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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, 27 Dec 2009 05:34:11 -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/2009/Dec/27/t1261917319foa8tv3d10gueob.htm/, Retrieved Thu, 02 May 2024 21:18:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70871, Retrieved Thu, 02 May 2024 21:18:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [Toon Nauwelaerts] [2009-12-27 12:06:14] [28075c6928548bea087cb2be962cfe7e]
- RMP   [Spectral Analysis] [Toon Nauwelaerts] [2009-12-27 12:12:13] [28075c6928548bea087cb2be962cfe7e]
- RMPD      [Cross Correlation Function] [Toon Nauwelaerts] [2009-12-27 12:34:11] [b7e924d6f720297f82cd59f42434ec05] [Current]
- RMPD        [ARIMA Backward Selection] [Toon Nauwelaerts] [2009-12-27 12:56:27] [28075c6928548bea087cb2be962cfe7e]
- RMPD        [ARIMA Backward Selection] [Toon Nauwelaerts] [2009-12-27 13:02:08] [28075c6928548bea087cb2be962cfe7e]
- RMPD        [ARIMA Forecasting] [Toon Nauwelaerts] [2009-12-27 13:10:02] [28075c6928548bea087cb2be962cfe7e]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13139.7
14532.2
15167
16071.1
14827.5
15082
14772.7
16083
14272.5
15223.3
14897.3
13062.6
12603.8
13629.8
14421.1
13978.3
12927.9
13429.9
13470.1
14785.8
14292
14308.8
14013
13240.9
12153.4
14289.7
15669.2
14169.5
14569.8
14469.1
14264.9
15320.9
14433.5
13691.5
14194.1
13519.2
11857.9
14616
15643.4
14077.2
14887.5
14159.9
14643
17192.5
15386.1
14287.1
17526.6
14497
14398.3
16629.6
16670.7
16614.8
16869.2
15663.9
16359.9
18447.7
16889
16505
18320.9
15052.1
15699.8
18135.3
16768.7
18883
19021
18101.9
17776.1
21489.9
17065.3
18690
18953.1
16398.9
16895.7
18553
19270
19422.1
17579.4
18637.3
18076.7
20438.6
18075.2
19563
19899.2
19227.5
17789.6
19220.8
21968.9
21131.5
19484.6
22404.1
21099
22486.5
23707.5
21897.5
23326.4
23765.4
20444
Dataseries Y:
Download CSV
Histogram 
7.3
7.1
7.1
6.8
6.5
6.3
6.1
6.1
6.3
6.3
6
6.2
6.4
6.8
7.5
7.5
7.6
7.6
7.4
7.3
7.1
6.9
6.8
7.5
7.6
7.8
8
8.1
8.2
8.3
8.2
8
7.9
7.6
7.6
8.2
8.3
8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.5
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.6
8.2
8.1
8
8.6
8.7
8.8
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8.1
8.2
8.1
8.1
7.9
7.9
7.9
8
8
7.9
8
7.7
7.2
7.5
7.3
7
7
7
7.2
7.3
7.1
6.8
6.6
6.2
6.2
6.8
6.9

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=70871&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=70871&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70871&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 series1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series2
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series2
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-15-0.0622782953954924
-140.174740102660122
-13-0.138179036249256
-120.096406880152147
-110.0278193682743521
-10-0.0850494287705394
-90.0140951280241222
-8-0.0715021959113114
-70.0261615353220061
-60.0464777631423194
-50.0698347155147553
-4-0.144807419170595
-30.0244287131244407
-20.0437459832466196
-1-0.0178700114498374
0-0.0586703355017234
10.173678318722023
2-0.212072094866025
30.0938102457885448
4-0.0342982173543382
5-0.0355405926374326
60.0947667075911145
7-0.0119821390907997
80.0396136348913476
90.107241227614062
10-0.129565669809284
110.00851590388877892
12-0.0488341254015474
13-0.064121059138559
140.0379114914222763
150.102848792479444

