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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 computationMon, 26 Nov 2007 11:37:47 -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/26/t119610170192cziqrqs2m7r3r.htm/, Retrieved Thu, 02 May 2024 21:46:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=6618, Retrieved Thu, 02 May 2024 21:46:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact214

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] [Opdracht 4 Questi...] [2007-11-26 18:37:47] [cb172450b25aceeff04d58e88e905157] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
476
475
470
461
455
456
517
525
523
519
509
512
519
517
510
509
501
507
569
580
578
565
547
555
562
561
555
544
537
543
594
611
613
611
594
595
591
589
584
573
567
569
621
629
628
612
595
597
593
590
580
574
573
573
620
626
620
588
566
557
Dataseries Y:
Download CSV
Histogram 
2,9
2,6
2,7
1,8
1,3
0,9
1,3
1,3
1,3
1,3
1,1
1,4
1,2
1,7
1,8
1,5
1
1,6
1,5
1,8
1,8
1,6
1,9
1,7
1,6
1,3
1,1
1,9
2,6
2,3
2,4
2,2
2
2,9
2,6
2,3
2,3
2,6
3,1
2,8
2,5
2,9
3,1
3,1
3,2
2,5
2,6
2,9
2,6
2,4
1,7
2
2,2
1,9
1,6
1,6
1,2
1,2
1,5
1,6

Tables (Output of Computation)





Summary of compuational 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 compuational 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=6618&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]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=6618&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6618&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 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 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])
-13-0.146053947178632
-120.120572820939895
-110.121035349633998
-10-0.121750166706702
-9-0.201326126123877
-80.144519192875473
-70.175151206843987
-60.242344011678147
-5-0.132093990229357
-4-0.335711735792197
-30.0537794727051879
-2-0.0686434630111173
-10.123810521798613
0-0.0701571932827255
1-0.0882378604158224
20.109619300502287
3-0.0570171735639052
4-0.202040943196581
5-0.193147777850884
60.037548920348501
70.0453908841166929
80.150426945741055
9-0.0903823116339337
10-0.0210450555816627
11-0.048018888060457
12-0.000588672883403101
130.128162496329486

\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
-13 & -0.146053947178632 \tabularnewline
-12 & 0.120572820939895 \tabularnewline
-11 & 0.121035349633998 \tabularnewline
-10 & -0.121750166706702 \tabularnewline
-9 & -0.201326126123877 \tabularnewline
-8 & 0.144519192875473 \tabularnewline
-7 & 0.175151206843987 \tabularnewline
-6 & 0.242344011678147 \tabularnewline
-5 & -0.132093990229357 \tabularnewline
-4 & -0.335711735792197 \tabularnewline
-3 & 0.0537794727051879 \tabularnewline
-2 & -0.0686434630111173 \tabularnewline
-1 & 0.123810521798613 \tabularnewline
0 & -0.0701571932827255 \tabularnewline
1 & -0.0882378604158224 \tabularnewline
2 & 0.109619300502287 \tabularnewline
3 & -0.0570171735639052 \tabularnewline
4 & -0.202040943196581 \tabularnewline
5 & -0.193147777850884 \tabularnewline
6 & 0.037548920348501 \tabularnewline
7 & 0.0453908841166929 \tabularnewline
8 & 0.150426945741055 \tabularnewline
9 & -0.0903823116339337 \tabularnewline
10 & -0.0210450555816627 \tabularnewline
11 & -0.048018888060457 \tabularnewline
12 & -0.000588672883403101 \tabularnewline
13 & 0.128162496329486 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6618&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]-13[/C][C]-0.146053947178632[/C][/ROW]
[ROW][C]-12[/C][C]0.120572820939895[/C][/ROW]
[ROW][C]-11[/C][C]0.121035349633998[/C][/ROW]
[ROW][C]-10[/C][C]-0.121750166706702[/C][/ROW]
[ROW][C]-9[/C][C]-0.201326126123877[/C][/ROW]
[ROW][C]-8[/C][C]0.144519192875473[/C][/ROW]
[ROW][C]-7[/C][C]0.175151206843987[/C][/ROW]
[ROW][C]-6[/C][C]0.242344011678147[/C][/ROW]
[ROW][C]-5[/C][C]-0.132093990229357[/C][/ROW]
[ROW][C]-4[/C][C]-0.335711735792197[/C][/ROW]
[ROW][C]-3[/C][C]0.0537794727051879[/C][/ROW]
[ROW][C]-2[/C][C]-0.0686434630111173[/C][/ROW]
[ROW][C]-1[/C][C]0.123810521798613[/C][/ROW]
[ROW][C]0[/C][C]-0.0701571932827255[/C][/ROW]
[ROW][C]1[/C][C]-0.0882378604158224[/C][/ROW]
[ROW][C]2[/C][C]0.109619300502287[/C][/ROW]
[ROW][C]3[/C][C]-0.0570171735639052[/C][/ROW]
[ROW][C]4[/C][C]-0.202040943196581[/C][/ROW]
[ROW][C]5[/C][C]-0.193147777850884[/C][/ROW]
[ROW][C]6[/C][C]0.037548920348501[/C][/ROW]
[ROW][C]7[/C][C]0.0453908841166929[/C][/ROW]
[ROW][C]8[/C][C]0.150426945741055[/C][/ROW]
[ROW][C]9[/C][C]-0.0903823116339337[/C][/ROW]
[ROW][C]10[/C][C]-0.0210450555816627[/C][/ROW]
[ROW][C]11[/C][C]-0.048018888060457[/C][/ROW]
[ROW][C]12[/C][C]-0.000588672883403101[/C][/ROW]
[ROW][C]13[/C][C]0.128162496329486[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6618&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6618&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])
-13-0.146053947178632
-120.120572820939895
-110.121035349633998
-10-0.121750166706702
-9-0.201326126123877
-80.144519192875473
-70.175151206843987
-60.242344011678147
-5-0.132093990229357
-4-0.335711735792197
-30.0537794727051879
-2-0.0686434630111173
-10.123810521798613
0-0.0701571932827255
1-0.0882378604158224
20.109619300502287
3-0.0570171735639052
4-0.202040943196581
5-0.193147777850884
60.037548920348501
70.0453908841166929
80.150426945741055
9-0.0903823116339337
10-0.0210450555816627
11-0.048018888060457
12-0.000588672883403101
130.128162496329486


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) 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')