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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 computationTue, 02 Dec 2008 12:59:22 -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/02/t1228248014r692u2zino467wn.htm/, Retrieved Fri, 17 May 2024 03:04:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28308, Retrieved Fri, 17 May 2024 03:04:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact149

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:40:39] [b98453cac15ba1066b407e146608df68]
F RMPD    [Cross Correlation Function] [workshop7, Q7] [2008-12-02 19:59:22] [a16dfd7e948381d8b6391003c5d09447] [Current]
Feedback Forum
2008-12-04 09:35:19 [Julie Govaerts] [reply] 
Correlatie tussen Yt en Xt +k --> X wordt verschoven in de tijd om na te gaan in welke mate Yt verklaard kan worden dmv het verleden van Xt (een leading indicator voor Yt)
Eerst zijn de waarden van k negatief = het verleden ; er na worden deze positief = in de toekomst = toekomstige waarden
De Rho waarde is de correlatiecoefficient.
Natuurlijk zoeken we hier de hoogste coëfficiënt
17 0.131434424090898
= zoveel terug in de tijd gaan om Yt te kunnen voorspellen dmv Xt
= vertraging op de gegevens

Grafisch
Alle negatieve lag-waarden stellen het verleden voor. Lag 0 stelt het heden voor en de positieve lag-waarden de toekomst.
2008-12-07 14:49:52 [Stephanie Vanderlinden] [reply] 
Hier had nog bij vermeldkunnen worden dat de negatieve lag waarden het verleden voorstellen,lag 0 stelt het heden voor en de positieve lag waarden stellen de toekomst voor.
2008-12-09 15:16:40 [Jonas Janssens] [reply] 
De cross correlation function geeft aan in welke mate het verleden van andere tijdsreeksen Yt kan verklaren.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-3
-2
0
1
11
14
14
16
14
10
15
18
18
12
8
2
-2
-1
1
-6
-16
-21
-38
-32
-22
-31
-22
-26
-19
-20
-24
-29
-28
-31
-30
-32
-38
-43
-51
-43
-43
-42
-47
-45
-38
-46
-38
-32
-27
-26
-21
-23
-24
-17
-23
-16
-22
-26
-25
-21
-21
-18
-12
-19
-31
-38
-38
-32
-43
-33
-28
-25
-19
-20
-21
-19
-17
-16
-10
-16
-10
-8
-7
-15
-7
-6
-6
2
-4
-4
-8
-10
-16
-14
-30
-33
-40
-38
-39
-46
-50
-55
-66
-63
-56
-66
-63
Dataseries Y:
Download CSV
Histogram 
7,5
7,2
6,9
6,7
6,4
6,3
6,8
7,3
7,1
7,1
6,8
6,5
6,3
6,1
6,1
6,3
6,3
6
6,2
6,4
6,8
7,5
7,5
7,6
7,6
7,4
7,3
7,1
6,9
6,8
7,5
7,6
7,8
8
8,1
8,2
8,3
8,2
8
7,9
7,6
7,6
8,2
8,3
8,4
8,4
8,4
8,6
8,9
8,8
8,3
7,5
7,2
7,5
8,8
9,3
9,3
8,7
8,2
8,3
8,5
8,6
8,6
8,2
8,1
8
8,6
8,7
8,8
8,5
8,4
8,5
8,7
8,7
8,6
8,5
8,3
8,1
8,2
8,1
8,1
7,9
7,9
7,9
8
8
7,9
8
7,7
7,2
7,5
7,3
7
7
7
7,2
7,3
7,1
6,8
6,6
6,2
6,2
6,8
6,9
6,8
6,7

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=28308&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=28308&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28308&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])
-17-0.369748613393925
-16-0.398322798154745
-15-0.42381175172379
-14-0.46033046381066
-13-0.491254486519096
-12-0.526316093852423
-11-0.540090062020324
-10-0.53930837460441
-9-0.523000768580217
-8-0.508417061137681
-7-0.48130544521945
-6-0.455903476075819
-5-0.445734408387368
-4-0.426903092369459
-3-0.39967290141651
-2-0.368861516386191
-1-0.332234824004189
0-0.279188339675271
1-0.241661739793304
2-0.194694172844266
3-0.144493725982337
4-0.0975387006235088
5-0.0645538666145794
6-0.0555109283174372
7-0.0426678881865223
8-0.0319717402707272
9-0.0211917917294348
100.00287540156756824
110.0339128686601811
120.056179203617417
130.0800166577846555
140.089980832889985
150.0979423440676155
160.121938848401614
170.131434424090898

