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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 13:27:02 -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/t1228249677nq1o13zgzohrwr2.htm/, Retrieved Sun, 19 May 2024 09:39:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28377, Retrieved Sun, 19 May 2024 09:39:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:40:39] [b98453cac15ba1066b407e146608df68]
- RMPD  [(Partial) Autocorrelation Function] [Non stationary ti...] [2008-11-29 13:09:02] [65eec331235880e0070acfba94c20cfa]
F RMPD      [Cross Correlation Function] [law of averages q9] [2008-12-02 20:27:02] [2a6ed4ba8662f0ce2b179e623f45ffb0] [Current]
Feedback Forum
2008-12-06 14:01:27 [Thomas Plasschaert] [reply] 
juist opgelost
2008-12-09 23:12:23 [Peter Van Doninck] [reply] 
correcte argumentatie!

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
61,5
88,5
93,3
89,2
101,3
97
102,2
100,3
78,2
105,9
119,9
108
77
93,1
109,5
100,4
99
113,9
102,1
101,6
84
110,7
111,6
110,7
73,1
87,5
109,6
99,3
92,1
109,3
94,5
91,4
82,9
103,3
96
104,8
65,8
78,7
100,3
85
94,5
97,9
91,9
87,2
84,4
99,2
105,4
110,9
69,8
86,8
106,7
88,8
96,9
108,1
93,7
94,8
79,8
95,6
107,9
104,9
61,9
85,7
92,4
86,4
99,3
95,5
97
102,1
77,8
105,5
113,2
108,8
66,9
89,3
93,6
92
99,5
98,6
94,6
96,7
75,3
102,5
115,1
104,7
71,4
Dataseries Y:
Download CSV
Histogram 
200,7
146,5
143,6
141,5
137,5
138,7
135,5
136,4
112,1
109
123,8
151,2
139,2
115,7
147,6
126,1
122,8
137,3
142
137,4
89,4
108
117,7
127,3
121
104,1
119,5
116,7
96,1
125
118,8
114,9
79,3
90,5
87,8
109,4
88,9
97,4
112
86,8
82,9
105,2
89,1
85,5
87,1
85,2
88,2
104
96,4
82,3
114,1
88,9
93,6
101,8
96,6
93,7
68,4
68,7
81,2
85,1
75,4
71,6
83
72,3
90,2
89
84,9
90,9
46,6
55,4
88,7
76
76,9
72,1
90
92,3
78
93,9
84,5
80,4
60,5
75,3
91,5
105,2
92,7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28377&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28377&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28377&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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 series1
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-16-0.0579173922743333
-150.0862674079163654
-14-0.173602278059432
-13-0.0284026044031562
-120.438284959488502
-11-0.394295995714315
-100.0165009773570993
-9-0.0843401067576477
-80.358689121926659
-7-0.217273332301391
-6-0.0473438977224557
-50.0469285850209004
-40.0206296125897869
-30.0945044564734336
-2-0.296520686468807
-10.0803467846136697
00.495143682481381
1-0.552334162533439
20.0670510657514002
30.0082972020093272
40.206122610587756
5-0.100536053899862
6-0.103349131578294
70.0927070227144026
8-0.0354893582113580
90.167679153359016
10-0.38012946741063
110.186839986041565
120.392997202463836
13-0.523112574352719
140.141347106781147
15-0.0196337858956304
160.103294970813559

