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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 computationTue, 02 Dec 2008 06:12:06 -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/02/t1228223570ubehupc7d6s9a58.htm/, Retrieved Sun, 19 May 2024 12:34:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27723, Retrieved Sun, 19 May 2024 12:34:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [] [2008-12-02 13:12:06] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-12-04 09:48:02 [72e979bcc364082694890d2eccc1a66f] [reply] 
In de figuur duiden de stippellijnen op het betrouwbaarheidsinterval. Er zijn heel wat metingen die hierbuiten vallen, zij zijn dus significant verschillend en kunnen niet toegewezen worden aan het toeval.
2008-12-05 19:10:12 [Bert Moons] [reply] 
Er is inderdaad een een kruiscorrelatie te trekken tussen de 2 variabelen. De kruiscorrelatie is significant met een lag tussen ongeveer -5 en de +10.
2008-12-06 16:21:22 [Bénédicte Soens] [reply] 
Goede methode. Het is duidelijk dat er correlatie is. Maar deze is op vele momenten niet aan toeval toe te wijzen doordat deze significant verschillend zijn van 0.
2008-12-07 12:26:20 [Lana Van Wesemael] [reply] 
Wanneer de staafjes binnen het betrouwbaarheidsinterval vallen is de correlatie waarschijnlijk te wijten aan het toeval. Wanneer ze buiten het 95% betrouwbaarheidsinterval vallen, is er wel degelijk sprake van een significante correlatie tussen de variabelen.
2008-12-07 21:43:52 [Ellen Van den Broeck] [reply] 
Wanneer de staafjes boven de blauwe stippellijn komen dan zijn deze gegevens significant verschillend van nul.
2008-12-09 13:57:54 [Jules De Bruycker] [reply] 
Er is inderderdaad correlatie. Het gaat hier om een ruwe reeks want de tijdreeks werd niet gedifferentieerd en niet getransformeerd (d=0 en D=0). In de tabel zien we dat voor k=0 de correlatie tussen Y[t] en X[t] 0.686100625132908 bedraagt, dit is de correlatie zonder verschuiving in de tijd.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4,56
4,41
4,33
4,20
4,25
4,25
4,19
4,17
4,21
4,21
4,17
4,16
4,19
4,08
4,06
3,98
3,82
3,82
3,72
3,56
3,57
3,49
3,32
3,23
3,04
3,00
2,82
2,73
2,59
2,58
2,53
2,31
2,31
2,30
2,07
2,07
2,06
2,06
2,05
2,05
2,05
2,05
2,05
2,06
2,07
2,08
2,05
2,03
2,02
2,02
2,01
2,01
2,01
2,01
2,01
2,01
2,03
2,04
2,03
2,05
2,08
2,06
2,09
2,19
2,56
2,54
2,63
2,78
2,84
3,02
3,28
3,29
3,29
3,29
3,32
3,34
3,32
3,30
3,30
3,30
3,31
3,35
3,48
3,76
4,06
4,51
4,52
4,53
4,63
4,79
4,77
4,77
4,77
4,81
4,83
4,76
4,61
Dataseries Y:
Download CSV
Histogram 
5,1
4,9
5,2
5,1
4,6
3,7
3,9
3,1
2,8
2,6
2,2
1,8
1,3
1,2
1,4
1,3
1,3
1,9
1,9
2,1
2,0
1,9
1,9
1,9
1,8
1,7
1,6
1,7
1,9
1,7
1,3
2,0
2,0
2,3
2,0
1,7
2,3
2,4
2,4
2,3
2,1
2,1
2,5
2,0
1,8
1,7
1,9
2,1
1,4
1,6
1,7
1,6
1,9
1,6
1,1
1,3
1,6
1,6
1,7
1,6
1,7
1,6
1,5
1,6
1,1
1,5
1,4
1,3
0,9
1,2
0,9
1,1
1,3
1,3
1,4
1,2
1,7
2,0
3,0
3,1
3,2
2,7
2,8
3,0
2,8
3,1
3,1
3,2
3,1
2,7
2,2
2,2
2,1
2,3
2,5
2,3
2,6

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=27723&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=27723&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27723&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 series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.169129545676857
-150.172171805220660
-140.177906832830929
-130.176892793867708
-120.175651459722506
-110.182276897256894
-100.192992083833293
-90.206664875265751
-80.217526979479629
-70.229041008148761
-60.252617564450730
-50.27332976828734
-40.311921162748927
-30.359456965549619
-20.407697275121354
-10.448226634750219
00.491764376665951
10.483288571048712
20.484305016525241
30.485358883283717
40.496601085110425
50.507090770225586
60.510890228017649
70.507598774020759
80.487700092008063
90.453397104262097
100.411925354021651
110.372128657369537
120.3254991474808
130.275366837145978
140.222152505217051
150.167439156148302
160.113272326251899

