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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 09:46:47 -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/t1228236474dyn9kmpgy2u4utj.htm/, Retrieved Fri, 17 May 2024 03:19:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28067, Retrieved Fri, 17 May 2024 03:19:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154

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] [non -stationary t...] [2008-12-02 16:46:47] [e7b1048c2c3a353441b9143db4404b91] [Current]
F    D    [Cross Correlation Function] [NonStationaryTime...] [2008-12-02 20:11:03] [9c2d53170eb755e9ae5fcf19d2174a32]
Feedback Forum
2008-12-08 18:47:52 [Jasmine Hendrikx] [reply] 
Eigen evaluatie:
De berekening is goed uitgevoerd en er is ook een goede uitleg gegeven over wat een cross correlatie functie juist doet. Er is dus duidelijk een verschil met de autocorrelatie. Bij autocorrelatie ga je de correlatie berekenen tussen Xt en Xt-1, tussen Xt en Xt-2,.. Het is dus de mate waarin Xt voorspeld kan worden op basis van zijn eigen verleden. Crosscorrelatie daarentegen gaat de correlatie tussen verschillende variabelen onderzoeken. Bijvoorbeeld de correlatie tussen Yt en Xt, tussen Yt en Xt-1, tussen Yt en Xt-2, tussen Yt en Xt+1. Je gaat dus kijken of de huidige waarden, de toekomstige of de verleden waarden van Xt kunnen helpen bij het voorspellen van Yt. Bij het bespreken van de grafiek zou ook gezegd kunnen worden dat het verticale lijntje bij k= -9, k=-3 significant verschillend is van 0, hoewel iets minder uitgesproken dan bij k= -6 en k= -12 (wanneer k negatief is). Wanneer k positief is, zien we inderdaad veel significante verschillen. Voor de rest is de bespreking goed en volledig.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
78,4
114,6
113,3
117,0
99,6
99,4
101,9
115,2
108,5
113,8
121,0
92,2
90,2
101,5
126,6
93,9
89,8
93,4
101,5
110,4
105,9
108,4
113,9
86,1
69,4
101,2
100,5
98,0
106,6
90,1
96,9
125,9
112,0
100,0
123,9
79,8
83,4
113,6
112,9
104,0
109,9
99,0
106,3
128,9
111,1
102,9
130,0
87,0
87,5
117,6
103,4
110,8
112,6
102,5
112,4
135,6
105,1
127,7
137,0
91,0
90,5
122,4
123,3
124,3
120,0
118,1
119,0
142,7
123,6
129,6
151,6
110,4
99,2
130,5
136,2
129,7
128,0
121,6
135,8
143,8
147,5
136,2
156,6
123,3
100,4
Dataseries Y:
Download CSV
Histogram 
97,8
107,4
117,5
105,6
97,4
99,5
98,0
104,3
100,6
101,1
103,9
96,9
95,5
108,4
117,0
103,8
100,8
110,6
104,0
112,6
107,3
98,9
109,8
104,9
102,2
123,9
124,9
112,7
121,9
100,6
104,3
120,4
107,5
102,9
125,6
107,5
108,8
128,4
121,1
119,5
128,7
108,7
105,5
119,8
111,3
110,6
120,1
97,5
107,7
127,3
117,2
119,8
116,2
111,0
112,4
130,6
109,1
118,8
123,9
101,6
112,8
128,0
129,6
125,8
119,5
115,7
113,6
129,7
112,0
116,8
127,0
112,1
114,2
121,1
131,6
125,0
120,4
117,7
117,5
120,6
127,5
112,3
124,5
115,2
105,4

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=28067&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=28067&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28067&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)12
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])
-16-0.0700213890544833
-150.0142990905644138
-14-0.193365488956066
-13-0.0450890745987972
-120.377965898280041
-110.0519906803063849
-10-0.0746958549880802
-90.225762577193397
-80.0630934719706794
-70.132622602806340
-60.426786401686938
-50.201701426202626
-40.119284736444523
-30.237122659296598
-2-0.0886280683008975
-10.108125980015205
00.686100625132908
10.230290912212587
20.139624344725165
30.441454371940561
40.232682044125126
50.306827743228845
60.590262231124296
70.308599562256039
80.281539285337359
90.32576371189541
10-0.0360541777512949
110.178304899355525
120.623018971583643
130.262213896511307
140.158442969008772
150.420419585992301
160.187429749797794

