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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 05:07:59 -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/t1228219770ds9ttaj4lh4d04b.htm/, Retrieved Tue, 28 May 2024 01:04:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27641, Retrieved Tue, 28 May 2024 01:04:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact215

Tree of Dependent Computations

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  [Spectral Analysis] [Non Stationary Ti...] [2008-12-02 11:59:49] [74be16979710d4c4e7c6647856088456]
F RM D      [Cross Correlation Function] [Non Stationary Ti...] [2008-12-02 12:07:59] [acca1d0ee7cc95ffc080d0867a313954] [Current]
F    D        [Cross Correlation Function] [Non Stationary Ti...] [2008-12-02 12:33:18] [8ac58ef7b35dc5a117bc162cf16850e9]
-   PD        [Cross Correlation Function] [cross correlation] [2008-12-24 12:11:16] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-04 13:41:01 [Glenn Maras] [reply] 
Goede uitleg over de tabel, die toont inderdaad de correlatie van het verleden van Xt en het heden van Yt. Maar er kon ook meer gezegd worden over de grafiek. Als we naar deze figuur kijken zien we dat er al zeker geen trend op te merken is en alleen enkele hoge waarden te zien zijn. Daarom is het waarschijnlijn dat D zal moeten aangepast worden en d hetzelfde zal blijven.
2008-12-06 11:11:00 [Annemiek Hoofman] [reply] 
De crosscorrelatie-grafiek lijkt mij zeer eigenaardig. De grootste coëfficiënt is als k = 0. Dit noemt men de leading indicator. Om het beter te zeggen: Als er deze maand iets verandert met Y zal diezelfde maand X veranderen.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110,40
96,40
101,90
106,20
81,00
94,70
101,00
109,40
102,30
90,70
96,20
96,10
106,00
103,10
102,00
104,70
86,00
92,10
106,90
112,60
101,70
92,00
97,40
97,00
105,40
102,70
98,10
104,50
87,40
89,90
109,80
111,70
98,60
96,90
95,10
97,00
112,70
102,90
97,40
111,40
87,40
96,80
114,10
110,30
103,90
101,60
94,60
95,90
104,70
102,80
98,10
113,90
80,90
95,70
113,20
105,90
108,80
102,30
99,00
100,70
115,50
Dataseries Y:
Download CSV
Histogram 
109,20
88,60
94,30
98,30
86,40
80,60
104,10
108,20
93,40
71,90
94,10
94,90
96,40
91,10
84,40
86,40
88,00
75,10
109,70
103,00
82,10
68,00
96,40
94,30
90,00
88,00
76,10
82,50
81,40
66,50
97,20
94,10
80,70
70,50
87,80
89,50
99,60
84,20
75,10
92,00
80,80
73,10
99,80
90,00
83,10
72,40
78,80
87,30
91,00
80,10
73,60
86,40
74,50
71,20
92,40
81,50
85,30
69,90
84,20
90,70
100,30

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27641&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27641&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27641&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






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])
-14-0.50485010462814
-13-0.0524241583333472
-120.360602741533168
-110.126286802187840
-10-0.0614338372385682
-9-0.115909526315568
-8-0.195853596114309
-7-0.147019121818716
-60.0592916155594943
-50.296893714690864
-40.189388136049227
-3-0.0827532370584942
-2-0.652877428364983
-1-0.0633027606078038
00.565259717157914
10.0247050959818376
2-0.0961364083866377
3-0.175880308930436
4-0.346319913189060
5-0.115015890365957
60.057953017981959
70.144617233393979
80.141615339571738
9-0.161642112619030
10-0.641162192575574
11-0.0255114170302718
120.408713756022164
130.0101451622436098
14-0.0526193380005553

