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R Software Module

rwasp_cross.wasp

Title produced by software

Cross Correlation Function

Date of computation

Wed, 03 Dec 2008 05:05:52 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/03/t12283062288orcx9nxzd9oerg.htm/, Retrieved Tue, 21 May 2024 04:31:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28639, Retrieved Tue, 21 May 2024 04:31:43 +0000

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Estimated Impact

183

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F       [Cross Correlation Function] [Cross Correlation...] [2008-12-03 12:05:52] [44fbdf1868a3b8f737edae4578b93508] [Current]

Feedback Forum

2008-12-06 15:38:06 [Sofie Sergoynne] [reply] 
Student geeft vrij weinig informatie. Dit kon veel uitgebreider. 
Met de crosscorrelation gaan we proberen een voorspelling te maken van y(t) aan de hand van het verleden van  
x(t). Er zijn vele verticale staafjes die significant zijn. We kunnen dus een goede voorspelling maken van y(t) aan de hand van het verleden van x(t).
2008-12-08 15:30:12 [Kevin Vermeiren] [reply] 
De student heeft de vraag verkeerd opgelost. Hier diende nog geen differentiatie te gebeuren. Ook werd er niets vermeld over de Cross Correlation Function zelf. Hier mocht vermeld worden dat de Cross Correlation Function de correlatie tussen verschillende reeksen gaat berekenen. Bij deze function kunnen we de volgende vraag stellen kan het verleden van Xt ons helpen bij het voorspellen van Yt of met andere woorden op basis waarvan kan kik Yt voorspellen. Uiteraard hangt dit van Xt af, dit kan gebasseerd zijn op het verleden of de toekomst. Er zijn mensen die bij deze functie spreken over een trend terwijl dit helemaal niet kan. De verticale lijnen stellen immers de correlatie coëfficiënten voor. De k-waarde stelt de verschuiving in de tijd voor. K= -9 wil zeggen correlatie tussen Yt en Xt-9, k=-8 is correlatie tussen Yt en Xt-8. Negatieve waarden duiden aan in welke mate kan ik Yt verklaren op basis van het verleden van Xt. Positieve k-waarden hebben betrekking op de toekomstige evolutie. Vallen de verticale lijnen buiten het betrouwbaarheidsinterval (blauwe stippellijnen) kunnen we van een significant verschil spreken. In de grafiek zien we dat lag -13 significant verschillend is. Als Xt vandaag verandert gaat er met een vertraging van 13 maanden een effect merkbaar zijn op Yt.

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Dataseries X:

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Dataseries Y:

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Histogram

4,70
4,75
4,75
4,75
4,75
4,75
4,75
4,58
4,50
4,50
4,49
4,03
3,75
3,39
3,25
3,25
3,25
3,25
3,25
3,25
3,25
3,25
3,25
3,25
3,25
3,25
2,85
2,75
2,75
2,55
2,50
2,50
2,10
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,00
2,21
2,25
2,25
2,45
2,50
2,50
2,64
2,75
2,93
3,00
3,17
3,25
3,39
3,50
3,50
3,65
3,75
3,75
3,90
4,00
4,00
4,00
4,00
4,00
4,00
4,00
4,00
4,00
4,00
4,00
4,00
4,18
4,25
4,25
3,97

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 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=28639&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=28639&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28639&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Cross Correlation Function
Parameter	Value
Box-Cox transformation parameter (lambda) of X series	1
Degree of non-seasonal differencing (d) of X series	1
Degree of seasonal differencing (D) of X series	1
Seasonal Period (s)	12
Box-Cox transformation parameter (lambda) of Y series	1
Degree of non-seasonal differencing (d) of Y series	1
Degree of seasonal differencing (D) of Y series	1
k	rho(Y[t],X[t+k])
-16	0.0383896130157162
-15	-0.0431592598655148
-14	0.117329362335501
-13	0.216693499873761
-12	-0.0786348913274296
-11	-0.0212109474417028
-10	0.0242333757798274
-9	0.0895110248135392
-8	-0.0275696231430755
-7	-0.0404718644260075
-6	0.0253485671639796
-5	-0.0266903446624256
-4	0.00357910963910154
-3	0.00498379724514848
-2	-0.205685876947638
-1	-0.211887826014668
0	-0.0423984864712752
1	-0.0200897210762981
2	0.0503321094012996
3	-0.141713429768418
4	-0.112855429567069
5	0.0566306181683683
6	-0.107880227598250
7	-0.132371095911512
8	-0.0790302740385703
9	0.0995025763506258
10	0.0276603307400809
11	-0.121859987352061
12	-0.227801522942538
13	-0.0278454034984875
14	-0.103758518281093
15	-0.0268668221445029
16	0.0215095875745859

