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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 14:28:41 -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/t1228253341g8ele6m0r7yh5sp.htm/, Retrieved Sun, 19 May 2024 08:50:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28474, Retrieved Sun, 19 May 2024 08:50:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact183

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] [] [2008-12-02 21:28:41] [d41d8cd98f00b204e9800998ecf8427e] [Current]
F   PD      [Cross Correlation Function] [] [2008-12-02 21:39:53] [74be16979710d4c4e7c6647856088456]
- RMPD        [Variance Reduction Matrix] [] [2008-12-04 15:53:33] [996314793dac993597edc1ca2281ff39]
- RMPD        [(Partial) Autocorrelation Function] [] [2008-12-04 16:04:36] [996314793dac993597edc1ca2281ff39]
- RMPD        [(Partial) Autocorrelation Function] [] [2008-12-04 16:11:33] [996314793dac993597edc1ca2281ff39]
- RMPD        [Standard Deviation-Mean Plot] [] [2008-12-04 16:15:59] [996314793dac993597edc1ca2281ff39]
- RMPD        [Spectral Analysis] [] [2008-12-04 16:21:06] [996314793dac993597edc1ca2281ff39]
Feedback Forum
2008-12-04 15:47:17 [Angelique Van de Vijver] [reply] 
Goede berekening en conclusies van de student.
De kruiscorrelatiefunctie heeft inderdaad niks te maken met een trend zoals de student aangeeft. Het geef het verband weer tussen 2 verschillende tijdreeksen.
De student heeft niks vermeld over de k-waarde. Als deze k-waarde negatief is dan kan je het verleden gebruiken van X om Y te voorspellen. Als deze k-waarde positief is dan kan je Y voorspellen m.b.v. de toekomst van X.
Een leading indicator is een voorspellende waarde, iets dat vooroploopt. Het is een indicator die je op voorhand zegt wat het verloop van een andere variabele is.
De k-waarden -12, 0 en 12 geven de hoogste correlatiewaarden. Dit zie je op de grafiek en ook in de tabel. Hier is de kruiscorrelatie dus het hoogst.
2008-12-10 08:59:05 [Peter Van Doninck] [reply] 
Duidelijke theorie: bij cross correlatie gaan we niet op zoek naar een lange termijntrend en gaan we ook niet differentiëren. Het klopt dat we op -12, 12 en 0 grote waarden verkrijgen. Deze duiden op cross correlatie.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100,8
100,7
86,2
83,2
71,7
77,5
89,8
80,3
78,7
93,8
57,6
60,6
91
85,3
77,4
77,3
68,3
69,9
81,7
75,1
69,9
84
54,3
60
89,9
77
85,3
77,6
69,2
75,5
85,7
72,2
79,9
85,3
52,2
61,2
82,4
85,4
78,2
70,2
70,2
69,3
77,5
66,1
69
79,2
56,2
63,3
77,8
92
78,1
65,1
71,1
70,9
72
81,9
70,6
72,5
65,1
61,1
Dataseries Y:
Download CSV
Histogram 
127,5
128,6
116,6
127,4
105
108,3
125
111,6
106,5
130,3
115
116,1
134
126,5
125,8
136,4
114,9
110,9
125,5
116,8
116,8
125,5
104,2
115,1
132,8
123,3
124,8
122
117,4
117,9
137,4
114,6
124,7
129,6
109,4
120,9
134,9
136,3
133,2
127,2
122,7
120,5
137,8
119,1
124,3
134,4
121,1
122,2
127,7
137,4
132,2
129,2
124,9
124,8
128,2
134,4
118,6
132,6
123,2
112,3

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28474&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28474&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28474&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'George Udny Yule' @ 72.249.76.132






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])
-14-0.206976225248515
-130.000296089983289110
-120.45670148088246
-11-0.0777900680652565
-10-0.320779920396384
-90.0807190501368602
-8-0.235210533230332
-7-0.20209367417502
-60.223492357382856
-5-0.170873844942394
-4-0.393729530525745
-3-0.117098528385658
-2-0.339771835312393
-1-0.118820223007067
00.552013596338645
1-0.163772790988161
2-0.30982387724197
30.124040891360780
4-0.29583624531687
5-0.141575285373761
60.254837723824361
7-0.121364183802788
8-0.242178271798323
9-0.062625097216656
10-0.287950654885141
11-0.042649196476392
120.427941348358654
13-0.00500869281943723
14-0.141239792742081

