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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationMon, 03 Dec 2007 03:09:55 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2007/Dec/03/t11966758898tdv66s47epod2g.htm/, Retrieved Sat, 04 May 2024 02:53:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=2300, Retrieved Sat, 04 May 2024 02:53:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsex012008
Estimated Impact456

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [omzet vs aantal] [2007-12-03 10:09:55] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
589
606
566
487
442
463
547
432
513
602
637
913
576
634
563
513
483
477
524
470
427
537
662
1079
816
705
653
584
508
446
604
446
512
533
791
1206
783
567
473
412
314
323
438
429
468
518
555
816
673
593
569
505
447
433
549
553
505
601
706
852
643
448
551
476
416
331
435
395
405
619
596
889
668
555
620
472
460
417
582
525
507
750
899
1075
993
777
675
655
535
491
686
637
652
794
859
1049
1022
762
762
563
573
473
527
710
630
706
870
1069
1021
799
694
521
622
614
661
630
Dataseries Y:
Download CSV
Histogram 
122302.01
109264.65
103674.75
103890.3
75512.66
83121.3
125096.81
74206.73
88481.63
111598.17
146919.48
150790.85
113780.5
110870.76
118785.32
112820.5
102188.92
97092.73
114067.82
89690.15
89267.9
96198.64
129599.75
169424.7
152510.91
121850.2
144737.64
121381.88
106894.86
94305.06
116800.42
77584.28
100680.88
106634.05
168390.77
211971.89
136163.28
168950.25
89816.88
85406.93
66055.52
73311.68
85674.51
82822.59
94277.63
100991.65
149245.88
208517.17
40733.51
121352.23
104020.11
99566.82
101352.17
106628.41
109696.95
248696.37
105628.33
120449.17
136547.7
140896.42
131509.91
95450.31
133592.64
110332.9
88110.54
64931.25
98446.22
84212.38
77519.55
124806.02
102185.94
151348.79
124378.28
101433.13
126724.22
87461.88
95288.27
129055.33
107753.06
96364.03
71662.75
125666.24
456841.51
167642.32
167154.73
139685.18
119275.2
122746.05
107337.43
112584.89
133183.08
121152.57
119815.6
122858.44
152077.17
157221.96
140435.08
101455.09
104791.29
77226.59
84477.43
66227.74
89076.23
108924.43
83926.11
91764.8
120892.76
129952.42
135865.14
105512.77
96486.62
78064.88
92370.22
98454.46
96703.93
83170.95

Tables (Output of Computation)





Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2300&T=0

[TABLE]
[ROW][C]Summary of compuational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2300&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2300&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 compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






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)12
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])
-17-0.357144294886409
-16-0.285452843309513
-15-0.212251050243329
-14-0.0938511301813533
-130.117436369378211
-120.29406040700545
-110.307861423385869
-100.0812501693432608
-9-0.0911361296618104
-8-0.125460410395322
-7-0.245315978307244
-6-0.336279348211163
-5-0.272303855408064
-4-0.155466204829861
-3-0.0529511353546788
-20.0774274205429954
-10.297336605356138
00.555891068970592
10.460756623519948
20.241114841568807
30.0123786944804443
4-0.102651574034357
5-0.159824067486857
6-0.299371895933753
7-0.233155323225039
8-0.0866019889690887
9-0.0218884907721187
100.0604457669007405
110.236518099494551
120.40005326744743
130.384564097709222
140.199340515077752
15-0.027902679926241
16-0.0634391905075318
17-0.183264894971528

