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Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationFri, 07 Dec 2007 05:57:00 -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/Dec/07/t119703144388d81c7u81w4n1g.htm/, Retrieved Sun, 28 Apr 2024 20:03:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=2794, Retrieved Sun, 28 Apr 2024 20:03:25 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [Cross Correlation...] [2007-12-07 12:57:00] [ca5e0f9f346e091f4d0fe7e17f7dba21] [Current]
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Dataseries X:
101,5
126,6
93,9
89,8
93,4
101,5
110,4
105,9
108,4
113,9
86,1
69,4
101,2
100,5
98
106,6
90,1
96,9
125,9
112
100
123,9
79,8
83,4
113,6
112,9
104
109,9
99
106,3
128,9
111,1
102,9
130
87
87,5
117,6
103,4
110,8
112,6
102,5
112,4
135,6
105,1
127,7
137
91
90,5
122,4
123,3
124,3
120
118,1
119
142,7
123,6
129,6
146,9
108,7
99,4
Dataseries Y:
108,4
117
103,8
100,8
110,6
104
112,6
107,3
98,9
109,8
104,9
102,2
123,9
124,9
112,7
121,9
100,6
104,3
120,4
107,5
102,9
125,6
107,5
108,8
128,4
121,1
119,5
128,7
108,7
105,5
119,8
111,3
110,6
120,1
97,5
107,7
127,3
117,2
119,8
116,2
111
112,4
130,6
109,1
118,8
123,9
101,6
112,8
128
129,6
125,8
119,5
115,7
113,6
129,7
112
116,8
126,3
112,9
115,9




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=2794&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=2794&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2794&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 series0
Seasonal Period (s)1
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])
-14-0.154551749335059
-13-0.162033062153822
-120.392888869954226
-11-0.0335546592720612
-10-0.258796149779621
-90.238456354495266
-8-0.00771371043986249
-70.0141436883390533
-60.495932857774105
-50.134157381804131
-4-0.0678174140700726
-30.258740823186658
-2-0.208886070342851
-1-0.0831380831491027
00.704708274931032
10.0923446333059778
20.0202025631748818
30.474930722786954
40.0565212654825738
50.147917956470911
60.595921589492243
70.137338354446251
80.102532953501372
90.270281875043985
10-0.271111443911877
11-0.0799682960495309
120.464955587953066
13-0.0141831147245367
14-0.00353054147341108

\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) & 1 \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 & -0.154551749335059 \tabularnewline
-13 & -0.162033062153822 \tabularnewline
-12 & 0.392888869954226 \tabularnewline
-11 & -0.0335546592720612 \tabularnewline
-10 & -0.258796149779621 \tabularnewline
-9 & 0.238456354495266 \tabularnewline
-8 & -0.00771371043986249 \tabularnewline
-7 & 0.0141436883390533 \tabularnewline
-6 & 0.495932857774105 \tabularnewline
-5 & 0.134157381804131 \tabularnewline
-4 & -0.0678174140700726 \tabularnewline
-3 & 0.258740823186658 \tabularnewline
-2 & -0.208886070342851 \tabularnewline
-1 & -0.0831380831491027 \tabularnewline
0 & 0.704708274931032 \tabularnewline
1 & 0.0923446333059778 \tabularnewline
2 & 0.0202025631748818 \tabularnewline
3 & 0.474930722786954 \tabularnewline
4 & 0.0565212654825738 \tabularnewline
5 & 0.147917956470911 \tabularnewline
6 & 0.595921589492243 \tabularnewline
7 & 0.137338354446251 \tabularnewline
8 & 0.102532953501372 \tabularnewline
9 & 0.270281875043985 \tabularnewline
10 & -0.271111443911877 \tabularnewline
11 & -0.0799682960495309 \tabularnewline
12 & 0.464955587953066 \tabularnewline
13 & -0.0141831147245367 \tabularnewline
14 & -0.00353054147341108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2794&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]1[/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]-0.154551749335059[/C][/ROW]
[ROW][C]-13[/C][C]-0.162033062153822[/C][/ROW]
[ROW][C]-12[/C][C]0.392888869954226[/C][/ROW]
[ROW][C]-11[/C][C]-0.0335546592720612[/C][/ROW]
[ROW][C]-10[/C][C]-0.258796149779621[/C][/ROW]
[ROW][C]-9[/C][C]0.238456354495266[/C][/ROW]
[ROW][C]-8[/C][C]-0.00771371043986249[/C][/ROW]
[ROW][C]-7[/C][C]0.0141436883390533[/C][/ROW]
[ROW][C]-6[/C][C]0.495932857774105[/C][/ROW]
[ROW][C]-5[/C][C]0.134157381804131[/C][/ROW]
[ROW][C]-4[/C][C]-0.0678174140700726[/C][/ROW]
[ROW][C]-3[/C][C]0.258740823186658[/C][/ROW]
[ROW][C]-2[/C][C]-0.208886070342851[/C][/ROW]
[ROW][C]-1[/C][C]-0.0831380831491027[/C][/ROW]
[ROW][C]0[/C][C]0.704708274931032[/C][/ROW]
[ROW][C]1[/C][C]0.0923446333059778[/C][/ROW]
[ROW][C]2[/C][C]0.0202025631748818[/C][/ROW]
[ROW][C]3[/C][C]0.474930722786954[/C][/ROW]
[ROW][C]4[/C][C]0.0565212654825738[/C][/ROW]
[ROW][C]5[/C][C]0.147917956470911[/C][/ROW]
[ROW][C]6[/C][C]0.595921589492243[/C][/ROW]
[ROW][C]7[/C][C]0.137338354446251[/C][/ROW]
[ROW][C]8[/C][C]0.102532953501372[/C][/ROW]
[ROW][C]9[/C][C]0.270281875043985[/C][/ROW]
[ROW][C]10[/C][C]-0.271111443911877[/C][/ROW]
[ROW][C]11[/C][C]-0.0799682960495309[/C][/ROW]
[ROW][C]12[/C][C]0.464955587953066[/C][/ROW]
[ROW][C]13[/C][C]-0.0141831147245367[/C][/ROW]
[ROW][C]14[/C][C]-0.00353054147341108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2794&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2794&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 series0
Seasonal Period (s)1
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])
-14-0.154551749335059
-13-0.162033062153822
-120.392888869954226
-11-0.0335546592720612
-10-0.258796149779621
-90.238456354495266
-8-0.00771371043986249
-70.0141436883390533
-60.495932857774105
-50.134157381804131
-4-0.0678174140700726
-30.258740823186658
-2-0.208886070342851
-1-0.0831380831491027
00.704708274931032
10.0923446333059778
20.0202025631748818
30.474930722786954
40.0565212654825738
50.147917956470911
60.595921589492243
70.137338354446251
80.102532953501372
90.270281875043985
10-0.271111443911877
11-0.0799682960495309
120.464955587953066
13-0.0141831147245367
14-0.00353054147341108



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