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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 06:42:47 -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/t1228311807oast63l2gvflxwp.htm/, Retrieved Sun, 19 May 2024 04:22:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28707, Retrieved Sun, 19 May 2024 04:22:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsCross correlation
Estimated Impact217

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
- RMPD  [Cross Correlation Function] [non stationary ti...] [2008-12-02 19:52:31] [47f64d63202c1921bd27f3073f07a153]
F   PD    [Cross Correlation Function] [non stationary ti...] [2008-12-02 20:08:18] [47f64d63202c1921bd27f3073f07a153]
F   P       [Cross Correlation Function] [non stat time ser...] [2008-12-03 11:54:56] [c96f3dce3a823a83b6ede18389e1cfd4]
F   P           [Cross Correlation Function] [non stat time ser...] [2008-12-03 13:42:47] [3bdbbe597ac6c61989658933956ee6ac] [Current]
Feedback Forum
2008-12-06 14:19:36 [Thomas Plasschaert] [reply] 
goede heruitrekening
2008-12-08 14:43:46 [Sam De Cuyper] [reply] 
Goede resultaten en interpretatie.
2008-12-10 00:06:14 [Peter Van Doninck] [reply] 
goede conclusie.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.5
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.6
8.2
8.1
8
8.6
8.7
8.8
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8.1
8.2
8.1
8.1
7.9
7.9
7.9
8
8
7.9
8
7.7
7.2
7.5
7.3
7
7
7
7.2
7.3
7.1
6.8
6.6
6.2
6.2
6.8
6.9
6.8
Dataseries Y:
Download CSV
Histogram 
7.6
7.9
7.9
8.1
8.2
8
7.5
6.8
6.5
6.6
7.6
8
8
7.7
7.5
7.6
7.7
7.9
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.1
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.7
6.4
6.3
6.2
6.5
6.8
6.8
6.5
6.3
5.9
5.9
6.4
6.4
6.5

Tables (Output of Computation)





Summary of computational 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 computational 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=28707&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]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=28707&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28707&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series-0.8
Degree of non-seasonal differencing (d) of X series2
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1.8
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-130.171037354661246
-120.0308412855794718
-11-0.00529292158655459
-100.0824929933197233
-9-0.167888506182735
-8-0.091653878859252
-70.0827049033748374
-60.0656296880163144
-50.145065486864864
-40.0140504419279036
-3-0.29398567536961
-2-0.184115791587947
-1-0.192639278412249
00.246391621806179
10.417072751644317
20.180132009234780
3-0.135698732172296
4-0.289125281583213
5-0.129833917724480
60.0329746240872741
70.150107087753461
80.216362908689795
9-0.117058227703332
10-0.162403444530631
11-0.162526300567020
12-0.0128478998952833
130.135264432673810

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & -0.8 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 2 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1.8 \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
-13 & 0.171037354661246 \tabularnewline
-12 & 0.0308412855794718 \tabularnewline
-11 & -0.00529292158655459 \tabularnewline
-10 & 0.0824929933197233 \tabularnewline
-9 & -0.167888506182735 \tabularnewline
-8 & -0.091653878859252 \tabularnewline
-7 & 0.0827049033748374 \tabularnewline
-6 & 0.0656296880163144 \tabularnewline
-5 & 0.145065486864864 \tabularnewline
-4 & 0.0140504419279036 \tabularnewline
-3 & -0.29398567536961 \tabularnewline
-2 & -0.184115791587947 \tabularnewline
-1 & -0.192639278412249 \tabularnewline
0 & 0.246391621806179 \tabularnewline
1 & 0.417072751644317 \tabularnewline
2 & 0.180132009234780 \tabularnewline
3 & -0.135698732172296 \tabularnewline
4 & -0.289125281583213 \tabularnewline
5 & -0.129833917724480 \tabularnewline
6 & 0.0329746240872741 \tabularnewline
7 & 0.150107087753461 \tabularnewline
8 & 0.216362908689795 \tabularnewline
9 & -0.117058227703332 \tabularnewline
10 & -0.162403444530631 \tabularnewline
11 & -0.162526300567020 \tabularnewline
12 & -0.0128478998952833 \tabularnewline
13 & 0.135264432673810 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28707&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]-0.8[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of X series[/C][C]2[/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]1.8[/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]-13[/C][C]0.171037354661246[/C][/ROW]
[ROW][C]-12[/C][C]0.0308412855794718[/C][/ROW]
[ROW][C]-11[/C][C]-0.00529292158655459[/C][/ROW]
[ROW][C]-10[/C][C]0.0824929933197233[/C][/ROW]
[ROW][C]-9[/C][C]-0.167888506182735[/C][/ROW]
[ROW][C]-8[/C][C]-0.091653878859252[/C][/ROW]
[ROW][C]-7[/C][C]0.0827049033748374[/C][/ROW]
[ROW][C]-6[/C][C]0.0656296880163144[/C][/ROW]
[ROW][C]-5[/C][C]0.145065486864864[/C][/ROW]
[ROW][C]-4[/C][C]0.0140504419279036[/C][/ROW]
[ROW][C]-3[/C][C]-0.29398567536961[/C][/ROW]
[ROW][C]-2[/C][C]-0.184115791587947[/C][/ROW]
[ROW][C]-1[/C][C]-0.192639278412249[/C][/ROW]
[ROW][C]0[/C][C]0.246391621806179[/C][/ROW]
[ROW][C]1[/C][C]0.417072751644317[/C][/ROW]
[ROW][C]2[/C][C]0.180132009234780[/C][/ROW]
[ROW][C]3[/C][C]-0.135698732172296[/C][/ROW]
[ROW][C]4[/C][C]-0.289125281583213[/C][/ROW]
[ROW][C]5[/C][C]-0.129833917724480[/C][/ROW]
[ROW][C]6[/C][C]0.0329746240872741[/C][/ROW]
[ROW][C]7[/C][C]0.150107087753461[/C][/ROW]
[ROW][C]8[/C][C]0.216362908689795[/C][/ROW]
[ROW][C]9[/C][C]-0.117058227703332[/C][/ROW]
[ROW][C]10[/C][C]-0.162403444530631[/C][/ROW]
[ROW][C]11[/C][C]-0.162526300567020[/C][/ROW]
[ROW][C]12[/C][C]-0.0128478998952833[/C][/ROW]
[ROW][C]13[/C][C]0.135264432673810[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28707&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28707&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 series-0.8
Degree of non-seasonal differencing (d) of X series2
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1.8
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-130.171037354661246
-120.0308412855794718
-11-0.00529292158655459
-100.0824929933197233
-9-0.167888506182735
-8-0.091653878859252
-70.0827049033748374
-60.0656296880163144
-50.145065486864864
-40.0140504419279036
-3-0.29398567536961
-2-0.184115791587947
-1-0.192639278412249
00.246391621806179
10.417072751644317
20.180132009234780
3-0.135698732172296
4-0.289125281583213
5-0.129833917724480
60.0329746240872741
70.150107087753461
80.216362908689795
9-0.117058227703332
10-0.162403444530631
11-0.162526300567020
12-0.0128478998952833
130.135264432673810


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = -0.8 ; par2 = 2 ; par3 = 1 ; par4 = 12 ; par5 = 1.8 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = -0.8 ; par2 = 2 ; par3 = 1 ; par4 = 12 ; par5 = 1.8 ; 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')