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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 computationWed, 03 Dec 2008 04:41:20 -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/2008/Dec/03/t1228304533p1l23p4svzpklat.htm/, Retrieved Tue, 21 May 2024 03:16:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28634, Retrieved Tue, 21 May 2024 03:16:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact197

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] [Q7 zonder aanpass...] [2008-12-03 11:21:09] [f77c9ab3b413812d7baee6b7ec69a15d]
F   PD  [Cross Correlation Function] [Q7 met seizoenali...] [2008-12-03 11:23:43] [f77c9ab3b413812d7baee6b7ec69a15d]
F   P       [Cross Correlation Function] [Q9 voor d=1 en D=0] [2008-12-03 11:41:20] [3fc0b50a130253095e963177b0139835] [Current]
Feedback Forum
2008-12-04 17:22:47 [Loïque Verhasselt] [reply] 
Q9:Door de foute interpretaties en berekeningen in Q8 krijgen we een foute cross correlatiefunctie. Omdat ik niet over de tijdreeksen beschik kan ik deze ook niet oplossen. Zie assessment Q8
2008-12-08 13:29:18 [Anouk Greeve] [reply] 
Aangezien Q8 niet helemaal correct was, kunnen we in Q9 niet verder.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.02
100.67
100.47
100.38
100.33
100.34
100.37
100.39
100.21
100.21
100.22
100.28
100.25
100.25
100.21
100.16
100.18
100.1
99.96
99.88
99.88
99.86
99.84
99.8
99.82
99.81
99.92
100.03
99.99
100.02
100.01
100.13
100.33
100.13
99.96
100.05
99.83
99.8
100.01
100.1
100.13
100.16
100.41
101.34
101.65
101.85
102.07
102.12
102.14
102.21
102.28
102.19
102.33
102.54
102.44
102.78
102.9
103.08
102.77
102.65
102.71
103.29
102.86
103.45
103.72
103.65
103.83
104.45
105.14
105.07
105.31
105.19
105.3
105.02
105.17
105.28
105.45
105.38
105.8
105.96
105.08
105.11
105.61
105.5
Dataseries Y:
Download CSV
Histogram 
103.68
103.64
103.37
104.3
104.15
104.09
104.21
104.27
104
103.36
104.2
104.12
103.79
104.65
103.84
103.98
103.83
104.34
103.76
103.57
103.06
103.06
102.6
103.41
103.15
103.33
103.96
104.91
104.23
103.68
104.16
104.49
104.23
104.21
103.74
103.96
104.02
104.15
103.74
103.23
103.69
103.46
102.14
102.39
102.19
102.02
102.64
103.52
103.32
103.65
104.25
101.74
102.08
101.35
102.79
102.21
101.78
101.25
101.8
103
104.17
104.08
105.24
104.72
104.77
104.39
104.14
105.15
105.07
104.54
106.03
107.24
108.2
109.15
110.1
109.48
109.96
110.13
110.53
110.82
110.06
110.05
109.49
109.95

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28634&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28634&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28634&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.115150195259463
-150.166834388799464
-14-0.0752587067235661
-13-0.0732490681268925
-12-0.126389173584046
-110.121254450818889
-100.0487450908700858
-90.0551780898679537
-8-0.161801434040997
-70.142378624901963
-60.164688849542407
-50.0491509456246743
-40.204738103804101
-30.152951158420067
-2-0.0136401170306954
-10.00213132318054614
0-0.0128010977744782
10.0127095934403298
2-0.110001392854602
30.052249950109272
4-0.118819854181777
50.107055314852546
6-0.00695475043082488
70.0578485420547898
80.0959395080599164
90.0486763746816926
10-0.231692755923864
110.0280046911618218
12-0.0558111044571641
13-0.177828480096759
140.0997561595292586
15-0.0779580632627149
160.0711735102965002