\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 & 2 \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 & 2 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-15 & -0.0622782953954924 \tabularnewline
-14 & 0.174740102660122 \tabularnewline
-13 & -0.138179036249256 \tabularnewline
-12 & 0.096406880152147 \tabularnewline
-11 & 0.0278193682743521 \tabularnewline
-10 & -0.0850494287705394 \tabularnewline
-9 & 0.0140951280241222 \tabularnewline
-8 & -0.0715021959113114 \tabularnewline
-7 & 0.0261615353220061 \tabularnewline
-6 & 0.0464777631423194 \tabularnewline
-5 & 0.0698347155147553 \tabularnewline
-4 & -0.144807419170595 \tabularnewline
-3 & 0.0244287131244407 \tabularnewline
-2 & 0.0437459832466196 \tabularnewline
-1 & -0.0178700114498374 \tabularnewline
0 & -0.0586703355017234 \tabularnewline
1 & 0.173678318722023 \tabularnewline
2 & -0.212072094866025 \tabularnewline
3 & 0.0938102457885448 \tabularnewline
4 & -0.0342982173543382 \tabularnewline
5 & -0.0355405926374326 \tabularnewline
6 & 0.0947667075911145 \tabularnewline
7 & -0.0119821390907997 \tabularnewline
8 & 0.0396136348913476 \tabularnewline
9 & 0.107241227614062 \tabularnewline
10 & -0.129565669809284 \tabularnewline
11 & 0.00851590388877892 \tabularnewline
12 & -0.0488341254015474 \tabularnewline
13 & -0.064121059138559 \tabularnewline
14 & 0.0379114914222763 \tabularnewline
15 & 0.102848792479444 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70871&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]2[/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]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]-15[/C][C]-0.0622782953954924[/C][/ROW]
[ROW][C]-14[/C][C]0.174740102660122[/C][/ROW]
[ROW][C]-13[/C][C]-0.138179036249256[/C][/ROW]
[ROW][C]-12[/C][C]0.096406880152147[/C][/ROW]
[ROW][C]-11[/C][C]0.0278193682743521[/C][/ROW]
[ROW][C]-10[/C][C]-0.0850494287705394[/C][/ROW]
[ROW][C]-9[/C][C]0.0140951280241222[/C][/ROW]
[ROW][C]-8[/C][C]-0.0715021959113114[/C][/ROW]
[ROW][C]-7[/C][C]0.0261615353220061[/C][/ROW]
[ROW][C]-6[/C][C]0.0464777631423194[/C][/ROW]
[ROW][C]-5[/C][C]0.0698347155147553[/C][/ROW]
[ROW][C]-4[/C][C]-0.144807419170595[/C][/ROW]
[ROW][C]-3[/C][C]0.0244287131244407[/C][/ROW]
[ROW][C]-2[/C][C]0.0437459832466196[/C][/ROW]
[ROW][C]-1[/C][C]-0.0178700114498374[/C][/ROW]
[ROW][C]0[/C][C]-0.0586703355017234[/C][/ROW]
[ROW][C]1[/C][C]0.173678318722023[/C][/ROW]
[ROW][C]2[/C][C]-0.212072094866025[/C][/ROW]
[ROW][C]3[/C][C]0.0938102457885448[/C][/ROW]
[ROW][C]4[/C][C]-0.0342982173543382[/C][/ROW]
[ROW][C]5[/C][C]-0.0355405926374326[/C][/ROW]
[ROW][C]6[/C][C]0.0947667075911145[/C][/ROW]
[ROW][C]7[/C][C]-0.0119821390907997[/C][/ROW]
[ROW][C]8[/C][C]0.0396136348913476[/C][/ROW]
[ROW][C]9[/C][C]0.107241227614062[/C][/ROW]
[ROW][C]10[/C][C]-0.129565669809284[/C][/ROW]
[ROW][C]11[/C][C]0.00851590388877892[/C][/ROW]
[ROW][C]12[/C][C]-0.0488341254015474[/C][/ROW]
[ROW][C]13[/C][C]-0.064121059138559[/C][/ROW]
[ROW][C]14[/C][C]0.0379114914222763[/C][/ROW]
[ROW][C]15[/C][C]0.102848792479444[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70871&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70871&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 series2
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series2
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-15-0.0622782953954924
-140.174740102660122
-13-0.138179036249256
-120.096406880152147
-110.0278193682743521
-10-0.0850494287705394
-90.0140951280241222
-8-0.0715021959113114
-70.0261615353220061
-60.0464777631423194
-50.0698347155147553
-4-0.144807419170595
-30.0244287131244407
-20.0437459832466196
-1-0.0178700114498374
0-0.0586703355017234
10.173678318722023
2-0.212072094866025
30.0938102457885448
4-0.0342982173543382
5-0.0355405926374326
60.0947667075911145
7-0.0119821390907997
80.0396136348913476
90.107241227614062
10-0.129565669809284
110.00851590388877892
12-0.0488341254015474
13-0.064121059138559
140.0379114914222763
150.102848792479444


Figures (Output of Computation)

Input Parameters & R Code

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