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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 computationFri, 28 Nov 2008 07:06:52 -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/Nov/28/t1227881280824rrcy0wno4oar.htm/, Retrieved Tue, 28 May 2024 00:49:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26114, Retrieved Tue, 28 May 2024 00:49:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180

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    D  [Univariate Data Series] [q5] [2008-11-28 13:33:02] [e43247bc0ab243a5af99ac7f55ba0b41]
- RMP     [Box-Cox Normality Plot] [q5 box cox normality] [2008-11-28 13:40:30] [e43247bc0ab243a5af99ac7f55ba0b41]
F RMPD      [Cross Correlation Function] [q7] [2008-11-28 14:01:57] [e43247bc0ab243a5af99ac7f55ba0b41]
F    D          [Cross Correlation Function] [] [2008-11-28 14:06:52] [f24298b2e4c2a19d76cf4460ec5d2246] [Current]
Feedback Forum
2008-12-06 15:39:04 [Wim Golsteyn] [reply] 
De reeks is nu stationair gemaakt door D en d aan te passen, wat nodig is om een model te kunnen opstellen.
2008-12-07 15:16:57 [Chi-Kwong Man] [reply] 
Tijdreeks is nu stationair gemaakt.
2008-12-08 17:32:13 [Lindsay Heyndrickx] [reply] 
Dit is correct. Hier werd de tijdreeks stationair gemaakt. De lange termijn trend en de seizonaliteit werden eruit gehaald. Zo krijgen we een correcter beeld van de correlatie dan in q7.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7.8
7.6
7.5
7.6
7.5
7.3
7.6
7.5
7.6
7.9
7.9
8.1
8.2
8.0
7.5
6.8
6.5
6.6
7.6
8.0
8.0
7.7
7.5
7.6
7.7
7.9
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.1
7.9
7.3
6.9
6.6
6.7
6.9
7.0
7.1
7.2
7.1
6.9
7.0
6.8
6.4
6.7
6.7
6.4
6.3
6.2
6.5
6.8
6.8
6.5
6.3
5.9
5.9
6.4
6.4
Dataseries Y:
Download CSV
Histogram 
9.0
9.1
8.7
8.2
7.9
7.9
9.1
9.4
9.5
9.1
9.0
9.3
9.9
9.8
9.4
8.3
8.0
8.5
10.4
11.1
10.9
9.9
9.2
9.2
9.5
9.6
9.5
9.1
8.9
9.0
10.1
10.3
10.2
9.6
9.2
9.3
9.4
9.4
9.2
9.0
9.0
9.0
9.8
10.0
9.9
9.3
9.0
9.0
9.1
9.1
9.1
9.2
8.8
8.3
8.4
8.1
7.8
7.9
7.9
8.0
7.9
7.5
7.2
6.9
6.6
6.7
7.3
7.5

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=26114&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=26114&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26114&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 series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-15-0.239635205432394
-14-0.0866087987085784
-130.0200288276351517
-120.337841069966043
-110.127344345600814
-10-0.0462215050637655
-9-0.116304691803540
-8-0.00734589422346472
-70.203796404275998
-60.254483410519776
-50.0373176493934073
-4-0.299717551699424
-3-0.411685753733839
-2-0.216705043021761
-10.0703430193408576
00.588261008852756
10.266759143586482
2-0.0559725959915867
3-0.266595229288883
4-0.207632877875477
50.0755823304462849
60.24608215641808
70.154410887896752
8-0.0859019506176709
9-0.276153707990031
10-0.181039980679225
11-0.0182138775260924
120.344302240646316
130.0901663077542573
14-0.0608680490279187
15-0.177431280733034

