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Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationThu, 29 Nov 2007 16:16:23 -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/30/t1196377581lq9z75g2ps8hukv.htm/, Retrieved Sun, 28 Apr 2024 17:08:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=7614, Retrieved Sun, 28 Apr 2024 17:08:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact217
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [Workshop 4 Q6 ] [2007-11-29 23:16:23] [44cf2be50bc8700e14714598feda9df9] [Current]
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Dataseries X:
15761.3
16943.0
15070.3
13659.6
14768.9
14725.1
15998.1
15370.6
14956.9
15469.7
15101.8
11703.7
16283.6
16726.5
14968.9
14861.0
14583.3
15305.8
17903.9
16379.4
15420.3
17870.5
15912.8
13866.5
17823.2
17872.0
17420.4
16704.4
15991.2
16583.6
19123.5
17838.7
17209.4
18586.5
16258.1
15141.6
19202.1
17746.5
19090.1
18040.3
17515.5
17751.8
21072.4
17170.0
19439.5
19795.4
17574.9
16165.4
19464.6
19932.1
19961.2
17343.4
18924.2
18574.1
21350.6
18840.1
20304.8
21132.4
19753.9
18009.9
20390.4
Dataseries Y:
0.9808
0.9811
1.0014
1.0183
1.0622
1.0773
1.0807
1.0848
1.1582
1.1663
1.1372
1.1139
1.1222
1.1692
1.1702
1.2286
1.2613
1.2646
1.2262
1.1985
1.2007
1.2138
1.2266
1.2176
1.2218
1.2490
1.2991
1.3408
1.3119
1.3014
1.3201
1.2938
1.2694
1.2165
1.2037
1.2292
1.2256
1.2015
1.1786
1.1856
1.2103
1.1938
1.2020
1.2271
1.2770
1.2650
1.2684
1.2811
1.2727
1.2611
1.2881
1.3213
1.2999
1.3074
1.3242
1.3516
1.3511
1.3419
1.3716
1.3622
1.3896




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=7614&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=7614&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=7614&T=0

As an alternative you can also use a 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 series1
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1.1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-130.040948810760078
-120.0503178451874771
-11-0.00864513651824456
-10-0.0802466104510305
-9-0.128643952588727
-8-0.162853310543859
-7-0.210079527809982
-6-0.275034962358321
-5-0.341605850101474
-4-0.407093465304991
-3-0.47307243968031
-2-0.561917107759988
-1-0.67282093056232
0-0.751336324285515
1-0.700769417097611
2-0.600706200602573
3-0.534632552736359
4-0.447965249189347
5-0.341033586292116
6-0.218749603967794
7-0.147536165998775
8-0.115869724925832
9-0.122346600118626
10-0.105059751856136
11-0.0612763319063758
12-0.0207307288853736
130.0197629172484807

\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 & 1.1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & 0.040948810760078 \tabularnewline
-12 & 0.0503178451874771 \tabularnewline
-11 & -0.00864513651824456 \tabularnewline
-10 & -0.0802466104510305 \tabularnewline
-9 & -0.128643952588727 \tabularnewline
-8 & -0.162853310543859 \tabularnewline
-7 & -0.210079527809982 \tabularnewline
-6 & -0.275034962358321 \tabularnewline
-5 & -0.341605850101474 \tabularnewline
-4 & -0.407093465304991 \tabularnewline
-3 & -0.47307243968031 \tabularnewline
-2 & -0.561917107759988 \tabularnewline
-1 & -0.67282093056232 \tabularnewline
0 & -0.751336324285515 \tabularnewline
1 & -0.700769417097611 \tabularnewline
2 & -0.600706200602573 \tabularnewline
3 & -0.534632552736359 \tabularnewline
4 & -0.447965249189347 \tabularnewline
5 & -0.341033586292116 \tabularnewline
6 & -0.218749603967794 \tabularnewline
7 & -0.147536165998775 \tabularnewline
8 & -0.115869724925832 \tabularnewline
9 & -0.122346600118626 \tabularnewline
10 & -0.105059751856136 \tabularnewline
11 & -0.0612763319063758 \tabularnewline
12 & -0.0207307288853736 \tabularnewline
13 & 0.0197629172484807 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=7614&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]1.1[/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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]0.040948810760078[/C][/ROW]
[ROW][C]-12[/C][C]0.0503178451874771[/C][/ROW]
[ROW][C]-11[/C][C]-0.00864513651824456[/C][/ROW]
[ROW][C]-10[/C][C]-0.0802466104510305[/C][/ROW]
[ROW][C]-9[/C][C]-0.128643952588727[/C][/ROW]
[ROW][C]-8[/C][C]-0.162853310543859[/C][/ROW]
[ROW][C]-7[/C][C]-0.210079527809982[/C][/ROW]
[ROW][C]-6[/C][C]-0.275034962358321[/C][/ROW]
[ROW][C]-5[/C][C]-0.341605850101474[/C][/ROW]
[ROW][C]-4[/C][C]-0.407093465304991[/C][/ROW]
[ROW][C]-3[/C][C]-0.47307243968031[/C][/ROW]
[ROW][C]-2[/C][C]-0.561917107759988[/C][/ROW]
[ROW][C]-1[/C][C]-0.67282093056232[/C][/ROW]
[ROW][C]0[/C][C]-0.751336324285515[/C][/ROW]
[ROW][C]1[/C][C]-0.700769417097611[/C][/ROW]
[ROW][C]2[/C][C]-0.600706200602573[/C][/ROW]
[ROW][C]3[/C][C]-0.534632552736359[/C][/ROW]
[ROW][C]4[/C][C]-0.447965249189347[/C][/ROW]
[ROW][C]5[/C][C]-0.341033586292116[/C][/ROW]
[ROW][C]6[/C][C]-0.218749603967794[/C][/ROW]
[ROW][C]7[/C][C]-0.147536165998775[/C][/ROW]
[ROW][C]8[/C][C]-0.115869724925832[/C][/ROW]
[ROW][C]9[/C][C]-0.122346600118626[/C][/ROW]
[ROW][C]10[/C][C]-0.105059751856136[/C][/ROW]
[ROW][C]11[/C][C]-0.0612763319063758[/C][/ROW]
[ROW][C]12[/C][C]-0.0207307288853736[/C][/ROW]
[ROW][C]13[/C][C]0.0197629172484807[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=7614&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=7614&T=1

As an alternative you can also use a 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 series1.1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-130.040948810760078
-120.0503178451874771
-11-0.00864513651824456
-10-0.0802466104510305
-9-0.128643952588727
-8-0.162853310543859
-7-0.210079527809982
-6-0.275034962358321
-5-0.341605850101474
-4-0.407093465304991
-3-0.47307243968031
-2-0.561917107759988
-1-0.67282093056232
0-0.751336324285515
1-0.700769417097611
2-0.600706200602573
3-0.534632552736359
4-0.447965249189347
5-0.341033586292116
6-0.218749603967794
7-0.147536165998775
8-0.115869724925832
9-0.122346600118626
10-0.105059751856136
11-0.0612763319063758
12-0.0207307288853736
130.0197629172484807



Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1.1 ; par6 = 0 ; par7 = 1 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1.1 ; par6 = 0 ; 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) 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')