894.51
770.97
722.85
696.07
811.00
834.23
849.46
911.93
984.20
946.65
969.18
899.54
988.84
880.13
836.42
876.78
868.84
863.87
885.55
792.98
785.66
729.56
672.49
572.95
509.42
530.77
497.14
392.33
393.13
389.25
372.69
356.30
387.31
442.63
445.02
531.54
581.40
686.42
746.32
792.16
686.87
428.67
363.76
435.34
471.05
580.61
665.05
724.65
544.68
533.68
419.25
431.69
380.29
390.91
451.33
518.11
545.90
493.21
576.50
679.14
739.60
710.26
719.43
786.23
832.74
833.73
821.45
764.06
807.42
828.93
804.86
766.85
758.54
711.30
518.63
604.25
538.65
515.42
514.74
560.44
564.83
608.19
625.44
590.04
461.97
619.76
781.35
669.21
794.68
765.52
755.92
703.50
504.19
480.18
422.67
635.18
688.50
804.74
802.68
815.08
724.27
698.89
707.05
735.06
822.93
782.90
736.33
692.23
730.05
795.33
745.38
703.52
695.08
729.65
741.11
778.90
953.28
1029.75
784.60
890.59
919.01
883.12
781.78
670.40
686.87
649.08
565.85
550.17
570.16
585.78
578.11
571.58
531.69
491.41
452.50
438.18
452.63
468.55
494.38
598.70
743.92
851.67
816.09
759.53
763.00
680.63
872.07
993.43
948.72
803.98
835.81
848.19
758.31
632.01
626.40
696.51
789.61
919.14
1011.02
1075.88
1207.03
1279.62
1240.37
1232.59
1269.13
1132.67
1116.30
1201.30
1302.47
1314.46
1242.01
1187.80
1111.50
1092.00
997.42
965.39
949.20
933.82
888.22
881.00
838.01
746.90
729.34
724.00
698.30
721.50
750.70
811.52
835.11
860.21
899.66
938.51
993.14
1007.93
1065.33
1117.15
1033.19
829.43
802.25
809.47
733.88
842.82
919.00
963.85
960.71
941.62
925.86
1094.77
1282.67
1362.51
1305.91
1303.04
1284.83
1248.47
1249.77
1290.07
1406.03
1446.90
1494.10
1494.48
1427.50
1431.89
1345.15
1283.16
1382.74
1309.63
1265.04
1123.16
1012.05
939.90
988.92
1022.33
872.76
812.12
923.37
1011.63
1020.35
1061.21
1013.27
983.52
1015.32
1042.61
1064.51
1269.03
1237.71
963.08
843.92
781.90
690.03
682.50
602.10
696.08
820.91
787.52
707.08
649.34
664.20
668.50
713.42
684.26
636.00
656.50
605.81
619.93
654.88
673.50
763.31
796.35
914.30
994.83
977.79
956.73
908.42
932.47
913.67
915.07
895.93
826.27
876.35
853.76
827.65
816.91
739.92
697.25
577.60
639.59
618.99
528.58
440.03
416.93
421.04
458.60
458.05
445.50
474.29
554.30
606.50
621.91
570.54
502.19
502.70
395.98
377.12
415.10
510.03
525.10
458.10
344.10
344.88
237.60
236.90
208.07
184.70
218.92
310.10
410.00
581.40
724.24
754.61
876.80
1030.65
1093.61
1111.43
1062.86
1082.70
1074.12
1083.63
1140.90
1228.01
1313.60
1347.50
1342.30
1256.90
1265.07
1297.37
1285.00
1185.03
1134.70
1135.81
1085.82
1037.59
1077.60
1192.77
1115.10
1042.95
1099.04
1111.99
1067.73
1026.41
902.80
736.19
562.85
505.10
526.78
745.95
743.57
816.86
954.23
876.50
870.19
875.00
871.97
777.70
674.89
719.40
717.30
853.78
750.04
748.71
811.35
943.73
1043.49
1201.53
1149.40
914.00
Dataseries Y:
Download CSV
Histogram 
88.0
61.3
295.7
279.0
245.3
195.7
133.3
129.0
138.5
115.3
104.7
108.2
95.7
100.2
117.2
87.0
101.0
103.0
68.0
65.0
55.3
53.0
31.3
27.7
62.3
42.7
18.3
33.3
-8.7
7.3
-12.3
31.7
20.0
13.3
418.7
448.3
433.0
408.7
378.7
368.0
372.7
339.3
287.7
260.7
258.7
224.0
229.7
201.0
216.3
170.3
163.0
139.3
96.7
96.7
123.3
123.3
123.3
126.7
103.3
76.7
66.7
73.3
76.7
68.3
50.0
60.0
53.3
36.7
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
46.7
53.3
60.0
113.3
76.7
73.3
153.3
156.7
156.7
163.3
123.3
115.0
83.3
58.3
36.7
20.0
26.7
26.7
36.7
41.7
-5.0
63.3
81.7
93.3
100.0
93.3
150.0
160.0
115.0
100.0
110.0
111.7
90.0
86.7
60.0
75.0
76.7
221.7
250.0
286.7
370.0
400.0
400.0
411.7
