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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 11:15:57 -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/Dec/02/t1228241805vx0z49x7gy5j7a6.htm/, Retrieved Sat, 25 May 2024 13:05:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28195, Retrieved Sat, 25 May 2024 13:05:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact216

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [Non Stationary Ti...] [2008-12-02 18:15:57] [1828943283e41f5e3270e2e73d6433b4] [Current]
Feedback Forum
2008-12-04 10:27:49 [Steven Vercammen] [reply] 
De uitleg mag uitgebreider. Het feit dat er veel minder correlaties significant zijn betekent dat er sprake was van een schijncorrelatie. Er was een ‘Z’ die invloed had op beide variabelen en dus zorgde voor een schijnbare correlatie tussen X en Y. In bovenstaande grafiek is de invloed van die Z geëlimineerd. Dit geeft dus een betrouwbaarder beeld en toont aan dat we Y veel minder goed kunnen voorspellen op basis van vroegere en toekomstige waarden van X.
  2008-12-06 14:02:08 [Bert Moons] [reply] 
is het niet eerder zo dat door de differentiaties de correlaties met opzet verwijderd worden? De Z waarde is bij partiële correlaties denk ik.
2008-12-06 14:14:47 [Bert Moons] [reply] 
Door de transformaties werd de reeks stationair gemaakt, de LT trend is volledig verdwenen. Er was geen seizonaliteit in het begin dus er moest niet seizonaal gedifferentieerd worden (D=0). Opvallend is hier de correlatie bij lag 0.
2008-12-07 11:31:07 [Steven Vanhooreweghe] [reply] 
Wat hier over het hoofd word gezien is het staafje dat op tijdstip 12 onder het betrouwbaarheidsinterval ligt. Tijdstip 12 is een belangrijk tijdstip dus dit kan nog wijzen op seizoenaliteit.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4,8
5,5
5,4
5,9
5,8
5,1
4,1
4,4
3,6
3,5
3,1
2,9
2,2
1,4
1,2
1,3
1,3
1,3
1,8
1,8
1,8
1,7
2,1
2
1,7
1,9
2,3
2,4
2,5
2,8
2,6
2,2
2,8
2,8
2,8
2,3
2,2
3
2,9
2,7
2,7
2,3
2,4
2,8
2,3
2
1,9
2,3
2,7
1,8
2
2,1
2
2,4
1,7
1
1,2
1,4
1,7
1,8
Dataseries Y:
Download CSV
Histogram 
19,2
26,6
26,6
31,4
31,2
26,4
20,7
20,7
15
13,3
8,7
10,2
4,3
-0,1
-4,6
-3,9
-3,5
-3,4
-2,5
-1,1
0,3
-0,9
3,6
2,7
-0,2
-1
5,8
6,4
9,6
13,2
10,6
10,9
12,9
15,9
12,2
9,1
9
17,4
14,7
17
13,7
9,5
14,8
13,6
12,6
8,9
10,2
12,7
16
10,4
9,9
9,5
8,6
10
3,5
-4,2
-4,4
-1,5
-0,1
0,8

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=28195&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=28195&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28195&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 series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-14-0.0206482055857075
-13-0.154640020006701
-12-0.234072424917523
-110.0576034404241439
-100.192746645185483
-9-0.107981773046411
-80.00490648713362482
-7-0.114306856834126
-60.0407047570438186
-50.139005068509391
-40.109820265336385
-3-0.0479286071642797
-20.00321456571565975
-10.141883425390393
00.825007260718134
10.162630239997366
2-0.161173606978299
3-0.0149162035639519
40.0466773501212517
5-0.00190681498309575
60.198019559499132
7-0.192456868589036
8-0.065975621659886
9-0.0915848705595464
100.00170548398242048
110.163587675468583
12-0.330951726340343
13-0.237280328320819
140.121837989116685

