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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 06:44:21 -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/t1228225508zlw5zwpdyibfezw.htm/, Retrieved Sun, 19 May 2024 09:16:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27775, Retrieved Sun, 19 May 2024 09:16:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact195

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] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-   P       [Cross Correlation Function] [] [2008-12-02 13:49:03] [74be16979710d4c4e7c6647856088456]
F   P       [Cross Correlation Function] [] [2008-12-02 13:49:03] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-04 09:59:30 [72e979bcc364082694890d2eccc1a66f] [reply] 
Deze figuur is beter gedifferentieerd. Tweemaal trendmatig differentiëren leidt dus tot de correcte oplossing. De student heeft deze opdracht goed uitgewerkt.
2008-12-06 11:02:12 [Bert Moons] [reply] 
De trend was naar mijn mening reeds weg bij 1 maal differentiëren, een 2de maal kan echter geen kwaad.
De uitkomsten zijn inderdaad zeer verschillend, aangezien er een LT-trend was , en deze hier uitgefilterd werd.
2008-12-06 16:31:21 [Bénédicte Soens] [reply] 
Hier is er duidelijk een stationaire tijdreeks bekomen. Ook al zou het misschien genoeg zijn door maar 1 keer te differentieren

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=27775&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=27775&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27775&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 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])
-16-0.0364523238675263
-150.145363945163987
-14-0.0374181532176044
-13-0.00451275761005841
-12-0.065089539317987
-110.0608041744381833
-100.0370817671004444
-9-0.0356089876605198
-8-0.0604831571874706
-7-0.0749976101713744
-6-0.00641621305046123
-5-0.0886088697734089
-40.09326973929772
-30.0538007043564002
-2-0.0653369740781416
-1-0.145686260469728
00.209747818138458
1-0.136572224137937
20.00823336781396976
30.160828151508516
4-0.0262959964918402
50.0611862105090456
60.0713891708973502
7-0.0291458454591348
8-0.0156695449735690
9-0.167391337487309
100.187506828968800
110.0248007941744120
12-0.0761519975830726
13-0.0123349931286144
14-0.119331141411975
15-0.0353045551436310
16-0.0125035406788691

\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 & -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 & -0.0364523238675263 \tabularnewline
-15 & 0.145363945163987 \tabularnewline
-14 & -0.0374181532176044 \tabularnewline
-13 & -0.00451275761005841 \tabularnewline
-12 & -0.065089539317987 \tabularnewline
-11 & 0.0608041744381833 \tabularnewline
-10 & 0.0370817671004444 \tabularnewline
-9 & -0.0356089876605198 \tabularnewline
-8 & -0.0604831571874706 \tabularnewline
-7 & -0.0749976101713744 \tabularnewline
-6 & -0.00641621305046123 \tabularnewline
-5 & -0.0886088697734089 \tabularnewline
-4 & 0.09326973929772 \tabularnewline
-3 & 0.0538007043564002 \tabularnewline
-2 & -0.0653369740781416 \tabularnewline
-1 & -0.145686260469728 \tabularnewline
0 & 0.209747818138458 \tabularnewline
1 & -0.136572224137937 \tabularnewline
2 & 0.00823336781396976 \tabularnewline
3 & 0.160828151508516 \tabularnewline
4 & -0.0262959964918402 \tabularnewline
5 & 0.0611862105090456 \tabularnewline
6 & 0.0713891708973502 \tabularnewline
7 & -0.0291458454591348 \tabularnewline
8 & -0.0156695449735690 \tabularnewline
9 & -0.167391337487309 \tabularnewline
10 & 0.187506828968800 \tabularnewline
11 & 0.0248007941744120 \tabularnewline
12 & -0.0761519975830726 \tabularnewline
13 & -0.0123349931286144 \tabularnewline
14 & -0.119331141411975 \tabularnewline
15 & -0.0353045551436310 \tabularnewline
16 & -0.0125035406788691 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27775&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]-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]-0.0364523238675263[/C][/ROW]
[ROW][C]-15[/C][C]0.145363945163987[/C][/ROW]
[ROW][C]-14[/C][C]-0.0374181532176044[/C][/ROW]
[ROW][C]-13[/C][C]-0.00451275761005841[/C][/ROW]
[ROW][C]-12[/C][C]-0.065089539317987[/C][/ROW]
[ROW][C]-11[/C][C]0.0608041744381833[/C][/ROW]
[ROW][C]-10[/C][C]0.0370817671004444[/C][/ROW]
[ROW][C]-9[/C][C]-0.0356089876605198[/C][/ROW]
[ROW][C]-8[/C][C]-0.0604831571874706[/C][/ROW]
[ROW][C]-7[/C][C]-0.0749976101713744[/C][/ROW]
[ROW][C]-6[/C][C]-0.00641621305046123[/C][/ROW]
[ROW][C]-5[/C][C]-0.0886088697734089[/C][/ROW]
[ROW][C]-4[/C][C]0.09326973929772[/C][/ROW]
[ROW][C]-3[/C][C]0.0538007043564002[/C][/ROW]
[ROW][C]-2[/C][C]-0.0653369740781416[/C][/ROW]
[ROW][C]-1[/C][C]-0.145686260469728[/C][/ROW]
[ROW][C]0[/C][C]0.209747818138458[/C][/ROW]
[ROW][C]1[/C][C]-0.136572224137937[/C][/ROW]
[ROW][C]2[/C][C]0.00823336781396976[/C][/ROW]
[ROW][C]3[/C][C]0.160828151508516[/C][/ROW]
[ROW][C]4[/C][C]-0.0262959964918402[/C][/ROW]
[ROW][C]5[/C][C]0.0611862105090456[/C][/ROW]
[ROW][C]6[/C][C]0.0713891708973502[/C][/ROW]
[ROW][C]7[/C][C]-0.0291458454591348[/C][/ROW]
[ROW][C]8[/C][C]-0.0156695449735690[/C][/ROW]
[ROW][C]9[/C][C]-0.167391337487309[/C][/ROW]
[ROW][C]10[/C][C]0.187506828968800[/C][/ROW]
[ROW][C]11[/C][C]0.0248007941744120[/C][/ROW]
[ROW][C]12[/C][C]-0.0761519975830726[/C][/ROW]
[ROW][C]13[/C][C]-0.0123349931286144[/C][/ROW]
[ROW][C]14[/C][C]-0.119331141411975[/C][/ROW]
[ROW][C]15[/C][C]-0.0353045551436310[/C][/ROW]
[ROW][C]16[/C][C]-0.0125035406788691[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27775&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27775&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 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])
-16-0.0364523238675263
-150.145363945163987
-14-0.0374181532176044
-13-0.00451275761005841
-12-0.065089539317987
-110.0608041744381833
-100.0370817671004444
-9-0.0356089876605198
-8-0.0604831571874706
-7-0.0749976101713744
-6-0.00641621305046123
-5-0.0886088697734089
-40.09326973929772
-30.0538007043564002
-2-0.0653369740781416
-1-0.145686260469728
00.209747818138458
1-0.136572224137937
20.00823336781396976
30.160828151508516
4-0.0262959964918402
50.0611862105090456
60.0713891708973502
7-0.0291458454591348
8-0.0156695449735690
9-0.167391337487309
100.187506828968800
110.0248007941744120
12-0.0761519975830726
13-0.0123349931286144
14-0.119331141411975
15-0.0353045551436310
16-0.0125035406788691


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0 ; par4 = 12 ; par5 = -0.5 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; 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')