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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 computationSun, 25 Nov 2007 08:19:15 -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/25/t1196003617m1n8xgsmz94n1gs.htm/, Retrieved Sat, 04 May 2024 05:56:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=6485, Retrieved Sat, 04 May 2024 05:56:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact184

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] [Totaal en machine...] [2007-11-25 15:19:15] [919a0fafa615dc0e3c6b6d22cd1007a0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
112.6
113.8
107.8
103.2
103.3
101.2
107.7
110.4
101.9
115.9
89.9
88.6
117.2
123.9
100
103.6
94.1
98.7
119.5
112.7
104.4
124.7
89.1
97
121.6
118.8
114
111.5
97.2
102.5
113.4
109.8
104.9
126.1
80
96.8
117.2
112.3
117.3
111.1
102.2
104.3
122.9
107.6
121.3
131.5
89
104.4
128.9
135.9
133.3
121.3
120.5
120.4
137.9
126.1
133.2
146.6
103.4
117.2
Dataseries Y:
Download CSV
Histogram 
106.8
113.7
102.5
96.6
92.1
95.6
102.3
98.6
98.2
104.5
84
73.8
103.9
106
97.2
102.6
89
93.8
116.7
106.8
98.5
118.7
90
91.9
113.3
113.1
104.1
108.7
96.7
101
116.9
105.8
99
129.4
83
88.9
115.9
104.2
113.4
112.2
100.8
107.3
126.6
102.9
117.9
128.8
87.5
93.8
122.7
126.2
124.6
116.7
115.2
111.1
129.9
113.3
118.5
133.5
102.1
102.4

Tables (Output of Computation)





Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6485&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6485&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6485&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 time5 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






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 series1
krho(Y[t],X[t+k])
-130.0370917683100851
-120.256810752730900
-11-0.177290030543156
-100.196107281670325
-90.161560187160695
-8-0.0480579953269876
-70.197677527917861
-60.10230253890886
-5-0.029541038853089
-40.161073417705934
-30.0633954391746503
-2-0.059352439108144
-10.0940624693915078
0-0.190439300268184
1-0.0690001999464688
20.0380338973503241
3-0.119335033290024
4-0.0247601173266201
50.055690794008488
6-0.0961443337013047
70.111980767520834
80.159424229189611
90.0401995614007164
100.061840667348995
110.157002969917026
120.0598452553469295
130.170971014288187

\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 & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & 0.0370917683100851 \tabularnewline
-12 & 0.256810752730900 \tabularnewline
-11 & -0.177290030543156 \tabularnewline
-10 & 0.196107281670325 \tabularnewline
-9 & 0.161560187160695 \tabularnewline
-8 & -0.0480579953269876 \tabularnewline
-7 & 0.197677527917861 \tabularnewline
-6 & 0.10230253890886 \tabularnewline
-5 & -0.029541038853089 \tabularnewline
-4 & 0.161073417705934 \tabularnewline
-3 & 0.0633954391746503 \tabularnewline
-2 & -0.059352439108144 \tabularnewline
-1 & 0.0940624693915078 \tabularnewline
0 & -0.190439300268184 \tabularnewline
1 & -0.0690001999464688 \tabularnewline
2 & 0.0380338973503241 \tabularnewline
3 & -0.119335033290024 \tabularnewline
4 & -0.0247601173266201 \tabularnewline
5 & 0.055690794008488 \tabularnewline
6 & -0.0961443337013047 \tabularnewline
7 & 0.111980767520834 \tabularnewline
8 & 0.159424229189611 \tabularnewline
9 & 0.0401995614007164 \tabularnewline
10 & 0.061840667348995 \tabularnewline
11 & 0.157002969917026 \tabularnewline
12 & 0.0598452553469295 \tabularnewline
13 & 0.170971014288187 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6485&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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]0.0370917683100851[/C][/ROW]
[ROW][C]-12[/C][C]0.256810752730900[/C][/ROW]
[ROW][C]-11[/C][C]-0.177290030543156[/C][/ROW]
[ROW][C]-10[/C][C]0.196107281670325[/C][/ROW]
[ROW][C]-9[/C][C]0.161560187160695[/C][/ROW]
[ROW][C]-8[/C][C]-0.0480579953269876[/C][/ROW]
[ROW][C]-7[/C][C]0.197677527917861[/C][/ROW]
[ROW][C]-6[/C][C]0.10230253890886[/C][/ROW]
[ROW][C]-5[/C][C]-0.029541038853089[/C][/ROW]
[ROW][C]-4[/C][C]0.161073417705934[/C][/ROW]
[ROW][C]-3[/C][C]0.0633954391746503[/C][/ROW]
[ROW][C]-2[/C][C]-0.059352439108144[/C][/ROW]
[ROW][C]-1[/C][C]0.0940624693915078[/C][/ROW]
[ROW][C]0[/C][C]-0.190439300268184[/C][/ROW]
[ROW][C]1[/C][C]-0.0690001999464688[/C][/ROW]
[ROW][C]2[/C][C]0.0380338973503241[/C][/ROW]
[ROW][C]3[/C][C]-0.119335033290024[/C][/ROW]
[ROW][C]4[/C][C]-0.0247601173266201[/C][/ROW]
[ROW][C]5[/C][C]0.055690794008488[/C][/ROW]
[ROW][C]6[/C][C]-0.0961443337013047[/C][/ROW]
[ROW][C]7[/C][C]0.111980767520834[/C][/ROW]
[ROW][C]8[/C][C]0.159424229189611[/C][/ROW]
[ROW][C]9[/C][C]0.0401995614007164[/C][/ROW]
[ROW][C]10[/C][C]0.061840667348995[/C][/ROW]
[ROW][C]11[/C][C]0.157002969917026[/C][/ROW]
[ROW][C]12[/C][C]0.0598452553469295[/C][/ROW]
[ROW][C]13[/C][C]0.170971014288187[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6485&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6485&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 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 series1
krho(Y[t],X[t+k])
-130.0370917683100851
-120.256810752730900
-11-0.177290030543156
-100.196107281670325
-90.161560187160695
-8-0.0480579953269876
-70.197677527917861
-60.10230253890886
-5-0.029541038853089
-40.161073417705934
-30.0633954391746503
-2-0.059352439108144
-10.0940624693915078
0-0.190439300268184
1-0.0690001999464688
20.0380338973503241
3-0.119335033290024
4-0.0247601173266201
50.055690794008488
6-0.0961443337013047
70.111980767520834
80.159424229189611
90.0401995614007164
100.061840667348995
110.157002969917026
120.0598452553469295
130.170971014288187


Figures (Output of Computation)

Input Parameters & R Code

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