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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 14:54:57 -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/t1229550925c7w6415k6nuwwg5.htm/, Retrieved Sun, 19 May 2024 04:07:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=34583, Retrieved Sun, 19 May 2024 04:07:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact167

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 21:54:57] [c4d631a082add458929a68f815b04b21] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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
Dataseries Y:
Download CSV
Histogram 
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

Tables (Output of Computation)





Summary of computational 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 computational 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=34583&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]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=34583&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34583&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 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)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])
-130.222940319352107
-120.293653164864672
-110.363795689570743
-100.412617646340291
-90.461398504345714
-80.525452324871411
-70.595516730586526
-60.661717113956927
-50.716969329924537
-40.770962666316763
-30.823796302547969
-20.88009475013666
-10.942872393583857
00.979047303453821
10.929550385265479
20.88636023455848
30.85808323498069
40.825348683495939
50.770976946806274
60.709481394361163
70.651447411188065
80.600888730707498
90.558891770730833
100.516890290620173
110.449034155547116
120.370023494308719
130.296614034522655

\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 & 0.222940319352107 \tabularnewline
-12 & 0.293653164864672 \tabularnewline
-11 & 0.363795689570743 \tabularnewline
-10 & 0.412617646340291 \tabularnewline
-9 & 0.461398504345714 \tabularnewline
-8 & 0.525452324871411 \tabularnewline
-7 & 0.595516730586526 \tabularnewline
-6 & 0.661717113956927 \tabularnewline
-5 & 0.716969329924537 \tabularnewline
-4 & 0.770962666316763 \tabularnewline
-3 & 0.823796302547969 \tabularnewline
-2 & 0.88009475013666 \tabularnewline
-1 & 0.942872393583857 \tabularnewline
0 & 0.979047303453821 \tabularnewline
1 & 0.929550385265479 \tabularnewline
2 & 0.88636023455848 \tabularnewline
3 & 0.85808323498069 \tabularnewline
4 & 0.825348683495939 \tabularnewline
5 & 0.770976946806274 \tabularnewline
6 & 0.709481394361163 \tabularnewline
7 & 0.651447411188065 \tabularnewline
8 & 0.600888730707498 \tabularnewline
9 & 0.558891770730833 \tabularnewline
10 & 0.516890290620173 \tabularnewline
11 & 0.449034155547116 \tabularnewline
12 & 0.370023494308719 \tabularnewline
13 & 0.296614034522655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34583&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]0.222940319352107[/C][/ROW]
[ROW][C]-12[/C][C]0.293653164864672[/C][/ROW]
[ROW][C]-11[/C][C]0.363795689570743[/C][/ROW]
[ROW][C]-10[/C][C]0.412617646340291[/C][/ROW]
[ROW][C]-9[/C][C]0.461398504345714[/C][/ROW]
[ROW][C]-8[/C][C]0.525452324871411[/C][/ROW]
[ROW][C]-7[/C][C]0.595516730586526[/C][/ROW]
[ROW][C]-6[/C][C]0.661717113956927[/C][/ROW]
[ROW][C]-5[/C][C]0.716969329924537[/C][/ROW]
[ROW][C]-4[/C][C]0.770962666316763[/C][/ROW]
[ROW][C]-3[/C][C]0.823796302547969[/C][/ROW]
[ROW][C]-2[/C][C]0.88009475013666[/C][/ROW]
[ROW][C]-1[/C][C]0.942872393583857[/C][/ROW]
[ROW][C]0[/C][C]0.979047303453821[/C][/ROW]
[ROW][C]1[/C][C]0.929550385265479[/C][/ROW]
[ROW][C]2[/C][C]0.88636023455848[/C][/ROW]
[ROW][C]3[/C][C]0.85808323498069[/C][/ROW]
[ROW][C]4[/C][C]0.825348683495939[/C][/ROW]
[ROW][C]5[/C][C]0.770976946806274[/C][/ROW]
[ROW][C]6[/C][C]0.709481394361163[/C][/ROW]
[ROW][C]7[/C][C]0.651447411188065[/C][/ROW]
[ROW][C]8[/C][C]0.600888730707498[/C][/ROW]
[ROW][C]9[/C][C]0.558891770730833[/C][/ROW]
[ROW][C]10[/C][C]0.516890290620173[/C][/ROW]
[ROW][C]11[/C][C]0.449034155547116[/C][/ROW]
[ROW][C]12[/C][C]0.370023494308719[/C][/ROW]
[ROW][C]13[/C][C]0.296614034522655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34583&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34583&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])
-130.222940319352107
-120.293653164864672
-110.363795689570743
-100.412617646340291
-90.461398504345714
-80.525452324871411
-70.595516730586526
-60.661717113956927
-50.716969329924537
-40.770962666316763
-30.823796302547969
-20.88009475013666
-10.942872393583857
00.979047303453821
10.929550385265479
20.88636023455848
30.85808323498069
40.825348683495939
50.770976946806274
60.709481394361163
70.651447411188065
80.600888730707498
90.558891770730833
100.516890290620173
110.449034155547116
120.370023494308719
130.296614034522655


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')