353.3
313.3
301.7
296.7
355.0
376.7
363.3
348.3
328.3
331.7
316.7
430.0
286.7
250.0
223.3
210.0
186.7
183.3
168.3
156.7
145.0
135.0
116.7
93.3
81.7
75.0
48.3
71.7
58.3
50.0
15.0
-1.7
-13.3
0.0
-1.7
-10.0
-8.3
-11.7
-16.7
-20.0
-25.8
-1.7
5.0
6.7
15.0
43.3
66.7
86.7
95.0
111.7
111.7
138.3
131.7
160.0
131.7
120.0
118.3
119.2
100.0
95.0
78.3
65.0
55.0
131.7
116.7
86.7
73.3
65.0
70.0
70.0
60.0
88.3
73.3
73.3
60.0
48.3
31.7
40.0
11.7
-78.3
-110.0
-108.3
-103.3
-110.0
-95.0
-108.3
-143.3
-108.3
-131.7
-70.0
-76.7
-125.0
-106.7
-90.0
-81.7
-35.0
50.0
90.0
91.7
123.3
98.3
110.0
110.0
138.3
156.7
183.3
200.0
185.0
145.0
123.3
75.0
61.7
46.7
216.7
186.7
161.7
151.7
155.0
136.7
110.0
110.0
86.7
48.3
-10.0
-48.3
-38.3
-33.3
-40.0
-30.0
-18.3
-90.0
-60.0
-51.7
-46.7
10.0
30.0
30.0
23.3
6.7
-21.7
-30.0
-51.7
-56.7
-40.0
-31.7
-31.7
-30.0
-25.0
-10.0
36.7
55.0
63.3
55.0
61.7
61.7
70.0
70.0
80.0
80.0
90.0
100.0
98.3
76.7
70.0
56.7
33.3
-8.3
-25.0
-26.7
-29.2
-30.0
-30.0
73.3
95.0
101.7
78.3
90.0
96.7
155.0
198.3
253.3
280.0
265.0
191.7
140.0
45.0
-51.7
-101.7
-108.3
-140.0
-150.0
-153.3
-145.0
-101.7
-60.0
-25.0
1.7
26.7
28.3
-27.5
-53.3
-58.3
-60.0
-75.0
-53.3
-50.0
-33.3
-33.3
-20.0
-5.0
1.7
-5.0
-16.7
-23.3
-31.7
-36.7
-26.7
-23.3
-81.7
-85.0
6.7
241.7
236.7
228.3
200.0
215.0
221.7
201.7
135.0
91.7
-13.3
-70.0
-61.7
-61.7
-36.7
-158.3
-153.3
-143.3
-98.3
-40.0
-20.0
-53.3
-90.0
-45.0
-36.7
-48.3

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-220.124853956245585
-210.123657644130298
-200.113195434922484
-190.100507621104026
-180.0904004230850213
-170.0774369288864095
-160.0681898934654856
-150.0545324226210655
-140.0399865872705492
-130.026763126304143
-120.011383242328604
-11-0.00704920029051092
-10-0.0322018923296364
-9-0.0542841521252702
-8-0.0888656564367709
-7-0.12909482160932
-6-0.173424633768228
-5-0.230890873801542
-4-0.295095998076536
-3-0.355671465790944
-2-0.41849443948973
-1-0.468055035736377
0-0.498205086959898
1-0.50444653842674
2-0.493421718277897
3-0.475251583953769
4-0.457670478886773
5-0.443677689495996
6-0.423722668029297
7-0.394481859792488
8-0.364163381398348
9-0.323912465870736
10-0.291093170054574
11-0.259969304394652
12-0.232596394655788
13-0.200246613383931
14-0.161050752858126
15-0.121094803346649
16-0.0862956460893512
17-0.0616608925123904
18-0.0422245560798096
19-0.0319012644646597
20-0.0226723669712038
21-0.00487544378908174
220.00863102249675203

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 1 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-22 & 0.124853956245585 \tabularnewline
-21 & 0.123657644130298 \tabularnewline
-20 & 0.113195434922484 \tabularnewline
-19 & 0.100507621104026 \tabularnewline
-18 & 0.0904004230850213 \tabularnewline
-17 & 0.0774369288864095 \tabularnewline
-16 & 0.0681898934654856 \tabularnewline
-15 & 0.0545324226210655 \tabularnewline
-14 & 0.0399865872705492 \tabularnewline
-13 & 0.026763126304143 \tabularnewline
-12 & 0.011383242328604 \tabularnewline
-11 & -0.00704920029051092 \tabularnewline
-10 & -0.0322018923296364 \tabularnewline
-9 & -0.0542841521252702 \tabularnewline
-8 & -0.0888656564367709 \tabularnewline
-7 & -0.12909482160932 \tabularnewline
-6 & -0.173424633768228 \tabularnewline
-5 & -0.230890873801542 \tabularnewline
-4 & -0.295095998076536 \tabularnewline
-3 & -0.355671465790944 \tabularnewline
-2 & -0.41849443948973 \tabularnewline
-1 & -0.468055035736377 \tabularnewline