\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 & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \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 & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-16 & 0.169129545676857 \tabularnewline
-15 & 0.172171805220660 \tabularnewline
-14 & 0.177906832830929 \tabularnewline
-13 & 0.176892793867708 \tabularnewline
-12 & 0.175651459722506 \tabularnewline
-11 & 0.182276897256894 \tabularnewline
-10 & 0.192992083833293 \tabularnewline
-9 & 0.206664875265751 \tabularnewline
-8 & 0.217526979479629 \tabularnewline
-7 & 0.229041008148761 \tabularnewline
-6 & 0.252617564450730 \tabularnewline
-5 & 0.27332976828734 \tabularnewline
-4 & 0.311921162748927 \tabularnewline
-3 & 0.359456965549619 \tabularnewline
-2 & 0.407697275121354 \tabularnewline
-1 & 0.448226634750219 \tabularnewline
0 & 0.491764376665951 \tabularnewline
1 & 0.483288571048712 \tabularnewline
2 & 0.484305016525241 \tabularnewline
3 & 0.485358883283717 \tabularnewline
4 & 0.496601085110425 \tabularnewline
5 & 0.507090770225586 \tabularnewline
6 & 0.510890228017649 \tabularnewline
7 & 0.507598774020759 \tabularnewline
8 & 0.487700092008063 \tabularnewline
9 & 0.453397104262097 \tabularnewline
10 & 0.411925354021651 \tabularnewline
11 & 0.372128657369537 \tabularnewline
12 & 0.3254991474808 \tabularnewline
13 & 0.275366837145978 \tabularnewline
14 & 0.222152505217051 \tabularnewline
15 & 0.167439156148302 \tabularnewline
16 & 0.113272326251899 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27723&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]0[/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]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]0[/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]-16[/C][C]0.169129545676857[/C][/ROW]
[ROW][C]-15[/C][C]0.172171805220660[/C][/ROW]
[ROW][C]-14[/C][C]0.177906832830929[/C][/ROW]
[ROW][C]-13[/C][C]0.176892793867708[/C][/ROW]
[ROW][C]-12[/C][C]0.175651459722506[/C][/ROW]
[ROW][C]-11[/C][C]0.182276897256894[/C][/ROW]
[ROW][C]-10[/C][C]0.192992083833293[/C][/ROW]
[ROW][C]-9[/C][C]0.206664875265751[/C][/ROW]
[ROW][C]-8[/C][C]0.217526979479629[/C][/ROW]
[ROW][C]-7[/C][C]0.229041008148761[/C][/ROW]
[ROW][C]-6[/C][C]0.252617564450730[/C][/ROW]
[ROW][C]-5[/C][C]0.27332976828734[/C][/ROW]
[ROW][C]-4[/C][C]0.311921162748927[/C][/ROW]
[ROW][C]-3[/C][C]0.359456965549619[/C][/ROW]
[ROW][C]-2[/C][C]0.407697275121354[/C][/ROW]
[ROW][C]-1[/C][C]0.448226634750219[/C][/ROW]
[ROW][C]0[/C][C]0.491764376665951[/C][/ROW]
[ROW][C]1[/C][C]0.483288571048712[/C][/ROW]
[ROW][C]2[/C][C]0.484305016525241[/C][/ROW]
[ROW][C]3[/C][C]0.485358883283717[/C][/ROW]
[ROW][C]4[/C][C]0.496601085110425[/C][/ROW]
[ROW][C]5[/C][C]0.507090770225586[/C][/ROW]
[ROW][C]6[/C][C]0.510890228017649[/C][/ROW]
[ROW][C]7[/C][C]0.507598774020759[/C][/ROW]
[ROW][C]8[/C][C]0.487700092008063[/C][/ROW]
[ROW][C]9[/C][C]0.453397104262097[/C][/ROW]
[ROW][C]10[/C][C]0.411925354021651[/C][/ROW]
[ROW][C]11[/C][C]0.372128657369537[/C][/ROW]
[ROW][C]12[/C][C]0.3254991474808[/C][/ROW]
[ROW][C]13[/C][C]0.275366837145978[/C][/ROW]
[ROW][C]14[/C][C]0.222152505217051[/C][/ROW]
[ROW][C]15[/C][C]0.167439156148302[/C][/ROW]
[ROW][C]16[/C][C]0.113272326251899[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27723&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27723&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 series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.169129545676857
-150.172171805220660
-140.177906832830929
-130.176892793867708
-120.175651459722506
-110.182276897256894
-100.192992083833293
-90.206664875265751
-80.217526979479629
-70.229041008148761
-60.252617564450730
-50.27332976828734
-40.311921162748927
-30.359456965549619
-20.407697275121354
-10.448226634750219
00.491764376665951
10.483288571048712
20.484305016525241
30.485358883283717
40.496601085110425
50.507090770225586
60.510890228017649
70.507598774020759
80.487700092008063
90.453397104262097
100.411925354021651
110.372128657369537
120.3254991474808
130.275366837145978
140.222152505217051
150.167439156148302
160.113272326251899


Figures (Output of Computation)

Input Parameters & R Code

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