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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 09:48:02 -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/t1228236601t6w965emg49d5n0.htm/, Retrieved Thu, 23 May 2024 10:09:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28069, Retrieved Thu, 23 May 2024 10:09:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact200

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] [cross correlation] [2008-12-02 16:48:02] [32a7b12f2bdf14b45f7a9a96ba1ab98d] [Current]
Feedback Forum
2008-12-04 09:11:31 [Julie Govaerts] [reply] 
Correlatie tussen Yt en Xt +k --> X wordt verschoven in de tijd om na te gaan in welke mate Yt verklaard kan worden dmv het verleden van Xt (een leading indicator voor Yt) DUS het is niet Xt die een leading indicator heeft maar er zelf eventueel 1 is voor Yt

in de tabel --> Eerst zijn de waarden van k negatief = het verleden ; er na worden deze positief = in de toekomst = toekomstige waarden
De Rho waarde is de correlatiecoefficient.
Natuurlijk zoeken we hier de hoogste coëfficiënt --> 0 0.929594575625931
Deze valt dan ook duidelijk op nul. Dwz dat er geen vertraging of versnelling is op de gegevens, want de hoogste correlatie vind je op het huidige moment.

Grafisch
Alle negatieve lag-waarden stellen het verleden voor. Lag 0 stelt het heden voor en de positieve lag-waarden de toekomst. De hoogste correlatie (die het 95% betrouwbaarheidsinterval overschrijdt) ligt op lag 0 en wil dus zeggen dat er geen vertraging of versnelling is van de gegevens, want de hoogste correlatie ligt op het huidige moment.

de correlaties zijn echter nog nonsens, omdat deze tijdsreeksen nog vol zitten met seizoenaliteit en een trend. Ze moeten dus eerst nog gedifferentieerd worden! (volgende vraag)

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3,253
3,233
3,196
3,138
3,091
3,17
3,378
3,468
3,33
3,413
3,356
3,525
3,633
3,597
3,6
3,522
3,503
3,532
3,686
3,748
3,672
3,843
3,905
3,999
4,07
4,084
4,042
3,951
3,933
3,958
4,147
4,221
4,058
4,057
4,089
4,268
4,309
4,303
4,177
4,117
4,065
3,983
4,091
4,067
4,024
3,868
3,8
3,804
3,862
3,792
3,674
3,56
3,489
3,412
3,674
3,672
3,463
3,429
3,4
3,533
Dataseries Y:
Download CSV
Histogram 
11,836
11,85
11,897
12,082
11,936
11,928
12,646
12,747
12,447
12,445
12,257
12,878
13,69
13,665
13,78
13,608
13,375
13,376
13,918
14,304
13,877
14,543
14,291
14,788
15,241
15,265
15,322
15,175
14,817
14,579
15,247
15,385
14,891
14,766
14,42
14,85
15,117
15,352
15,099
15,291
15,208
14,995
15,454
15,251
14,975
14,005
13,55
13,422
13,848
13,376
13,038
12,974
12,554
11,971
12,916
12,757
11,924
11,693
11,382
11,821

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=28069&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=28069&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28069&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)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])
-14-0.180520806410882
-13-0.0945926094342653
-12-0.0209652757573449
-110.0229995454748145
-100.0886696607555842
-90.183937787555729
-80.298967183024573
-70.418946365193872
-60.521840646109108
-50.585194360499534
-40.621909376039641
-30.685815473307652
-20.773553963799869
-10.864906585255423
00.929594575625931
10.860174755351746
20.78821343687617
30.745349953056033
40.709084883618785
50.67223919992684
60.619031015567343
70.546906141561835
80.470749050519605
90.406970152319427
100.362288023786942
110.316272590264809
120.268110126460641
130.185631813680882
140.101482118091820

