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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 computationTue, 02 Dec 2008 13:56:08 -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/t1228251417244e9b2hzrrs6tp.htm/, Retrieved Sun, 19 May 2024 11:36:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28447, Retrieved Sun, 19 May 2024 11:36:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact161

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Spectral Analysis] [Q6 - 3e methode -...] [2008-12-02 20:05:14] [7a664918911e34206ce9d0436dd7c1c8]
F RMPD    [Cross Correlation Function] [Q7 - cross correl...] [2008-12-02 20:56:08] [98255691c21504803b38711776845ae0] [Current]
F RMPD      [ARIMA Forecasting] [arima forecasting] [2008-12-14 12:01:13] [7a664918911e34206ce9d0436dd7c1c8]
F             [ARIMA Forecasting] [] [2008-12-16 22:07:39] [74be16979710d4c4e7c6647856088456]
- RMPD          [Standard Deviation-Mean Plot] [] [2008-12-22 16:20:28] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-06 14:05:30 [Natalie De Wilde] [reply] 
Goed, de bespreking had misschien toch wat uitgebreider gemogen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-19
0
-18
-17
0
-16
-1
-11
7
-2
-9
-8
4
-7
2
1
3
5
0
11
5
-1
17
0
4
13
0
-7
19
9
2
9
8
-2
-6
-5
11
Dataseries Y:
Download CSV
Histogram 
-1
4
9
18
13
9
5
9
9
17
7
4
8
1
4
2
1
5
15
11
4
11
2
3
7
6
4
5
3
2
1
1
0
9
3
4
-3

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=28447&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=28447&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28447&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 series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
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])
-12-0.100098130035939
-11-0.0159261652918508
-10-0.0465417508328226
-9-0.150071658403304
-8-0.229588956405438
-7-0.375620521407501
-6-0.230871291615988
-5-0.19333538677737
-4-0.300437418085292
-3-0.304030549056924
-2-0.209621803787486
-1-0.224493584707888
0-0.28987122624932
1-0.124110292641858
2-0.241622078935389
3-0.015350030720084
4-0.00147050639341575
50.0357601231057787
6-0.164820077693134
70.0482053449958297
8-0.134216560137762
9-0.0919750348402133
100.225303480630341
110.147829247893953
120.0445232685007055

\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 & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \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
-12 & -0.100098130035939 \tabularnewline
-11 & -0.0159261652918508 \tabularnewline
-10 & -0.0465417508328226 \tabularnewline
-9 & -0.150071658403304 \tabularnewline
-8 & -0.229588956405438 \tabularnewline
-7 & -0.375620521407501 \tabularnewline
-6 & -0.230871291615988 \tabularnewline
-5 & -0.19333538677737 \tabularnewline
-4 & -0.300437418085292 \tabularnewline
-3 & -0.304030549056924 \tabularnewline
-2 & -0.209621803787486 \tabularnewline
-1 & -0.224493584707888 \tabularnewline
0 & -0.28987122624932 \tabularnewline
1 & -0.124110292641858 \tabularnewline
2 & -0.241622078935389 \tabularnewline
3 & -0.015350030720084 \tabularnewline
4 & -0.00147050639341575 \tabularnewline
5 & 0.0357601231057787 \tabularnewline
6 & -0.164820077693134 \tabularnewline
7 & 0.0482053449958297 \tabularnewline
8 & -0.134216560137762 \tabularnewline
9 & -0.0919750348402133 \tabularnewline
10 & 0.225303480630341 \tabularnewline
11 & 0.147829247893953 \tabularnewline
12 & 0.0445232685007055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28447&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]0[/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]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]-12[/C][C]-0.100098130035939[/C][/ROW]
[ROW][C]-11[/C][C]-0.0159261652918508[/C][/ROW]
[ROW][C]-10[/C][C]-0.0465417508328226[/C][/ROW]
[ROW][C]-9[/C][C]-0.150071658403304[/C][/ROW]
[ROW][C]-8[/C][C]-0.229588956405438[/C][/ROW]
[ROW][C]-7[/C][C]-0.375620521407501[/C][/ROW]
[ROW][C]-6[/C][C]-0.230871291615988[/C][/ROW]
[ROW][C]-5[/C][C]-0.19333538677737[/C][/ROW]
[ROW][C]-4[/C][C]-0.300437418085292[/C][/ROW]
[ROW][C]-3[/C][C]-0.304030549056924[/C][/ROW]
[ROW][C]-2[/C][C]-0.209621803787486[/C][/ROW]
[ROW][C]-1[/C][C]-0.224493584707888[/C][/ROW]
[ROW][C]0[/C][C]-0.28987122624932[/C][/ROW]
[ROW][C]1[/C][C]-0.124110292641858[/C][/ROW]
[ROW][C]2[/C][C]-0.241622078935389[/C][/ROW]
[ROW][C]3[/C][C]-0.015350030720084[/C][/ROW]
[ROW][C]4[/C][C]-0.00147050639341575[/C][/ROW]
[ROW][C]5[/C][C]0.0357601231057787[/C][/ROW]
[ROW][C]6[/C][C]-0.164820077693134[/C][/ROW]
[ROW][C]7[/C][C]0.0482053449958297[/C][/ROW]
[ROW][C]8[/C][C]-0.134216560137762[/C][/ROW]
[ROW][C]9[/C][C]-0.0919750348402133[/C][/ROW]
[ROW][C]10[/C][C]0.225303480630341[/C][/ROW]
[ROW][C]11[/C][C]0.147829247893953[/C][/ROW]
[ROW][C]12[/C][C]0.0445232685007055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28447&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28447&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 series0
Degree of seasonal differencing (D) of X series0
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])
-12-0.100098130035939
-11-0.0159261652918508
-10-0.0465417508328226
-9-0.150071658403304
-8-0.229588956405438
-7-0.375620521407501
-6-0.230871291615988
-5-0.19333538677737
-4-0.300437418085292
-3-0.304030549056924
-2-0.209621803787486
-1-0.224493584707888
0-0.28987122624932
1-0.124110292641858
2-0.241622078935389
3-0.015350030720084
4-0.00147050639341575
50.0357601231057787
6-0.164820077693134
70.0482053449958297
8-0.134216560137762
9-0.0919750348402133
100.225303480630341
110.147829247893953
120.0445232685007055


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 = 1 ; par2 = 0 ; par3 = 0 ; 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')