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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 computationMon, 01 Dec 2008 12:35:49 -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/01/t1228160168zsht149of4mc1k2.htm/, Retrieved Sun, 05 May 2024 09:36:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27239, Retrieved Sun, 05 May 2024 09:36:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact251

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
- RMPD  [Standard Deviation-Mean Plot] [Q5] [2008-11-29 20:10:39] [57fa5e3679c393aa19449b2f1be9928b]
-   P     [Standard Deviation-Mean Plot] [Q5] [2008-11-29 20:18:39] [57fa5e3679c393aa19449b2f1be9928b]
- RM        [Variance Reduction Matrix] [Q6 Variance] [2008-11-29 20:25:29] [57fa5e3679c393aa19449b2f1be9928b]
- RM          [(Partial) Autocorrelation Function] [Q6 ACF] [2008-11-29 20:35:57] [57fa5e3679c393aa19449b2f1be9928b]
-               [(Partial) Autocorrelation Function] [Q6 aangepaste ACF] [2008-11-29 20:44:03] [57fa5e3679c393aa19449b2f1be9928b]
- RM D            [Cross Correlation Function] [Q7] [2008-11-29 20:55:14] [57fa5e3679c393aa19449b2f1be9928b]
-   P               [Cross Correlation Function] [] [2008-11-30 11:20:03] [a4ee3bef49b119f4bd2e925060c84f5e]
F    D                [Cross Correlation Function] [] [2008-12-01 19:24:37] [d134696a922d84037f02d49ded84b0bd]
F    D                    [Cross Correlation Function] [] [2008-12-01 19:35:49] [db9a5fd0f9c3e1245d8075d8bb09236d] [Current]
Feedback Forum
2008-12-07 14:09:48 [Stijn Van de Velde] [reply] 
Hiervoor heb ik de verkeerde gegevens gebruikt. De lambda waarde waren wel correct, maar voor d en D heb ik de verkeerde waarden genomen (zie Q8).

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9097.4
12639.8
13040.1
11687.3
11191.7
11391.9
11793.1
13933.2
12778.1
11810.3
13698.4
11956.6
10723.8
13938.9
13979.8
13807.4
12973.9
12509.8
12934.1
14908.3
13772.1
13012.6
14049.9
11816.5
11593.2
14466.2
13615.9
14733.9
13880.7
13527.5
13584
16170.2
13260.6
14741.9
15486.5
13154.5
12621.2
15031.6
15452.4
15428
13105.9
14716.8
14180
16202.2
14392.4
15140.6
15960.1
14351.3
13230.2
15202.1
17157.3
16159.1
13405.7
17224.7
17338.4
17370.6
18817.8
16593.2
17979.5
17015.2
Dataseries Y:
Download CSV
Histogram 
8638.7
11063.7
11855.7
10684.5
11337.4
10478
11123.9
12909.3
11339.9
10462.2
12733.5
10519.2
10414.9
12476.8
12384.6
12266.7
12919.9
11497.3
12142
13919.4
12656.8
12034.1
13199.7
10881.3
11301.2
13643.9
12517
13981.1
14275.7
13435
13565.7
16216.3
12970
14079.9
14235
12213.4
12581
14130.4
14210.8
14378.5
13142.8
13714.7
13621.9
15379.8
13306.3
14391.2
14909.9
14025.4
12951.2
14344.3
16213.3
15544.5
14750.6
17292.7
17568.5
17930.8
18644.7
16694.8
17242.8
16979.9

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27239&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]0 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=27239&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27239&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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series0.2
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series0.4
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.140008336129725
-120.00421046243464701
-11-0.129709423214782
-10-0.224719937415074
-90.0284651954407576
-8-0.102054843057384
-7-0.255180984326245
-60.0274052138577839
-5-0.0214659481774029
-4-0.0128819818471754
-30.36622410211305
-20.222728271684136
-10.201512316890002
00.862292628254829
10.261644115601271
20.314089927044283
30.524938898889262
40.223845874282149
50.114624900817126
60.258226032117444
7-0.0467499359923966
80.00568041875534816
90.0808167104222037
10-0.215318295957152
11-0.232620265405720
12-0.109327476357289
13-0.301075837247033