\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
-14 & -0.180520806410882 \tabularnewline
-13 & -0.0945926094342653 \tabularnewline
-12 & -0.0209652757573449 \tabularnewline
-11 & 0.0229995454748145 \tabularnewline
-10 & 0.0886696607555842 \tabularnewline
-9 & 0.183937787555729 \tabularnewline
-8 & 0.298967183024573 \tabularnewline
-7 & 0.418946365193872 \tabularnewline
-6 & 0.521840646109108 \tabularnewline
-5 & 0.585194360499534 \tabularnewline
-4 & 0.621909376039641 \tabularnewline
-3 & 0.685815473307652 \tabularnewline
-2 & 0.773553963799869 \tabularnewline
-1 & 0.864906585255423 \tabularnewline
0 & 0.929594575625931 \tabularnewline
1 & 0.860174755351746 \tabularnewline
2 & 0.78821343687617 \tabularnewline
3 & 0.745349953056033 \tabularnewline
4 & 0.709084883618785 \tabularnewline
5 & 0.67223919992684 \tabularnewline
6 & 0.619031015567343 \tabularnewline
7 & 0.546906141561835 \tabularnewline
8 & 0.470749050519605 \tabularnewline
9 & 0.406970152319427 \tabularnewline
10 & 0.362288023786942 \tabularnewline
11 & 0.316272590264809 \tabularnewline
12 & 0.268110126460641 \tabularnewline
13 & 0.185631813680882 \tabularnewline
14 & 0.101482118091820 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28069&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]-14[/C][C]-0.180520806410882[/C][/ROW]
[ROW][C]-13[/C][C]-0.0945926094342653[/C][/ROW]
[ROW][C]-12[/C][C]-0.0209652757573449[/C][/ROW]
[ROW][C]-11[/C][C]0.0229995454748145[/C][/ROW]
[ROW][C]-10[/C][C]0.0886696607555842[/C][/ROW]
[ROW][C]-9[/C][C]0.183937787555729[/C][/ROW]
[ROW][C]-8[/C][C]0.298967183024573[/C][/ROW]
[ROW][C]-7[/C][C]0.418946365193872[/C][/ROW]
[ROW][C]-6[/C][C]0.521840646109108[/C][/ROW]
[ROW][C]-5[/C][C]0.585194360499534[/C][/ROW]
[ROW][C]-4[/C][C]0.621909376039641[/C][/ROW]
[ROW][C]-3[/C][C]0.685815473307652[/C][/ROW]
[ROW][C]-2[/C][C]0.773553963799869[/C][/ROW]
[ROW][C]-1[/C][C]0.864906585255423[/C][/ROW]
[ROW][C]0[/C][C]0.929594575625931[/C][/ROW]
[ROW][C]1[/C][C]0.860174755351746[/C][/ROW]
[ROW][C]2[/C][C]0.78821343687617[/C][/ROW]
[ROW][C]3[/C][C]0.745349953056033[/C][/ROW]
[ROW][C]4[/C][C]0.709084883618785[/C][/ROW]
[ROW][C]5[/C][C]0.67223919992684[/C][/ROW]
[ROW][C]6[/C][C]0.619031015567343[/C][/ROW]
[ROW][C]7[/C][C]0.546906141561835[/C][/ROW]
[ROW][C]8[/C][C]0.470749050519605[/C][/ROW]
[ROW][C]9[/C][C]0.406970152319427[/C][/ROW]
[ROW][C]10[/C][C]0.362288023786942[/C][/ROW]
[ROW][C]11[/C][C]0.316272590264809[/C][/ROW]
[ROW][C]12[/C][C]0.268110126460641[/C][/ROW]
[ROW][C]13[/C][C]0.185631813680882[/C][/ROW]
[ROW][C]14[/C][C]0.101482118091820[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28069&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28069&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])
-14-0.180520806410882
-13-0.0945926094342653
-12-0.0209652757573449
-110.0229995454748145
-100.0886696607555842
-90.183937787555729
-80.298967183024573
-70.418946365193872
-60.521840646109108
-50.585194360499534
-40.621909376039641
-30.685815473307652
-20.773553963799869
-10.864906585255423
00.929594575625931
10.860174755351746
20.78821343687617
30.745349953056033
40.709084883618785
50.67223919992684
60.619031015567343
70.546906141561835
80.470749050519605
90.406970152319427
100.362288023786942
110.316272590264809
120.268110126460641
130.185631813680882
140.101482118091820


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')