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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 13:41:40 -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/t1228250562f7cxzwfqnp5njut.htm/, Retrieved Fri, 17 May 2024 06:17:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28416, Retrieved Fri, 17 May 2024 06:17:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146

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 stat time ser...] [2008-12-02 20:41:40] [c5d6d05aee6be5527ac4a30a8c3b8fe5] [Current]
Feedback Forum
2008-12-06 14:09:57 [Thomas Plasschaert] [reply] 
juiste uitwerking van de vraag
2008-12-08 13:54:30 [Katja van Hek] [reply] 
Er is hier inderdaad geen sprake meer van autocorrelatie.
2008-12-08 17:01:09 [Jonas Janssens] [reply] 
Juiste berekeningen en uitleg.
2008-12-08 19:45:09 [5faab2fc6fb120339944528a32d48a04] [reply] 
Door differentiatie is er inderdaad geen sprake meer van autocorrelatie.
2008-12-09 22:54:46 [Gert-Jan Geudens] [reply] 
Correct. Er is nog wel een lichte correlatie bij k = ongeveer -2. Het is zeer duidelijk dat de gevonden correlatie in Q7, duidelijk een nonsenscorrelatie was.
2008-12-09 23:07:06 [Gert-Jan Geudens] [reply] 
Graag zouden we hier nog graag even het volgende toevoegen aan onze vorige feedback. Je hebt de lambda niet ingegeven en dus zijn de gevonden resultaten niet correct.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105,4
107,1
110,7
117,1
118,7
126,5
127,5
134,6
131,8
135,9
142,7
141,7
153,4
145
137,7
148,3
152,2
169,4
168,6
161,1
174,1
179
190,6
190
181,6
174,8
180,5
196,8
193,8
197
216,3
221,4
217,9
229,7
227,4
204,2
196,6
198,8
207,5
190,7
201,6
210,5
223,5
223,8
231,2
244
234,7
250,2
265,7
287,6
283,3
295,4
312,3
333,8
347,7
383,2
407,1
413,6
362,7
321,9
239,4
Dataseries Y:
Download CSV
Histogram 
109,1
111,4
114,1
121,8
127,6
129,9
128
123,5
124
127,4
127,6
128,4
131,4
135,1
134
144,5
147,3
150,9
148,7
141,4
138,9
139,8
145,6
147,9
148,5
151,1
157,5
167,5
172,3
173,5
187,5
205,5
195,1
204,5
204,5
201,7
207
206,6
210,6
211,1
215
223,9
238,2
238,9
229,6
232,2
222,1
221,6
227,3
221
213,6
243,4
253,8
265,3
268,2
268,5
266,9
268,4
250,8
231,2
192

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=28416&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=28416&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28416&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 series2
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])
-140.0545125187309012
-13-0.0848533991643492
-120.142570452737942
-11-0.0608089364812745
-100.0210505004312697
-9-0.0492999619406107
-8-0.0857306774032623
-7-0.0161781854159614
-6-0.0129306861218951
-50.0743569321614704
-4-0.0883594487383662
-3-0.0184583560210838
-20.274712942044851
-10.358755344631534
0-0.0825600473583195
10.223279423568953
2-0.0132495751738984
30.0379390455732733
4-0.159535986152933
5-0.085376089100707
6-0.131111372921861
7-0.102808966355430
8-0.108903923760482
90.0674008398704485
100.183932990007192
110.0445537794003050
120.0169734265556673
130.264606350954408
14-0.0739455437730965

