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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, 03 Dec 2008 03:56:49 -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/03/t1228301901e9h22uzhwasyh3h.htm/, Retrieved Sun, 19 May 2024 00:53:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28611, Retrieved Sun, 19 May 2024 00:53:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact299

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]
F           [Standard Deviation-Mean Plot] [] [2008-11-30 10:50:04] [a4ee3bef49b119f4bd2e925060c84f5e]
F             [Standard Deviation-Mean Plot] [q5 / 7] [2008-11-30 17:57:37] [4300be8b33fd3dcdacd2aa9800ceba23]
- R             [Standard Deviation-Mean Plot] [question 5] [2008-12-01 14:10:37] [379d6c32f73e3218fd773d79e4063d07]
-                 [Standard Deviation-Mean Plot] [wx] [2008-12-02 14:05:35] [98f6eecc397b06503dbf024e1e936f30]
F RMPD                [Cross Correlation Function] [] [2008-12-03 10:56:49] [ba8414dd214a21fbd6c7bde748ac585f] [Current]
Feedback Forum
2008-12-06 11:33:40 [Carole Thielens] [reply] 
De student vergeleek deze cross correlatie functie niet met de cross correlation functie voor differentiatie. Ik begrijp ook niet goed hoe hij deze grafiek heeft kunnen opmaken als hij in Q8 de waarden voor lambda, d en D niet gezocht heeft?
2008-12-08 22:11:32 [Katja van Hek] [reply] 
ik ben het eens met bovenstaande studente. Ik zie niet hoe hij aan de waarden van lambda, d en D is gekomen die hij hier heeft gebruikt om tot deze cross correlation te komen. Er is ook geen vergelijking gemaakt met de cross correlation grafiek uit Q7.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.36
1.95
2.16
2.76
2.09
1.49
1.17
1.3
1.26
2.17
2.03
2.18
2.61
2.58
3.86
3.81
2.41
1.47
1.33
1.38
1.57
2.6
2.18
2.36
2.24
2.41
2.51
2.98
1.87
1.9
1.47
1.45
2.71
2.9
2.11
2.18
2.24
2.05
2.42
2.77
1.99
1.47
1.09
0.93
1.32
2.03
2.04
2.78
2.8
3.03
3.11
2.75
2.78
1.76
1.29
1.28
1.43
1.71
1.89
1.84
2.08
2.09
2.36
2.99
2.75
1.58
1.69
1.3
1.97
1.84
1.96
1.86
2.75
2.62
2.41
3.61
2.03
1.45
1.4
1.3
1.58
2.1
2.27
2.54
Dataseries Y:
Download CSV
Histogram 
1.43
1.43
1.43
1.43
1.43
1.43
1.44
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.57
1.58
1.58
1.58
1.58
1.59
1.60
1.60
1.61
1.61
1.61
1.62
1.63
1.63
1.64
1.64
1.64
1.64
1.64
1.65
1.65
1.65
1.65
1.65
1.66
1.66
1.67
1.68
1.68
1.68
1.68
1.69
1.70
1.70
1.71
1.72
1.73
1.74
1.74
1.75
1.75
1.75
1.76
1.79
1.83
1.84
1.85

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=28611&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=28611&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28611&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 series-0.7
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 series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-15-0.122962250813796
-14-0.109003785690586
-13-0.0962512107397236
-12-0.0908987100117162
-11-0.052629389514537
-10-0.0241107347602709
-9-0.0184792937017401
-8-0.00995460472947026
-7-0.0303803063199552
-6-0.0238424319010727
-5-0.0165589103371160
-40.0079050429502416
-3-0.0049023981958064
-20.00703819201742943
-10.0045864416424008
00.0181359652049308
1-0.00116755144651062
20.00185727460884384
30.0345708830519466
40.034979739238803
50.0371936327391415
60.0259644377088313
70.0136122901524363
80.0237460280210631
90.0624649078916249
100.0855234083930534
110.108577893220613
120.121942851043367
130.124943727566220
140.116613816263764
150.0794587873862846

