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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 computationTue, 02 Dec 2008 13:08:18 -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/t1228248539bkvgufbrj0bngow.htm/, Retrieved Tue, 28 May 2024 01:48:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28327, Retrieved Tue, 28 May 2024 01:48:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsnon stationary time series crosscorrelation
Estimated Impact175

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]
- RMPD  [Cross Correlation Function] [non stationary ti...] [2008-12-02 19:52:31] [47f64d63202c1921bd27f3073f07a153]
F   PD      [Cross Correlation Function] [non stationary ti...] [2008-12-02 20:08:18] [74c7506a1ea162af3aa8be25bcd05d28] [Current]
F   P         [Cross Correlation Function] [non stat time ser...] [2008-12-03 11:54:56] [c96f3dce3a823a83b6ede18389e1cfd4]
F   P           [Cross Correlation Function] [non stat time ser...] [2008-12-03 13:42:47] [c96f3dce3a823a83b6ede18389e1cfd4]
F   P         [Cross Correlation Function] [non stationary ti...] [2008-12-03 13:42:33] [47f64d63202c1921bd27f3073f07a153]
Feedback Forum
2008-12-08 16:24:56 [6066575aa30c0611e452e930b1dff53d] [reply] 
Deze vraag werd ook zeer goed beantwoord. Er werd ook vermeld dat het hier gaat 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] 0.91 bedraagt, dit is de correlatie zonder verschuiving in de tijd. In de grafiek van de cross correlation function worden deze cijfers grafisch voorgesteld. In deze grafiek zien we dat er veel verticale lijnen buiten het 95% betrouwbaarheidsinterval liggen. Al de verticale lijnen die buiten dit betrouwbaarheidsinterval liggen, zijn significant verschillend van nul. We zien ook dat rechts van nul, waar de k waarde positief is, zeer veel verticale lijnen buiten het 95% betrouwbaarheidsinterval liggen.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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
6.8
Dataseries Y:
Download CSV
Histogram 
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
6.8
6.8
6.5
6.3
5.9
5.9
6.4
6.4
6.5

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=28327&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=28327&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28327&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 series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
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])
-140.149990559589973
-130.185861325964253
-120.225297525036757
-110.230031305698896
-100.247280712672970
-90.273097296793963
-80.320058642919908
-70.374685950976791
-60.410742592989367
-50.42140749629168
-40.449612300735022
-30.541245758598121
-20.683668326476845
-10.837063189610136
00.919639434552627
10.811423616715479
20.652148324297912
30.524483155580876
40.459041264932223
50.463415390546591
60.495400497340574
70.498141170375977
80.484834579971819
90.479019341050503
100.476425946526319
110.46456693306283
120.430352149647916
130.341028055775768
140.260096528602442

\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) & 1 \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
-14 & 0.149990559589973 \tabularnewline
-13 & 0.185861325964253 \tabularnewline
-12 & 0.225297525036757 \tabularnewline
-11 & 0.230031305698896 \tabularnewline
-10 & 0.247280712672970 \tabularnewline
-9 & 0.273097296793963 \tabularnewline
-8 & 0.320058642919908 \tabularnewline
-7 & 0.374685950976791 \tabularnewline
-6 & 0.410742592989367 \tabularnewline
-5 & 0.42140749629168 \tabularnewline
-4 & 0.449612300735022 \tabularnewline
-3 & 0.541245758598121 \tabularnewline
-2 & 0.683668326476845 \tabularnewline
-1 & 0.837063189610136 \tabularnewline
0 & 0.919639434552627 \tabularnewline
1 & 0.811423616715479 \tabularnewline
2 & 0.652148324297912 \tabularnewline
3 & 0.524483155580876 \tabularnewline
4 & 0.459041264932223 \tabularnewline
5 & 0.463415390546591 \tabularnewline
6 & 0.495400497340574 \tabularnewline
7 & 0.498141170375977 \tabularnewline
8 & 0.484834579971819 \tabularnewline
9 & 0.479019341050503 \tabularnewline
10 & 0.476425946526319 \tabularnewline
11 & 0.46456693306283 \tabularnewline
12 & 0.430352149647916 \tabularnewline
13 & 0.341028055775768 \tabularnewline
14 & 0.260096528602442 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28327&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]1[/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]-14[/C][C]0.149990559589973[/C][/ROW]
[ROW][C]-13[/C][C]0.185861325964253[/C][/ROW]
[ROW][C]-12[/C][C]0.225297525036757[/C][/ROW]
[ROW][C]-11[/C][C]0.230031305698896[/C][/ROW]
[ROW][C]-10[/C][C]0.247280712672970[/C][/ROW]
[ROW][C]-9[/C][C]0.273097296793963[/C][/ROW]
[ROW][C]-8[/C][C]0.320058642919908[/C][/ROW]
[ROW][C]-7[/C][C]0.374685950976791[/C][/ROW]
[ROW][C]-6[/C][C]0.410742592989367[/C][/ROW]
[ROW][C]-5[/C][C]0.42140749629168[/C][/ROW]
[ROW][C]-4[/C][C]0.449612300735022[/C][/ROW]
[ROW][C]-3[/C][C]0.541245758598121[/C][/ROW]
[ROW][C]-2[/C][C]0.683668326476845[/C][/ROW]
[ROW][C]-1[/C][C]0.837063189610136[/C][/ROW]
[ROW][C]0[/C][C]0.919639434552627[/C][/ROW]
[ROW][C]1[/C][C]0.811423616715479[/C][/ROW]
[ROW][C]2[/C][C]0.652148324297912[/C][/ROW]
[ROW][C]3[/C][C]0.524483155580876[/C][/ROW]
[ROW][C]4[/C][C]0.459041264932223[/C][/ROW]
[ROW][C]5[/C][C]0.463415390546591[/C][/ROW]
[ROW][C]6[/C][C]0.495400497340574[/C][/ROW]
[ROW][C]7[/C][C]0.498141170375977[/C][/ROW]
[ROW][C]8[/C][C]0.484834579971819[/C][/ROW]
[ROW][C]9[/C][C]0.479019341050503[/C][/ROW]
[ROW][C]10[/C][C]0.476425946526319[/C][/ROW]
[ROW][C]11[/C][C]0.46456693306283[/C][/ROW]
[ROW][C]12[/C][C]0.430352149647916[/C][/ROW]
[ROW][C]13[/C][C]0.341028055775768[/C][/ROW]
[ROW][C]14[/C][C]0.260096528602442[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28327&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28327&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)1
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])
-140.149990559589973
-130.185861325964253
-120.225297525036757
-110.230031305698896
-100.247280712672970
-90.273097296793963
-80.320058642919908
-70.374685950976791
-60.410742592989367
-50.42140749629168
-40.449612300735022
-30.541245758598121
-20.683668326476845
-10.837063189610136
00.919639434552627
10.811423616715479
20.652148324297912
30.524483155580876
40.459041264932223
50.463415390546591
60.495400497340574
70.498141170375977
80.484834579971819
90.479019341050503
100.476425946526319
110.46456693306283
120.430352149647916
130.341028055775768
140.260096528602442


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 500 ; par2 = 0.5 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; 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')