\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 & -0.50485010462814 \tabularnewline
-13 & -0.0524241583333472 \tabularnewline
-12 & 0.360602741533168 \tabularnewline
-11 & 0.126286802187840 \tabularnewline
-10 & -0.0614338372385682 \tabularnewline
-9 & -0.115909526315568 \tabularnewline
-8 & -0.195853596114309 \tabularnewline
-7 & -0.147019121818716 \tabularnewline
-6 & 0.0592916155594943 \tabularnewline
-5 & 0.296893714690864 \tabularnewline
-4 & 0.189388136049227 \tabularnewline
-3 & -0.0827532370584942 \tabularnewline
-2 & -0.652877428364983 \tabularnewline
-1 & -0.0633027606078038 \tabularnewline
0 & 0.565259717157914 \tabularnewline
1 & 0.0247050959818376 \tabularnewline
2 & -0.0961364083866377 \tabularnewline
3 & -0.175880308930436 \tabularnewline
4 & -0.346319913189060 \tabularnewline
5 & -0.115015890365957 \tabularnewline
6 & 0.057953017981959 \tabularnewline
7 & 0.144617233393979 \tabularnewline
8 & 0.141615339571738 \tabularnewline
9 & -0.161642112619030 \tabularnewline
10 & -0.641162192575574 \tabularnewline
11 & -0.0255114170302718 \tabularnewline
12 & 0.408713756022164 \tabularnewline
13 & 0.0101451622436098 \tabularnewline
14 & -0.0526193380005553 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27641&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]-0.50485010462814[/C][/ROW]
[ROW][C]-13[/C][C]-0.0524241583333472[/C][/ROW]
[ROW][C]-12[/C][C]0.360602741533168[/C][/ROW]
[ROW][C]-11[/C][C]0.126286802187840[/C][/ROW]
[ROW][C]-10[/C][C]-0.0614338372385682[/C][/ROW]
[ROW][C]-9[/C][C]-0.115909526315568[/C][/ROW]
[ROW][C]-8[/C][C]-0.195853596114309[/C][/ROW]
[ROW][C]-7[/C][C]-0.147019121818716[/C][/ROW]
[ROW][C]-6[/C][C]0.0592916155594943[/C][/ROW]
[ROW][C]-5[/C][C]0.296893714690864[/C][/ROW]
[ROW][C]-4[/C][C]0.189388136049227[/C][/ROW]
[ROW][C]-3[/C][C]-0.0827532370584942[/C][/ROW]
[ROW][C]-2[/C][C]-0.652877428364983[/C][/ROW]
[ROW][C]-1[/C][C]-0.0633027606078038[/C][/ROW]
[ROW][C]0[/C][C]0.565259717157914[/C][/ROW]
[ROW][C]1[/C][C]0.0247050959818376[/C][/ROW]
[ROW][C]2[/C][C]-0.0961364083866377[/C][/ROW]
[ROW][C]3[/C][C]-0.175880308930436[/C][/ROW]
[ROW][C]4[/C][C]-0.346319913189060[/C][/ROW]
[ROW][C]5[/C][C]-0.115015890365957[/C][/ROW]
[ROW][C]6[/C][C]0.057953017981959[/C][/ROW]
[ROW][C]7[/C][C]0.144617233393979[/C][/ROW]
[ROW][C]8[/C][C]0.141615339571738[/C][/ROW]
[ROW][C]9[/C][C]-0.161642112619030[/C][/ROW]
[ROW][C]10[/C][C]-0.641162192575574[/C][/ROW]
[ROW][C]11[/C][C]-0.0255114170302718[/C][/ROW]
[ROW][C]12[/C][C]0.408713756022164[/C][/ROW]
[ROW][C]13[/C][C]0.0101451622436098[/C][/ROW]
[ROW][C]14[/C][C]-0.0526193380005553[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27641&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27641&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])
-14-0.50485010462814
-13-0.0524241583333472
-120.360602741533168
-110.126286802187840
-10-0.0614338372385682
-9-0.115909526315568
-8-0.195853596114309
-7-0.147019121818716
-60.0592916155594943
-50.296893714690864
-40.189388136049227
-3-0.0827532370584942
-2-0.652877428364983
-1-0.0633027606078038
00.565259717157914
10.0247050959818376
2-0.0961364083866377
3-0.175880308930436
4-0.346319913189060
5-0.115015890365957
60.057953017981959
70.144617233393979
80.141615339571738
9-0.161642112619030
10-0.641162192575574
11-0.0255114170302718
120.408713756022164
130.0101451622436098
14-0.0526193380005553


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = -0.3 ; par3 = 1 ; par4 = 0 ; par5 = 12 ;
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')