0 & -0.498205086959898 \tabularnewline
1 & -0.50444653842674 \tabularnewline
2 & -0.493421718277897 \tabularnewline
3 & -0.475251583953769 \tabularnewline
4 & -0.457670478886773 \tabularnewline
5 & -0.443677689495996 \tabularnewline
6 & -0.423722668029297 \tabularnewline
7 & -0.394481859792488 \tabularnewline
8 & -0.364163381398348 \tabularnewline
9 & -0.323912465870736 \tabularnewline
10 & -0.291093170054574 \tabularnewline
11 & -0.259969304394652 \tabularnewline
12 & -0.232596394655788 \tabularnewline
13 & -0.200246613383931 \tabularnewline
14 & -0.161050752858126 \tabularnewline
15 & -0.121094803346649 \tabularnewline
16 & -0.0862956460893512 \tabularnewline
17 & -0.0616608925123904 \tabularnewline
18 & -0.0422245560798096 \tabularnewline
19 & -0.0319012644646597 \tabularnewline
20 & -0.0226723669712038 \tabularnewline
21 & -0.00487544378908174 \tabularnewline
22 & 0.00863102249675203 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Cross Correlation Function[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of X series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of X series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]1[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-22[/C][C]0.124853956245585[/C][/ROW]
[ROW][C]-21[/C][C]0.123657644130298[/C][/ROW]
[ROW][C]-20[/C][C]0.113195434922484[/C][/ROW]
[ROW][C]-19[/C][C]0.100507621104026[/C][/ROW]
[ROW][C]-18[/C][C]0.0904004230850213[/C][/ROW]
[ROW][C]-17[/C][C]0.0774369288864095[/C][/ROW]
[ROW][C]-16[/C][C]0.0681898934654856[/C][/ROW]
[ROW][C]-15[/C][C]0.0545324226210655[/C][/ROW]
[ROW][C]-14[/C][C]0.0399865872705492[/C][/ROW]
[ROW][C]-13[/C][C]0.026763126304143[/C][/ROW]
[ROW][C]-12[/C][C]0.011383242328604[/C][/ROW]
[ROW][C]-11[/C][C]-0.00704920029051092[/C][/ROW]
[ROW][C]-10[/C][C]-0.0322018923296364[/C][/ROW]
[ROW][C]-9[/C][C]-0.0542841521252702[/C][/ROW]
[ROW][C]-8[/C][C]-0.0888656564367709[/C][/ROW]
[ROW][C]-7[/C][C]-0.12909482160932[/C][/ROW]
[ROW][C]-6[/C][C]-0.173424633768228[/C][/ROW]
[ROW][C]-5[/C][C]-0.230890873801542[/C][/ROW]
[ROW][C]-4[/C][C]-0.295095998076536[/C][/ROW]
[ROW][C]-3[/C][C]-0.355671465790944[/C][/ROW]
[ROW][C]-2[/C][C]-0.41849443948973[/C][/ROW]
[ROW][C]-1[/C][C]-0.468055035736377[/C][/ROW]
[ROW][C]0[/C][C]-0.498205086959898[/C][/ROW]
[ROW][C]1[/C][C]-0.50444653842674[/C][/ROW]
[ROW][C]2[/C][C]-0.493421718277897[/C][/ROW]
[ROW][C]3[/C][C]-0.475251583953769[/C][/ROW]
[ROW][C]4[/C][C]-0.457670478886773[/C][/ROW]
[ROW][C]5[/C][C]-0.443677689495996[/C][/ROW]
[ROW][C]6[/C][C]-0.423722668029297[/C][/ROW]
[ROW][C]7[/C][C]-0.394481859792488[/C][/ROW]
[ROW][C]8[/C][C]-0.364163381398348[/C][/ROW]
[ROW][C]9[/C][C]-0.323912465870736[/C][/ROW]
[ROW][C]10[/C][C]-0.291093170054574[/C][/ROW]
[ROW][C]11[/C][C]-0.259969304394652[/C][/ROW]
[ROW][C]12[/C][C]-0.232596394655788[/C][/ROW]
[ROW][C]13[/C][C]-0.200246613383931[/C][/ROW]
[ROW][C]14[/C][C]-0.161050752858126[/C][/ROW]
[ROW][C]15[/C][C]-0.121094803346649[/C][/ROW]
[ROW][C]16[/C][C]-0.0862956460893512[/C][/ROW]
[ROW][C]17[/C][C]-0.0616608925123904[/C][/ROW]
[ROW][C]18[/C][C]-0.0422245560798096[/C][/ROW]
[ROW][C]19[/C][C]-0.0319012644646597[/C][/ROW]
[ROW][C]20[/C][C]-0.0226723669712038[/C][/ROW]
[ROW][C]21[/C][C]-0.00487544378908174[/C][/ROW]
[ROW][C]22[/C][C]0.00863102249675203[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

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

Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-220.124853956245585
