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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationTue, 02 Dec 2008 03:58:04 -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/t12282155114ux967tu2ytmrm3.htm/, Retrieved Sun, 19 May 2024 10:22:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27601, Retrieved Sun, 19 May 2024 10:22:29 +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     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:31:28] [b98453cac15ba1066b407e146608df68]
F RMPD    [Cross Correlation Function] [Workshop 4] [2008-12-02 10:58:04] [34b2bf1c29318ebc3536134756a32b87] [Current]
Feedback Forum
2008-12-06 19:14:13 [Stefan Temmerman] [reply] 
Y(t)wordt voorspeld aan de hand van X(t)(k=0) of het verleden van X(t). Hier vallen een paar correlaties buiten het betrouwbaarheidsinterval, dit wil zeggen dat Y(t) kan voorspeld worden door X(t).
2008-12-08 20:02:40 [An Knapen] [reply] 
De student heeft duidelijk de gebruikte tijdreeksen vermeld.
Het cross correlation plot wordt inderdaad gebruikt om een verband te zoeken tussen twee verschillende variabelen. In dit voorbeeld wordt de correlatie tussen de olieprijzen en de vervaardiging van olie.
Op de grafiek kunnen we duidelijk zien dat de correlatie niet altijd positief is. In het begin is er een negatieve correlatie merkbaar. Dit heeft de student ook exact vermeld. Verder kunnen we opmerken dat de staafjes niet altijd binnen het betrouwbaarheidsinterval liggen. Ze zijn met andere woorden siginificant.
Dit betekent dus dat we met behulp van de waarden van x, de y waarden kunnen voorspellen. Aangezien de staafjes enkel significant zijn bij een waarde van k>0, betekent dit dat we de toekomstige waarden van x nodig hebben om y te bepalen(dus niet het verleden van x)

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
87,0
96,3
107,1
115,2
106,1
89,5
91,3
97,6
100,7
104,6
94,7
101,8
102,5
105,3
110,3
109,8
117,3
118,8
131,3
125,9
133,1
147,0
145,8
164,4
149,8
137,7
151,7
156,8
180,0
180,4
170,4
191,6
199,5
218,2
217,5
205,0
194,0
199,3
219,3
211,1
215,2
240,2
242,2
240,7
255,4
253,0
218,2
203,7
205,6
215,6
188,5
202,9
214,0
230,3
230,0
241,0
259,6
247,8
270,3
289,7
Dataseries Y:
Download CSV
Histogram 
106,7
101,1
97,8
113,8
107,1
117,5
113,7
106,6
109,8
108,8
102,0
114,5
116,5
108,6
113,9
109,3
112,5
123,4
115,2
110,8
120,4
117,6
111,2
131,1
118,9
115,7
119,6
113,1
106,4
115,5
111,8
109,6
121,5
109,5
109,0
113,4
112,7
114,4
109,2
116,2
113,8
123,6
112,6
117,7
113,3
110,7
114,7
116,9
120,6
111,6
111,9
116,1
111,9
125,1
115,1
116,7
115,8
116,8
113,0
106,5

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=27601&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=27601&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27601&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])
-14-0.0803006037856932
-13-0.0480950491664458
-12-0.0516475471253057
-11-0.0595622288497007
-100.00364597796691961
-90.0621285398568565
-80.0648214038882413
-70.0462242747478695
-60.0480296396100966
-50.0330462248600298
-40.0854180767397203
-30.107236036551465
-20.182452244959524
-10.211103835508267
00.249717791484955
10.297545926248175
20.311923834609141
30.326851637441637
40.312146838626612
50.321047999156973
60.323684576357806
70.220969266559349
80.271025742814268
90.262935102611362
100.276969694595590
110.295928301200173
120.280109690756847
130.222005194141713
140.207591041129590