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0.2 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 0.4 \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 & -0.140008336129725 \tabularnewline
-12 & 0.00421046243464701 \tabularnewline
-11 & -0.129709423214782 \tabularnewline
-10 & -0.224719937415074 \tabularnewline
-9 & 0.0284651954407576 \tabularnewline
-8 & -0.102054843057384 \tabularnewline
-7 & -0.255180984326245 \tabularnewline
-6 & 0.0274052138577839 \tabularnewline
-5 & -0.0214659481774029 \tabularnewline
-4 & -0.0128819818471754 \tabularnewline
-3 & 0.36622410211305 \tabularnewline
-2 & 0.222728271684136 \tabularnewline
-1 & 0.201512316890002 \tabularnewline
0 & 0.862292628254829 \tabularnewline
1 & 0.261644115601271 \tabularnewline
2 & 0.314089927044283 \tabularnewline
3 & 0.524938898889262 \tabularnewline
4 & 0.223845874282149 \tabularnewline
5 & 0.114624900817126 \tabularnewline
6 & 0.258226032117444 \tabularnewline
7 & -0.0467499359923966 \tabularnewline
8 & 0.00568041875534816 \tabularnewline
9 & 0.0808167104222037 \tabularnewline
10 & -0.215318295957152 \tabularnewline
11 & -0.232620265405720 \tabularnewline
12 & -0.109327476357289 \tabularnewline
13 & -0.301075837247033 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27239&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]0.2[/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]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]0.4[/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]-0.140008336129725[/C][/ROW]
[ROW][C]-12[/C][C]0.00421046243464701[/C][/ROW]
[ROW][C]-11[/C][C]-0.129709423214782[/C][/ROW]
[ROW][C]-10[/C][C]-0.224719937415074[/C][/ROW]
[ROW][C]-9[/C][C]0.0284651954407576[/C][/ROW]
[ROW][C]-8[/C][C]-0.102054843057384[/C][/ROW]
[ROW][C]-7[/C][C]-0.255180984326245[/C][/ROW]
[ROW][C]-6[/C][C]0.0274052138577839[/C][/ROW]
[ROW][C]-5[/C][C]-0.0214659481774029[/C][/ROW]
[ROW][C]-4[/C][C]-0.0128819818471754[/C][/ROW]
[ROW][C]-3[/C][C]0.36622410211305[/C][/ROW]
[ROW][C]-2[/C][C]0.222728271684136[/C][/ROW]
[ROW][C]-1[/C][C]0.201512316890002[/C][/ROW]
[ROW][C]0[/C][C]0.862292628254829[/C][/ROW]
[ROW][C]1[/C][C]0.261644115601271[/C][/ROW]
[ROW][C]2[/C][C]0.314089927044283[/C][/ROW]
[ROW][C]3[/C][C]0.524938898889262[/C][/ROW]
[ROW][C]4[/C][C]0.223845874282149[/C][/ROW]
[ROW][C]5[/C][C]0.114624900817126[/C][/ROW]
[ROW][C]6[/C][C]0.258226032117444[/C][/ROW]
[ROW][C]7[/C][C]-0.0467499359923966[/C][/ROW]
[ROW][C]8[/C][C]0.00568041875534816[/C][/ROW]
[ROW][C]9[/C][C]0.0808167104222037[/C][/ROW]
[ROW][C]10[/C][C]-0.215318295957152[/C][/ROW]
[ROW][C]11[/C][C]-0.232620265405720[/C][/ROW]
[ROW][C]12[/C][C]-0.109327476357289[/C][/ROW]
[ROW][C]13[/C][C]-0.301075837247033[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27239&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27239&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 series0.2
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series0.4
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.140008336129725
-120.00421046243464701
-11-0.129709423214782
-10-0.224719937415074
-90.0284651954407576
-8-0.102054843057384
-7-0.255180984326245
-60.0274052138577839
-5-0.0214659481774029
-4-0.0128819818471754
-30.36622410211305
-20.222728271684136
-10.201512316890002
00.862292628254829
10.261644115601271
20.314089927044283
30.524938898889262
40.223845874282149
50.114624900817126
60.258226032117444
7-0.0467499359923966
80.00568041875534816
90.0808167104222037
10-0.215318295957152
11-0.232620265405720
12-0.109327476357289
13-0.301075837247033


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 = 0.2 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 0.4 ; 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) 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')