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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 03:58:55 -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/t12281293974ccspr4vvo8z4mv.htm/, Retrieved Sun, 05 May 2024 19:39:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26869, Retrieved Sun, 05 May 2024 19:39:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

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 18:31:28] [b98453cac15ba1066b407e146608df68]
- RMPD  [Standard Deviation-Mean Plot] [Q5: Standard Devi...] [2008-11-30 15:50:04] [44ec60eb6065a3f81a5f756bd5af1faf]
- RMPD      [Cross Correlation Function] [Cross correlation...] [2008-12-01 10:58:55] [924502d03698cd41cacbcd1327858815] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7.8
7.6
7.5
7.6
7.5
7.3
7.6
7.5
7.6
7.9
7.9
8.1
8.2
8
7.5
6.8
6.5
6.6
7.6
8
8
7.7
7.5
7.6
7.7
7.9
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.1
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.7
6.4
6.3
6.2
6.5
Dataseries Y:
Download CSV
Histogram 
9
9.1
8.7
8.2
7.9
7.9
9.1
9.4
9.5
9.1
9
9.3
9.9
9.8
9.4
8.3
8
8.5
10.4
11.1
10.9
9.9
9.2
9.2
9.5
9.6
9.5
9.1
8.9
9
10.1
10.3
10.2
9.6
9.2
9.3
9.4
9.4
9.2
9
9
9
9.8
10
9.9
9.3
9
9
9.1
9.1
9.1
9.2
8.8
8.3
8.4
8.1
7.8
7.9
7.9
8

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=26869&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=26869&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26869&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 series2
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 series0
Degree of non-seasonal differencing (d) of Y series2
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-130.0695265568956715
-120.135469680374996
-11-0.0554749814201783
-10-0.0579908620549389
-9-0.0908622442178487
-80.178471853348942
-70.123816911760035
-60.000687562591474116
-5-0.141808422099384
-4-0.267241784753425
-3-0.164974848737790
-20.122812131426313
-10.277380899770727
00.249382145959831
1-0.125485781315052
2-0.222033167151899
3-0.264474242578808
4-0.0396098845979867
50.154595863672206
60.257553920280402
70.104481697390156
80.107026197639326
9-0.0694287323141893
10-0.177003619342830
11-0.195415419495406
12-0.0520865451053317
130.084554766877509

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 2 \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 & 0 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 2 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & 0.0695265568956715 \tabularnewline
-12 & 0.135469680374996 \tabularnewline
-11 & -0.0554749814201783 \tabularnewline
-10 & -0.0579908620549389 \tabularnewline
-9 & -0.0908622442178487 \tabularnewline
-8 & 0.178471853348942 \tabularnewline
-7 & 0.123816911760035 \tabularnewline
-6 & 0.000687562591474116 \tabularnewline
-5 & -0.141808422099384 \tabularnewline
-4 & -0.267241784753425 \tabularnewline
-3 & -0.164974848737790 \tabularnewline
-2 & 0.122812131426313 \tabularnewline
-1 & 0.277380899770727 \tabularnewline
0 & 0.249382145959831 \tabularnewline
1 & -0.125485781315052 \tabularnewline
2 & -0.222033167151899 \tabularnewline
3 & -0.264474242578808 \tabularnewline
4 & -0.0396098845979867 \tabularnewline
5 & 0.154595863672206 \tabularnewline
6 & 0.257553920280402 \tabularnewline
7 & 0.104481697390156 \tabularnewline
8 & 0.107026197639326 \tabularnewline
9 & -0.0694287323141893 \tabularnewline
10 & -0.177003619342830 \tabularnewline
11 & -0.195415419495406 \tabularnewline
12 & -0.0520865451053317 \tabularnewline
13 & 0.084554766877509 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26869&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]2[/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]0[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]2[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]0.0695265568956715[/C][/ROW]
[ROW][C]-12[/C][C]0.135469680374996[/C][/ROW]
[ROW][C]-11[/C][C]-0.0554749814201783[/C][/ROW]
[ROW][C]-10[/C][C]-0.0579908620549389[/C][/ROW]
[ROW][C]-9[/C][C]-0.0908622442178487[/C][/ROW]
[ROW][C]-8[/C][C]0.178471853348942[/C][/ROW]
[ROW][C]-7[/C][C]0.123816911760035[/C][/ROW]
[ROW][C]-6[/C][C]0.000687562591474116[/C][/ROW]
[ROW][C]-5[/C][C]-0.141808422099384[/C][/ROW]
[ROW][C]-4[/C][C]-0.267241784753425[/C][/ROW]
[ROW][C]-3[/C][C]-0.164974848737790[/C][/ROW]
[ROW][C]-2[/C][C]0.122812131426313[/C][/ROW]
[ROW][C]-1[/C][C]0.277380899770727[/C][/ROW]
[ROW][C]0[/C][C]0.249382145959831[/C][/ROW]
[ROW][C]1[/C][C]-0.125485781315052[/C][/ROW]
[ROW][C]2[/C][C]-0.222033167151899[/C][/ROW]
[ROW][C]3[/C][C]-0.264474242578808[/C][/ROW]
[ROW][C]4[/C][C]-0.0396098845979867[/C][/ROW]
[ROW][C]5[/C][C]0.154595863672206[/C][/ROW]
[ROW][C]6[/C][C]0.257553920280402[/C][/ROW]
[ROW][C]7[/C][C]0.104481697390156[/C][/ROW]
[ROW][C]8[/C][C]0.107026197639326[/C][/ROW]
[ROW][C]9[/C][C]-0.0694287323141893[/C][/ROW]
[ROW][C]10[/C][C]-0.177003619342830[/C][/ROW]
[ROW][C]11[/C][C]-0.195415419495406[/C][/ROW]
[ROW][C]12[/C][C]-0.0520865451053317[/C][/ROW]
[ROW][C]13[/C][C]0.084554766877509[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26869&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26869&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 series2
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 series0
Degree of non-seasonal differencing (d) of Y series2
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-130.0695265568956715
-120.135469680374996
-11-0.0554749814201783
-10-0.0579908620549389
-9-0.0908622442178487
-80.178471853348942
-70.123816911760035
-60.000687562591474116
-5-0.141808422099384
-4-0.267241784753425
-3-0.164974848737790
-20.122812131426313
-10.277380899770727
00.249382145959831
1-0.125485781315052
2-0.222033167151899
3-0.264474242578808
4-0.0396098845979867
50.154595863672206
60.257553920280402
70.104481697390156
80.107026197639326
9-0.0694287323141893
10-0.177003619342830
11-0.195415419495406
12-0.0520865451053317
130.084554766877509


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2.0 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 0.0 ; par6 = 2 ; par7 = 1 ;
Parameters (R input):
par1 = 2.0 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 0.0 ; par6 = 2 ; par7 = 1 ;
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')