\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
-14 & -0.0803006037856932 \tabularnewline
-13 & -0.0480950491664458 \tabularnewline
-12 & -0.0516475471253057 \tabularnewline
-11 & -0.0595622288497007 \tabularnewline
-10 & 0.00364597796691961 \tabularnewline
-9 & 0.0621285398568565 \tabularnewline
-8 & 0.0648214038882413 \tabularnewline
-7 & 0.0462242747478695 \tabularnewline
-6 & 0.0480296396100966 \tabularnewline
-5 & 0.0330462248600298 \tabularnewline
-4 & 0.0854180767397203 \tabularnewline
-3 & 0.107236036551465 \tabularnewline
-2 & 0.182452244959524 \tabularnewline
-1 & 0.211103835508267 \tabularnewline
0 & 0.249717791484955 \tabularnewline
1 & 0.297545926248175 \tabularnewline
2 & 0.311923834609141 \tabularnewline
3 & 0.326851637441637 \tabularnewline
4 & 0.312146838626612 \tabularnewline
5 & 0.321047999156973 \tabularnewline
6 & 0.323684576357806 \tabularnewline
7 & 0.220969266559349 \tabularnewline
8 & 0.271025742814268 \tabularnewline
9 & 0.262935102611362 \tabularnewline
10 & 0.276969694595590 \tabularnewline
11 & 0.295928301200173 \tabularnewline
12 & 0.280109690756847 \tabularnewline
13 & 0.222005194141713 \tabularnewline
14 & 0.207591041129590 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27601&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]-14[/C][C]-0.0803006037856932[/C][/ROW]
[ROW][C]-13[/C][C]-0.0480950491664458[/C][/ROW]
[ROW][C]-12[/C][C]-0.0516475471253057[/C][/ROW]
[ROW][C]-11[/C][C]-0.0595622288497007[/C][/ROW]
[ROW][C]-10[/C][C]0.00364597796691961[/C][/ROW]
[ROW][C]-9[/C][C]0.0621285398568565[/C][/ROW]
[ROW][C]-8[/C][C]0.0648214038882413[/C][/ROW]
[ROW][C]-7[/C][C]0.0462242747478695[/C][/ROW]
[ROW][C]-6[/C][C]0.0480296396100966[/C][/ROW]
[ROW][C]-5[/C][C]0.0330462248600298[/C][/ROW]
[ROW][C]-4[/C][C]0.0854180767397203[/C][/ROW]
[ROW][C]-3[/C][C]0.107236036551465[/C][/ROW]
[ROW][C]-2[/C][C]0.182452244959524[/C][/ROW]
[ROW][C]-1[/C][C]0.211103835508267[/C][/ROW]
[ROW][C]0[/C][C]0.249717791484955[/C][/ROW]
[ROW][C]1[/C][C]0.297545926248175[/C][/ROW]
[ROW][C]2[/C][C]0.311923834609141[/C][/ROW]
[ROW][C]3[/C][C]0.326851637441637[/C][/ROW]
[ROW][C]4[/C][C]0.312146838626612[/C][/ROW]
[ROW][C]5[/C][C]0.321047999156973[/C][/ROW]
[ROW][C]6[/C][C]0.323684576357806[/C][/ROW]
[ROW][C]7[/C][C]0.220969266559349[/C][/ROW]
[ROW][C]8[/C][C]0.271025742814268[/C][/ROW]
[ROW][C]9[/C][C]0.262935102611362[/C][/ROW]
[ROW][C]10[/C][C]0.276969694595590[/C][/ROW]
[ROW][C]11[/C][C]0.295928301200173[/C][/ROW]
[ROW][C]12[/C][C]0.280109690756847[/C][/ROW]
[ROW][C]13[/C][C]0.222005194141713[/C][/ROW]
[ROW][C]14[/C][C]0.207591041129590[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27601&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27601&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])
-14-0.0803006037856932
-13-0.0480950491664458
-12-0.0516475471253057
-11-0.0595622288497007
-100.00364597796691961
-90.0621285398568565
-80.0648214038882413
-70.0462242747478695
-60.0480296396100966
-50.0330462248600298
-40.0854180767397203
-30.107236036551465
-20.182452244959524
-10.211103835508267
00.249717791484955
10.297545926248175
20.311923834609141
30.326851637441637
40.312146838626612
50.321047999156973
60.323684576357806
70.220969266559349
80.271025742814268
90.262935102611362
100.276969694595590
110.295928301200173
120.280109690756847
130.222005194141713
140.207591041129590


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')