-210.123657644130298
-200.113195434922484
-190.100507621104026
-180.0904004230850213
-170.0774369288864095
-160.0681898934654856
-150.0545324226210655
-140.0399865872705492
-130.026763126304143
-120.011383242328604
-11-0.00704920029051092
-10-0.0322018923296364
-9-0.0542841521252702
-8-0.0888656564367709
-7-0.12909482160932
-6-0.173424633768228
-5-0.230890873801542
-4-0.295095998076536
-3-0.355671465790944
-2-0.41849443948973
-1-0.468055035736377
0-0.498205086959898
1-0.50444653842674
2-0.493421718277897
3-0.475251583953769
4-0.457670478886773
5-0.443677689495996
6-0.423722668029297
7-0.394481859792488
8-0.364163381398348
9-0.323912465870736
10-0.291093170054574
11-0.259969304394652
12-0.232596394655788
13-0.200246613383931
14-0.161050752858126
15-0.121094803346649
16-0.0862956460893512
17-0.0616608925123904
18-0.0422245560798096
19-0.0319012644646597
20-0.0226723669712038
21-0.00487544378908174
220.00863102249675203


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = na.fail ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
par6 <- as.numeric(par6)
par7 <- as.numeric(par7)
if (par8=='na.fail') par8 <- na.fail else par8 <- na.pass
ccf <- function (x, y, lag.max = NULL, type = c('correlation', 'covariance'),  plot = TRUE, na.action = na.fail, ...) {
type <- match.arg(type)
if (is.matrix(x) || is.matrix(y))
stop('univariate time series only')
X <- na.action(ts.intersect(as.ts(x), as.ts(y)))
colnames(X) <- c(deparse(substitute(x))[1L], deparse(substitute(y))[1L])
acf.out <- acf(X, lag.max = lag.max, plot = FALSE, type = type, na.action=na.action)
lag <- c(rev(acf.out$lag[-1, 2, 1]), acf.out$lag[, 1, 2])
y <- c(rev(acf.out$acf[-1, 2, 1]), acf.out$acf[, 1, 2])
acf.out$acf <- array(y, dim = c(length(y), 1L, 1L))
acf.out$lag <- array(lag, dim = c(length(y), 1L, 1L))
acf.out$snames <- paste(acf.out$snames, collapse = ' & ')
if (plot) {
plot(acf.out, ...)
return(invisible(acf.out))
}
else return(acf.out)
}
if (par1 == 0) {
x <- log(x)
} else {
x <- (x ^ par1 - 1) / par1
}
if (par5 == 0) {
y <- log(y)
} else {
y <- (y ^ par5 - 1) / par5
}
if (par2 > 0) x <- diff(x,lag=1,difference=par2)
if (par6 > 0) y <- diff(y,lag=1,difference=par6)
if (par3 > 0) x <- diff(x,lag=par4,difference=par3)
if (par7 > 0) y <- diff(y,lag=par4,difference=par7)
print(x)
print(y)
bitmap(file='test1.png')
(r <- ccf(x,y,na.action=par8,main='Cross Correlation Function',ylab='CCF',xlab='Lag (k)'))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Cross Correlation Function',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of X series',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of X series',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of X series',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal Period (s)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of Y series',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of Y series',header=TRUE)
a<-table.element(a,par6)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of Y series',header=TRUE)
a<-table.element(a,par7)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'k',header=TRUE)
a<-table.element(a,'rho(Y[t],X[t+k])',header=TRUE)
a<-table.row.end(a)
mylength <- length(r$acf)
myhalf <- floor((mylength-1)/2)
for (i in 1:mylength) {
a<-table.row.start(a)
a<-table.element(a,i-myhalf-1,header=TRUE)
a<-table.element(a,r$acf[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')