FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 01 Dec 2008 12:59:35 -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/01/t1228161842nwgm8jy8344cmq1.htm/, Retrieved Sun, 05 May 2024 12:25:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27300, Retrieved Sun, 05 May 2024 12:25:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [Q7 Non Stationary...] [2008-12-01 19:59:35] [70ba55c7ff8e068610dc28fc16e6d1e2] [Current]
Feedback Forum
2008-12-03 10:22:02 [Romina Machiels] [reply] 
Er werd een berekening gemaakt, maar er is geen uitleg gegeven.
2008-12-06 16:11:10 [Kevin Engels] [reply] 
Bij de cross correlatie functie gaan we zoeken naar een verband tussen de getallen van reeks x en die van reeks y. We zien hier een positieve correlatie.
2008-12-08 20:19:57 [Ruben Jacobs] [reply] 
De cross correlation function kan niet vergeleken worden met de autocorrelation function. Autocorrelatie meet in welke mate een variabele kan voorspeld worden door het verleden van diezelfde variabele.
Hier zie je hoeveel het verband is tussen tijdreeks x op moment t en tijdreeks y op moment t – of + k: in welke mate een variabele voorspeld kan worden door het verleden van een andere variabele.
Dit is berekend voor verschillende k-waarden (periodes dus).
Je ziet dat r meer verband (correlatie) heeft bij een lage k-waarde, dichtbij de normale t dus, en minder verband op momenten ver van t.

k=0 => dit is gewoon de correlatie tussen Yt en Xt. Dit resultaat is wat je dus ook zou krijgen als je gewoon de correlatie zou berekenen.
k=-1 => de correlatie tussen Yt en Xt-1 (verleden)
K=+1 => de correlatie tussen yt en Xt+1 (toekomst)

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7,8
7,6
7,5
7,6
7,5
7,3
7,6
7,5
7,6
7,9
7,9
8,1
8,2
8,0
7,5
6,8
6,5
6,6
7,6
8,0
8,0
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
7,0
7,1
7,2
7,1
6,9
7,0
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
Dataseries Y:
Download CSV
Histogram 
9,0
9,1
8,7
8,2
7,9
7,9
9,1
9,4
9,5
9,1
9,0
9,3
9,9
9,8
9,4
8,3
8,0
8,5
10,4
11,1
10,9
9,9
9,2
9,2
9,5
9,6
9,5
9,1
8,9
9,0
10,1
10,3
10,2
9,6
9,2
9,3
9,4
9,4
9,2
9,0
9,0
9,0
9,8
10,0
9,9
9,3
9,0
9,0
9,1
9,1
9,1
9,2
8,8
8,3
8,4
8,1
7,8
7,9
7,9
8,0
7,9
7,5
7,2
6,9
6,6
6,7
7,3
7,5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 0 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27300&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27300&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27300&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 time0 seconds
R Server'George Udny Yule' @ 72.249.76.132






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])
-150.136624798178720
-140.235232163625655
-130.346348804555672
-120.453249550309423
-110.487391501761162
-100.491999742106762
-90.51075199279971
-80.548627274673732
-70.574812518902828
-60.551323519968099
-50.465617335078044
-40.378729053581335
-30.375780853593256
-20.469274528972795
-10.596809593974356
00.706684285304976
10.654773476632952
20.539650426001266
30.422844009948712
40.360881639308075
50.350187097763021
60.32915669253383
70.259349839031943
80.164135390111071
90.0892073649176952
100.0712481663785791
110.0922083763699584
120.113621224618949
130.0650547019163851
140.00309981289984139
15-0.0470154059899226