\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 & 1 \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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & -0.0206482055857075 \tabularnewline
-13 & -0.154640020006701 \tabularnewline
-12 & -0.234072424917523 \tabularnewline
-11 & 0.0576034404241439 \tabularnewline
-10 & 0.192746645185483 \tabularnewline
-9 & -0.107981773046411 \tabularnewline
-8 & 0.00490648713362482 \tabularnewline
-7 & -0.114306856834126 \tabularnewline
-6 & 0.0407047570438186 \tabularnewline
-5 & 0.139005068509391 \tabularnewline
-4 & 0.109820265336385 \tabularnewline
-3 & -0.0479286071642797 \tabularnewline
-2 & 0.00321456571565975 \tabularnewline
-1 & 0.141883425390393 \tabularnewline
0 & 0.825007260718134 \tabularnewline
1 & 0.162630239997366 \tabularnewline
2 & -0.161173606978299 \tabularnewline
3 & -0.0149162035639519 \tabularnewline
4 & 0.0466773501212517 \tabularnewline
5 & -0.00190681498309575 \tabularnewline
6 & 0.198019559499132 \tabularnewline
7 & -0.192456868589036 \tabularnewline
8 & -0.065975621659886 \tabularnewline
9 & -0.0915848705595464 \tabularnewline
10 & 0.00170548398242048 \tabularnewline
11 & 0.163587675468583 \tabularnewline
12 & -0.330951726340343 \tabularnewline
13 & -0.237280328320819 \tabularnewline
14 & 0.121837989116685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28195&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]1[/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]1[/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]-0.0206482055857075[/C][/ROW]
[ROW][C]-13[/C][C]-0.154640020006701[/C][/ROW]
[ROW][C]-12[/C][C]-0.234072424917523[/C][/ROW]
[ROW][C]-11[/C][C]0.0576034404241439[/C][/ROW]
[ROW][C]-10[/C][C]0.192746645185483[/C][/ROW]
[ROW][C]-9[/C][C]-0.107981773046411[/C][/ROW]
[ROW][C]-8[/C][C]0.00490648713362482[/C][/ROW]
[ROW][C]-7[/C][C]-0.114306856834126[/C][/ROW]
[ROW][C]-6[/C][C]0.0407047570438186[/C][/ROW]
[ROW][C]-5[/C][C]0.139005068509391[/C][/ROW]
[ROW][C]-4[/C][C]0.109820265336385[/C][/ROW]
[ROW][C]-3[/C][C]-0.0479286071642797[/C][/ROW]
[ROW][C]-2[/C][C]0.00321456571565975[/C][/ROW]
[ROW][C]-1[/C][C]0.141883425390393[/C][/ROW]
[ROW][C]0[/C][C]0.825007260718134[/C][/ROW]
[ROW][C]1[/C][C]0.162630239997366[/C][/ROW]
[ROW][C]2[/C][C]-0.161173606978299[/C][/ROW]
[ROW][C]3[/C][C]-0.0149162035639519[/C][/ROW]
[ROW][C]4[/C][C]0.0466773501212517[/C][/ROW]
[ROW][C]5[/C][C]-0.00190681498309575[/C][/ROW]
[ROW][C]6[/C][C]0.198019559499132[/C][/ROW]
[ROW][C]7[/C][C]-0.192456868589036[/C][/ROW]
[ROW][C]8[/C][C]-0.065975621659886[/C][/ROW]
[ROW][C]9[/C][C]-0.0915848705595464[/C][/ROW]
[ROW][C]10[/C][C]0.00170548398242048[/C][/ROW]
[ROW][C]11[/C][C]0.163587675468583[/C][/ROW]
[ROW][C]12[/C][C]-0.330951726340343[/C][/ROW]
[ROW][C]13[/C][C]-0.237280328320819[/C][/ROW]
[ROW][C]14[/C][C]0.121837989116685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28195&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28195&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 series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-14-0.0206482055857075
-13-0.154640020006701
-12-0.234072424917523
-110.0576034404241439
-100.192746645185483
-9-0.107981773046411
-80.00490648713362482
-7-0.114306856834126
-60.0407047570438186
-50.139005068509391
-40.109820265336385
-3-0.0479286071642797
-20.00321456571565975
-10.141883425390393
00.825007260718134
10.162630239997366
2-0.161173606978299
3-0.0149162035639519
40.0466773501212517
5-0.00190681498309575
60.198019559499132
7-0.192456868589036
8-0.065975621659886
9-0.0915848705595464
100.00170548398242048
110.163587675468583
12-0.330951726340343
13-0.237280328320819
140.121837989116685


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; 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')