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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 computationFri, 28 Nov 2008 07:01:57 -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/28/t1227881024n68w2d3fz5ok48t.htm/, Retrieved Sun, 19 May 2024 08:55:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26113, Retrieved Sun, 19 May 2024 08:55:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
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]
F    D  [Univariate Data Series] [q5] [2008-11-28 13:33:02] [e43247bc0ab243a5af99ac7f55ba0b41]
- RMP     [Box-Cox Normality Plot] [q5 box cox normality] [2008-11-28 13:40:30] [e43247bc0ab243a5af99ac7f55ba0b41]
F RMPD        [Cross Correlation Function] [q7] [2008-11-28 14:01:57] [f24298b2e4c2a19d76cf4460ec5d2246] [Current]
F    D          [Cross Correlation Function] [] [2008-11-28 14:06:52] [e43247bc0ab243a5af99ac7f55ba0b41]
- RMPD          [Standard Deviation-Mean Plot] [q7 sd mean] [2008-12-01 11:18:30] [e43247bc0ab243a5af99ac7f55ba0b41]
F    D            [Standard Deviation-Mean Plot] [werkloosheid mann...] [2008-12-01 16:25:51] [e43247bc0ab243a5af99ac7f55ba0b41]
-    D              [Standard Deviation-Mean Plot] [sd mean vrouwen] [2008-12-11 14:51:13] [e43247bc0ab243a5af99ac7f55ba0b41]
-    D                [Standard Deviation-Mean Plot] [sd mean onder de 25j] [2008-12-11 15:16:09] [e43247bc0ab243a5af99ac7f55ba0b41]
-    D                [Standard Deviation-Mean Plot] [sd mean boven 25j] [2008-12-11 15:33:47] [e43247bc0ab243a5af99ac7f55ba0b41]
Feedback Forum
2008-12-06 15:55:24 [Wim Golsteyn] [reply] 
Deze grafiek gaat na over een verband is tussen het verleden van de ene reeks en het heden van de andere. Hier zien we echter dat de correlatie het grootst is bij 0, dwz dat het verband het grootst is wanneer beide reeksen in het heden bekeken worden.
2008-12-07 15:13:10 [Chi-Kwong Man] [reply] 
De cross correlation funcie geeft het verband weer van het verleden en het heden tussen Xt en Yt.
2008-12-08 17:27:15 [Lindsay Heyndrickx] [reply] 
Hier werd een nieuwe techniek toegepast die niet hetzelfde is als de autocorrelatiefunctie. Met deze functie kunnen we een tijdreeks verschuiven in de tijd. Zo kan je het verleden beginnen voorspellen van andere variabelen.
We zien dat er hier heel wat waarden zijn dat buiten het betrouwbaarheidsinterval vallen en deze zijn allemaal positief significant, dit betekent dat het verleden van Xt gecorreleerd is met het heden van Yt en omgekeerd (=simultaan effect).

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7,8
7,6
7,5
7,6
7,5
7,3
7,6
7,5
7,6
7,9
7,9
8,1
8,2
8,0
7,5
6,8
6,5
6,6
7,6
8,0
8,0
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
7,0
7,1
7,2
7,1
6,9
7,0
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
Dataseries Y:
Download CSV
Histogram 
9,0
9,1
8,7
8,2
7,9
7,9
9,1
9,4
9,5
9,1
9,0
9,3
9,9
9,8
9,4
8,3
8,0
8,5
10,4
11,1
10,9
9,9
9,2
9,2
9,5
9,6
9,5
9,1
8,9
9,0
10,1
10,3
10,2
9,6
9,2
9,3
9,4
9,4
9,2
9,0
9,0
9,0
9,8
10,0
9,9
9,3
9,0
9,0
9,1
9,1
9,1
9,2
8,8
8,3
8,4
8,1
7,8
7,9
7,9
8,0
7,9
7,5
7,2
6,9
6,6
6,7
7,3
7,5

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=26113&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=26113&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26113&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])
-150.136624798178720
-140.235232163625655
-130.346348804555672
-120.453249550309423
-110.487391501761162
-100.491999742106762
-90.51075199279971
-80.548627274673732
-70.574812518902828
-60.551323519968099
-50.465617335078044
-40.378729053581335
-30.375780853593256
-20.469274528972795
-10.596809593974356
00.706684285304976
10.654773476632952
20.539650426001266
30.422844009948712
40.360881639308075
50.350187097763021
60.32915669253383
70.259349839031943
80.164135390111071
90.0892073649176952
100.0712481663785791
110.0922083763699584
120.113621224618949
130.0650547019163851
140.00309981289984139
15-0.0470154059899226