\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 & 1 \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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-16 & 0.115150195259463 \tabularnewline
-15 & 0.166834388799464 \tabularnewline
-14 & -0.0752587067235661 \tabularnewline
-13 & -0.0732490681268925 \tabularnewline
-12 & -0.126389173584046 \tabularnewline
-11 & 0.121254450818889 \tabularnewline
-10 & 0.0487450908700858 \tabularnewline
-9 & 0.0551780898679537 \tabularnewline
-8 & -0.161801434040997 \tabularnewline
-7 & 0.142378624901963 \tabularnewline
-6 & 0.164688849542407 \tabularnewline
-5 & 0.0491509456246743 \tabularnewline
-4 & 0.204738103804101 \tabularnewline
-3 & 0.152951158420067 \tabularnewline
-2 & -0.0136401170306954 \tabularnewline
-1 & 0.00213132318054614 \tabularnewline
0 & -0.0128010977744782 \tabularnewline
1 & 0.0127095934403298 \tabularnewline
2 & -0.110001392854602 \tabularnewline
3 & 0.052249950109272 \tabularnewline
4 & -0.118819854181777 \tabularnewline
5 & 0.107055314852546 \tabularnewline
6 & -0.00695475043082488 \tabularnewline
7 & 0.0578485420547898 \tabularnewline
8 & 0.0959395080599164 \tabularnewline
9 & 0.0486763746816926 \tabularnewline
10 & -0.231692755923864 \tabularnewline
11 & 0.0280046911618218 \tabularnewline
12 & -0.0558111044571641 \tabularnewline
13 & -0.177828480096759 \tabularnewline
14 & 0.0997561595292586 \tabularnewline
15 & -0.0779580632627149 \tabularnewline
16 & 0.0711735102965002 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28634&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]1[/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]1[/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]-16[/C][C]0.115150195259463[/C][/ROW]
[ROW][C]-15[/C][C]0.166834388799464[/C][/ROW]
[ROW][C]-14[/C][C]-0.0752587067235661[/C][/ROW]
[ROW][C]-13[/C][C]-0.0732490681268925[/C][/ROW]
[ROW][C]-12[/C][C]-0.126389173584046[/C][/ROW]
[ROW][C]-11[/C][C]0.121254450818889[/C][/ROW]
[ROW][C]-10[/C][C]0.0487450908700858[/C][/ROW]
[ROW][C]-9[/C][C]0.0551780898679537[/C][/ROW]
[ROW][C]-8[/C][C]-0.161801434040997[/C][/ROW]
[ROW][C]-7[/C][C]0.142378624901963[/C][/ROW]
[ROW][C]-6[/C][C]0.164688849542407[/C][/ROW]
[ROW][C]-5[/C][C]0.0491509456246743[/C][/ROW]
[ROW][C]-4[/C][C]0.204738103804101[/C][/ROW]
[ROW][C]-3[/C][C]0.152951158420067[/C][/ROW]
[ROW][C]-2[/C][C]-0.0136401170306954[/C][/ROW]
[ROW][C]-1[/C][C]0.00213132318054614[/C][/ROW]
[ROW][C]0[/C][C]-0.0128010977744782[/C][/ROW]
[ROW][C]1[/C][C]0.0127095934403298[/C][/ROW]
[ROW][C]2[/C][C]-0.110001392854602[/C][/ROW]
[ROW][C]3[/C][C]0.052249950109272[/C][/ROW]
[ROW][C]4[/C][C]-0.118819854181777[/C][/ROW]
[ROW][C]5[/C][C]0.107055314852546[/C][/ROW]
[ROW][C]6[/C][C]-0.00695475043082488[/C][/ROW]
[ROW][C]7[/C][C]0.0578485420547898[/C][/ROW]
[ROW][C]8[/C][C]0.0959395080599164[/C][/ROW]
[ROW][C]9[/C][C]0.0486763746816926[/C][/ROW]
[ROW][C]10[/C][C]-0.231692755923864[/C][/ROW]
[ROW][C]11[/C][C]0.0280046911618218[/C][/ROW]
[ROW][C]12[/C][C]-0.0558111044571641[/C][/ROW]
[ROW][C]13[/C][C]-0.177828480096759[/C][/ROW]
[ROW][C]14[/C][C]0.0997561595292586[/C][/ROW]
[ROW][C]15[/C][C]-0.0779580632627149[/C][/ROW]
[ROW][C]16[/C][C]0.0711735102965002[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28634&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28634&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 series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.115150195259463
-150.166834388799464
-14-0.0752587067235661
-13-0.0732490681268925
-12-0.126389173584046
-110.121254450818889
-100.0487450908700858
-90.0551780898679537
-8-0.161801434040997
-70.142378624901963
-60.164688849542407
-50.0491509456246743
-40.204738103804101
-30.152951158420067
-2-0.0136401170306954
-10.00213132318054614
0-0.0128010977744782
10.0127095934403298
2-0.110001392854602
30.052249950109272
4-0.118819854181777
50.107055314852546
6-0.00695475043082488
70.0578485420547898
80.0959395080599164
90.0486763746816926
10-0.231692755923864
110.0280046911618218
12-0.0558111044571641
13-0.177828480096759
140.0997561595292586
15-0.0779580632627149
160.0711735102965002


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; 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) y <- 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')