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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 06:49:03 -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/t12282257957xfvwtnkd2t1tmg.htm/, Retrieved Sun, 19 May 2024 12:35:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27788, Retrieved Sun, 19 May 2024 12:35:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact210

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] [] [2008-12-02 13:40:49] [74be16979710d4c4e7c6647856088456]
F   P   [Cross Correlation Function] [] [2008-12-02 13:44:21] [74be16979710d4c4e7c6647856088456]
F   P       [Cross Correlation Function] [] [2008-12-02 13:49:03] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-12-04 09:57:45 [72e979bcc364082694890d2eccc1a66f] [reply] 
Deze figuur is nog te weinig gedifferentieerd. Tweemaal trendmatig differentiëren zal hier dus nodig zijn.
2008-12-06 10:58:48 [Bert Moons] [reply] 
De trend is naar mijn mening wel volledig weg (alle waarden liggen volledig in het toevalsinterval en dalen of stijgen niet meer op een uniforme manier). Een 2de maal differentiëren is mogelijk, maar ik denk overbodig.
2008-12-06 16:32:13 [Bénédicte Soens] [reply] 
Ik zie ook geen echte trend meer in deze voorstelling, dus kan een 2de maal differntieren onnuttig zijn. Misschien toch maar eens proberen en dan resultaat bekijken.
2008-12-07 12:28:47 [Lana Van Wesemael] [reply] 
Hier ben ik niet akkoord met de student. In de cross correlation function kunnen we geen trends aflezen. De cross correlation geeft wel de correlatie weer tussen 2 verschillende reeksen. Het was in Q9 gewoon de bedoeling om de gevonden waarden uit de vorige vraag in te vullen en dit dan te vergelijken met de cross correlation te gemaakt werd in Q7.
2008-12-07 21:47:21 [Ellen Van den Broeck] [reply] 
Ik zie ook geen trend meer.
2008-12-09 14:08:12 [Jules De Bruycker] [reply] 
Ik vind het moeilijk om hier nog een trend waar te nemen, ik kan de student hier niet volgen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.56
4.41
4.33
4.20
4.25
4.25
4.19
4.17
4.21
4.21
4.17
4.16
4.19
4.08
4.06
3.98
3.82
3.82
3.72
3.56
3.57
3.49
3.32
3.23
3.04
3.00
2.82
2.73
2.59
2.58
2.53
2.31
2.31
2.30
2.07
2.07
2.06
2.06
2.05
2.05
2.05
2.05
2.05
2.06
2.07
2.08
2.05
2.03
2.02
2.02
2.01
2.01
2.01
2.01
2.01
2.01
2.03
2.04
2.03
2.05
2.08
2.06
2.09
2.19
2.56
2.54
2.63
2.78
2.84
3.02
3.28
3.29
3.29
3.29
3.32
3.34
3.32
3.30
3.30
3.30
3.31
3.35
3.48
3.76
4.06
4.51
4.52
4.53
4.63
4.79
4.77
4.77
4.77
4.81
4.83
4.76
4.61
Dataseries Y:
Download CSV
Histogram 
5.1
4.9
5.2
5.1
4.6
3.7
3.9
3.1
2.8
2.6
2.2
1.8
1.3
1.2
1.4
1.3
1.3
1.9
1.9
2.1
2.0
1.9
1.9
1.9
1.8
1.7
1.6
1.7
1.9
1.7
1.3
2.0
2.0
2.3
2.0
1.7
2.3
2.4
2.4
2.3
2.1
2.1
2.5
2.0
1.8
1.7
1.9
2.1
1.4
1.6
1.7
1.6
1.9
1.6
1.1
1.3
1.6
1.6
1.7
1.6
1.7
1.6
1.5
1.6
1.1
1.5
1.4
1.3
0.9
1.2
0.9
1.1
1.3
1.3
1.4
1.2
1.7
2.0
3.0
3.1
3.2
2.7
2.8
3.0
2.8
3.1
3.1
3.2
3.1
2.7
2.2
2.2
2.1
2.3
2.5
2.3
2.6

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=27788&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=27788&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27788&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 series-0.5
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-16-0.0108070076577080
-15-0.0455467805711560
-140.109879304421038
-130.0664833888508641
-120.0703480612950768
-110.0429726826152915
-100.0916290443959148
-90.139545683999101
-80.114124223349849
-70.0638294274322133
-6-0.00815235967795531
-5-0.0167315972120066
-4-0.093098497719009
-30.00605877439768973
-20.0584441027616006
-1-0.0148619675992000
0-0.162081659068235
10.0541310596489284
2-0.0862462299354433
3-0.0718258524021504
40.094314188354494
50.0653677743915024
60.126288325295815
70.183011919270843
80.145878609845236
90.128941433198905
10-0.0340457705071162
110.151730466495693
120.183216734609487
130.103977225836308
140.096689724179535
15-0.0183909200339855
16-0.0636850139860025

