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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 computationMon, 01 Dec 2008 14:28:45 -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/01/t122816703383vddsp9bjxuen4.htm/, Retrieved Sun, 05 May 2024 08:52:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27423, Retrieved Sun, 05 May 2024 08:52:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:40:39] [b98453cac15ba1066b407e146608df68]
F RMPD    [Cross Correlation Function] [Q7] [2008-12-01 21:28:45] [787873b6436f665b5b192a0bdb2e43c9] [Current]
Feedback Forum
2008-12-08 10:05:51 [Joris Deboel] [reply] 
De techniek is juist enkel nog een correcte uitleg erbij en het is in orde.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,95
0,98
1,23
1,17
0,84
0,74
0,65
0,91
1,19
1,30
1,53
1,94
1,79
1,95
2,26
2,04
2,16
2,75
2,79
2,88
3,36
2,97
3,10
2,49
2,20
2,25
2,09
2,79
3,14
2,93
2,65
2,67
2,26
2,35
2,13
2,18
2,90
2,63
2,67
1,81
1,33
0,88
1,28
1,26
1,26
1,29
1,10
1,37
1.21
1.74
1.76
1.48
1.04
1.62
1.49
1.79
1.8
1.58
1.86
1.74
1.59
1.26
1.13
1.92
2.61
2.26
2.41
2.26
2.03
2.86
2.55
2.27
2.26
2.57
3.07
2.76
2.51
2.87
3.14
3.11
3.16
2.47
2.57
2.89
2.63
2.38
1.69
1.96
2.19
1.87
1.6
1.63
1.22
1.21
1.49
1.64
1.66
1.77
1.82
1.78
1.28
1.29
1.37
1.12
1.51
2.24
2.94
3.09
3,46
3,64
4,39
4,15
5,21
5,80
5,91
Dataseries Y:
Download CSV
Histogram 
13.92
13.22
13.31
12.91
13.19
12.92
13.43
13.72
13.97
14.91
14.46
14.12
14.23
15.04
14.80
14.49
15.14
14.34
15.12
15.14
14.34
14.36
14.91
15.56
16.50
15.57
15.14
15.19
15.07
14.48
14.27
14.72
14.65
14.38
13.95
14.85
14.87
14.83
15.03
15.47
16.21
16.55
17.04
17.22
17.47
17.75
17.84
18.47
18.38
18.55
18.39
18.88
20.21
19.67
20.09
18.78
19.74
20.64
20.34
21.75
22.10
22.81
22.91
22.46
21.78
25.05
23.70
23.02
24.34
24.15
25.85
26.42
26.54
26.36
26.99
27.52
26.63
26.26
24.86
26.84
26.57
24.67
27.24
27.77
27.61
27.27
28.46
26.97
29.95
29.88
29.67
31.19
30.24
30.03
31.02
30.45
31.70
32.10
32.32
32.18
33.43
33.07
35.32
35.17
35.29
37.89
38.32
37.07
39.77
39.20
40.46
44.95
41.69
41.88
45.86

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27423&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27423&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27423&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'George Udny Yule' @ 72.249.76.132






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])
-17-0.00479186598655025
-16-0.00587074621520246
-15-0.00934197190447173
-14-0.0260825695178232
-13-0.0415367888658148
-12-0.0506282747643316
-11-0.065436375091796
-10-0.0683764172378799
-9-0.0557554455084368
-8-0.0273199885503661
-70.00659598587029291
-60.0511536298504029
-50.0943864673534992
-40.151430496348075
-30.199773656948116
-20.275310565908975
-10.362267260210596
00.440252326718446
10.417815911904861
20.406465119614607
30.395185579198596
40.368107949730086
50.344352180232094
60.319918359920992
70.292042332218934
80.275484295230905
90.249132042613659
100.222121161075864
110.206869264864196
120.197488786096852
130.179079954705805
140.168360066513307
150.157560352160144
160.147862331616595
170.142104133538265

