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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 computationFri, 23 Nov 2007 01:51:42 -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/23/t11958075829smtpkdo4c8avi9.htm/, Retrieved Mon, 29 Apr 2024 03:31:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=6115, Retrieved Mon, 29 Apr 2024 03:31:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact212

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] [] [2007-11-23 08:51:42] [079615521100262cd8b5675a0217a3b1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
88,74
88,92
88,77
89,17
89,61
89,52
89,74
89,40
89,36
89,38
89,36
89,29
89,59
89,79
89,86
90,21
90,37
90,19
90,33
90,22
90,42
90,54
90,73
91,02
91,19
91,53
91,88
92,06
92,32
92,67
92,85
92,82
93,46
93,23
93,54
93,29
93,20
93,60
93,81
94,62
95,22
95,38
95,31
95,30
95,57
95,42
95,53
95,33
95,90
96,06
96,31
96,34
96,49
96,22
96,53
96,50
96,77
96,66
96,58
96,63
97,06
97,73
98,01
97,76
97,49
97,77
97,96
98,23
98,51
98,19
98,37
98,31
98,60
98,97
99,11
99,64
100,03
99,98
100,32
100,44
100,51
101,00
100,88
100,55
100,83
101,51
102,16
102,39
102,54
102,85
103,47
103,57
103,69
103,50
103,47
103,45
103,48
103,93
103,89
104,40
104,79
104,77
105,13
105,26
104,96
104,75
105,01
105,15
105,20
105,77
105,78
106,26
106,13
106,12
106,57
106,44
106,54
Dataseries Y:
Download CSV
Histogram 
88,95
88,81
88,90
90,15
90,92
90,78
90,81
89,46
89,22
88,89
89,41
89,59
90,25
90,20
90,27
90,71
91,18
90,66
89,72
88,72
88,91
89,15
89,15
89,08
89,28
89,47
89,53
90,72
90,91
91,38
91,49
90,90
90,93
90,57
91,28
90,83
91,50
91,58
92,49
94,16
95,46
95,80
95,32
95,41
95,35
95,68
95,59
94,96
96,92
96,06
96,59
96,67
97,27
96,38
96,47
96,05
96,76
96,51
96,55
95,97
97,00
97,46
97,90
98,42
98,54
99,00
98,94
99,02
100,07
98,72
98,73
98,04
99,08
99,22
99,57
100,44
100,84
100,75
100,49
99,98
99,96
99,76
100,11
99,79
100,29
101,12
102,65
102,71
103,39
102,80
102,07
102,15
101,21
101,27
101,86
101,65
101,94
102,62
102,71
103,39
104,51
104,09
104,29
104,57
105,39
105,15
106,13
105,46
106,47
106,62
106,52
108,04
107,15
107,32
107,76
107,26
107,89

Tables (Output of Computation)





Summary of compuational 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 compuational 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=6115&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]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=6115&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6115&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 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 series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t-k])
-17-0.192469097511668
-160.0860870313982716
-15-0.172780199215953
-140.168590321155393
-13-0.0233635047622764
-120.174752343676701
-110.140447951929067
-10-0.00113590630044887
-90.0707036883621715
-80.0519833218422955
-70.0397035069868281
-6-0.0432849938450096
-5-0.175538176212502
-4-0.111953504686581
-3-0.298831732796920
-20.168481492900283
-1-0.051558905312738
00.547669194204097
10.0573367850470632
20.0161129751877183
30.124491825691775
4-0.0512090007381664
5-0.00286462530275131
6-0.0959430001119355
7-0.314291416342159
8-0.163118724241986
9-0.0685133960892931
100.0826723171385068
110.0522550996511018
120.164707109847053
130.0589989960858926
140.118209785595540
15-0.0407497392996269
16-0.0270778904158413
17-0.0398172287391704

