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Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationThu, 22 Nov 2007 04:52:04 -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/22/t1195731849161rokppbfipfa4.htm/, Retrieved Thu, 02 May 2024 17:16:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=5935, Retrieved Thu, 02 May 2024 17:16:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsQ6
Estimated Impact226
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [Cross Correlation] [2007-11-22 11:52:04] [c8a4a40341940b3329d625726d352171] [Current]
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Dataseries X:
15859.4
15258.9
15498.6
15106.5
15023.6
12083.0
15761.3
16942.6
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
17422.0
16704.5
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
Dataseries Y:
12710.3
12120.8
12469.5
12054.6
12112.9
9617.2
12645.8
13581.3
12162.3
10969.7
11880.0
11887.6
12926.9
12300.0
12092.8
12380.8
12196.9
9455.0
13168.0
13427.9
11980.5
11884.8
11691.7
12233.8
14341.4
13130.7
12421.1
14285.8
12864.6
11160.2
14316.2
14388.7
14013.9
13419.0
12769.6
13315.5
15332.9
14243.0
13824.4
14962.9
13202.9
12199.0
15508.9
14199.8
15169.6
14058.0
13786.2
14147.9
16541.7
13587.5
15582.4
15802.8
14130.5
12923.2
15612.2
16033.7
16036.6
14037.8
15330.6
15038.3
17401.8




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=5935&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=5935&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5935&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 series0
Degree of seasonal differencing (D) of X series1
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])
-130.279076664313528
-120.374752142695155
-110.0511854017560024
-100.413872787354109
-90.269005687500982
-80.108270818631738
-70.282039283062446
-60.211617086322655
-50.0638828033923742
-40.323596806209968
-30.131473593485516
-20.00512737237257253
-10.22126907700475
0-0.0394899713187497
1-0.0117874691322641
20.181810337141005
3-0.121908810567629
4-0.0862751547218497
50.0254217182595639
6-0.14716391265108
7-0.123539852655861
8-0.0123837859106411
9-0.218743174394968
10-0.191247398476388
11-0.0608498165313204
12-0.266022159575433
13-0.168200117165143

\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 & 1 \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
-13 & 0.279076664313528 \tabularnewline
-12 & 0.374752142695155 \tabularnewline
-11 & 0.0511854017560024 \tabularnewline
-10 & 0.413872787354109 \tabularnewline
-9 & 0.269005687500982 \tabularnewline
-8 & 0.108270818631738 \tabularnewline
-7 & 0.282039283062446 \tabularnewline
-6 & 0.211617086322655 \tabularnewline
-5 & 0.0638828033923742 \tabularnewline
-4 & 0.323596806209968 \tabularnewline
-3 & 0.131473593485516 \tabularnewline
-2 & 0.00512737237257253 \tabularnewline
-1 & 0.22126907700475 \tabularnewline
0 & -0.0394899713187497 \tabularnewline
1 & -0.0117874691322641 \tabularnewline
2 & 0.181810337141005 \tabularnewline
3 & -0.121908810567629 \tabularnewline
4 & -0.0862751547218497 \tabularnewline
5 & 0.0254217182595639 \tabularnewline
6 & -0.14716391265108 \tabularnewline
7 & -0.123539852655861 \tabularnewline
8 & -0.0123837859106411 \tabularnewline
9 & -0.218743174394968 \tabularnewline
10 & -0.191247398476388 \tabularnewline
11 & -0.0608498165313204 \tabularnewline
12 & -0.266022159575433 \tabularnewline
13 & -0.168200117165143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5935&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]1[/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]-13[/C][C]0.279076664313528[/C][/ROW]
[ROW][C]-12[/C][C]0.374752142695155[/C][/ROW]
[ROW][C]-11[/C][C]0.0511854017560024[/C][/ROW]
[ROW][C]-10[/C][C]0.413872787354109[/C][/ROW]
[ROW][C]-9[/C][C]0.269005687500982[/C][/ROW]
[ROW][C]-8[/C][C]0.108270818631738[/C][/ROW]
[ROW][C]-7[/C][C]0.282039283062446[/C][/ROW]
[ROW][C]-6[/C][C]0.211617086322655[/C][/ROW]
[ROW][C]-5[/C][C]0.0638828033923742[/C][/ROW]
[ROW][C]-4[/C][C]0.323596806209968[/C][/ROW]
[ROW][C]-3[/C][C]0.131473593485516[/C][/ROW]
[ROW][C]-2[/C][C]0.00512737237257253[/C][/ROW]
[ROW][C]-1[/C][C]0.22126907700475[/C][/ROW]
[ROW][C]0[/C][C]-0.0394899713187497[/C][/ROW]
[ROW][C]1[/C][C]-0.0117874691322641[/C][/ROW]
[ROW][C]2[/C][C]0.181810337141005[/C][/ROW]
[ROW][C]3[/C][C]-0.121908810567629[/C][/ROW]
[ROW][C]4[/C][C]-0.0862751547218497[/C][/ROW]
[ROW][C]5[/C][C]0.0254217182595639[/C][/ROW]
[ROW][C]6[/C][C]-0.14716391265108[/C][/ROW]
[ROW][C]7[/C][C]-0.123539852655861[/C][/ROW]
[ROW][C]8[/C][C]-0.0123837859106411[/C][/ROW]
[ROW][C]9[/C][C]-0.218743174394968[/C][/ROW]
[ROW][C]10[/C][C]-0.191247398476388[/C][/ROW]
[ROW][C]11[/C][C]-0.0608498165313204[/C][/ROW]
[ROW][C]12[/C][C]-0.266022159575433[/C][/ROW]
[ROW][C]13[/C][C]-0.168200117165143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5935&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5935&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 series0
Degree of seasonal differencing (D) of X series1
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])
-130.279076664313528
-120.374752142695155
-110.0511854017560024
-100.413872787354109
-90.269005687500982
-80.108270818631738
-70.282039283062446
-60.211617086322655
-50.0638828033923742
-40.323596806209968
-30.131473593485516
-20.00512737237257253
-10.22126907700475
0-0.0394899713187497
1-0.0117874691322641
20.181810337141005
3-0.121908810567629
4-0.0862751547218497
50.0254217182595639
6-0.14716391265108
7-0.123539852655861
8-0.0123837859106411
9-0.218743174394968
10-0.191247398476388
11-0.0608498165313204
12-0.266022159575433
13-0.168200117165143



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