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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 computationSun, 19 Dec 2010 14:48:27 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/19/t1292769998nrvjebf9iadr17t.htm/, Retrieved Sun, 05 May 2024 04:46:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112450, Retrieved Sun, 05 May 2024 04:46:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121

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] [paperCCF1] [2010-12-19 14:45:22] [7e261c986c934df955dd3ac53e9d45c6]
-   PD    [Cross Correlation Function] [paperCCF2] [2010-12-19 14:48:27] [13dfa60174f50d862e8699db2153bfc5] [Current]
-   P       [Cross Correlation Function] [paperCCF2] [2010-12-22 14:53:54] [7e261c986c934df955dd3ac53e9d45c6]
-   P         [Cross Correlation Function] [Kristof Nagels] [2010-12-24 15:16:47] [8441f95c4a5787a301bc621ebc7904ca]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15
14.4
13
13.7
13.6
15.2
12.9
14
14.1
13.2
11.3
13.3
14.4
13.3
11.6
13.2
13.1
14.6
14
14.3
13.8
13.7
11
14.4
15.6
13.7
12.6
13.2
13.3
14.3
14
13.4
13.9
13.7
10.5
14.5
15
13.5
13.5
13.2
13.8
16.2
14.7
13.9
16
14.4
12.3
15.9
15.9
15.5
15.1
14.5
15.1
17.4
16.2
15.6
17.2
14.9
13.8
17.5
16.2
17.5
16.6
16.2
16.6
19.6
15.9
18
18.3
16.3
14.9
18.2
18.4
18.5
16
17.4
17.2
19.6
17.2
18.3
19.3
18.1
16.2
18.4
20.5
19
16.5
18.7
19
19.2
20.5
19.3
20.6
20.1
16.1
20.4
19.7
15.6
14.4
13.7
14.1
15
14.2
13.6
15.4
14.8
12.5
16.2
16.1
16
15.8
15.2
15.7
18.9
17.4
17
19.8
17.7
16
19.6
19.7
Dataseries Y:
Download CSV
Histogram 
6.7
6.7
6.5
6.3
6.3
6.3
6.5
6.6
6.5
6.3
6.3
6.5
7
7.1
7.3
7.3
7.4
7.4
7.3
7.4
7.5
7.7
7.7
7.7
7.7
7.7
7.8
8
8.1
8.1
8.2
8.2
8.2
8.1
8.1
8.2
8.3
8.3
8.4
8.5
8.5
8.4
8
7.9
8.1
8.5
8.8
8.8
8.6
8.3
8.3
8.3
8.4
8.4
8.5
8.6
8.6
8.6
8.6
8.6
8.5
8.4
8.4
8.4
8.5
8.5
8.6
8.6
8.4
8.2
8
8
8
8
7.9
7.9
7.8
7.8
8
7.8
7.4
7.2
7
7
7.2
7.2
7.2
7
6.9
6.8
6.8
6.8
6.9
7.2
7.2
7.2
7.1
7.2
7.3
7.5
7.6
7.7
7.7
7.7
7.8
8
8.1
8.1
8
8.1
8.2
8.3
8.4
8.4
8.4
8.5
8.5
8.6
8.6
8.5
8.5

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112450&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112450&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112450&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'RServer@AstonUniversity' @ vre.aston.ac.uk






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 series1
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])
-17-0.04347007029858
-16-0.0332780273315675
-150.0447038680451226
-14-0.0852846514879445
-13-0.0858133834330864
-120.0362794776188292
-11-0.100372520390647
-10-0.00546742803854612
-90.0699713772277001
-80.0105875808372441
-70.0339998097982399
-60.0615178674489972
-5-0.0427728235825486
-40.00431829211289869
-30.151526594618141
-2-0.0156230718150465
-10.109095384179139
00.0717792036898001
1-0.0524636362150286
20.0678766712196391
30.0266919308379502
40.0585239666234717
50.224271050915595
6-0.0220789400937203
7-0.055105408575305
80.0820222055235295
9-0.0750624106739885
100.00501068560352843
110.0579639583368522
12-0.0398093902275999
130.0270726394083434
140.073027561112547
15-0.032457562615062
160.00547012427488024
17-0.042887683250387

