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Author

*The author of this computation has been verified*

R Software Module

rwasp_cross.wasp

Title produced by software

Cross Correlation Function

Date of computation

Sun, 21 Dec 2008 14:49:43 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/21/t1229896200pwkfiwq4h45vcd7.htm/, Retrieved Sat, 25 May 2024 19:29:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35853, Retrieved Sat, 25 May 2024 19:29:51 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

125

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

F     [Standard Deviation-Mean Plot] [Paper ] [2008-12-21 17:01:52] [74be16979710d4c4e7c6647856088456]
F RMPD    [Cross Correlation Function] [Paper ] [2008-12-21 21:49:43] [3452c99afdd85d4fde81272403cd85da] [Current]

Feedback Forum

2009-01-08 14:04:44 [Aurélie Van Impe] [reply] 
Het heeft absoluut geen enkele zin om stationaire tijdreeksen met elkaar te vergelijken! Je hebt nu alle verklaarbare elementen uit deze reeksen gehaald, waardoor enkel nog een toevalsfactor overblijft. Toeval vergelijken met toeval heeft niet echt zin. Het is juist de bedoeling dat je verklaringen zoekt in de ene tijdreeks, voor het verloop van de andere tijdreeks, maar als je alle verklaarbare elementen eruit haalt, dan heb je niets meer om te vergelijken… 
 
Verder spreek je over een grafiek met een x en een y-as, maar ik zie die grafiek niet… Ik weet niet waar je het over hebt.

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Dataseries X:

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Histogram

Boxplots

1.8
1.7
1.4
1.2
1
1.7
2.4
2
2.1
2
1.8
2.7
2.3
1.9
2
2.3
2.8
2.4
2.3
2.7
2.7
2.9
3
2.2
2.3
2.8
2.8
2.8
2.2
2.6
2.8
2.5
2.4
2.3
1.9
1.7
2
2.1
1.7
1.8
1.8
1.8
1.3
1.3
1.3
1.2
1.4
2.2
2.9
3.1
3.5
3.6
4.4
4.1
5.1
5.8
5.9
5.4
5.5
4.8
3.2

Dataseries Y:

Download CSV

Histogram

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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=35853&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=35853&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35853&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	3 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

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	0
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])
-13	0.0307317573252478
-12	-0.156557945610757
-11	0.136839890360932
-10	0.0590656241006931
-9	-0.304736522223826
-8	0.0219379451747721
-7	0.152839587368538
-6	-0.0373249517876515
-5	-0.270175731761003
-4	-0.148666077670771
-3	-0.0294604247412537
-2	0.0733872687236564
-1	-0.0829266065930709
0	0.155841125574853
1	0.189316322963408
2	-0.109200145521054
3	0.0146446092458489
4	0.132376469394662
5	-0.0340878399902620
6	-0.0710258561968734
7	0.0690449923113317
8	0.170268560049049
9	-0.0788743534641416
10	0.0491607404166352
11	0.273249052395992
12	0.265691183889950
13	-0.0147587772136035

\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 & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & 0.0307317573252478 \tabularnewline
-12 & -0.156557945610757 \tabularnewline
-11 & 0.136839890360932 \tabularnewline
-10 & 0.0590656241006931 \tabularnewline
-9 & -0.304736522223826 \tabularnewline
-8 & 0.0219379451747721 \tabularnewline
-7 & 0.152839587368538 \tabularnewline
-6 & -0.0373249517876515 \tabularnewline
-5 & -0.270175731761003 \tabularnewline
-4 & -0.148666077670771 \tabularnewline
-3 & -0.0294604247412537 \tabularnewline
-2 & 0.0733872687236564 \tabularnewline
-1 & -0.0829266065930709 \tabularnewline
0 & 0.155841125574853 \tabularnewline
1 & 0.189316322963408 \tabularnewline
2 & -0.109200145521054 \tabularnewline
3 & 0.0146446092458489 \tabularnewline
4 & 0.132376469394662 \tabularnewline
5 & -0.0340878399902620 \tabularnewline
6 & -0.0710258561968734 \tabularnewline
7 & 0.0690449923113317 \tabularnewline
8 & 0.170268560049049 \tabularnewline
9 & -0.0788743534641416 \tabularnewline
10 & 0.0491607404166352 \tabularnewline
11 & 0.273249052395992 \tabularnewline
12 & 0.265691183889950 \tabularnewline
13 & -0.0147587772136035 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35853&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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]0.0307317573252478[/C][/ROW]
[ROW][C]-12[/C][C]-0.156557945610757[/C][/ROW]
[ROW][C]-11[/C][C]0.136839890360932[/C][/ROW]
[ROW][C]-10[/C][C]0.0590656241006931[/C][/ROW]
[ROW][C]-9[/C][C]-0.304736522223826[/C][/ROW]
[ROW][C]-8[/C][C]0.0219379451747721[/C][/ROW]
[ROW][C]-7[/C][C]0.152839587368538[/C][/ROW]
[ROW][C]-6[/C][C]-0.0373249517876515[/C][/ROW]
[ROW][C]-5[/C][C]-0.270175731761003[/C][/ROW]
[ROW][C]-4[/C][C]-0.148666077670771[/C][/ROW]
[ROW][C]-3[/C][C]-0.0294604247412537[/C][/ROW]
[ROW][C]-2[/C][C]0.0733872687236564[/C][/ROW]
[ROW][C]-1[/C][C]-0.0829266065930709[/C][/ROW]
[ROW][C]0[/C][C]0.155841125574853[/C][/ROW]
[ROW][C]1[/C][C]0.189316322963408[/C][/ROW]
[ROW][C]2[/C][C]-0.109200145521054[/C][/ROW]
[ROW][C]3[/C][C]0.0146446092458489[/C][/ROW]
[ROW][C]4[/C][C]0.132376469394662[/C][/ROW]
[ROW][C]5[/C][C]-0.0340878399902620[/C][/ROW]
[ROW][C]6[/C][C]-0.0710258561968734[/C][/ROW]
[ROW][C]7[/C][C]0.0690449923113317[/C][/ROW]
[ROW][C]8[/C][C]0.170268560049049[/C][/ROW]
[ROW][C]9[/C][C]-0.0788743534641416[/C][/ROW]
[ROW][C]10[/C][C]0.0491607404166352[/C][/ROW]
[ROW][C]11[/C][C]0.273249052395992[/C][/ROW]
[ROW][C]12[/C][C]0.265691183889950[/C][/ROW]
[ROW][C]13[/C][C]-0.0147587772136035[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35853&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35853&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	0
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])
-13	0.0307317573252478
-12	-0.156557945610757
-11	0.136839890360932
-10	0.0590656241006931
-9	-0.304736522223826
-8	0.0219379451747721
-7	0.152839587368538
-6	-0.0373249517876515
-5	-0.270175731761003
-4	-0.148666077670771
-3	-0.0294604247412537
-2	0.0733872687236564
-1	-0.0829266065930709
0	0.155841125574853
1	0.189316322963408
2	-0.109200145521054
3	0.0146446092458489
4	0.132376469394662
5	-0.0340878399902620
6	-0.0710258561968734
7	0.0690449923113317
8	0.170268560049049
9	-0.0788743534641416
10	0.0491607404166352
11	0.273249052395992
12	0.265691183889950
13	-0.0147587772136035

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 1 ;

Parameters (R input):

par1 = 1 ; par2 = 1 ; par3 = 0 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code