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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 09:18:06 -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/t1228234725isidfwhokqm8l95.htm/, Retrieved Sun, 19 May 2024 09:38:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28020, Retrieved Sun, 19 May 2024 09:38:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153

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] [Q7] [2008-12-02 16:18:06] [59094f58b9d90d3694e930ebd2901ecd] [Current]
Feedback Forum
2008-12-04 19:07:51 [c97d2ae59c98cf77a04815c1edffab5a] [reply] 
de student heeft de tabel van de cross correlatie niet weergegeven:
enige uitleg hierbij:
-in de tabel zie je de herhaling van de parameters die zijn ingevuld (links bovenaan)
-de berekende correlatie coëfficiënten zijn ook weergegeven in de grafiek hieronder
-k: het aantal verschuivingen in de tijd.
-K=0 => correlatie tussen y(t) en x(t) ZONDER verschuiving in de tijd
-Boven 0 in tabel (negatieve waarden van k):
= correlatie tussen het verleden van x(t) en huidige y(t)
-onderzoek of x(t) een leading indicator is (voorloper)/ een voorspellende kracht heeft op het verloop van y(t)
-Onder 0 (positieve waarden van k):
= toekomstige waarde van x(t) gecorreleerd met de huidige waarde van y(t)
= verleden van y(t) gecorreleerd met de huidige waarde van x(t)

de student zegt: De cross correlation functie berekent de correlatie tussen Yt en Yt+k => dit moet zijn tussen y(t) en x(t+k)

bij de grafiek:
-Betrouwbaarheidsinterval: correlaties die buiten de stippenlijn vallen zijn significant verschillend van 0, en dus niet te wijten aan toeval.
=>Al deze waarden die erbuiten vallen, gaan een effect hebben op het verloop van y(t)
- hier zijn er 3 correlaties die niet door toeval zijn veroorzaakt.
hierbij zal x(t) een vertraging van 3,4 of 5 periodes moeten ondergaan om y(t) te verklaren.



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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
99.2
99
100
111.6
122.2
117.6
121.1
136
154.2
153.6
158.5
140.6
136.2
168
154.3
149
165.5
Dataseries Y:
Download CSV
Histogram 
96.7
98.1
100
104.9
104.9
109.5
110.8
112.3
109.3
105.3
101.7
95.4
96.4
97.6
102.4
101.6
103.8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28020&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]3 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=28020&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28020&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 time3 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)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])
-90.236006556160983
-80.053221581239011
-7-0.169833066960884
-6-0.336047226350378
-5-0.517192563302102
-4-0.573470967646541
-3-0.5885180954614
-2-0.452605867411525
-1-0.202374044224987
00.0617694041009443
10.144479690299472
20.166031833197589
30.159983697754745
40.179177574128377
50.232674124098104
60.292424183865019
70.258003898887469
80.215196574475449
90.171680328730759

\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
-9 & 0.236006556160983 \tabularnewline
-8 & 0.053221581239011 \tabularnewline
-7 & -0.169833066960884 \tabularnewline
-6 & -0.336047226350378 \tabularnewline
-5 & -0.517192563302102 \tabularnewline
-4 & -0.573470967646541 \tabularnewline
-3 & -0.5885180954614 \tabularnewline
-2 & -0.452605867411525 \tabularnewline
-1 & -0.202374044224987 \tabularnewline
0 & 0.0617694041009443 \tabularnewline
1 & 0.144479690299472 \tabularnewline
2 & 0.166031833197589 \tabularnewline
3 & 0.159983697754745 \tabularnewline
4 & 0.179177574128377 \tabularnewline
5 & 0.232674124098104 \tabularnewline
6 & 0.292424183865019 \tabularnewline
7 & 0.258003898887469 \tabularnewline
8 & 0.215196574475449 \tabularnewline
9 & 0.171680328730759 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28020&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]-9[/C][C]0.236006556160983[/C][/ROW]
[ROW][C]-8[/C][C]0.053221581239011[/C][/ROW]
[ROW][C]-7[/C][C]-0.169833066960884[/C][/ROW]
[ROW][C]-6[/C][C]-0.336047226350378[/C][/ROW]
[ROW][C]-5[/C][C]-0.517192563302102[/C][/ROW]
[ROW][C]-4[/C][C]-0.573470967646541[/C][/ROW]
[ROW][C]-3[/C][C]-0.5885180954614[/C][/ROW]
[ROW][C]-2[/C][C]-0.452605867411525[/C][/ROW]
[ROW][C]-1[/C][C]-0.202374044224987[/C][/ROW]
[ROW][C]0[/C][C]0.0617694041009443[/C][/ROW]
[ROW][C]1[/C][C]0.144479690299472[/C][/ROW]
[ROW][C]2[/C][C]0.166031833197589[/C][/ROW]
[ROW][C]3[/C][C]0.159983697754745[/C][/ROW]
[ROW][C]4[/C][C]0.179177574128377[/C][/ROW]
[ROW][C]5[/C][C]0.232674124098104[/C][/ROW]
[ROW][C]6[/C][C]0.292424183865019[/C][/ROW]
[ROW][C]7[/C][C]0.258003898887469[/C][/ROW]
[ROW][C]8[/C][C]0.215196574475449[/C][/ROW]
[ROW][C]9[/C][C]0.171680328730759[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28020&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28020&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])
-90.236006556160983
-80.053221581239011
-7-0.169833066960884
-6-0.336047226350378
-5-0.517192563302102
-4-0.573470967646541
-3-0.5885180954614
-2-0.452605867411525
-1-0.202374044224987
00.0617694041009443
10.144479690299472
20.166031833197589
30.159983697754745
40.179177574128377
50.232674124098104
60.292424183865019
70.258003898887469
80.215196574475449
90.171680328730759


Figures (Output of Computation)

Input Parameters & R Code

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