\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 & 1 \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 & -0.04347007029858 \tabularnewline
-16 & -0.0332780273315675 \tabularnewline
-15 & 0.0447038680451226 \tabularnewline
-14 & -0.0852846514879445 \tabularnewline
-13 & -0.0858133834330864 \tabularnewline
-12 & 0.0362794776188292 \tabularnewline
-11 & -0.100372520390647 \tabularnewline
-10 & -0.00546742803854612 \tabularnewline
-9 & 0.0699713772277001 \tabularnewline
-8 & 0.0105875808372441 \tabularnewline
-7 & 0.0339998097982399 \tabularnewline
-6 & 0.0615178674489972 \tabularnewline
-5 & -0.0427728235825486 \tabularnewline
-4 & 0.00431829211289869 \tabularnewline
-3 & 0.151526594618141 \tabularnewline
-2 & -0.0156230718150465 \tabularnewline
-1 & 0.109095384179139 \tabularnewline
0 & 0.0717792036898001 \tabularnewline
1 & -0.0524636362150286 \tabularnewline
2 & 0.0678766712196391 \tabularnewline
3 & 0.0266919308379502 \tabularnewline
4 & 0.0585239666234717 \tabularnewline
5 & 0.224271050915595 \tabularnewline
6 & -0.0220789400937203 \tabularnewline
7 & -0.055105408575305 \tabularnewline
8 & 0.0820222055235295 \tabularnewline
9 & -0.0750624106739885 \tabularnewline
10 & 0.00501068560352843 \tabularnewline
11 & 0.0579639583368522 \tabularnewline
12 & -0.0398093902275999 \tabularnewline
13 & 0.0270726394083434 \tabularnewline
14 & 0.073027561112547 \tabularnewline
15 & -0.032457562615062 \tabularnewline
16 & 0.00547012427488024 \tabularnewline
17 & -0.042887683250387 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112450&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]1[/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]-0.04347007029858[/C][/ROW]
[ROW][C]-16[/C][C]-0.0332780273315675[/C][/ROW]
[ROW][C]-15[/C][C]0.0447038680451226[/C][/ROW]
[ROW][C]-14[/C][C]-0.0852846514879445[/C][/ROW]
[ROW][C]-13[/C][C]-0.0858133834330864[/C][/ROW]
[ROW][C]-12[/C][C]0.0362794776188292[/C][/ROW]
[ROW][C]-11[/C][C]-0.100372520390647[/C][/ROW]
[ROW][C]-10[/C][C]-0.00546742803854612[/C][/ROW]
[ROW][C]-9[/C][C]0.0699713772277001[/C][/ROW]
[ROW][C]-8[/C][C]0.0105875808372441[/C][/ROW]
[ROW][C]-7[/C][C]0.0339998097982399[/C][/ROW]
[ROW][C]-6[/C][C]0.0615178674489972[/C][/ROW]
[ROW][C]-5[/C][C]-0.0427728235825486[/C][/ROW]
[ROW][C]-4[/C][C]0.00431829211289869[/C][/ROW]
[ROW][C]-3[/C][C]0.151526594618141[/C][/ROW]
[ROW][C]-2[/C][C]-0.0156230718150465[/C][/ROW]
[ROW][C]-1[/C][C]0.109095384179139[/C][/ROW]
[ROW][C]0[/C][C]0.0717792036898001[/C][/ROW]
[ROW][C]1[/C][C]-0.0524636362150286[/C][/ROW]
[ROW][C]2[/C][C]0.0678766712196391[/C][/ROW]
[ROW][C]3[/C][C]0.0266919308379502[/C][/ROW]
[ROW][C]4[/C][C]0.0585239666234717[/C][/ROW]
[ROW][C]5[/C][C]0.224271050915595[/C][/ROW]
[ROW][C]6[/C][C]-0.0220789400937203[/C][/ROW]
[ROW][C]7[/C][C]-0.055105408575305[/C][/ROW]
[ROW][C]8[/C][C]0.0820222055235295[/C][/ROW]
[ROW][C]9[/C][C]-0.0750624106739885[/C][/ROW]
[ROW][C]10[/C][C]0.00501068560352843[/C][/ROW]
[ROW][C]11[/C][C]0.0579639583368522[/C][/ROW]
[ROW][C]12[/C][C]-0.0398093902275999[/C][/ROW]
[ROW][C]13[/C][C]0.0270726394083434[/C][/ROW]
[ROW][C]14[/C][C]0.073027561112547[/C][/ROW]
[ROW][C]15[/C][C]-0.032457562615062[/C][/ROW]
[ROW][C]16[/C][C]0.00547012427488024[/C][/ROW]
[ROW][C]17[/C][C]-0.042887683250387[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112450&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112450&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 series1
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])
-17-0.04347007029858
-16-0.0332780273315675
-150.0447038680451226
-14-0.0852846514879445
-13-0.0858133834330864
-120.0362794776188292
-11-0.100372520390647
-10-0.00546742803854612
-90.0699713772277001
-80.0105875808372441
-70.0339998097982399
-60.0615178674489972
-5-0.0427728235825486
-40.00431829211289869
-30.151526594618141
-2-0.0156230718150465
-10.109095384179139
00.0717792036898001
1-0.0524636362150286
20.0678766712196391
30.0266919308379502
40.0585239666234717
50.224271050915595
6-0.0220789400937203
7-0.055105408575305
80.0820222055235295
9-0.0750624106739885
100.00501068560352843
110.0579639583368522
12-0.0398093902275999
130.0270726394083434
140.073027561112547
15-0.032457562615062
160.00547012427488024
17-0.042887683250387


Figures (Output of Computation)

Input Parameters & R Code

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