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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 computationSat, 24 Nov 2007 11:04:24 -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/2007/Nov/24/t119592698693yse0tty8kxjra.htm/, Retrieved Fri, 03 May 2024 04:22:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=6353, Retrieved Fri, 03 May 2024 04:22:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact209

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [Cross Correlation...] [2007-11-24 18:04:24] [6b5c00822e2ce0f7cf73539c28d95782] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106,48
106,83
107,14
107,94
108,46
108,81
108,92
108,99
109,16
109,22
109,43
109,23
109,93
110,09
110,33
110,11
110,35
110,09
110,44
110,39
110,62
110,43
110,46
110,55
110,94
111,56
111,82
111,73
111,57
111,85
112,06
112,2
112,47
112,15
112,36
112,32
112,67
113,02
113,05
113,5
113,67
113,65
114
114,03
114,08
114,49
114,48
114,25
114,68
115,28
115,9
115,87
116,09
116,29
116,76
116,78
116,65
116,46
116,82
116,91
Dataseries Y:
Download CSV
Histogram 
103,7
103,75
103,85
104,02
104,13
104,17
104,18
104,2
104,5
104,78
104,88
104,89
104,9
104,95
105,24
105,35
105,44
105,46
105,47
105,48
105,75
106,1
106,19
106,23
106,24
106,25
106,35
106,48
106,52
106,55
106,55
106,56
106,89
107,09
107,24
107,28
107,3
107,31
107,47
107,35
107,31
107,32
107,32
107,34
107,53
107,72
107,75
107,79
107,81
107,9
107,8
107,86
107,8
107,74
107,75
107,83
107,8
107,81
107,86
107,83

Tables (Output of Computation)





Summary of compuational 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 compuational 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=6353&T=0

[TABLE]
[ROW][C]Summary of compuational 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=6353&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6353&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 compuational 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])
-140.178401528218461
-130.226117151717748
-120.271171672420362
-110.316991885476050
-100.365416717659090
-90.418493819330666
-80.474822408902887
-70.536160901010385
-60.595538088785421
-50.65473976814277
-40.712570237694782
-30.7697819573718
-20.827414834665487
-10.886217127317887
00.9438938146598
10.900691877843436
20.85928874584392
30.820239107516748
40.787592285385713
50.754630410892565
60.723372188473765
70.691853340972153
80.658270973142908
90.623363820748207
100.587629641932915
110.548427026069998
120.506015033597691
130.47082892742653
140.437481591565191

\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 & 0.178401528218461 \tabularnewline
-13 & 0.226117151717748 \tabularnewline
-12 & 0.271171672420362 \tabularnewline
-11 & 0.316991885476050 \tabularnewline
-10 & 0.365416717659090 \tabularnewline
-9 & 0.418493819330666 \tabularnewline
-8 & 0.474822408902887 \tabularnewline
-7 & 0.536160901010385 \tabularnewline
-6 & 0.595538088785421 \tabularnewline
-5 & 0.65473976814277 \tabularnewline
-4 & 0.712570237694782 \tabularnewline
-3 & 0.7697819573718 \tabularnewline
-2 & 0.827414834665487 \tabularnewline
-1 & 0.886217127317887 \tabularnewline
0 & 0.9438938146598 \tabularnewline
1 & 0.900691877843436 \tabularnewline
2 & 0.85928874584392 \tabularnewline
3 & 0.820239107516748 \tabularnewline
4 & 0.787592285385713 \tabularnewline
5 & 0.754630410892565 \tabularnewline
6 & 0.723372188473765 \tabularnewline
7 & 0.691853340972153 \tabularnewline
8 & 0.658270973142908 \tabularnewline
9 & 0.623363820748207 \tabularnewline
10 & 0.587629641932915 \tabularnewline
11 & 0.548427026069998 \tabularnewline
12 & 0.506015033597691 \tabularnewline
13 & 0.47082892742653 \tabularnewline
14 & 0.437481591565191 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6353&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]0.178401528218461[/C][/ROW]
[ROW][C]-13[/C][C]0.226117151717748[/C][/ROW]
[ROW][C]-12[/C][C]0.271171672420362[/C][/ROW]
[ROW][C]-11[/C][C]0.316991885476050[/C][/ROW]
[ROW][C]-10[/C][C]0.365416717659090[/C][/ROW]
[ROW][C]-9[/C][C]0.418493819330666[/C][/ROW]
[ROW][C]-8[/C][C]0.474822408902887[/C][/ROW]
[ROW][C]-7[/C][C]0.536160901010385[/C][/ROW]
[ROW][C]-6[/C][C]0.595538088785421[/C][/ROW]
[ROW][C]-5[/C][C]0.65473976814277[/C][/ROW]
[ROW][C]-4[/C][C]0.712570237694782[/C][/ROW]
[ROW][C]-3[/C][C]0.7697819573718[/C][/ROW]
[ROW][C]-2[/C][C]0.827414834665487[/C][/ROW]
[ROW][C]-1[/C][C]0.886217127317887[/C][/ROW]
[ROW][C]0[/C][C]0.9438938146598[/C][/ROW]
[ROW][C]1[/C][C]0.900691877843436[/C][/ROW]
[ROW][C]2[/C][C]0.85928874584392[/C][/ROW]
[ROW][C]3[/C][C]0.820239107516748[/C][/ROW]
[ROW][C]4[/C][C]0.787592285385713[/C][/ROW]
[ROW][C]5[/C][C]0.754630410892565[/C][/ROW]
[ROW][C]6[/C][C]0.723372188473765[/C][/ROW]
[ROW][C]7[/C][C]0.691853340972153[/C][/ROW]
[ROW][C]8[/C][C]0.658270973142908[/C][/ROW]
[ROW][C]9[/C][C]0.623363820748207[/C][/ROW]
[ROW][C]10[/C][C]0.587629641932915[/C][/ROW]
[ROW][C]11[/C][C]0.548427026069998[/C][/ROW]
[ROW][C]12[/C][C]0.506015033597691[/C][/ROW]
[ROW][C]13[/C][C]0.47082892742653[/C][/ROW]
[ROW][C]14[/C][C]0.437481591565191[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6353&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6353&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])
-140.178401528218461
-130.226117151717748
-120.271171672420362
-110.316991885476050
-100.365416717659090
-90.418493819330666
-80.474822408902887
-70.536160901010385
-60.595538088785421
-50.65473976814277
-40.712570237694782
-30.7697819573718
-20.827414834665487
-10.886217127317887
00.9438938146598
10.900691877843436
20.85928874584392
30.820239107516748
40.787592285385713
50.754630410892565
60.723372188473765
70.691853340972153
80.658270973142908
90.623363820748207
100.587629641932915
110.548427026069998
120.506015033597691
130.47082892742653
140.437481591565191


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) x <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',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')