\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
-17 & -0.00479186598655025 \tabularnewline
-16 & -0.00587074621520246 \tabularnewline
-15 & -0.00934197190447173 \tabularnewline
-14 & -0.0260825695178232 \tabularnewline
-13 & -0.0415367888658148 \tabularnewline
-12 & -0.0506282747643316 \tabularnewline
-11 & -0.065436375091796 \tabularnewline
-10 & -0.0683764172378799 \tabularnewline
-9 & -0.0557554455084368 \tabularnewline
-8 & -0.0273199885503661 \tabularnewline
-7 & 0.00659598587029291 \tabularnewline
-6 & 0.0511536298504029 \tabularnewline
-5 & 0.0943864673534992 \tabularnewline
-4 & 0.151430496348075 \tabularnewline
-3 & 0.199773656948116 \tabularnewline
-2 & 0.275310565908975 \tabularnewline
-1 & 0.362267260210596 \tabularnewline
0 & 0.440252326718446 \tabularnewline
1 & 0.417815911904861 \tabularnewline
2 & 0.406465119614607 \tabularnewline
3 & 0.395185579198596 \tabularnewline
4 & 0.368107949730086 \tabularnewline
5 & 0.344352180232094 \tabularnewline
6 & 0.319918359920992 \tabularnewline
7 & 0.292042332218934 \tabularnewline
8 & 0.275484295230905 \tabularnewline
9 & 0.249132042613659 \tabularnewline
10 & 0.222121161075864 \tabularnewline
11 & 0.206869264864196 \tabularnewline
12 & 0.197488786096852 \tabularnewline
13 & 0.179079954705805 \tabularnewline
14 & 0.168360066513307 \tabularnewline
15 & 0.157560352160144 \tabularnewline
16 & 0.147862331616595 \tabularnewline
17 & 0.142104133538265 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27423&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]-17[/C][C]-0.00479186598655025[/C][/ROW]
[ROW][C]-16[/C][C]-0.00587074621520246[/C][/ROW]
[ROW][C]-15[/C][C]-0.00934197190447173[/C][/ROW]
[ROW][C]-14[/C][C]-0.0260825695178232[/C][/ROW]
[ROW][C]-13[/C][C]-0.0415367888658148[/C][/ROW]
[ROW][C]-12[/C][C]-0.0506282747643316[/C][/ROW]
[ROW][C]-11[/C][C]-0.065436375091796[/C][/ROW]
[ROW][C]-10[/C][C]-0.0683764172378799[/C][/ROW]
[ROW][C]-9[/C][C]-0.0557554455084368[/C][/ROW]
[ROW][C]-8[/C][C]-0.0273199885503661[/C][/ROW]
[ROW][C]-7[/C][C]0.00659598587029291[/C][/ROW]
[ROW][C]-6[/C][C]0.0511536298504029[/C][/ROW]
[ROW][C]-5[/C][C]0.0943864673534992[/C][/ROW]
[ROW][C]-4[/C][C]0.151430496348075[/C][/ROW]
[ROW][C]-3[/C][C]0.199773656948116[/C][/ROW]
[ROW][C]-2[/C][C]0.275310565908975[/C][/ROW]
[ROW][C]-1[/C][C]0.362267260210596[/C][/ROW]
[ROW][C]0[/C][C]0.440252326718446[/C][/ROW]
[ROW][C]1[/C][C]0.417815911904861[/C][/ROW]
[ROW][C]2[/C][C]0.406465119614607[/C][/ROW]
[ROW][C]3[/C][C]0.395185579198596[/C][/ROW]
[ROW][C]4[/C][C]0.368107949730086[/C][/ROW]
[ROW][C]5[/C][C]0.344352180232094[/C][/ROW]
[ROW][C]6[/C][C]0.319918359920992[/C][/ROW]
[ROW][C]7[/C][C]0.292042332218934[/C][/ROW]
[ROW][C]8[/C][C]0.275484295230905[/C][/ROW]
[ROW][C]9[/C][C]0.249132042613659[/C][/ROW]
[ROW][C]10[/C][C]0.222121161075864[/C][/ROW]
[ROW][C]11[/C][C]0.206869264864196[/C][/ROW]
[ROW][C]12[/C][C]0.197488786096852[/C][/ROW]
[ROW][C]13[/C][C]0.179079954705805[/C][/ROW]
[ROW][C]14[/C][C]0.168360066513307[/C][/ROW]
[ROW][C]15[/C][C]0.157560352160144[/C][/ROW]
[ROW][C]16[/C][C]0.147862331616595[/C][/ROW]
[ROW][C]17[/C][C]0.142104133538265[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27423&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27423&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])
-17-0.00479186598655025
-16-0.00587074621520246
-15-0.00934197190447173
-14-0.0260825695178232
-13-0.0415367888658148
-12-0.0506282747643316
-11-0.065436375091796
-10-0.0683764172378799
-9-0.0557554455084368
-8-0.0273199885503661
-70.00659598587029291
-60.0511536298504029
-50.0943864673534992
-40.151430496348075
-30.199773656948116
-20.275310565908975
-10.362267260210596
00.440252326718446
10.417815911904861
20.406465119614607
30.395185579198596
40.368107949730086
50.344352180232094
60.319918359920992
70.292042332218934
80.275484295230905
90.249132042613659
100.222121161075864
110.206869264864196
120.197488786096852
130.179079954705805
140.168360066513307
150.157560352160144
160.147862331616595
170.142104133538265


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')