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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 computationTue, 27 Nov 2007 03:47:14 -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/27/t11961598922tleitur26f1tyy.htm/, Retrieved Sun, 05 May 2024 20:25:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=6842, Retrieved Sun, 05 May 2024 20:25:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsgroep MENS
Estimated Impact191

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] [energie en voedin...] [2007-11-27 10:47:14] [68ccea1ea79fa519d33f2664ba3973dd] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.8
103.4
104.9
105.1
105.6
104.5
105.5
105.1
106.9
106.6
106.6
106.5
109.7
109.5
109.2
109.1
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.0
109.2
113.3
112.3
112.3
116.3
118.3
119.4
119.4
119.4
120.1
121.7
123.7
123.7
128.5
127.1
122.6
119.8
122.7
123.4
123.8
121.8
121.2
121.2
121.2
121.2
129.6
131.0
131.0
129.8
129.8
134.9
131.2
127.1
130.5
130.5
131.7
131.7
131.7
Dataseries Y:
Download CSV
Histogram 
100.0
100.0
100.0
100.1
100.0
100.0
99.8
100.0
99.9
99.2
98.7
98.7
98.9
99.2
99.8
100.5
100.1
100.5
98.4
98.6
99.0
99.1
98.9
98.5
96.9
96.8
97.0
97.0
96.9
97.1
97.2
97.9
98.9
99.2
99.5
99.3
99.9
100.0
100.3
100.5
100.7
100.9
100.8
100.9
101.0
100.3
100.1
99.8
99.9
99.9
100.2
99.7
100.4
100.9
101.3
101.4
101.3
100.9
100.9
100.9
101.1
101.1
101.3
101.8
102.9
103.2
103.3
104.5
105.0
104.9
104.9
105.4
106.0

Tables (Output of Computation)





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=6842&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=6842&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6842&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 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])
-150.444548248609067
-140.437697833270517
-130.43633151113496
-120.471341694295437
-110.515617773052758
-100.55505734618752
-90.587454858867919
-80.616351742551836
-70.663046377109011
-60.68355906194861
-50.686928855221178
-40.705282342480908
-30.716595082236766
-20.729701312362767
-10.735914047432224
00.745577216390946
10.683289577822784
20.625453447394864
30.575550567815842
40.531527080533671
50.480349124035905
60.426392762307672
70.381354364296278
80.330610388433747
90.274927799798965
100.230076648726767
110.184555589915794
120.141963925641705
130.101290886019132
140.0641022150061952
150.031107309852579

\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
-15 & 0.444548248609067 \tabularnewline
-14 & 0.437697833270517 \tabularnewline
-13 & 0.43633151113496 \tabularnewline
-12 & 0.471341694295437 \tabularnewline
-11 & 0.515617773052758 \tabularnewline
-10 & 0.55505734618752 \tabularnewline
-9 & 0.587454858867919 \tabularnewline
-8 & 0.616351742551836 \tabularnewline
-7 & 0.663046377109011 \tabularnewline
-6 & 0.68355906194861 \tabularnewline
-5 & 0.686928855221178 \tabularnewline
-4 & 0.705282342480908 \tabularnewline
-3 & 0.716595082236766 \tabularnewline
-2 & 0.729701312362767 \tabularnewline
-1 & 0.735914047432224 \tabularnewline
0 & 0.745577216390946 \tabularnewline
1 & 0.683289577822784 \tabularnewline
2 & 0.625453447394864 \tabularnewline
3 & 0.575550567815842 \tabularnewline
4 & 0.531527080533671 \tabularnewline
5 & 0.480349124035905 \tabularnewline
6 & 0.426392762307672 \tabularnewline
7 & 0.381354364296278 \tabularnewline
8 & 0.330610388433747 \tabularnewline
9 & 0.274927799798965 \tabularnewline
10 & 0.230076648726767 \tabularnewline
11 & 0.184555589915794 \tabularnewline
12 & 0.141963925641705 \tabularnewline
13 & 0.101290886019132 \tabularnewline
14 & 0.0641022150061952 \tabularnewline
15 & 0.031107309852579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6842&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]-15[/C][C]0.444548248609067[/C][/ROW]
[ROW][C]-14[/C][C]0.437697833270517[/C][/ROW]
[ROW][C]-13[/C][C]0.43633151113496[/C][/ROW]
[ROW][C]-12[/C][C]0.471341694295437[/C][/ROW]
[ROW][C]-11[/C][C]0.515617773052758[/C][/ROW]
[ROW][C]-10[/C][C]0.55505734618752[/C][/ROW]
[ROW][C]-9[/C][C]0.587454858867919[/C][/ROW]
[ROW][C]-8[/C][C]0.616351742551836[/C][/ROW]
[ROW][C]-7[/C][C]0.663046377109011[/C][/ROW]
[ROW][C]-6[/C][C]0.68355906194861[/C][/ROW]
[ROW][C]-5[/C][C]0.686928855221178[/C][/ROW]
[ROW][C]-4[/C][C]0.705282342480908[/C][/ROW]
[ROW][C]-3[/C][C]0.716595082236766[/C][/ROW]
[ROW][C]-2[/C][C]0.729701312362767[/C][/ROW]
[ROW][C]-1[/C][C]0.735914047432224[/C][/ROW]
[ROW][C]0[/C][C]0.745577216390946[/C][/ROW]
[ROW][C]1[/C][C]0.683289577822784[/C][/ROW]
[ROW][C]2[/C][C]0.625453447394864[/C][/ROW]
[ROW][C]3[/C][C]0.575550567815842[/C][/ROW]
[ROW][C]4[/C][C]0.531527080533671[/C][/ROW]
[ROW][C]5[/C][C]0.480349124035905[/C][/ROW]
[ROW][C]6[/C][C]0.426392762307672[/C][/ROW]
[ROW][C]7[/C][C]0.381354364296278[/C][/ROW]
[ROW][C]8[/C][C]0.330610388433747[/C][/ROW]
[ROW][C]9[/C][C]0.274927799798965[/C][/ROW]
[ROW][C]10[/C][C]0.230076648726767[/C][/ROW]
[ROW][C]11[/C][C]0.184555589915794[/C][/ROW]
[ROW][C]12[/C][C]0.141963925641705[/C][/ROW]
[ROW][C]13[/C][C]0.101290886019132[/C][/ROW]
[ROW][C]14[/C][C]0.0641022150061952[/C][/ROW]
[ROW][C]15[/C][C]0.031107309852579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6842&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6842&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)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])
-150.444548248609067
-140.437697833270517
-130.43633151113496
-120.471341694295437
-110.515617773052758
-100.55505734618752
-90.587454858867919
-80.616351742551836
-70.663046377109011
-60.68355906194861
-50.686928855221178
-40.705282342480908
-30.716595082236766
-20.729701312362767
-10.735914047432224
00.745577216390946
10.683289577822784
20.625453447394864
30.575550567815842
40.531527080533671
50.480349124035905
60.426392762307672
70.381354364296278
80.330610388433747
90.274927799798965
100.230076648726767
110.184555589915794
120.141963925641705
130.101290886019132
140.0641022150061952
150.031107309852579


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
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',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')