\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
-15 & 0.136624798178720 \tabularnewline
-14 & 0.235232163625655 \tabularnewline
-13 & 0.346348804555672 \tabularnewline
-12 & 0.453249550309423 \tabularnewline
-11 & 0.487391501761162 \tabularnewline
-10 & 0.491999742106762 \tabularnewline
-9 & 0.51075199279971 \tabularnewline
-8 & 0.548627274673732 \tabularnewline
-7 & 0.574812518902828 \tabularnewline
-6 & 0.551323519968099 \tabularnewline
-5 & 0.465617335078044 \tabularnewline
-4 & 0.378729053581335 \tabularnewline
-3 & 0.375780853593256 \tabularnewline
-2 & 0.469274528972795 \tabularnewline
-1 & 0.596809593974356 \tabularnewline
0 & 0.706684285304976 \tabularnewline
1 & 0.654773476632952 \tabularnewline
2 & 0.539650426001266 \tabularnewline
3 & 0.422844009948712 \tabularnewline
4 & 0.360881639308075 \tabularnewline
5 & 0.350187097763021 \tabularnewline
6 & 0.32915669253383 \tabularnewline
7 & 0.259349839031943 \tabularnewline
8 & 0.164135390111071 \tabularnewline
9 & 0.0892073649176952 \tabularnewline
10 & 0.0712481663785791 \tabularnewline
11 & 0.0922083763699584 \tabularnewline
12 & 0.113621224618949 \tabularnewline
13 & 0.0650547019163851 \tabularnewline
14 & 0.00309981289984139 \tabularnewline
15 & -0.0470154059899226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27300&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]-15[/C][C]0.136624798178720[/C][/ROW]
[ROW][C]-14[/C][C]0.235232163625655[/C][/ROW]
[ROW][C]-13[/C][C]0.346348804555672[/C][/ROW]
[ROW][C]-12[/C][C]0.453249550309423[/C][/ROW]
[ROW][C]-11[/C][C]0.487391501761162[/C][/ROW]
[ROW][C]-10[/C][C]0.491999742106762[/C][/ROW]
[ROW][C]-9[/C][C]0.51075199279971[/C][/ROW]
[ROW][C]-8[/C][C]0.548627274673732[/C][/ROW]
[ROW][C]-7[/C][C]0.574812518902828[/C][/ROW]
[ROW][C]-6[/C][C]0.551323519968099[/C][/ROW]
[ROW][C]-5[/C][C]0.465617335078044[/C][/ROW]
[ROW][C]-4[/C][C]0.378729053581335[/C][/ROW]
[ROW][C]-3[/C][C]0.375780853593256[/C][/ROW]
[ROW][C]-2[/C][C]0.469274528972795[/C][/ROW]
[ROW][C]-1[/C][C]0.596809593974356[/C][/ROW]
[ROW][C]0[/C][C]0.706684285304976[/C][/ROW]
[ROW][C]1[/C][C]0.654773476632952[/C][/ROW]
[ROW][C]2[/C][C]0.539650426001266[/C][/ROW]
[ROW][C]3[/C][C]0.422844009948712[/C][/ROW]
[ROW][C]4[/C][C]0.360881639308075[/C][/ROW]
[ROW][C]5[/C][C]0.350187097763021[/C][/ROW]
[ROW][C]6[/C][C]0.32915669253383[/C][/ROW]
[ROW][C]7[/C][C]0.259349839031943[/C][/ROW]
[ROW][C]8[/C][C]0.164135390111071[/C][/ROW]
[ROW][C]9[/C][C]0.0892073649176952[/C][/ROW]
[ROW][C]10[/C][C]0.0712481663785791[/C][/ROW]
[ROW][C]11[/C][C]0.0922083763699584[/C][/ROW]
[ROW][C]12[/C][C]0.113621224618949[/C][/ROW]
[ROW][C]13[/C][C]0.0650547019163851[/C][/ROW]
[ROW][C]14[/C][C]0.00309981289984139[/C][/ROW]
[ROW][C]15[/C][C]-0.0470154059899226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27300&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27300&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])
-150.136624798178720
-140.235232163625655
-130.346348804555672
-120.453249550309423
-110.487391501761162
-100.491999742106762
-90.51075199279971
-80.548627274673732
-70.574812518902828
-60.551323519968099
-50.465617335078044
-40.378729053581335
-30.375780853593256
-20.469274528972795
-10.596809593974356
00.706684285304976
10.654773476632952
20.539650426001266
30.422844009948712
40.360881639308075
50.350187097763021
60.32915669253383
70.259349839031943
80.164135390111071
90.0892073649176952
100.0712481663785791
110.0922083763699584
120.113621224618949
130.0650547019163851
140.00309981289984139
15-0.0470154059899226


Figures (Output of Computation)

Input Parameters & R Code

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