\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 & 1 \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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-16 & -0.0579173922743333 \tabularnewline
-15 & 0.0862674079163654 \tabularnewline
-14 & -0.173602278059432 \tabularnewline
-13 & -0.0284026044031562 \tabularnewline
-12 & 0.438284959488502 \tabularnewline
-11 & -0.394295995714315 \tabularnewline
-10 & 0.0165009773570993 \tabularnewline
-9 & -0.0843401067576477 \tabularnewline
-8 & 0.358689121926659 \tabularnewline
-7 & -0.217273332301391 \tabularnewline
-6 & -0.0473438977224557 \tabularnewline
-5 & 0.0469285850209004 \tabularnewline
-4 & 0.0206296125897869 \tabularnewline
-3 & 0.0945044564734336 \tabularnewline
-2 & -0.296520686468807 \tabularnewline
-1 & 0.0803467846136697 \tabularnewline
0 & 0.495143682481381 \tabularnewline
1 & -0.552334162533439 \tabularnewline
2 & 0.0670510657514002 \tabularnewline
3 & 0.0082972020093272 \tabularnewline
4 & 0.206122610587756 \tabularnewline
5 & -0.100536053899862 \tabularnewline
6 & -0.103349131578294 \tabularnewline
7 & 0.0927070227144026 \tabularnewline
8 & -0.0354893582113580 \tabularnewline
9 & 0.167679153359016 \tabularnewline
10 & -0.38012946741063 \tabularnewline
11 & 0.186839986041565 \tabularnewline
12 & 0.392997202463836 \tabularnewline
13 & -0.523112574352719 \tabularnewline
14 & 0.141347106781147 \tabularnewline
15 & -0.0196337858956304 \tabularnewline
16 & 0.103294970813559 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28377&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]1[/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]1[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-16[/C][C]-0.0579173922743333[/C][/ROW]
[ROW][C]-15[/C][C]0.0862674079163654[/C][/ROW]
[ROW][C]-14[/C][C]-0.173602278059432[/C][/ROW]
[ROW][C]-13[/C][C]-0.0284026044031562[/C][/ROW]
[ROW][C]-12[/C][C]0.438284959488502[/C][/ROW]
[ROW][C]-11[/C][C]-0.394295995714315[/C][/ROW]
[ROW][C]-10[/C][C]0.0165009773570993[/C][/ROW]
[ROW][C]-9[/C][C]-0.0843401067576477[/C][/ROW]
[ROW][C]-8[/C][C]0.358689121926659[/C][/ROW]
[ROW][C]-7[/C][C]-0.217273332301391[/C][/ROW]
[ROW][C]-6[/C][C]-0.0473438977224557[/C][/ROW]
[ROW][C]-5[/C][C]0.0469285850209004[/C][/ROW]
[ROW][C]-4[/C][C]0.0206296125897869[/C][/ROW]
[ROW][C]-3[/C][C]0.0945044564734336[/C][/ROW]
[ROW][C]-2[/C][C]-0.296520686468807[/C][/ROW]
[ROW][C]-1[/C][C]0.0803467846136697[/C][/ROW]
[ROW][C]0[/C][C]0.495143682481381[/C][/ROW]
[ROW][C]1[/C][C]-0.552334162533439[/C][/ROW]
[ROW][C]2[/C][C]0.0670510657514002[/C][/ROW]
[ROW][C]3[/C][C]0.0082972020093272[/C][/ROW]
[ROW][C]4[/C][C]0.206122610587756[/C][/ROW]
[ROW][C]5[/C][C]-0.100536053899862[/C][/ROW]
[ROW][C]6[/C][C]-0.103349131578294[/C][/ROW]
[ROW][C]7[/C][C]0.0927070227144026[/C][/ROW]
[ROW][C]8[/C][C]-0.0354893582113580[/C][/ROW]
[ROW][C]9[/C][C]0.167679153359016[/C][/ROW]
[ROW][C]10[/C][C]-0.38012946741063[/C][/ROW]
[ROW][C]11[/C][C]0.186839986041565[/C][/ROW]
[ROW][C]12[/C][C]0.392997202463836[/C][/ROW]
[ROW][C]13[/C][C]-0.523112574352719[/C][/ROW]
[ROW][C]14[/C][C]0.141347106781147[/C][/ROW]
[ROW][C]15[/C][C]-0.0196337858956304[/C][/ROW]
[ROW][C]16[/C][C]0.103294970813559[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28377&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28377&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 series1
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-16-0.0579173922743333
-150.0862674079163654
-14-0.173602278059432
-13-0.0284026044031562
-120.438284959488502
-11-0.394295995714315
-100.0165009773570993
-9-0.0843401067576477
-80.358689121926659
-7-0.217273332301391
-6-0.0473438977224557
-50.0469285850209004
-40.0206296125897869
-30.0945044564734336
-2-0.296520686468807
-10.0803467846136697
00.495143682481381
1-0.552334162533439
20.0670510657514002
30.0082972020093272
40.206122610587756
5-0.100536053899862
6-0.103349131578294
70.0927070227144026
8-0.0354893582113580
90.167679153359016
10-0.38012946741063
110.186839986041565
120.392997202463836
13-0.523112574352719
140.141347106781147
15-0.0196337858956304
160.103294970813559


Figures (Output of Computation)

Input Parameters & R Code

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