\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
-17 & -0.369748613393925 \tabularnewline
-16 & -0.398322798154745 \tabularnewline
-15 & -0.42381175172379 \tabularnewline
-14 & -0.46033046381066 \tabularnewline
-13 & -0.491254486519096 \tabularnewline
-12 & -0.526316093852423 \tabularnewline
-11 & -0.540090062020324 \tabularnewline
-10 & -0.53930837460441 \tabularnewline
-9 & -0.523000768580217 \tabularnewline
-8 & -0.508417061137681 \tabularnewline
-7 & -0.48130544521945 \tabularnewline
-6 & -0.455903476075819 \tabularnewline
-5 & -0.445734408387368 \tabularnewline
-4 & -0.426903092369459 \tabularnewline
-3 & -0.39967290141651 \tabularnewline
-2 & -0.368861516386191 \tabularnewline
-1 & -0.332234824004189 \tabularnewline
0 & -0.279188339675271 \tabularnewline
1 & -0.241661739793304 \tabularnewline
2 & -0.194694172844266 \tabularnewline
3 & -0.144493725982337 \tabularnewline
4 & -0.0975387006235088 \tabularnewline
5 & -0.0645538666145794 \tabularnewline
6 & -0.0555109283174372 \tabularnewline
7 & -0.0426678881865223 \tabularnewline
8 & -0.0319717402707272 \tabularnewline
9 & -0.0211917917294348 \tabularnewline
10 & 0.00287540156756824 \tabularnewline
11 & 0.0339128686601811 \tabularnewline
12 & 0.056179203617417 \tabularnewline
13 & 0.0800166577846555 \tabularnewline
14 & 0.089980832889985 \tabularnewline
15 & 0.0979423440676155 \tabularnewline
16 & 0.121938848401614 \tabularnewline
17 & 0.131434424090898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28308&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]-17[/C][C]-0.369748613393925[/C][/ROW]
[ROW][C]-16[/C][C]-0.398322798154745[/C][/ROW]
[ROW][C]-15[/C][C]-0.42381175172379[/C][/ROW]
[ROW][C]-14[/C][C]-0.46033046381066[/C][/ROW]
[ROW][C]-13[/C][C]-0.491254486519096[/C][/ROW]
[ROW][C]-12[/C][C]-0.526316093852423[/C][/ROW]
[ROW][C]-11[/C][C]-0.540090062020324[/C][/ROW]
[ROW][C]-10[/C][C]-0.53930837460441[/C][/ROW]
[ROW][C]-9[/C][C]-0.523000768580217[/C][/ROW]
[ROW][C]-8[/C][C]-0.508417061137681[/C][/ROW]
[ROW][C]-7[/C][C]-0.48130544521945[/C][/ROW]
[ROW][C]-6[/C][C]-0.455903476075819[/C][/ROW]
[ROW][C]-5[/C][C]-0.445734408387368[/C][/ROW]
[ROW][C]-4[/C][C]-0.426903092369459[/C][/ROW]
[ROW][C]-3[/C][C]-0.39967290141651[/C][/ROW]
[ROW][C]-2[/C][C]-0.368861516386191[/C][/ROW]
[ROW][C]-1[/C][C]-0.332234824004189[/C][/ROW]
[ROW][C]0[/C][C]-0.279188339675271[/C][/ROW]
[ROW][C]1[/C][C]-0.241661739793304[/C][/ROW]
[ROW][C]2[/C][C]-0.194694172844266[/C][/ROW]
[ROW][C]3[/C][C]-0.144493725982337[/C][/ROW]
[ROW][C]4[/C][C]-0.0975387006235088[/C][/ROW]
[ROW][C]5[/C][C]-0.0645538666145794[/C][/ROW]
[ROW][C]6[/C][C]-0.0555109283174372[/C][/ROW]
[ROW][C]7[/C][C]-0.0426678881865223[/C][/ROW]
[ROW][C]8[/C][C]-0.0319717402707272[/C][/ROW]
[ROW][C]9[/C][C]-0.0211917917294348[/C][/ROW]
[ROW][C]10[/C][C]0.00287540156756824[/C][/ROW]
[ROW][C]11[/C][C]0.0339128686601811[/C][/ROW]
[ROW][C]12[/C][C]0.056179203617417[/C][/ROW]
[ROW][C]13[/C][C]0.0800166577846555[/C][/ROW]
[ROW][C]14[/C][C]0.089980832889985[/C][/ROW]
[ROW][C]15[/C][C]0.0979423440676155[/C][/ROW]
[ROW][C]16[/C][C]0.121938848401614[/C][/ROW]
[ROW][C]17[/C][C]0.131434424090898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28308&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28308&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])
-17-0.369748613393925
-16-0.398322798154745
-15-0.42381175172379
-14-0.46033046381066
-13-0.491254486519096
-12-0.526316093852423
-11-0.540090062020324
-10-0.53930837460441
-9-0.523000768580217
-8-0.508417061137681
-7-0.48130544521945
-6-0.455903476075819
-5-0.445734408387368
-4-0.426903092369459
-3-0.39967290141651
-2-0.368861516386191
-1-0.332234824004189
0-0.279188339675271
1-0.241661739793304
2-0.194694172844266
3-0.144493725982337
4-0.0975387006235088
5-0.0645538666145794
6-0.0555109283174372
7-0.0426678881865223
8-0.0319717402707272
9-0.0211917917294348
100.00287540156756824
110.0339128686601811
120.056179203617417
130.0800166577846555
140.089980832889985
150.0979423440676155
160.121938848401614
170.131434424090898


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')