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Description of Statistical Computation

Author's title

Cross Correlation Function hoeveelheid uitvoer (x) en hoeveelheid invoer (y...

Author*The author of this computation has been verified*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationFri, 28 Nov 2008 06:30:46 -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/Nov/28/t1227879106aqdn2a9vleyvyqi.htm/, Retrieved Thu, 30 Oct 2025 23:19:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26099, Retrieved Thu, 30 Oct 2025 23:19:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact364

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:40:39] [b98453cac15ba1066b407e146608df68]
F       [Law of Averages] [Random Walk Simul...] [2008-11-27 19:45:04] [58bf45a666dc5198906262e8815a9722]
- RMPD    [Cross Correlation Function] [Cross Correlation...] [2008-11-27 21:51:52] [58bf45a666dc5198906262e8815a9722]
F   P         [Cross Correlation Function] [Cross Correlation...] [2008-11-28 13:30:46] [63db34dadd44fb018112addcdefe949f] [Current]
Feedback Forum
2008-12-04 15:52:00 [Matthieu Blondeau] [reply] 
De studente heeft hier gelijk.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106
82
114
118
105
105
103
107
123
112
104
122
108
94
120
118
117
113
106
108
122
115
110
120
104
96
121
111
120
114
107
108
127
105
119
121
106
97
119
122
121
106
114
112
127
109
118
123
115
105
116
131
121
104
127
126
124
132
117
123
Dataseries Y:
Download CSV
Histogram 
101
88
108
116
104
110
105
107
124
109
102
125
102
101
116
114
115
119
108
110
120
113
111
121
99
104
117
108
122
122
111
111
131
108
118
119
104
105
118
124
123
114
119
116
129
112
123
124
117
110
118
135
127
117
137
130
132
142
122
126

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26099&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26099&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26099&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series2
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series0.7
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.123285134961142
-120.0697589434924293
-11-0.0741635206691525
-10-0.133995521026413
-90.106627311139854
-80.101282379847497
-7-0.174511987104959
-60.185024896269223
-5-0.0465134282459645
-4-0.0542001690830295
-30.298004913303569
-20.0321558830157541
-1-0.228159079121396
00.934447937888823
1-0.0925622130907974
2-0.0603517980705737
30.389067534425028
40.00518847450247714
5-0.08937296106675
60.251484448830115
7-0.072178036554753
80.00254300881165220
90.222355713903678
10-0.0763539860638515
11-0.110069241077512
120.122179226024754
13-0.115238451191427

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 2 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 0.7 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & -0.123285134961142 \tabularnewline
-12 & 0.0697589434924293 \tabularnewline
-11 & -0.0741635206691525 \tabularnewline
-10 & -0.133995521026413 \tabularnewline
-9 & 0.106627311139854 \tabularnewline
-8 & 0.101282379847497 \tabularnewline
-7 & -0.174511987104959 \tabularnewline
-6 & 0.185024896269223 \tabularnewline
-5 & -0.0465134282459645 \tabularnewline
-4 & -0.0542001690830295 \tabularnewline
-3 & 0.298004913303569 \tabularnewline
-2 & 0.0321558830157541 \tabularnewline
-1 & -0.228159079121396 \tabularnewline
0 & 0.934447937888823 \tabularnewline
1 & -0.0925622130907974 \tabularnewline
2 & -0.0603517980705737 \tabularnewline
3 & 0.389067534425028 \tabularnewline
4 & 0.00518847450247714 \tabularnewline
5 & -0.08937296106675 \tabularnewline
6 & 0.251484448830115 \tabularnewline
7 & -0.072178036554753 \tabularnewline
8 & 0.00254300881165220 \tabularnewline
9 & 0.222355713903678 \tabularnewline
10 & -0.0763539860638515 \tabularnewline
11 & -0.110069241077512 \tabularnewline
12 & 0.122179226024754 \tabularnewline
13 & -0.115238451191427 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26099&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]2[/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]1[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]0.7[/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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]-0.123285134961142[/C][/ROW]
[ROW][C]-12[/C][C]0.0697589434924293[/C][/ROW]
[ROW][C]-11[/C][C]-0.0741635206691525[/C][/ROW]
[ROW][C]-10[/C][C]-0.133995521026413[/C][/ROW]
[ROW][C]-9[/C][C]0.106627311139854[/C][/ROW]
[ROW][C]-8[/C][C]0.101282379847497[/C][/ROW]
[ROW][C]-7[/C][C]-0.174511987104959[/C][/ROW]
[ROW][C]-6[/C][C]0.185024896269223[/C][/ROW]
[ROW][C]-5[/C][C]-0.0465134282459645[/C][/ROW]
[ROW][C]-4[/C][C]-0.0542001690830295[/C][/ROW]
[ROW][C]-3[/C][C]0.298004913303569[/C][/ROW]
[ROW][C]-2[/C][C]0.0321558830157541[/C][/ROW]
[ROW][C]-1[/C][C]-0.228159079121396[/C][/ROW]
[ROW][C]0[/C][C]0.934447937888823[/C][/ROW]
[ROW][C]1[/C][C]-0.0925622130907974[/C][/ROW]
[ROW][C]2[/C][C]-0.0603517980705737[/C][/ROW]
[ROW][C]3[/C][C]0.389067534425028[/C][/ROW]
[ROW][C]4[/C][C]0.00518847450247714[/C][/ROW]
[ROW][C]5[/C][C]-0.08937296106675[/C][/ROW]
[ROW][C]6[/C][C]0.251484448830115[/C][/ROW]
[ROW][C]7[/C][C]-0.072178036554753[/C][/ROW]
[ROW][C]8[/C][C]0.00254300881165220[/C][/ROW]
[ROW][C]9[/C][C]0.222355713903678[/C][/ROW]
[ROW][C]10[/C][C]-0.0763539860638515[/C][/ROW]
[ROW][C]11[/C][C]-0.110069241077512[/C][/ROW]
[ROW][C]12[/C][C]0.122179226024754[/C][/ROW]
[ROW][C]13[/C][C]-0.115238451191427[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26099&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26099&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 series2
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series0.7
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.123285134961142
-120.0697589434924293
-11-0.0741635206691525
-10-0.133995521026413
-90.106627311139854
-80.101282379847497
-7-0.174511987104959
-60.185024896269223
-5-0.0465134282459645
-4-0.0542001690830295
-30.298004913303569
-20.0321558830157541
-1-0.228159079121396
00.934447937888823
1-0.0925622130907974
2-0.0603517980705737
30.389067534425028
40.00518847450247714
5-0.08937296106675
60.251484448830115
7-0.072178036554753
80.00254300881165220
90.222355713903678
10-0.0763539860638515
11-0.110069241077512
120.122179226024754
13-0.115238451191427


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2.0 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 0.7 ; par6 = 0 ; par7 = 1 ;
Parameters (R input):
par1 = 2.0 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 0.7 ; par6 = 0 ; par7 = 1 ;
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')