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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 15:07:24 -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/t1228169327kk1t0k3meiu87l7.htm/, Retrieved Sun, 05 May 2024 09:31:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27448, Retrieved Sun, 05 May 2024 09:31:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact189
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]
F RMPD    [Cross Correlation Function] [Non stationary ti...] [2008-12-01 22:07:24] [d6e9f26c3644bfc30f06303d9993b878] [Current]
Feedback Forum
2008-12-08 18:37:17 [Peter Melgers] [reply
Interpretatie tabel:

De crosscorrelatie is niet hetzelfde als de autocorrelatie! Op de y-as is de correlatie terug te vinden (gelegen tussen -1 en +1). Op de x-as is het aantal perioden met hoeveel dat we gaan vertragen (linkerkant) met hoeveel dat we gaan versnellen (rechterkant) weergegeven.

De CCF geeft weer in welke mate een bepaalde variabele voorspelt kan worden door het verleden van een andere variabele.

In de tabel kunnen we het volgende terugvinden: rho(Y[t],X[t+k]). De waarden voor k zijn ook gegeven in de tabel.

Voorbeeld:

k = -14 de correlatie tussen Yt en Xt-14 wordt weergegeven
nl. -0.00206772237903045
vertraging met 14 perioden

k = 0 de correlatie tussen Yt en Xt

k = 5 de correlatie tussen Yt en Xt+5
versnelling met 5 perioden


Post a new message
Dataseries X:
116.1
102.5
102.0
101.3
100.6
100.9
104.2
108.3
108.9
109.9
106.8
112.7
113.4
101.3
97.8
95.0
93.8
94.5
101.4
105.8
106.6
109.7
108.8
113.4
113.7
103.6
98.2
95.5
94.4
95.9
103.2
104.1
127.6
130.3
133.0
140.4
123.5
116.9
115.9
113.1
112.1
112.4
118.9
117.4
115.6
120.7
114.9
122.0
119.6
114.6
118.4
110.9
111.6
114.6
112.1
117.4
114.8
123.4
118.1
121.9
123.3
Dataseries Y:
100.3
97.6
89.1
99.1
94.9
96.5
92.6
80.8
89.5
101.4
95.9
92.3
91.2
88.3
80.7
89.9
87.2
86.9
82.8
72.6
81.3
91.2
87.3
83.4
81.7
80.2
74.1
80.6
79.0
79.3
71.2
78.1
68.2
81.0
106.9
123.7
73.7
69.2
72.5
75.7
73.5
70.4
65.7
68.1
62.4
64.7
77.7
85.9
61.0
57.4
75.1
75.9
71.8
72.3
67.3
71.5
67.6
74.2
77.6
76.4
74.2




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=27448&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=27448&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27448&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 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])
-14-0.0664610329649337
-13-0.0778272519500454
-12-0.0684987380611167
-11-0.109534882801539
-10-0.234874767074836
-9-0.351596911846083
-8-0.444800942596340
-7-0.505615530339256
-6-0.464301151963452
-5-0.380802049811753
-4-0.342588132738846
-3-0.200031484260371
-2-0.132444216650178
-1-0.102400113683410
0-0.0826798096433685
1-0.198818848786986
2-0.314113014091646
3-0.37584491346215
4-0.444769239367064
5-0.468341135401358
6-0.410825955191684
7-0.318434152832965
8-0.276628589803539
9-0.273151240882441
10-0.238707127453795
11-0.234836957422613
12-0.202831329388275
13-0.221139536707715
14-0.260814837003886