\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 & 1 \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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t-k]) \tabularnewline
-17 & -0.192469097511668 \tabularnewline
-16 & 0.0860870313982716 \tabularnewline
-15 & -0.172780199215953 \tabularnewline
-14 & 0.168590321155393 \tabularnewline
-13 & -0.0233635047622764 \tabularnewline
-12 & 0.174752343676701 \tabularnewline
-11 & 0.140447951929067 \tabularnewline
-10 & -0.00113590630044887 \tabularnewline
-9 & 0.0707036883621715 \tabularnewline
-8 & 0.0519833218422955 \tabularnewline
-7 & 0.0397035069868281 \tabularnewline
-6 & -0.0432849938450096 \tabularnewline
-5 & -0.175538176212502 \tabularnewline
-4 & -0.111953504686581 \tabularnewline
-3 & -0.298831732796920 \tabularnewline
-2 & 0.168481492900283 \tabularnewline
-1 & -0.051558905312738 \tabularnewline
0 & 0.547669194204097 \tabularnewline
1 & 0.0573367850470632 \tabularnewline
2 & 0.0161129751877183 \tabularnewline
3 & 0.124491825691775 \tabularnewline
4 & -0.0512090007381664 \tabularnewline
5 & -0.00286462530275131 \tabularnewline
6 & -0.0959430001119355 \tabularnewline
7 & -0.314291416342159 \tabularnewline
8 & -0.163118724241986 \tabularnewline
9 & -0.0685133960892931 \tabularnewline
10 & 0.0826723171385068 \tabularnewline
11 & 0.0522550996511018 \tabularnewline
12 & 0.164707109847053 \tabularnewline
13 & 0.0589989960858926 \tabularnewline
14 & 0.118209785595540 \tabularnewline
15 & -0.0407497392996269 \tabularnewline
16 & -0.0270778904158413 \tabularnewline
17 & -0.0398172287391704 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6115&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]1[/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]1[/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]-17[/C][C]-0.192469097511668[/C][/ROW]
[ROW][C]-16[/C][C]0.0860870313982716[/C][/ROW]
[ROW][C]-15[/C][C]-0.172780199215953[/C][/ROW]
[ROW][C]-14[/C][C]0.168590321155393[/C][/ROW]
[ROW][C]-13[/C][C]-0.0233635047622764[/C][/ROW]
[ROW][C]-12[/C][C]0.174752343676701[/C][/ROW]
[ROW][C]-11[/C][C]0.140447951929067[/C][/ROW]
[ROW][C]-10[/C][C]-0.00113590630044887[/C][/ROW]
[ROW][C]-9[/C][C]0.0707036883621715[/C][/ROW]
[ROW][C]-8[/C][C]0.0519833218422955[/C][/ROW]
[ROW][C]-7[/C][C]0.0397035069868281[/C][/ROW]
[ROW][C]-6[/C][C]-0.0432849938450096[/C][/ROW]
[ROW][C]-5[/C][C]-0.175538176212502[/C][/ROW]
[ROW][C]-4[/C][C]-0.111953504686581[/C][/ROW]
[ROW][C]-3[/C][C]-0.298831732796920[/C][/ROW]
[ROW][C]-2[/C][C]0.168481492900283[/C][/ROW]
[ROW][C]-1[/C][C]-0.051558905312738[/C][/ROW]
[ROW][C]0[/C][C]0.547669194204097[/C][/ROW]
[ROW][C]1[/C][C]0.0573367850470632[/C][/ROW]
[ROW][C]2[/C][C]0.0161129751877183[/C][/ROW]
[ROW][C]3[/C][C]0.124491825691775[/C][/ROW]
[ROW][C]4[/C][C]-0.0512090007381664[/C][/ROW]
[ROW][C]5[/C][C]-0.00286462530275131[/C][/ROW]
[ROW][C]6[/C][C]-0.0959430001119355[/C][/ROW]
[ROW][C]7[/C][C]-0.314291416342159[/C][/ROW]
[ROW][C]8[/C][C]-0.163118724241986[/C][/ROW]
[ROW][C]9[/C][C]-0.0685133960892931[/C][/ROW]
[ROW][C]10[/C][C]0.0826723171385068[/C][/ROW]
[ROW][C]11[/C][C]0.0522550996511018[/C][/ROW]
[ROW][C]12[/C][C]0.164707109847053[/C][/ROW]
[ROW][C]13[/C][C]0.0589989960858926[/C][/ROW]
[ROW][C]14[/C][C]0.118209785595540[/C][/ROW]
[ROW][C]15[/C][C]-0.0407497392996269[/C][/ROW]
[ROW][C]16[/C][C]-0.0270778904158413[/C][/ROW]
[ROW][C]17[/C][C]-0.0398172287391704[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6115&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6115&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 series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t-k])
-17-0.192469097511668
-160.0860870313982716
-15-0.172780199215953
-140.168590321155393
-13-0.0233635047622764
-120.174752343676701
-110.140447951929067
-10-0.00113590630044887
-90.0707036883621715
-80.0519833218422955
-70.0397035069868281
-6-0.0432849938450096
-5-0.175538176212502
-4-0.111953504686581
-3-0.298831732796920
-20.168481492900283
-1-0.051558905312738
00.547669194204097
10.0573367850470632
20.0161129751877183
30.124491825691775
4-0.0512090007381664
5-0.00286462530275131
6-0.0959430001119355
7-0.314291416342159
8-0.163118724241986
9-0.0685133960892931
100.0826723171385068
110.0522550996511018
120.164707109847053
130.0589989960858926
140.118209785595540
15-0.0407497392996269
16-0.0270778904158413
17-0.0398172287391704


Figures (Output of Computation)

Input Parameters & R Code

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