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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, 21 Dec 2010 14:29:32 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t1292941833fckq9wucs0ruep0.htm/, Retrieved Sat, 18 May 2024 17:53:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113617, Retrieved Sat, 18 May 2024 17:53:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Kendall tau matri...] [2007-12-13 16:32:12] [707a919fab5d6f3020ea3c395672cd86]
- RMPD  [Cross Correlation Function] [Stefan Temmerman] [2008-12-11 13:49:31] [4c0c0466a42d9212e91e81695c3ab4a9]
- RM D      [Cross Correlation Function] [paper] [2010-12-21 14:29:32] [a4671b53c9c003ef222bf9d29c2203ca] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10570
10297
10635
10872
10296
10383
10431
10574
10653
10805
10872
10625
10407
10463
10556
10646
10702
11353
11346
11451
11964
12574
13031
13812
14544
14931
14886
16005
17064
15168
16050
15839
15137
14954
15648
15305
15579
16348
15928
16171
15937
15713
15594
15683
16438
17032
17696
17745
19394
20148
20108
18584
18441
18391
19178
18079
18483
19644
19195
19650
20830
23595
22937
21814
21928
21777
21383
21467
22052
22680
24320
24977
25204
Dataseries Y:
Download CSV
Histogram 
6
6
6
6
5
6
5
6
5
7
4
5
6
6
5
3
2
3
3
2
0
4
4
5
6
6
5
5
3
5
5
5
3
6
6
4
6
5
4
5
5
4
3
2
3
2
-1
0
-2
1
-2
-2
-2
-6
-4
-2
0
-5
-4
-5
-1
-2
-4
-1
1
1
-2
1
1
3
3
1
1

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=113617&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=113617&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113617&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 series-0.6
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series2
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series2
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-130.0313898382452651
-120.0420100171870227
-11-0.176774919177771
-100.262814690352851
-9-0.282570260186653
-80.180665217855586
-7-0.234570942681544
-60.337869713402249
-5-0.245746188750158
-40.068922616768803
-3-0.0124436075441760
-2-0.0993318324890262
-10.207443335578125
0-0.223148968291910
10.193198859394047
2-0.23072483441381
30.128346704900680
4-0.0665370194693759
50.112426624672224
6-0.0870756440107823
70.00686960074892482
8-0.0237744997420325
9-0.0436492613905941
100.193754129325291
11-0.257742381658802
120.183902692386244
13-0.0388093101306112

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & -0.6 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 1 \tabularnewline
Degree of seasonal differencing (D) of X series & 2 \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 & 2 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & 0.0313898382452651 \tabularnewline
-12 & 0.0420100171870227 \tabularnewline
-11 & -0.176774919177771 \tabularnewline
-10 & 0.262814690352851 \tabularnewline
-9 & -0.282570260186653 \tabularnewline
-8 & 0.180665217855586 \tabularnewline
-7 & -0.234570942681544 \tabularnewline
-6 & 0.337869713402249 \tabularnewline
-5 & -0.245746188750158 \tabularnewline
-4 & 0.068922616768803 \tabularnewline
-3 & -0.0124436075441760 \tabularnewline
-2 & -0.0993318324890262 \tabularnewline
-1 & 0.207443335578125 \tabularnewline
0 & -0.223148968291910 \tabularnewline
1 & 0.193198859394047 \tabularnewline
2 & -0.23072483441381 \tabularnewline
3 & 0.128346704900680 \tabularnewline
4 & -0.0665370194693759 \tabularnewline
5 & 0.112426624672224 \tabularnewline
6 & -0.0870756440107823 \tabularnewline
7 & 0.00686960074892482 \tabularnewline
8 & -0.0237744997420325 \tabularnewline
9 & -0.0436492613905941 \tabularnewline
10 & 0.193754129325291 \tabularnewline
11 & -0.257742381658802 \tabularnewline
12 & 0.183902692386244 \tabularnewline
13 & -0.0388093101306112 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113617&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]-0.6[/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]2[/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]2[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]0.0313898382452651[/C][/ROW]
[ROW][C]-12[/C][C]0.0420100171870227[/C][/ROW]
[ROW][C]-11[/C][C]-0.176774919177771[/C][/ROW]
[ROW][C]-10[/C][C]0.262814690352851[/C][/ROW]
[ROW][C]-9[/C][C]-0.282570260186653[/C][/ROW]
[ROW][C]-8[/C][C]0.180665217855586[/C][/ROW]
[ROW][C]-7[/C][C]-0.234570942681544[/C][/ROW]
[ROW][C]-6[/C][C]0.337869713402249[/C][/ROW]
[ROW][C]-5[/C][C]-0.245746188750158[/C][/ROW]
[ROW][C]-4[/C][C]0.068922616768803[/C][/ROW]
[ROW][C]-3[/C][C]-0.0124436075441760[/C][/ROW]
[ROW][C]-2[/C][C]-0.0993318324890262[/C][/ROW]
[ROW][C]-1[/C][C]0.207443335578125[/C][/ROW]
[ROW][C]0[/C][C]-0.223148968291910[/C][/ROW]
[ROW][C]1[/C][C]0.193198859394047[/C][/ROW]
[ROW][C]2[/C][C]-0.23072483441381[/C][/ROW]
[ROW][C]3[/C][C]0.128346704900680[/C][/ROW]
[ROW][C]4[/C][C]-0.0665370194693759[/C][/ROW]
[ROW][C]5[/C][C]0.112426624672224[/C][/ROW]
[ROW][C]6[/C][C]-0.0870756440107823[/C][/ROW]
[ROW][C]7[/C][C]0.00686960074892482[/C][/ROW]
[ROW][C]8[/C][C]-0.0237744997420325[/C][/ROW]
[ROW][C]9[/C][C]-0.0436492613905941[/C][/ROW]
[ROW][C]10[/C][C]0.193754129325291[/C][/ROW]
[ROW][C]11[/C][C]-0.257742381658802[/C][/ROW]
[ROW][C]12[/C][C]0.183902692386244[/C][/ROW]
[ROW][C]13[/C][C]-0.0388093101306112[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113617&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113617&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 series-0.6
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series2
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series2
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-130.0313898382452651
-120.0420100171870227
-11-0.176774919177771
-100.262814690352851
-9-0.282570260186653
-80.180665217855586
-7-0.234570942681544
-60.337869713402249
-5-0.245746188750158
-40.068922616768803
-3-0.0124436075441760
-2-0.0993318324890262
-10.207443335578125
0-0.223148968291910
10.193198859394047
2-0.23072483441381
30.128346704900680
4-0.0665370194693759
50.112426624672224
6-0.0870756440107823
70.00686960074892482
8-0.0237744997420325
9-0.0436492613905941
100.193754129325291
11-0.257742381658802
120.183902692386244
13-0.0388093101306112


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = -0.6 ; par2 = 1 ; par3 = 2 ; par4 = 12 ; par5 = 1 ; par6 = 2 ; par7 = 1 ;
Parameters (R input):
par1 = -0.6 ; par2 = 1 ; par3 = 2 ; par4 = 12 ; par5 = 1 ; par6 = 2 ; par7 = 1 ;
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')