\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 & -0.0664610329649337 \tabularnewline
-13 & -0.0778272519500454 \tabularnewline
-12 & -0.0684987380611167 \tabularnewline
-11 & -0.109534882801539 \tabularnewline
-10 & -0.234874767074836 \tabularnewline
-9 & -0.351596911846083 \tabularnewline
-8 & -0.444800942596340 \tabularnewline
-7 & -0.505615530339256 \tabularnewline
-6 & -0.464301151963452 \tabularnewline
-5 & -0.380802049811753 \tabularnewline
-4 & -0.342588132738846 \tabularnewline
-3 & -0.200031484260371 \tabularnewline
-2 & -0.132444216650178 \tabularnewline
-1 & -0.102400113683410 \tabularnewline
0 & -0.0826798096433685 \tabularnewline
1 & -0.198818848786986 \tabularnewline
2 & -0.314113014091646 \tabularnewline
3 & -0.37584491346215 \tabularnewline
4 & -0.444769239367064 \tabularnewline
5 & -0.468341135401358 \tabularnewline
6 & -0.410825955191684 \tabularnewline
7 & -0.318434152832965 \tabularnewline
8 & -0.276628589803539 \tabularnewline
9 & -0.273151240882441 \tabularnewline
10 & -0.238707127453795 \tabularnewline
11 & -0.234836957422613 \tabularnewline
12 & -0.202831329388275 \tabularnewline
13 & -0.221139536707715 \tabularnewline
14 & -0.260814837003886 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27448&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]-0.0664610329649337[/C][/ROW]
[ROW][C]-13[/C][C]-0.0778272519500454[/C][/ROW]
[ROW][C]-12[/C][C]-0.0684987380611167[/C][/ROW]
[ROW][C]-11[/C][C]-0.109534882801539[/C][/ROW]
[ROW][C]-10[/C][C]-0.234874767074836[/C][/ROW]
[ROW][C]-9[/C][C]-0.351596911846083[/C][/ROW]
[ROW][C]-8[/C][C]-0.444800942596340[/C][/ROW]
[ROW][C]-7[/C][C]-0.505615530339256[/C][/ROW]
[ROW][C]-6[/C][C]-0.464301151963452[/C][/ROW]
[ROW][C]-5[/C][C]-0.380802049811753[/C][/ROW]
[ROW][C]-4[/C][C]-0.342588132738846[/C][/ROW]
[ROW][C]-3[/C][C]-0.200031484260371[/C][/ROW]
[ROW][C]-2[/C][C]-0.132444216650178[/C][/ROW]
[ROW][C]-1[/C][C]-0.102400113683410[/C][/ROW]
[ROW][C]0[/C][C]-0.0826798096433685[/C][/ROW]
[ROW][C]1[/C][C]-0.198818848786986[/C][/ROW]
[ROW][C]2[/C][C]-0.314113014091646[/C][/ROW]
[ROW][C]3[/C][C]-0.37584491346215[/C][/ROW]
[ROW][C]4[/C][C]-0.444769239367064[/C][/ROW]
[ROW][C]5[/C][C]-0.468341135401358[/C][/ROW]
[ROW][C]6[/C][C]-0.410825955191684[/C][/ROW]
[ROW][C]7[/C][C]-0.318434152832965[/C][/ROW]
[ROW][C]8[/C][C]-0.276628589803539[/C][/ROW]
[ROW][C]9[/C][C]-0.273151240882441[/C][/ROW]
[ROW][C]10[/C][C]-0.238707127453795[/C][/ROW]
[ROW][C]11[/C][C]-0.234836957422613[/C][/ROW]
[ROW][C]12[/C][C]-0.202831329388275[/C][/ROW]
[ROW][C]13[/C][C]-0.221139536707715[/C][/ROW]
[ROW][C]14[/C][C]-0.260814837003886[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27448&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27448&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
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])
-14-0.0664610329649337
-13-0.0778272519500454
-12-0.0684987380611167
-11-0.109534882801539
-10-0.234874767074836
-9-0.351596911846083
-8-0.444800942596340
-7-0.505615530339256
-6-0.464301151963452
-5-0.380802049811753
-4-0.342588132738846
-3-0.200031484260371
-2-0.132444216650178
-1-0.102400113683410
0-0.0826798096433685
1-0.198818848786986
2-0.314113014091646
3-0.37584491346215
4-0.444769239367064
5-0.468341135401358
6-0.410825955191684
7-0.318434152832965
8-0.276628589803539
9-0.273151240882441
10-0.238707127453795
11-0.234836957422613
12-0.202831329388275
13-0.221139536707715
14-0.260814837003886



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')