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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 12:32:48 -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/t1228246539nkx55qr10aoa26h.htm/, Retrieved Sat, 18 May 2024 08:25:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28246, Retrieved Sat, 18 May 2024 08:25:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

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] [nsts Q7] [2008-12-02 19:32:48] [821c4b3d195be8e737cf8c9dc649d3cf] [Current]
F    D    [Cross Correlation Function] [nsts Q8] [2008-12-02 20:12:04] [3a9fc6d5b5e0e816787b7dbace57e7cd]
Feedback Forum
2008-12-08 16:33:11 [Charis Berrevoets] [reply] 
Wat je hier zegt klopt maar je had ook de tabel kunnen bespreken om je conclusie verder met bewijzen te staven.
Bijvoorbeeld:
bij k=-4 zien we een correlatie tussen x en y van 0,53
bij k=4 zien we een correlatie tussen x en y van 0,60
Je kan dus inderdaad, zoals je zelf al zei informatie voor y(t) halen uit het verleden en de toekomst van x(t).
2008-12-08 19:37:46 [Gert-Jan Geudens] [reply] 
Goede conclusie maar je had ook nog kunnen vermelden dat er een redelijke grote correlatie is tussen xt en yt in de periode k = -5 en +5. Of er sprake is van een nonsenscorrelatie zullen we berekenen in Q9.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109,57
107,08
110,33
110,36
106,5
104,3
107,21
109,34
108,2
109,86
108,68
113,38
117,12
116,23
114,75
115,81
115,86
117,8
117,11
116,31
118,38
121,57
121,65
124,2
126,12
128,6
128,16
130,12
135,83
138,05
134,99
132,38
128,94
128,12
127,84
132,43
134,13
134,78
133,13
129,08
134,48
132,86
134,08
134,54
134,51
135,97
136,09
139,14
135,63
136,55
138,83
138,84
135,37
132,22
134,75
135,98
136,06
138,05
139,59
140,58
Dataseries Y:
Download CSV
Histogram 
377,2
332,2
364,8
352,4
341,6
298,2
355,3
330,9
314,5
418,9
433,2
367
422,9
352,1
419,8
432,7
414,2
387,7
297,2
357,4
384,2
425,2
385,3
355,4
409,8
421,2
421,8
464,2
494
404,2
411,4
403,4
403,3
520,9
439,8
434,8
476,5
454,3
522
498,4
439,9
450,7
447,1
451,3
466,8
498
533,6
451,9
477,1
410,4
469,5
485,4
406,7
439,7
412,2
440,2
411,1
477,7
463,2
320,5

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'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28246&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28246&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28246&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'Herman Ole Andreas Wold' @ 193.190.124.10:1001






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])
-140.195103976157979
-130.251681564257730
-120.238265760841685
-110.287431640399995
-100.323920324028572
-90.318757466853466
-80.380600513975211
-70.400312855429773
-60.421626608156360
-50.468353580392985
-40.533635314479274
-30.58669673914887
-20.583708755789858
-10.627843394150585
00.661520230627637
10.709029348971536
20.683335964042
30.634939200194095
40.606886703691076
50.54236996274649
60.493562531891862
70.480960778564144
80.482293851812332
90.450116768547687
100.391252455615663
110.380605916944592
120.36819206062676
130.364207379555673
140.317960262429143

\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 & 0.195103976157979 \tabularnewline
-13 & 0.251681564257730 \tabularnewline
-12 & 0.238265760841685 \tabularnewline
-11 & 0.287431640399995 \tabularnewline
-10 & 0.323920324028572 \tabularnewline
-9 & 0.318757466853466 \tabularnewline
-8 & 0.380600513975211 \tabularnewline
-7 & 0.400312855429773 \tabularnewline
-6 & 0.421626608156360 \tabularnewline
-5 & 0.468353580392985 \tabularnewline
-4 & 0.533635314479274 \tabularnewline
-3 & 0.58669673914887 \tabularnewline
-2 & 0.583708755789858 \tabularnewline
-1 & 0.627843394150585 \tabularnewline
0 & 0.661520230627637 \tabularnewline
1 & 0.709029348971536 \tabularnewline
2 & 0.683335964042 \tabularnewline
3 & 0.634939200194095 \tabularnewline
4 & 0.606886703691076 \tabularnewline
5 & 0.54236996274649 \tabularnewline
6 & 0.493562531891862 \tabularnewline
7 & 0.480960778564144 \tabularnewline
8 & 0.482293851812332 \tabularnewline
9 & 0.450116768547687 \tabularnewline
10 & 0.391252455615663 \tabularnewline
11 & 0.380605916944592 \tabularnewline
12 & 0.36819206062676 \tabularnewline
13 & 0.364207379555673 \tabularnewline
14 & 0.317960262429143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28246&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]0.195103976157979[/C][/ROW]
[ROW][C]-13[/C][C]0.251681564257730[/C][/ROW]
[ROW][C]-12[/C][C]0.238265760841685[/C][/ROW]
[ROW][C]-11[/C][C]0.287431640399995[/C][/ROW]
[ROW][C]-10[/C][C]0.323920324028572[/C][/ROW]
[ROW][C]-9[/C][C]0.318757466853466[/C][/ROW]
[ROW][C]-8[/C][C]0.380600513975211[/C][/ROW]
[ROW][C]-7[/C][C]0.400312855429773[/C][/ROW]
[ROW][C]-6[/C][C]0.421626608156360[/C][/ROW]
[ROW][C]-5[/C][C]0.468353580392985[/C][/ROW]
[ROW][C]-4[/C][C]0.533635314479274[/C][/ROW]
[ROW][C]-3[/C][C]0.58669673914887[/C][/ROW]
[ROW][C]-2[/C][C]0.583708755789858[/C][/ROW]
[ROW][C]-1[/C][C]0.627843394150585[/C][/ROW]
[ROW][C]0[/C][C]0.661520230627637[/C][/ROW]
[ROW][C]1[/C][C]0.709029348971536[/C][/ROW]
[ROW][C]2[/C][C]0.683335964042[/C][/ROW]
[ROW][C]3[/C][C]0.634939200194095[/C][/ROW]
[ROW][C]4[/C][C]0.606886703691076[/C][/ROW]
[ROW][C]5[/C][C]0.54236996274649[/C][/ROW]
[ROW][C]6[/C][C]0.493562531891862[/C][/ROW]
[ROW][C]7[/C][C]0.480960778564144[/C][/ROW]
[ROW][C]8[/C][C]0.482293851812332[/C][/ROW]
[ROW][C]9[/C][C]0.450116768547687[/C][/ROW]
[ROW][C]10[/C][C]0.391252455615663[/C][/ROW]
[ROW][C]11[/C][C]0.380605916944592[/C][/ROW]
[ROW][C]12[/C][C]0.36819206062676[/C][/ROW]
[ROW][C]13[/C][C]0.364207379555673[/C][/ROW]
[ROW][C]14[/C][C]0.317960262429143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28246&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28246&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])
-140.195103976157979
-130.251681564257730
-120.238265760841685
-110.287431640399995
-100.323920324028572
-90.318757466853466
-80.380600513975211
-70.400312855429773
-60.421626608156360
-50.468353580392985
-40.533635314479274
-30.58669673914887
-20.583708755789858
-10.627843394150585
00.661520230627637
10.709029348971536
20.683335964042
30.634939200194095
40.606886703691076
50.54236996274649
60.493562531891862
70.480960778564144
80.482293851812332
90.450116768547687
100.391252455615663
110.380605916944592
120.36819206062676
130.364207379555673
140.317960262429143


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')