\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 & -0.5 \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
-16 & -0.0108070076577080 \tabularnewline
-15 & -0.0455467805711560 \tabularnewline
-14 & 0.109879304421038 \tabularnewline
-13 & 0.0664833888508641 \tabularnewline
-12 & 0.0703480612950768 \tabularnewline
-11 & 0.0429726826152915 \tabularnewline
-10 & 0.0916290443959148 \tabularnewline
-9 & 0.139545683999101 \tabularnewline
-8 & 0.114124223349849 \tabularnewline
-7 & 0.0638294274322133 \tabularnewline
-6 & -0.00815235967795531 \tabularnewline
-5 & -0.0167315972120066 \tabularnewline
-4 & -0.093098497719009 \tabularnewline
-3 & 0.00605877439768973 \tabularnewline
-2 & 0.0584441027616006 \tabularnewline
-1 & -0.0148619675992000 \tabularnewline
0 & -0.162081659068235 \tabularnewline
1 & 0.0541310596489284 \tabularnewline
2 & -0.0862462299354433 \tabularnewline
3 & -0.0718258524021504 \tabularnewline
4 & 0.094314188354494 \tabularnewline
5 & 0.0653677743915024 \tabularnewline
6 & 0.126288325295815 \tabularnewline
7 & 0.183011919270843 \tabularnewline
8 & 0.145878609845236 \tabularnewline
9 & 0.128941433198905 \tabularnewline
10 & -0.0340457705071162 \tabularnewline
11 & 0.151730466495693 \tabularnewline
12 & 0.183216734609487 \tabularnewline
13 & 0.103977225836308 \tabularnewline
14 & 0.096689724179535 \tabularnewline
15 & -0.0183909200339855 \tabularnewline
16 & -0.0636850139860025 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27788&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]-0.5[/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]-16[/C][C]-0.0108070076577080[/C][/ROW]
[ROW][C]-15[/C][C]-0.0455467805711560[/C][/ROW]
[ROW][C]-14[/C][C]0.109879304421038[/C][/ROW]
[ROW][C]-13[/C][C]0.0664833888508641[/C][/ROW]
[ROW][C]-12[/C][C]0.0703480612950768[/C][/ROW]
[ROW][C]-11[/C][C]0.0429726826152915[/C][/ROW]
[ROW][C]-10[/C][C]0.0916290443959148[/C][/ROW]
[ROW][C]-9[/C][C]0.139545683999101[/C][/ROW]
[ROW][C]-8[/C][C]0.114124223349849[/C][/ROW]
[ROW][C]-7[/C][C]0.0638294274322133[/C][/ROW]
[ROW][C]-6[/C][C]-0.00815235967795531[/C][/ROW]
[ROW][C]-5[/C][C]-0.0167315972120066[/C][/ROW]
[ROW][C]-4[/C][C]-0.093098497719009[/C][/ROW]
[ROW][C]-3[/C][C]0.00605877439768973[/C][/ROW]
[ROW][C]-2[/C][C]0.0584441027616006[/C][/ROW]
[ROW][C]-1[/C][C]-0.0148619675992000[/C][/ROW]
[ROW][C]0[/C][C]-0.162081659068235[/C][/ROW]
[ROW][C]1[/C][C]0.0541310596489284[/C][/ROW]
[ROW][C]2[/C][C]-0.0862462299354433[/C][/ROW]
[ROW][C]3[/C][C]-0.0718258524021504[/C][/ROW]
[ROW][C]4[/C][C]0.094314188354494[/C][/ROW]
[ROW][C]5[/C][C]0.0653677743915024[/C][/ROW]
[ROW][C]6[/C][C]0.126288325295815[/C][/ROW]
[ROW][C]7[/C][C]0.183011919270843[/C][/ROW]
[ROW][C]8[/C][C]0.145878609845236[/C][/ROW]
[ROW][C]9[/C][C]0.128941433198905[/C][/ROW]
[ROW][C]10[/C][C]-0.0340457705071162[/C][/ROW]
[ROW][C]11[/C][C]0.151730466495693[/C][/ROW]
[ROW][C]12[/C][C]0.183216734609487[/C][/ROW]
[ROW][C]13[/C][C]0.103977225836308[/C][/ROW]
[ROW][C]14[/C][C]0.096689724179535[/C][/ROW]
[ROW][C]15[/C][C]-0.0183909200339855[/C][/ROW]
[ROW][C]16[/C][C]-0.0636850139860025[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27788&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27788&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 series-0.5
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-16-0.0108070076577080
-15-0.0455467805711560
-140.109879304421038
-130.0664833888508641
-120.0703480612950768
-110.0429726826152915
-100.0916290443959148
-90.139545683999101
-80.114124223349849
-70.0638294274322133
-6-0.00815235967795531
-5-0.0167315972120066
-4-0.093098497719009
-30.00605877439768973
-20.0584441027616006
-1-0.0148619675992000
0-0.162081659068235
10.0541310596489284
2-0.0862462299354433
3-0.0718258524021504
40.094314188354494
50.0653677743915024
60.126288325295815
70.183011919270843
80.145878609845236
90.128941433198905
10-0.0340457705071162
110.151730466495693
120.183216734609487
130.103977225836308
140.096689724179535
15-0.0183909200339855
16-0.0636850139860025


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.5 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.5 ; 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')