\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 & 1 \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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-15 & -0.239635205432394 \tabularnewline
-14 & -0.0866087987085784 \tabularnewline
-13 & 0.0200288276351517 \tabularnewline
-12 & 0.337841069966043 \tabularnewline
-11 & 0.127344345600814 \tabularnewline
-10 & -0.0462215050637655 \tabularnewline
-9 & -0.116304691803540 \tabularnewline
-8 & -0.00734589422346472 \tabularnewline
-7 & 0.203796404275998 \tabularnewline
-6 & 0.254483410519776 \tabularnewline
-5 & 0.0373176493934073 \tabularnewline
-4 & -0.299717551699424 \tabularnewline
-3 & -0.411685753733839 \tabularnewline
-2 & -0.216705043021761 \tabularnewline
-1 & 0.0703430193408576 \tabularnewline
0 & 0.588261008852756 \tabularnewline
1 & 0.266759143586482 \tabularnewline
2 & -0.0559725959915867 \tabularnewline
3 & -0.266595229288883 \tabularnewline
4 & -0.207632877875477 \tabularnewline
5 & 0.0755823304462849 \tabularnewline
6 & 0.24608215641808 \tabularnewline
7 & 0.154410887896752 \tabularnewline
8 & -0.0859019506176709 \tabularnewline
9 & -0.276153707990031 \tabularnewline
10 & -0.181039980679225 \tabularnewline
11 & -0.0182138775260924 \tabularnewline
12 & 0.344302240646316 \tabularnewline
13 & 0.0901663077542573 \tabularnewline
14 & -0.0608680490279187 \tabularnewline
15 & -0.177431280733034 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26114&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]1[/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]1[/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]-15[/C][C]-0.239635205432394[/C][/ROW]
[ROW][C]-14[/C][C]-0.0866087987085784[/C][/ROW]
[ROW][C]-13[/C][C]0.0200288276351517[/C][/ROW]
[ROW][C]-12[/C][C]0.337841069966043[/C][/ROW]
[ROW][C]-11[/C][C]0.127344345600814[/C][/ROW]
[ROW][C]-10[/C][C]-0.0462215050637655[/C][/ROW]
[ROW][C]-9[/C][C]-0.116304691803540[/C][/ROW]
[ROW][C]-8[/C][C]-0.00734589422346472[/C][/ROW]
[ROW][C]-7[/C][C]0.203796404275998[/C][/ROW]
[ROW][C]-6[/C][C]0.254483410519776[/C][/ROW]
[ROW][C]-5[/C][C]0.0373176493934073[/C][/ROW]
[ROW][C]-4[/C][C]-0.299717551699424[/C][/ROW]
[ROW][C]-3[/C][C]-0.411685753733839[/C][/ROW]
[ROW][C]-2[/C][C]-0.216705043021761[/C][/ROW]
[ROW][C]-1[/C][C]0.0703430193408576[/C][/ROW]
[ROW][C]0[/C][C]0.588261008852756[/C][/ROW]
[ROW][C]1[/C][C]0.266759143586482[/C][/ROW]
[ROW][C]2[/C][C]-0.0559725959915867[/C][/ROW]
[ROW][C]3[/C][C]-0.266595229288883[/C][/ROW]
[ROW][C]4[/C][C]-0.207632877875477[/C][/ROW]
[ROW][C]5[/C][C]0.0755823304462849[/C][/ROW]
[ROW][C]6[/C][C]0.24608215641808[/C][/ROW]
[ROW][C]7[/C][C]0.154410887896752[/C][/ROW]
[ROW][C]8[/C][C]-0.0859019506176709[/C][/ROW]
[ROW][C]9[/C][C]-0.276153707990031[/C][/ROW]
[ROW][C]10[/C][C]-0.181039980679225[/C][/ROW]
[ROW][C]11[/C][C]-0.0182138775260924[/C][/ROW]
[ROW][C]12[/C][C]0.344302240646316[/C][/ROW]
[ROW][C]13[/C][C]0.0901663077542573[/C][/ROW]
[ROW][C]14[/C][C]-0.0608680490279187[/C][/ROW]
[ROW][C]15[/C][C]-0.177431280733034[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26114&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26114&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 series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-15-0.239635205432394
-14-0.0866087987085784
-130.0200288276351517
-120.337841069966043
-110.127344345600814
-10-0.0462215050637655
-9-0.116304691803540
-8-0.00734589422346472
-70.203796404275998
-60.254483410519776
-50.0373176493934073
-4-0.299717551699424
-3-0.411685753733839
-2-0.216705043021761
-10.0703430193408576
00.588261008852756
10.266759143586482
2-0.0559725959915867
3-0.266595229288883
4-0.207632877875477
50.0755823304462849
60.24608215641808
70.154410887896752
8-0.0859019506176709
9-0.276153707990031
10-0.181039980679225
11-0.0182138775260924
120.344302240646316
130.0901663077542573
14-0.0608680490279187
15-0.177431280733034


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 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; 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')