\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
-15 & 0.136624798178720 \tabularnewline
-14 & 0.235232163625655 \tabularnewline
-13 & 0.346348804555672 \tabularnewline
-12 & 0.453249550309423 \tabularnewline
-11 & 0.487391501761162 \tabularnewline
-10 & 0.491999742106762 \tabularnewline
-9 & 0.51075199279971 \tabularnewline
-8 & 0.548627274673732 \tabularnewline
-7 & 0.574812518902828 \tabularnewline
-6 & 0.551323519968099 \tabularnewline
-5 & 0.465617335078044 \tabularnewline
-4 & 0.378729053581335 \tabularnewline
-3 & 0.375780853593256 \tabularnewline
-2 & 0.469274528972795 \tabularnewline
-1 & 0.596809593974356 \tabularnewline
0 & 0.706684285304976 \tabularnewline
1 & 0.654773476632952 \tabularnewline
2 & 0.539650426001266 \tabularnewline
3 & 0.422844009948712 \tabularnewline
4 & 0.360881639308075 \tabularnewline
5 & 0.350187097763021 \tabularnewline
6 & 0.32915669253383 \tabularnewline
7 & 0.259349839031943 \tabularnewline
8 & 0.164135390111071 \tabularnewline
9 & 0.0892073649176952 \tabularnewline
10 & 0.0712481663785791 \tabularnewline
11 & 0.0922083763699584 \tabularnewline
12 & 0.113621224618949 \tabularnewline
13 & 0.0650547019163851 \tabularnewline
14 & 0.00309981289984139 \tabularnewline
15 & -0.0470154059899226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26113&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]-15[/C][C]0.136624798178720[/C][/ROW]
[ROW][C]-14[/C][C]0.235232163625655[/C][/ROW]
[ROW][C]-13[/C][C]0.346348804555672[/C][/ROW]
[ROW][C]-12[/C][C]0.453249550309423[/C][/ROW]
[ROW][C]-11[/C][C]0.487391501761162[/C][/ROW]
[ROW][C]-10[/C][C]0.491999742106762[/C][/ROW]
[ROW][C]-9[/C][C]0.51075199279971[/C][/ROW]
[ROW][C]-8[/C][C]0.548627274673732[/C][/ROW]
[ROW][C]-7[/C][C]0.574812518902828[/C][/ROW]
[ROW][C]-6[/C][C]0.551323519968099[/C][/ROW]
[ROW][C]-5[/C][C]0.465617335078044[/C][/ROW]
[ROW][C]-4[/C][C]0.378729053581335[/C][/ROW]
[ROW][C]-3[/C][C]0.375780853593256[/C][/ROW]
[ROW][C]-2[/C][C]0.469274528972795[/C][/ROW]
[ROW][C]-1[/C][C]0.596809593974356[/C][/ROW]
[ROW][C]0[/C][C]0.706684285304976[/C][/ROW]
[ROW][C]1[/C][C]0.654773476632952[/C][/ROW]
[ROW][C]2[/C][C]0.539650426001266[/C][/ROW]
[ROW][C]3[/C][C]0.422844009948712[/C][/ROW]
[ROW][C]4[/C][C]0.360881639308075[/C][/ROW]
[ROW][C]5[/C][C]0.350187097763021[/C][/ROW]
[ROW][C]6[/C][C]0.32915669253383[/C][/ROW]
[ROW][C]7[/C][C]0.259349839031943[/C][/ROW]
[ROW][C]8[/C][C]0.164135390111071[/C][/ROW]
[ROW][C]9[/C][C]0.0892073649176952[/C][/ROW]
[ROW][C]10[/C][C]0.0712481663785791[/C][/ROW]
[ROW][C]11[/C][C]0.0922083763699584[/C][/ROW]
[ROW][C]12[/C][C]0.113621224618949[/C][/ROW]
[ROW][C]13[/C][C]0.0650547019163851[/C][/ROW]
[ROW][C]14[/C][C]0.00309981289984139[/C][/ROW]
[ROW][C]15[/C][C]-0.0470154059899226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26113&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26113&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])
-150.136624798178720
-140.235232163625655
-130.346348804555672
-120.453249550309423
-110.487391501761162
-100.491999742106762
-90.51075199279971
-80.548627274673732
-70.574812518902828
-60.551323519968099
-50.465617335078044
-40.378729053581335
-30.375780853593256
-20.469274528972795
-10.596809593974356
00.706684285304976
10.654773476632952
20.539650426001266
30.422844009948712
40.360881639308075
50.350187097763021
60.32915669253383
70.259349839031943
80.164135390111071
90.0892073649176952
100.0712481663785791
110.0922083763699584
120.113621224618949
130.0650547019163851
140.00309981289984139
15-0.0470154059899226


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')