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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 computationWed, 17 Dec 2008 14:40:54 -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/2008/Dec/17/t1229550711d49hoimnjfernqf.htm/, Retrieved Sun, 19 May 2024 05:35:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=34582, Retrieved Sun, 19 May 2024 05:35:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

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] [cross] [2008-12-17 21:40:54] [c4d631a082add458929a68f815b04b21] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
117,09
116,77
119,39
122,49
124,08
118,29
112,94
113,79
114,43
118,7
120,36
118,27
118,34
117,82
117,65
118,18
121,02
124,78
131,16
130,14
131,75
134,73
135,35
140,32
136,35
131,6
128,9
133,89
138,25
146,23
144,76
149,3
156,8
159,08
165,12
163,14
153,43
151,01
154,72
154,58
155,63
161,67
163,51
162,91
164,8
164,98
154,54
148,6
149,19
150,61
Dataseries Y:
Download CSV
Histogram 
87
96,3
107,1
115,2
106,1
89,5
91,3
97,6
100,7
104,6
94,7
101,8
102,5
105,3
110,3
109,8
117,3
118,8
131,3
125,9
133,1
147
145,8
164,4
149,8
137,7
151,7
156,8
180
180,4
170,4
191,6
199,5
218,2
217,5
205
194
199,3
219,3
211,1
215,2
240,2
242,2
240,7
255,4
253
218,2
203,7
205,6
215,6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 2 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=34582&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]2 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=34582&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34582&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 time2 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 series0
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])
-130.296614034522655
-120.370023494308719
-110.449034155547116
-100.516890290620173
-90.558891770730833
-80.600888730707497
-70.651447411188065
-60.709481394361163
-50.770976946806274
-40.825348683495939
-30.85808323498069
-20.88636023455848
-10.929550385265479
00.979047303453821
10.942872393583857
20.880094750136659
30.823796302547969
40.770962666316763
50.716969329924537
60.661717113956927
70.595516730586526
80.525452324871411
90.461398504345714
100.412617646340291
110.363795689570743
120.293653164864672
130.222940319352107

\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) & 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 & 0.296614034522655 \tabularnewline
-12 & 0.370023494308719 \tabularnewline
-11 & 0.449034155547116 \tabularnewline
-10 & 0.516890290620173 \tabularnewline
-9 & 0.558891770730833 \tabularnewline
-8 & 0.600888730707497 \tabularnewline
-7 & 0.651447411188065 \tabularnewline
-6 & 0.709481394361163 \tabularnewline
-5 & 0.770976946806274 \tabularnewline
-4 & 0.825348683495939 \tabularnewline
-3 & 0.85808323498069 \tabularnewline
-2 & 0.88636023455848 \tabularnewline
-1 & 0.929550385265479 \tabularnewline
0 & 0.979047303453821 \tabularnewline
1 & 0.942872393583857 \tabularnewline
2 & 0.880094750136659 \tabularnewline
3 & 0.823796302547969 \tabularnewline
4 & 0.770962666316763 \tabularnewline
5 & 0.716969329924537 \tabularnewline
6 & 0.661717113956927 \tabularnewline
7 & 0.595516730586526 \tabularnewline
8 & 0.525452324871411 \tabularnewline
9 & 0.461398504345714 \tabularnewline
10 & 0.412617646340291 \tabularnewline
11 & 0.363795689570743 \tabularnewline
12 & 0.293653164864672 \tabularnewline
13 & 0.222940319352107 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34582&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]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]0.296614034522655[/C][/ROW]
[ROW][C]-12[/C][C]0.370023494308719[/C][/ROW]
[ROW][C]-11[/C][C]0.449034155547116[/C][/ROW]
[ROW][C]-10[/C][C]0.516890290620173[/C][/ROW]
[ROW][C]-9[/C][C]0.558891770730833[/C][/ROW]
[ROW][C]-8[/C][C]0.600888730707497[/C][/ROW]
[ROW][C]-7[/C][C]0.651447411188065[/C][/ROW]
[ROW][C]-6[/C][C]0.709481394361163[/C][/ROW]
[ROW][C]-5[/C][C]0.770976946806274[/C][/ROW]
[ROW][C]-4[/C][C]0.825348683495939[/C][/ROW]
[ROW][C]-3[/C][C]0.85808323498069[/C][/ROW]
[ROW][C]-2[/C][C]0.88636023455848[/C][/ROW]
[ROW][C]-1[/C][C]0.929550385265479[/C][/ROW]
[ROW][C]0[/C][C]0.979047303453821[/C][/ROW]
[ROW][C]1[/C][C]0.942872393583857[/C][/ROW]
[ROW][C]2[/C][C]0.880094750136659[/C][/ROW]
[ROW][C]3[/C][C]0.823796302547969[/C][/ROW]
[ROW][C]4[/C][C]0.770962666316763[/C][/ROW]
[ROW][C]5[/C][C]0.716969329924537[/C][/ROW]
[ROW][C]6[/C][C]0.661717113956927[/C][/ROW]
[ROW][C]7[/C][C]0.595516730586526[/C][/ROW]
[ROW][C]8[/C][C]0.525452324871411[/C][/ROW]
[ROW][C]9[/C][C]0.461398504345714[/C][/ROW]
[ROW][C]10[/C][C]0.412617646340291[/C][/ROW]
[ROW][C]11[/C][C]0.363795689570743[/C][/ROW]
[ROW][C]12[/C][C]0.293653164864672[/C][/ROW]
[ROW][C]13[/C][C]0.222940319352107[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34582&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34582&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 series0
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])
-130.296614034522655
-120.370023494308719
-110.449034155547116
-100.516890290620173
-90.558891770730833
-80.600888730707497
-70.651447411188065
-60.709481394361163
-50.770976946806274
-40.825348683495939
-30.85808323498069
-20.88636023455848
-10.929550385265479
00.979047303453821
10.942872393583857
20.880094750136659
30.823796302547969
40.770962666316763
50.716969329924537
60.661717113956927
70.595516730586526
80.525452324871411
90.461398504345714
100.412617646340291
110.363795689570743
120.293653164864672
130.222940319352107


Figures (Output of Computation)

Input Parameters & R Code

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