\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) & 12 \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
-16 & -0.0700213890544833 \tabularnewline
-15 & 0.0142990905644138 \tabularnewline
-14 & -0.193365488956066 \tabularnewline
-13 & -0.0450890745987972 \tabularnewline
-12 & 0.377965898280041 \tabularnewline
-11 & 0.0519906803063849 \tabularnewline
-10 & -0.0746958549880802 \tabularnewline
-9 & 0.225762577193397 \tabularnewline
-8 & 0.0630934719706794 \tabularnewline
-7 & 0.132622602806340 \tabularnewline
-6 & 0.426786401686938 \tabularnewline
-5 & 0.201701426202626 \tabularnewline
-4 & 0.119284736444523 \tabularnewline
-3 & 0.237122659296598 \tabularnewline
-2 & -0.0886280683008975 \tabularnewline
-1 & 0.108125980015205 \tabularnewline
0 & 0.686100625132908 \tabularnewline
1 & 0.230290912212587 \tabularnewline
2 & 0.139624344725165 \tabularnewline
3 & 0.441454371940561 \tabularnewline
4 & 0.232682044125126 \tabularnewline
5 & 0.306827743228845 \tabularnewline
6 & 0.590262231124296 \tabularnewline
7 & 0.308599562256039 \tabularnewline
8 & 0.281539285337359 \tabularnewline
9 & 0.32576371189541 \tabularnewline
10 & -0.0360541777512949 \tabularnewline
11 & 0.178304899355525 \tabularnewline
12 & 0.623018971583643 \tabularnewline
13 & 0.262213896511307 \tabularnewline
14 & 0.158442969008772 \tabularnewline
15 & 0.420419585992301 \tabularnewline
16 & 0.187429749797794 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28067&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]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]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]-16[/C][C]-0.0700213890544833[/C][/ROW]
[ROW][C]-15[/C][C]0.0142990905644138[/C][/ROW]
[ROW][C]-14[/C][C]-0.193365488956066[/C][/ROW]
[ROW][C]-13[/C][C]-0.0450890745987972[/C][/ROW]
[ROW][C]-12[/C][C]0.377965898280041[/C][/ROW]
[ROW][C]-11[/C][C]0.0519906803063849[/C][/ROW]
[ROW][C]-10[/C][C]-0.0746958549880802[/C][/ROW]
[ROW][C]-9[/C][C]0.225762577193397[/C][/ROW]
[ROW][C]-8[/C][C]0.0630934719706794[/C][/ROW]
[ROW][C]-7[/C][C]0.132622602806340[/C][/ROW]
[ROW][C]-6[/C][C]0.426786401686938[/C][/ROW]
[ROW][C]-5[/C][C]0.201701426202626[/C][/ROW]
[ROW][C]-4[/C][C]0.119284736444523[/C][/ROW]
[ROW][C]-3[/C][C]0.237122659296598[/C][/ROW]
[ROW][C]-2[/C][C]-0.0886280683008975[/C][/ROW]
[ROW][C]-1[/C][C]0.108125980015205[/C][/ROW]
[ROW][C]0[/C][C]0.686100625132908[/C][/ROW]
[ROW][C]1[/C][C]0.230290912212587[/C][/ROW]
[ROW][C]2[/C][C]0.139624344725165[/C][/ROW]
[ROW][C]3[/C][C]0.441454371940561[/C][/ROW]
[ROW][C]4[/C][C]0.232682044125126[/C][/ROW]
[ROW][C]5[/C][C]0.306827743228845[/C][/ROW]
[ROW][C]6[/C][C]0.590262231124296[/C][/ROW]
[ROW][C]7[/C][C]0.308599562256039[/C][/ROW]
[ROW][C]8[/C][C]0.281539285337359[/C][/ROW]
[ROW][C]9[/C][C]0.32576371189541[/C][/ROW]
[ROW][C]10[/C][C]-0.0360541777512949[/C][/ROW]
[ROW][C]11[/C][C]0.178304899355525[/C][/ROW]
[ROW][C]12[/C][C]0.623018971583643[/C][/ROW]
[ROW][C]13[/C][C]0.262213896511307[/C][/ROW]
[ROW][C]14[/C][C]0.158442969008772[/C][/ROW]
[ROW][C]15[/C][C]0.420419585992301[/C][/ROW]
[ROW][C]16[/C][C]0.187429749797794[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28067&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28067&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)12
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])
-16-0.0700213890544833
-150.0142990905644138
-14-0.193365488956066
-13-0.0450890745987972
-120.377965898280041
-110.0519906803063849
-10-0.0746958549880802
-90.225762577193397
-80.0630934719706794
-70.132622602806340
-60.426786401686938
-50.201701426202626
-40.119284736444523
-30.237122659296598
-2-0.0886280683008975
-10.108125980015205
00.686100625132908
10.230290912212587
20.139624344725165
30.441454371940561
40.232682044125126
50.306827743228845
60.590262231124296
70.308599562256039
80.281539285337359
90.32576371189541
10-0.0360541777512949
110.178304899355525
120.623018971583643
130.262213896511307
140.158442969008772
150.420419585992301
160.187429749797794


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; 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')