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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 computationSat, 29 Nov 2008 07:28:53 -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/Nov/29/t12279689702fbu3tob6h8h62l.htm/, Retrieved Sun, 19 May 2024 07:17:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26296, Retrieved Sun, 19 May 2024 07:17:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD    [Cross Correlation Function] [Q7] [2008-11-29 14:28:53] [d96f761aa3e94002e7c05c3c847d2c79] [Current]
Feedback Forum
2008-12-04 15:58:45 [339a57d8a4d5d113e4804fc423e4a59e] [reply] 
Door met de cross correlation function te werken, kan men de correlatie tussen 2 data uit 2 verschillende reeksen berekenen. Dit verschilt dus van de autocorrelatie waarbij men de correlatie enkel kan berekenen tuseen 2 data uit 1 datareeks. De software gaat na in welke mate het verleden gecorreleerd is met het heden. De 'k'-waarde bepaald hier het aantal perioden dat men terug of vooruit gaat.

In deze vraag was het nog niet nodig om 'd' en 'D' aan te passen. De student heeft dit hier echter wel gedaan.
2008-12-04 16:22:59 [Matthieu Blondeau] [reply] 
De 'd' en de 'D' moesten gelijk zijn aan 0 en de lambda verandert worden in 1.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
577992
565464
547344
554788
562325
560854
555332
543599
536662
542722
593530
610763
612613
611324
594167
595454
590865
589379
584428
573100
567456
569028
620735
628884
628232
612117
595404
597141
593408
590072
579799
574205
572775
572942
619567
625809
619916
587625
565742
557274
560576
548854
531673
525919
511038
498662
555362
564591
541657
527070
509846
514258
516922
507561
492622
490243
469357
477580
528379
533590
Dataseries Y:
Download CSV
Histogram 
157928
147768
137507
136919
136151
133001
125554
119647
114158
116193
152803
161761
160942
149470
139208
134588
130322
126611
122401
117352
112135
112879
148729
157230
157221
146681
136524
132111
125326
122716
116615
113719
110737
112093
143565
149946
149147
134339
122683
115614
116566
111272
104609
101802
94542
93051
124129
130374
123946
114971
105531
104919
104782
101281
94545
93248
84031
87486
115867
120327

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26296&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26296&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26296&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series2
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 series2
krho(Y[t],X[t+k])
-12-0.100128572336190
-110.127677530667994
-10-0.0825417682619398
-9-0.0336432267629482
-80.0276408607470650
-70.0737011412820242
-6-0.0979591982523176
-5-0.0156104048198003
-40.168741508317772
-3-0.176875336259694
-20.104246975590482
-1-0.430327983587352
00.785962527585892
1-0.463282395438254
20.0967555577861373
3-0.103510584867682
40.275547702148716
5-0.426692890942749
60.269899978193275
7-0.0247945610906564
80.00948416343414372
9-0.0115934007532749
10-0.106840777323005
110.32531825470987
12-0.370374150267279

\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 & 2 \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 & 2 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-12 & -0.100128572336190 \tabularnewline
-11 & 0.127677530667994 \tabularnewline
-10 & -0.0825417682619398 \tabularnewline
-9 & -0.0336432267629482 \tabularnewline
-8 & 0.0276408607470650 \tabularnewline
-7 & 0.0737011412820242 \tabularnewline
-6 & -0.0979591982523176 \tabularnewline
-5 & -0.0156104048198003 \tabularnewline
-4 & 0.168741508317772 \tabularnewline
-3 & -0.176875336259694 \tabularnewline
-2 & 0.104246975590482 \tabularnewline
-1 & -0.430327983587352 \tabularnewline
0 & 0.785962527585892 \tabularnewline
1 & -0.463282395438254 \tabularnewline
2 & 0.0967555577861373 \tabularnewline
3 & -0.103510584867682 \tabularnewline
4 & 0.275547702148716 \tabularnewline
5 & -0.426692890942749 \tabularnewline
6 & 0.269899978193275 \tabularnewline
7 & -0.0247945610906564 \tabularnewline
8 & 0.00948416343414372 \tabularnewline
9 & -0.0115934007532749 \tabularnewline
10 & -0.106840777323005 \tabularnewline
11 & 0.32531825470987 \tabularnewline
12 & -0.370374150267279 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26296&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]2[/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]2[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-12[/C][C]-0.100128572336190[/C][/ROW]
[ROW][C]-11[/C][C]0.127677530667994[/C][/ROW]
[ROW][C]-10[/C][C]-0.0825417682619398[/C][/ROW]
[ROW][C]-9[/C][C]-0.0336432267629482[/C][/ROW]
[ROW][C]-8[/C][C]0.0276408607470650[/C][/ROW]
[ROW][C]-7[/C][C]0.0737011412820242[/C][/ROW]
[ROW][C]-6[/C][C]-0.0979591982523176[/C][/ROW]
[ROW][C]-5[/C][C]-0.0156104048198003[/C][/ROW]
[ROW][C]-4[/C][C]0.168741508317772[/C][/ROW]
[ROW][C]-3[/C][C]-0.176875336259694[/C][/ROW]
[ROW][C]-2[/C][C]0.104246975590482[/C][/ROW]
[ROW][C]-1[/C][C]-0.430327983587352[/C][/ROW]
[ROW][C]0[/C][C]0.785962527585892[/C][/ROW]
[ROW][C]1[/C][C]-0.463282395438254[/C][/ROW]
[ROW][C]2[/C][C]0.0967555577861373[/C][/ROW]
[ROW][C]3[/C][C]-0.103510584867682[/C][/ROW]
[ROW][C]4[/C][C]0.275547702148716[/C][/ROW]
[ROW][C]5[/C][C]-0.426692890942749[/C][/ROW]
[ROW][C]6[/C][C]0.269899978193275[/C][/ROW]
[ROW][C]7[/C][C]-0.0247945610906564[/C][/ROW]
[ROW][C]8[/C][C]0.00948416343414372[/C][/ROW]
[ROW][C]9[/C][C]-0.0115934007532749[/C][/ROW]
[ROW][C]10[/C][C]-0.106840777323005[/C][/ROW]
[ROW][C]11[/C][C]0.32531825470987[/C][/ROW]
[ROW][C]12[/C][C]-0.370374150267279[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26296&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26296&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 series2
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 series2
krho(Y[t],X[t+k])
-12-0.100128572336190
-110.127677530667994
-10-0.0825417682619398
-9-0.0336432267629482
-80.0276408607470650
-70.0737011412820242
-6-0.0979591982523176
-5-0.0156104048198003
-40.168741508317772
-3-0.176875336259694
-20.104246975590482
-1-0.430327983587352
00.785962527585892
1-0.463282395438254
20.0967555577861373
3-0.103510584867682
40.275547702148716
5-0.426692890942749
60.269899978193275
7-0.0247945610906564
80.00948416343414372
9-0.0115934007532749
10-0.106840777323005
110.32531825470987
12-0.370374150267279


Figures (Output of Computation)

Input Parameters & R Code

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