\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 & -0.206976225248515 \tabularnewline
-13 & 0.000296089983289110 \tabularnewline
-12 & 0.45670148088246 \tabularnewline
-11 & -0.0777900680652565 \tabularnewline
-10 & -0.320779920396384 \tabularnewline
-9 & 0.0807190501368602 \tabularnewline
-8 & -0.235210533230332 \tabularnewline
-7 & -0.20209367417502 \tabularnewline
-6 & 0.223492357382856 \tabularnewline
-5 & -0.170873844942394 \tabularnewline
-4 & -0.393729530525745 \tabularnewline
-3 & -0.117098528385658 \tabularnewline
-2 & -0.339771835312393 \tabularnewline
-1 & -0.118820223007067 \tabularnewline
0 & 0.552013596338645 \tabularnewline
1 & -0.163772790988161 \tabularnewline
2 & -0.30982387724197 \tabularnewline
3 & 0.124040891360780 \tabularnewline
4 & -0.29583624531687 \tabularnewline
5 & -0.141575285373761 \tabularnewline
6 & 0.254837723824361 \tabularnewline
7 & -0.121364183802788 \tabularnewline
8 & -0.242178271798323 \tabularnewline
9 & -0.062625097216656 \tabularnewline
10 & -0.287950654885141 \tabularnewline
11 & -0.042649196476392 \tabularnewline
12 & 0.427941348358654 \tabularnewline
13 & -0.00500869281943723 \tabularnewline
14 & -0.141239792742081 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28474&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]-0.206976225248515[/C][/ROW]
[ROW][C]-13[/C][C]0.000296089983289110[/C][/ROW]
[ROW][C]-12[/C][C]0.45670148088246[/C][/ROW]
[ROW][C]-11[/C][C]-0.0777900680652565[/C][/ROW]
[ROW][C]-10[/C][C]-0.320779920396384[/C][/ROW]
[ROW][C]-9[/C][C]0.0807190501368602[/C][/ROW]
[ROW][C]-8[/C][C]-0.235210533230332[/C][/ROW]
[ROW][C]-7[/C][C]-0.20209367417502[/C][/ROW]
[ROW][C]-6[/C][C]0.223492357382856[/C][/ROW]
[ROW][C]-5[/C][C]-0.170873844942394[/C][/ROW]
[ROW][C]-4[/C][C]-0.393729530525745[/C][/ROW]
[ROW][C]-3[/C][C]-0.117098528385658[/C][/ROW]
[ROW][C]-2[/C][C]-0.339771835312393[/C][/ROW]
[ROW][C]-1[/C][C]-0.118820223007067[/C][/ROW]
[ROW][C]0[/C][C]0.552013596338645[/C][/ROW]
[ROW][C]1[/C][C]-0.163772790988161[/C][/ROW]
[ROW][C]2[/C][C]-0.30982387724197[/C][/ROW]
[ROW][C]3[/C][C]0.124040891360780[/C][/ROW]
[ROW][C]4[/C][C]-0.29583624531687[/C][/ROW]
[ROW][C]5[/C][C]-0.141575285373761[/C][/ROW]
[ROW][C]6[/C][C]0.254837723824361[/C][/ROW]
[ROW][C]7[/C][C]-0.121364183802788[/C][/ROW]
[ROW][C]8[/C][C]-0.242178271798323[/C][/ROW]
[ROW][C]9[/C][C]-0.062625097216656[/C][/ROW]
[ROW][C]10[/C][C]-0.287950654885141[/C][/ROW]
[ROW][C]11[/C][C]-0.042649196476392[/C][/ROW]
[ROW][C]12[/C][C]0.427941348358654[/C][/ROW]
[ROW][C]13[/C][C]-0.00500869281943723[/C][/ROW]
[ROW][C]14[/C][C]-0.141239792742081[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28474&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28474&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])
-14-0.206976225248515
-130.000296089983289110
-120.45670148088246
-11-0.0777900680652565
-10-0.320779920396384
-90.0807190501368602
-8-0.235210533230332
-7-0.20209367417502
-60.223492357382856
-5-0.170873844942394
-4-0.393729530525745
-3-0.117098528385658
-2-0.339771835312393
-1-0.118820223007067
00.552013596338645
1-0.163772790988161
2-0.30982387724197
30.124040891360780
4-0.29583624531687
5-0.141575285373761
60.254837723824361
7-0.121364183802788
8-0.242178271798323
9-0.062625097216656
10-0.287950654885141
11-0.042649196476392
120.427941348358654
13-0.00500869281943723
14-0.141239792742081


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')