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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 computationMon, 01 Dec 2008 13:53:01 -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/t1228164824b42n9cvgjm54i2h.htm/, Retrieved Sun, 05 May 2024 11:36:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27392, Retrieved Sun, 05 May 2024 11:36:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175

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] [Cross Corr] [2008-12-01 19:38:27] [bc937651ef42bf891200cf0e0edc7238]
F    D    [Cross Correlation Function] [Task 7: Q7] [2008-12-01 20:53:01] [96c9291ce335a5c9abba7b920811c2df] [Current]
Feedback Forum
2008-12-08 19:05:42 [8e2cc0b2ef568da46d009b2f601285b2] [reply] 
Je hebt de CCF willen berekenen met niet stationaire tijdreeksen vandaar de onrealistische correlatie. Deze ontstaat doordat er nog mogelijk een lange termijn en seizoenale trend aanwezig is in de gebruikte reeksen. Verder geef je ook geen besluit of commentaar/antwoord buiten 1 grafiek.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6
4
2
0
4
4
4
7
4
6
5
8
9
8
9
10
11
11
10
11
12
10
12
12
9
11
11
11
9
11
11
12
12
11
10
10
6
8
9
9
8
9
11
12
12
11
10
13
11
12
11
12
11
12
12
12
12
12
11
9
Dataseries Y:
Download CSV
Histogram 
59
44
41
35
34
36
39
40
30
33
30
32
41
40
41
40
39
34
34
46
45
44
40
39
37
39
35
26
26
33
27
30
26
27
18
19
13
14
41
21
16
17
9
14
14
16
11
10
6
9
5
7
2
0
8
13
11
19
23
23

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=27392&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=27392&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27392&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 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])
-14-0.400570402769551
-13-0.396128723083826
-12-0.407189524810127
-11-0.433717333162614
-10-0.427445154527552
-9-0.391125832286115
-8-0.345263792653751
-7-0.334301847330657
-6-0.340076905966817
-5-0.376033912395543
-4-0.38528550004401
-3-0.374259700106404
-2-0.402057505791959
-1-0.401085260161374
0-0.406980933069389
1-0.454403863278707
2-0.454730467807006
3-0.429444276958915
4-0.322551888764774
5-0.294640563764532
6-0.23260249478633
7-0.202463353235344
8-0.200512476512723
9-0.110534466207035
10-0.0888241337716032
11-0.0348530310933887
12-0.0217701665742149
13-0.00351507827958718
140.00566555979720037

\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
-14 & -0.400570402769551 \tabularnewline
-13 & -0.396128723083826 \tabularnewline
-12 & -0.407189524810127 \tabularnewline
-11 & -0.433717333162614 \tabularnewline
-10 & -0.427445154527552 \tabularnewline
-9 & -0.391125832286115 \tabularnewline
-8 & -0.345263792653751 \tabularnewline
-7 & -0.334301847330657 \tabularnewline
-6 & -0.340076905966817 \tabularnewline
-5 & -0.376033912395543 \tabularnewline
-4 & -0.38528550004401 \tabularnewline
-3 & -0.374259700106404 \tabularnewline
-2 & -0.402057505791959 \tabularnewline
-1 & -0.401085260161374 \tabularnewline
0 & -0.406980933069389 \tabularnewline
1 & -0.454403863278707 \tabularnewline
2 & -0.454730467807006 \tabularnewline
3 & -0.429444276958915 \tabularnewline
4 & -0.322551888764774 \tabularnewline
5 & -0.294640563764532 \tabularnewline
6 & -0.23260249478633 \tabularnewline
7 & -0.202463353235344 \tabularnewline
8 & -0.200512476512723 \tabularnewline
9 & -0.110534466207035 \tabularnewline
10 & -0.0888241337716032 \tabularnewline
11 & -0.0348530310933887 \tabularnewline
12 & -0.0217701665742149 \tabularnewline
13 & -0.00351507827958718 \tabularnewline
14 & 0.00566555979720037 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27392&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]-14[/C][C]-0.400570402769551[/C][/ROW]
[ROW][C]-13[/C][C]-0.396128723083826[/C][/ROW]
[ROW][C]-12[/C][C]-0.407189524810127[/C][/ROW]
[ROW][C]-11[/C][C]-0.433717333162614[/C][/ROW]
[ROW][C]-10[/C][C]-0.427445154527552[/C][/ROW]
[ROW][C]-9[/C][C]-0.391125832286115[/C][/ROW]
[ROW][C]-8[/C][C]-0.345263792653751[/C][/ROW]
[ROW][C]-7[/C][C]-0.334301847330657[/C][/ROW]
[ROW][C]-6[/C][C]-0.340076905966817[/C][/ROW]
[ROW][C]-5[/C][C]-0.376033912395543[/C][/ROW]
[ROW][C]-4[/C][C]-0.38528550004401[/C][/ROW]
[ROW][C]-3[/C][C]-0.374259700106404[/C][/ROW]
[ROW][C]-2[/C][C]-0.402057505791959[/C][/ROW]
[ROW][C]-1[/C][C]-0.401085260161374[/C][/ROW]
[ROW][C]0[/C][C]-0.406980933069389[/C][/ROW]
[ROW][C]1[/C][C]-0.454403863278707[/C][/ROW]
[ROW][C]2[/C][C]-0.454730467807006[/C][/ROW]
[ROW][C]3[/C][C]-0.429444276958915[/C][/ROW]
[ROW][C]4[/C][C]-0.322551888764774[/C][/ROW]
[ROW][C]5[/C][C]-0.294640563764532[/C][/ROW]
[ROW][C]6[/C][C]-0.23260249478633[/C][/ROW]
[ROW][C]7[/C][C]-0.202463353235344[/C][/ROW]
[ROW][C]8[/C][C]-0.200512476512723[/C][/ROW]
[ROW][C]9[/C][C]-0.110534466207035[/C][/ROW]
[ROW][C]10[/C][C]-0.0888241337716032[/C][/ROW]
[ROW][C]11[/C][C]-0.0348530310933887[/C][/ROW]
[ROW][C]12[/C][C]-0.0217701665742149[/C][/ROW]
[ROW][C]13[/C][C]-0.00351507827958718[/C][/ROW]
[ROW][C]14[/C][C]0.00566555979720037[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27392&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27392&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])
-14-0.400570402769551
-13-0.396128723083826
-12-0.407189524810127
-11-0.433717333162614
-10-0.427445154527552
-9-0.391125832286115
-8-0.345263792653751
-7-0.334301847330657
-6-0.340076905966817
-5-0.376033912395543
-4-0.38528550004401
-3-0.374259700106404
-2-0.402057505791959
-1-0.401085260161374
0-0.406980933069389
1-0.454403863278707
2-0.454730467807006
3-0.429444276958915
4-0.322551888764774
5-0.294640563764532
6-0.23260249478633
7-0.202463353235344
8-0.200512476512723
9-0.110534466207035
10-0.0888241337716032
11-0.0348530310933887
12-0.0217701665742149
13-0.00351507827958718
140.00566555979720037


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')