\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 & 2 \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 & 0.0545125187309012 \tabularnewline
-13 & -0.0848533991643492 \tabularnewline
-12 & 0.142570452737942 \tabularnewline
-11 & -0.0608089364812745 \tabularnewline
-10 & 0.0210505004312697 \tabularnewline
-9 & -0.0492999619406107 \tabularnewline
-8 & -0.0857306774032623 \tabularnewline
-7 & -0.0161781854159614 \tabularnewline
-6 & -0.0129306861218951 \tabularnewline
-5 & 0.0743569321614704 \tabularnewline
-4 & -0.0883594487383662 \tabularnewline
-3 & -0.0184583560210838 \tabularnewline
-2 & 0.274712942044851 \tabularnewline
-1 & 0.358755344631534 \tabularnewline
0 & -0.0825600473583195 \tabularnewline
1 & 0.223279423568953 \tabularnewline
2 & -0.0132495751738984 \tabularnewline
3 & 0.0379390455732733 \tabularnewline
4 & -0.159535986152933 \tabularnewline
5 & -0.085376089100707 \tabularnewline
6 & -0.131111372921861 \tabularnewline
7 & -0.102808966355430 \tabularnewline
8 & -0.108903923760482 \tabularnewline
9 & 0.0674008398704485 \tabularnewline
10 & 0.183932990007192 \tabularnewline
11 & 0.0445537794003050 \tabularnewline
12 & 0.0169734265556673 \tabularnewline
13 & 0.264606350954408 \tabularnewline
14 & -0.0739455437730965 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28416&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]2[/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]0.0545125187309012[/C][/ROW]
[ROW][C]-13[/C][C]-0.0848533991643492[/C][/ROW]
[ROW][C]-12[/C][C]0.142570452737942[/C][/ROW]
[ROW][C]-11[/C][C]-0.0608089364812745[/C][/ROW]
[ROW][C]-10[/C][C]0.0210505004312697[/C][/ROW]
[ROW][C]-9[/C][C]-0.0492999619406107[/C][/ROW]
[ROW][C]-8[/C][C]-0.0857306774032623[/C][/ROW]
[ROW][C]-7[/C][C]-0.0161781854159614[/C][/ROW]
[ROW][C]-6[/C][C]-0.0129306861218951[/C][/ROW]
[ROW][C]-5[/C][C]0.0743569321614704[/C][/ROW]
[ROW][C]-4[/C][C]-0.0883594487383662[/C][/ROW]
[ROW][C]-3[/C][C]-0.0184583560210838[/C][/ROW]
[ROW][C]-2[/C][C]0.274712942044851[/C][/ROW]
[ROW][C]-1[/C][C]0.358755344631534[/C][/ROW]
[ROW][C]0[/C][C]-0.0825600473583195[/C][/ROW]
[ROW][C]1[/C][C]0.223279423568953[/C][/ROW]
[ROW][C]2[/C][C]-0.0132495751738984[/C][/ROW]
[ROW][C]3[/C][C]0.0379390455732733[/C][/ROW]
[ROW][C]4[/C][C]-0.159535986152933[/C][/ROW]
[ROW][C]5[/C][C]-0.085376089100707[/C][/ROW]
[ROW][C]6[/C][C]-0.131111372921861[/C][/ROW]
[ROW][C]7[/C][C]-0.102808966355430[/C][/ROW]
[ROW][C]8[/C][C]-0.108903923760482[/C][/ROW]
[ROW][C]9[/C][C]0.0674008398704485[/C][/ROW]
[ROW][C]10[/C][C]0.183932990007192[/C][/ROW]
[ROW][C]11[/C][C]0.0445537794003050[/C][/ROW]
[ROW][C]12[/C][C]0.0169734265556673[/C][/ROW]
[ROW][C]13[/C][C]0.264606350954408[/C][/ROW]
[ROW][C]14[/C][C]-0.0739455437730965[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28416&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28416&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 series2
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])
-140.0545125187309012
-13-0.0848533991643492
-120.142570452737942
-11-0.0608089364812745
-100.0210505004312697
-9-0.0492999619406107
-8-0.0857306774032623
-7-0.0161781854159614
-6-0.0129306861218951
-50.0743569321614704
-4-0.0883594487383662
-3-0.0184583560210838
-20.274712942044851
-10.358755344631534
0-0.0825600473583195
10.223279423568953
2-0.0132495751738984
30.0379390455732733
4-0.159535986152933
5-0.085376089100707
6-0.131111372921861
7-0.102808966355430
8-0.108903923760482
90.0674008398704485
100.183932990007192
110.0445537794003050
120.0169734265556673
130.264606350954408
14-0.0739455437730965


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; 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')