\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 & 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 & 1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-15 & -0.122962250813796 \tabularnewline
-14 & -0.109003785690586 \tabularnewline
-13 & -0.0962512107397236 \tabularnewline
-12 & -0.0908987100117162 \tabularnewline
-11 & -0.052629389514537 \tabularnewline
-10 & -0.0241107347602709 \tabularnewline
-9 & -0.0184792937017401 \tabularnewline
-8 & -0.00995460472947026 \tabularnewline
-7 & -0.0303803063199552 \tabularnewline
-6 & -0.0238424319010727 \tabularnewline
-5 & -0.0165589103371160 \tabularnewline
-4 & 0.0079050429502416 \tabularnewline
-3 & -0.0049023981958064 \tabularnewline
-2 & 0.00703819201742943 \tabularnewline
-1 & 0.0045864416424008 \tabularnewline
0 & 0.0181359652049308 \tabularnewline
1 & -0.00116755144651062 \tabularnewline
2 & 0.00185727460884384 \tabularnewline
3 & 0.0345708830519466 \tabularnewline
4 & 0.034979739238803 \tabularnewline
5 & 0.0371936327391415 \tabularnewline
6 & 0.0259644377088313 \tabularnewline
7 & 0.0136122901524363 \tabularnewline
8 & 0.0237460280210631 \tabularnewline
9 & 0.0624649078916249 \tabularnewline
10 & 0.0855234083930534 \tabularnewline
11 & 0.108577893220613 \tabularnewline
12 & 0.121942851043367 \tabularnewline
13 & 0.124943727566220 \tabularnewline
14 & 0.116613816263764 \tabularnewline
15 & 0.0794587873862846 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28611&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]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]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]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-15[/C][C]-0.122962250813796[/C][/ROW]
[ROW][C]-14[/C][C]-0.109003785690586[/C][/ROW]
[ROW][C]-13[/C][C]-0.0962512107397236[/C][/ROW]
[ROW][C]-12[/C][C]-0.0908987100117162[/C][/ROW]
[ROW][C]-11[/C][C]-0.052629389514537[/C][/ROW]
[ROW][C]-10[/C][C]-0.0241107347602709[/C][/ROW]
[ROW][C]-9[/C][C]-0.0184792937017401[/C][/ROW]
[ROW][C]-8[/C][C]-0.00995460472947026[/C][/ROW]
[ROW][C]-7[/C][C]-0.0303803063199552[/C][/ROW]
[ROW][C]-6[/C][C]-0.0238424319010727[/C][/ROW]
[ROW][C]-5[/C][C]-0.0165589103371160[/C][/ROW]
[ROW][C]-4[/C][C]0.0079050429502416[/C][/ROW]
[ROW][C]-3[/C][C]-0.0049023981958064[/C][/ROW]
[ROW][C]-2[/C][C]0.00703819201742943[/C][/ROW]
[ROW][C]-1[/C][C]0.0045864416424008[/C][/ROW]
[ROW][C]0[/C][C]0.0181359652049308[/C][/ROW]
[ROW][C]1[/C][C]-0.00116755144651062[/C][/ROW]
[ROW][C]2[/C][C]0.00185727460884384[/C][/ROW]
[ROW][C]3[/C][C]0.0345708830519466[/C][/ROW]
[ROW][C]4[/C][C]0.034979739238803[/C][/ROW]
[ROW][C]5[/C][C]0.0371936327391415[/C][/ROW]
[ROW][C]6[/C][C]0.0259644377088313[/C][/ROW]
[ROW][C]7[/C][C]0.0136122901524363[/C][/ROW]
[ROW][C]8[/C][C]0.0237460280210631[/C][/ROW]
[ROW][C]9[/C][C]0.0624649078916249[/C][/ROW]
[ROW][C]10[/C][C]0.0855234083930534[/C][/ROW]
[ROW][C]11[/C][C]0.108577893220613[/C][/ROW]
[ROW][C]12[/C][C]0.121942851043367[/C][/ROW]
[ROW][C]13[/C][C]0.124943727566220[/C][/ROW]
[ROW][C]14[/C][C]0.116613816263764[/C][/ROW]
[ROW][C]15[/C][C]0.0794587873862846[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28611&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28611&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 series0
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 series0
krho(Y[t],X[t+k])
-15-0.122962250813796
-14-0.109003785690586
-13-0.0962512107397236
-12-0.0908987100117162
-11-0.052629389514537
-10-0.0241107347602709
-9-0.0184792937017401
-8-0.00995460472947026
-7-0.0303803063199552
-6-0.0238424319010727
-5-0.0165589103371160
-40.0079050429502416
-3-0.0049023981958064
-20.00703819201742943
-10.0045864416424008
00.0181359652049308
1-0.00116755144651062
20.00185727460884384
30.0345708830519466
40.034979739238803
50.0371936327391415
60.0259644377088313
70.0136122901524363
80.0237460280210631
90.0624649078916249
100.0855234083930534
110.108577893220613
120.121942851043367
130.124943727566220
140.116613816263764
150.0794587873862846


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = -0.7 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = -0.7 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; 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')