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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 computationWed, 28 Nov 2007 01:51:13 -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/2007/Nov/28/t1196239318npzt7oy54inbpeb.htm/, Retrieved Thu, 02 May 2024 10:31:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=6974, Retrieved Thu, 02 May 2024 10:31:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordscross correlatie Tinne Van der Eycken
Estimated Impact219

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [Workshop 3] [2007-11-28 08:51:13] [c8635c97647ba59406cb570a9fab7b02] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109,2
126,3
104
96
262
89,8
86
92,7
126,8
92,8
87,8
100
72,4
104,9
52,3
65,3
110,2
54,4
47,5
65,2
69,8
53,6
116,1
56,6
47,2
90,6
60,4
59,3
131,6
59,4
65,5
70,5
81
73,3
107,5
88,9
55,8
80,5
86,3
112,6
148,6
47,1
57,8
81
60,1
76,1
82,5
66,8
58,7
54,2
103,3
77,8
118,4
64,9
40,8
77,7
66,8
69,2
82,4
62,7
58,2
Dataseries Y:
Download CSV
Histogram 
86,5
104,1
110,9
114,5
112,2
96,4
92
102
99,7
102
98,9
87,4
94,4
109,3
116,4
101
105,5
97,8
95,5
113,7
103,7
100,8
113,8
84,6
95,3
110
107,5
107,6
116
96,9
97
108,1
101,9
107,2
110,2
78,7
96,5
115,2
104,7
109,1
108,4
95,5
97,8
115,1
96,2
112
111,8
82,5
100,8
116
116,3
116,6
112,9
100,9
104,1
117,4
103,3
111,6
115
92,6
105,2

Tables (Output of Computation)





Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6974&T=0

[TABLE]
[ROW][C]Summary of compuational 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]4 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=6974&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6974&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 compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series-0.7
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
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 series1
krho(Y[t],X[t+k])
-13-0.00675768081765082
-120.266598895464679
-11-0.322217189984607
-10-0.0844306815260882
-90.0379455978070337
-8-0.125138324932281
-70.225340406009670
-60.0639853421774783
-5-0.219844586574014
-40.131250610152779
-30.114704736534821
-2-0.0248666254517433
-10.0585443667117581
0-0.299664073742950
1-0.167975161904144
20.0738909862786928
30.119590683680628
4-0.00467161631336512
50.0841589508913727
6-0.0243689416904296
7-0.00715246348694528
80.101824584538043
9-0.02970473482535
10-0.132752188460310
110.133945755662522
120.0128646675198714
130.0864447326514692

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & -0.7 \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) & 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 & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & -0.00675768081765082 \tabularnewline
-12 & 0.266598895464679 \tabularnewline
-11 & -0.322217189984607 \tabularnewline
-10 & -0.0844306815260882 \tabularnewline
-9 & 0.0379455978070337 \tabularnewline
-8 & -0.125138324932281 \tabularnewline
-7 & 0.225340406009670 \tabularnewline
-6 & 0.0639853421774783 \tabularnewline
-5 & -0.219844586574014 \tabularnewline
-4 & 0.131250610152779 \tabularnewline
-3 & 0.114704736534821 \tabularnewline
-2 & -0.0248666254517433 \tabularnewline
-1 & 0.0585443667117581 \tabularnewline
0 & -0.299664073742950 \tabularnewline
1 & -0.167975161904144 \tabularnewline
2 & 0.0738909862786928 \tabularnewline
3 & 0.119590683680628 \tabularnewline
4 & -0.00467161631336512 \tabularnewline
5 & 0.0841589508913727 \tabularnewline
6 & -0.0243689416904296 \tabularnewline
7 & -0.00715246348694528 \tabularnewline
8 & 0.101824584538043 \tabularnewline
9 & -0.02970473482535 \tabularnewline
10 & -0.132752188460310 \tabularnewline
11 & 0.133945755662522 \tabularnewline
12 & 0.0128646675198714 \tabularnewline
13 & 0.0864447326514692 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6974&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]-0.7[/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]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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]-0.00675768081765082[/C][/ROW]
[ROW][C]-12[/C][C]0.266598895464679[/C][/ROW]
[ROW][C]-11[/C][C]-0.322217189984607[/C][/ROW]
[ROW][C]-10[/C][C]-0.0844306815260882[/C][/ROW]
[ROW][C]-9[/C][C]0.0379455978070337[/C][/ROW]
[ROW][C]-8[/C][C]-0.125138324932281[/C][/ROW]
[ROW][C]-7[/C][C]0.225340406009670[/C][/ROW]
[ROW][C]-6[/C][C]0.0639853421774783[/C][/ROW]
[ROW][C]-5[/C][C]-0.219844586574014[/C][/ROW]
[ROW][C]-4[/C][C]0.131250610152779[/C][/ROW]
[ROW][C]-3[/C][C]0.114704736534821[/C][/ROW]
[ROW][C]-2[/C][C]-0.0248666254517433[/C][/ROW]
[ROW][C]-1[/C][C]0.0585443667117581[/C][/ROW]
[ROW][C]0[/C][C]-0.299664073742950[/C][/ROW]
[ROW][C]1[/C][C]-0.167975161904144[/C][/ROW]
[ROW][C]2[/C][C]0.0738909862786928[/C][/ROW]
[ROW][C]3[/C][C]0.119590683680628[/C][/ROW]
[ROW][C]4[/C][C]-0.00467161631336512[/C][/ROW]
[ROW][C]5[/C][C]0.0841589508913727[/C][/ROW]
[ROW][C]6[/C][C]-0.0243689416904296[/C][/ROW]
[ROW][C]7[/C][C]-0.00715246348694528[/C][/ROW]
[ROW][C]8[/C][C]0.101824584538043[/C][/ROW]
[ROW][C]9[/C][C]-0.02970473482535[/C][/ROW]
[ROW][C]10[/C][C]-0.132752188460310[/C][/ROW]
[ROW][C]11[/C][C]0.133945755662522[/C][/ROW]
[ROW][C]12[/C][C]0.0128646675198714[/C][/ROW]
[ROW][C]13[/C][C]0.0864447326514692[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6974&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6974&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 series-0.7
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
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 series1
krho(Y[t],X[t+k])
-13-0.00675768081765082
-120.266598895464679
-11-0.322217189984607
-10-0.0844306815260882
-90.0379455978070337
-8-0.125138324932281
-70.225340406009670
-60.0639853421774783
-5-0.219844586574014
-40.131250610152779
-30.114704736534821
-2-0.0248666254517433
-10.0585443667117581
0-0.299664073742950
1-0.167975161904144
20.0738909862786928
30.119590683680628
4-0.00467161631336512
50.0841589508913727
6-0.0243689416904296
7-0.00715246348694528
80.101824584538043
9-0.02970473482535
10-0.132752188460310
110.133945755662522
120.0128646675198714
130.0864447326514692


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = Default ; par2 = -0.7 ; par3 = 0 ; par4 = 1 ; par5 = 12 ;
Parameters (R input):
par1 = -0.7 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; 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) x <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',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')