\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 & 1 \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 & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-16 & 0.0383896130157162 \tabularnewline
-15 & -0.0431592598655148 \tabularnewline
-14 & 0.117329362335501 \tabularnewline
-13 & 0.216693499873761 \tabularnewline
-12 & -0.0786348913274296 \tabularnewline
-11 & -0.0212109474417028 \tabularnewline
-10 & 0.0242333757798274 \tabularnewline
-9 & 0.0895110248135392 \tabularnewline
-8 & -0.0275696231430755 \tabularnewline
-7 & -0.0404718644260075 \tabularnewline
-6 & 0.0253485671639796 \tabularnewline
-5 & -0.0266903446624256 \tabularnewline
-4 & 0.00357910963910154 \tabularnewline
-3 & 0.00498379724514848 \tabularnewline
-2 & -0.205685876947638 \tabularnewline
-1 & -0.211887826014668 \tabularnewline
0 & -0.0423984864712752 \tabularnewline
1 & -0.0200897210762981 \tabularnewline
2 & 0.0503321094012996 \tabularnewline
3 & -0.141713429768418 \tabularnewline
4 & -0.112855429567069 \tabularnewline
5 & 0.0566306181683683 \tabularnewline
6 & -0.107880227598250 \tabularnewline
7 & -0.132371095911512 \tabularnewline
8 & -0.0790302740385703 \tabularnewline
9 & 0.0995025763506258 \tabularnewline
10 & 0.0276603307400809 \tabularnewline
11 & -0.121859987352061 \tabularnewline
12 & -0.227801522942538 \tabularnewline
13 & -0.0278454034984875 \tabularnewline
14 & -0.103758518281093 \tabularnewline
15 & -0.0268668221445029 \tabularnewline
16 & 0.0215095875745859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28639&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]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[/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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-16[/C][C]0.0383896130157162[/C][/ROW]
[ROW][C]-15[/C][C]-0.0431592598655148[/C][/ROW]
[ROW][C]-14[/C][C]0.117329362335501[/C][/ROW]
[ROW][C]-13[/C][C]0.216693499873761[/C][/ROW]
[ROW][C]-12[/C][C]-0.0786348913274296[/C][/ROW]
[ROW][C]-11[/C][C]-0.0212109474417028[/C][/ROW]
[ROW][C]-10[/C][C]0.0242333757798274[/C][/ROW]
[ROW][C]-9[/C][C]0.0895110248135392[/C][/ROW]
[ROW][C]-8[/C][C]-0.0275696231430755[/C][/ROW]
[ROW][C]-7[/C][C]-0.0404718644260075[/C][/ROW]
[ROW][C]-6[/C][C]0.0253485671639796[/C][/ROW]
[ROW][C]-5[/C][C]-0.0266903446624256[/C][/ROW]
[ROW][C]-4[/C][C]0.00357910963910154[/C][/ROW]
[ROW][C]-3[/C][C]0.00498379724514848[/C][/ROW]
[ROW][C]-2[/C][C]-0.205685876947638[/C][/ROW]
[ROW][C]-1[/C][C]-0.211887826014668[/C][/ROW]
[ROW][C]0[/C][C]-0.0423984864712752[/C][/ROW]
[ROW][C]1[/C][C]-0.0200897210762981[/C][/ROW]
[ROW][C]2[/C][C]0.0503321094012996[/C][/ROW]
[ROW][C]3[/C][C]-0.141713429768418[/C][/ROW]
[ROW][C]4[/C][C]-0.112855429567069[/C][/ROW]
[ROW][C]5[/C][C]0.0566306181683683[/C][/ROW]
[ROW][C]6[/C][C]-0.107880227598250[/C][/ROW]
[ROW][C]7[/C][C]-0.132371095911512[/C][/ROW]
[ROW][C]8[/C][C]-0.0790302740385703[/C][/ROW]
[ROW][C]9[/C][C]0.0995025763506258[/C][/ROW]
[ROW][C]10[/C][C]0.0276603307400809[/C][/ROW]
[ROW][C]11[/C][C]-0.121859987352061[/C][/ROW]
[ROW][C]12[/C][C]-0.227801522942538[/C][/ROW]
[ROW][C]13[/C][C]-0.0278454034984875[/C][/ROW]
[ROW][C]14[/C][C]-0.103758518281093[/C][/ROW]
[ROW][C]15[/C][C]-0.0268668221445029[/C][/ROW]
[ROW][C]16[/C][C]0.0215095875745859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28639&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28639&T=1

As an alternative you can also use a QR Code:

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

Cross Correlation Function
Parameter	Value
Box-Cox transformation parameter (lambda) of X series	1
Degree of non-seasonal differencing (d) of X series	1
Degree of seasonal differencing (D) of X series	1
Seasonal Period (s)	12
Box-Cox transformation parameter (lambda) of Y series	1
Degree of non-seasonal differencing (d) of Y series	1
Degree of seasonal differencing (D) of Y series	1
k	rho(Y[t],X[t+k])
-16	0.0383896130157162
-15	-0.0431592598655148
-14	0.117329362335501
-13	0.216693499873761
-12	-0.0786348913274296
-11	-0.0212109474417028
-10	0.0242333757798274
-9	0.0895110248135392
-8	-0.0275696231430755
-7	-0.0404718644260075
-6	0.0253485671639796
-5	-0.0266903446624256
-4	0.00357910963910154
-3	0.00498379724514848
-2	-0.205685876947638
-1	-0.211887826014668
0	-0.0423984864712752
1	-0.0200897210762981
2	0.0503321094012996
3	-0.141713429768418
4	-0.112855429567069
5	0.0566306181683683
6	-0.107880227598250
7	-0.132371095911512
8	-0.0790302740385703
9	0.0995025763506258
10	0.0276603307400809
11	-0.121859987352061
12	-0.227801522942538
13	-0.0278454034984875
14	-0.103758518281093
15	-0.0268668221445029
16	0.0215095875745859

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 1 ;

Parameters (R input):

par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; 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')

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