\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) & 12 \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
-17 & -0.357144294886409 \tabularnewline
-16 & -0.285452843309513 \tabularnewline
-15 & -0.212251050243329 \tabularnewline
-14 & -0.0938511301813533 \tabularnewline
-13 & 0.117436369378211 \tabularnewline
-12 & 0.29406040700545 \tabularnewline
-11 & 0.307861423385869 \tabularnewline
-10 & 0.0812501693432608 \tabularnewline
-9 & -0.0911361296618104 \tabularnewline
-8 & -0.125460410395322 \tabularnewline
-7 & -0.245315978307244 \tabularnewline
-6 & -0.336279348211163 \tabularnewline
-5 & -0.272303855408064 \tabularnewline
-4 & -0.155466204829861 \tabularnewline
-3 & -0.0529511353546788 \tabularnewline
-2 & 0.0774274205429954 \tabularnewline
-1 & 0.297336605356138 \tabularnewline
0 & 0.555891068970592 \tabularnewline
1 & 0.460756623519948 \tabularnewline
2 & 0.241114841568807 \tabularnewline
3 & 0.0123786944804443 \tabularnewline
4 & -0.102651574034357 \tabularnewline
5 & -0.159824067486857 \tabularnewline
6 & -0.299371895933753 \tabularnewline
7 & -0.233155323225039 \tabularnewline
8 & -0.0866019889690887 \tabularnewline
9 & -0.0218884907721187 \tabularnewline
10 & 0.0604457669007405 \tabularnewline
11 & 0.236518099494551 \tabularnewline
12 & 0.40005326744743 \tabularnewline
13 & 0.384564097709222 \tabularnewline
14 & 0.199340515077752 \tabularnewline
15 & -0.027902679926241 \tabularnewline
16 & -0.0634391905075318 \tabularnewline
17 & -0.183264894971528 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2300&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]12[/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]-17[/C][C]-0.357144294886409[/C][/ROW]
[ROW][C]-16[/C][C]-0.285452843309513[/C][/ROW]
[ROW][C]-15[/C][C]-0.212251050243329[/C][/ROW]
[ROW][C]-14[/C][C]-0.0938511301813533[/C][/ROW]
[ROW][C]-13[/C][C]0.117436369378211[/C][/ROW]
[ROW][C]-12[/C][C]0.29406040700545[/C][/ROW]
[ROW][C]-11[/C][C]0.307861423385869[/C][/ROW]
[ROW][C]-10[/C][C]0.0812501693432608[/C][/ROW]
[ROW][C]-9[/C][C]-0.0911361296618104[/C][/ROW]
[ROW][C]-8[/C][C]-0.125460410395322[/C][/ROW]
[ROW][C]-7[/C][C]-0.245315978307244[/C][/ROW]
[ROW][C]-6[/C][C]-0.336279348211163[/C][/ROW]
[ROW][C]-5[/C][C]-0.272303855408064[/C][/ROW]
[ROW][C]-4[/C][C]-0.155466204829861[/C][/ROW]
[ROW][C]-3[/C][C]-0.0529511353546788[/C][/ROW]
[ROW][C]-2[/C][C]0.0774274205429954[/C][/ROW]
[ROW][C]-1[/C][C]0.297336605356138[/C][/ROW]
[ROW][C]0[/C][C]0.555891068970592[/C][/ROW]
[ROW][C]1[/C][C]0.460756623519948[/C][/ROW]
[ROW][C]2[/C][C]0.241114841568807[/C][/ROW]
[ROW][C]3[/C][C]0.0123786944804443[/C][/ROW]
[ROW][C]4[/C][C]-0.102651574034357[/C][/ROW]
[ROW][C]5[/C][C]-0.159824067486857[/C][/ROW]
[ROW][C]6[/C][C]-0.299371895933753[/C][/ROW]
[ROW][C]7[/C][C]-0.233155323225039[/C][/ROW]
[ROW][C]8[/C][C]-0.0866019889690887[/C][/ROW]
[ROW][C]9[/C][C]-0.0218884907721187[/C][/ROW]
[ROW][C]10[/C][C]0.0604457669007405[/C][/ROW]
[ROW][C]11[/C][C]0.236518099494551[/C][/ROW]
[ROW][C]12[/C][C]0.40005326744743[/C][/ROW]
[ROW][C]13[/C][C]0.384564097709222[/C][/ROW]
[ROW][C]14[/C][C]0.199340515077752[/C][/ROW]
[ROW][C]15[/C][C]-0.027902679926241[/C][/ROW]
[ROW][C]16[/C][C]-0.0634391905075318[/C][/ROW]
[ROW][C]17[/C][C]-0.183264894971528[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2300&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2300&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)12
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])
-17-0.357144294886409
-16-0.285452843309513
-15-0.212251050243329
-14-0.0938511301813533
-130.117436369378211
-120.29406040700545
-110.307861423385869
-100.0812501693432608
-9-0.0911361296618104
-8-0.125460410395322
-7-0.245315978307244
-6-0.336279348211163
-5-0.272303855408064
-4-0.155466204829861
-3-0.0529511353546788
-20.0774274205429954
-10.297336605356138
00.555891068970592
10.460756623519948
20.241114841568807
30.0123786944804443
4-0.102651574034357
5-0.159824067486857
6-0.299371895933753
7-0.233155323225039
8-0.0866019889690887
9-0.0218884907721187
100.0604457669007405
110.236518099494551
120.40005326744743
130.384564097709222
140.199340515077752
15-0.027902679926241
16-0.0634391905075318
17-0.183264894971528


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
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 (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) x <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,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')