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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, 21 Dec 2010 12:37:58 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t12929350546en9ja39umwjzlb.htm/, Retrieved Fri, 17 May 2024 23:08:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113412, Retrieved Fri, 17 May 2024 23:08:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

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] [cross correlatie ...] [2008-12-14 14:01:08] [4e6222b6603c6cf58bf3d7a9b1dc61c3]
-  M D  [Cross Correlation Function] [Cross correlatie ...] [2010-12-03 09:32:54] [ff7c1e95cf99a1dae07ec89975494dde]
-   P     [Cross Correlation Function] [Crosscorrelatie n...] [2010-12-21 12:35:08] [ff7c1e95cf99a1dae07ec89975494dde]
-   P         [Cross Correlation Function] [Crosscorrelatie d...] [2010-12-21 12:37:58] [2fa539864aa87c5da4977c85c6885fac] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.81
0.81
0.81
0.79
0.78
0.78
0.77
0.78
0.77
0.78
0.79
0.79
0.79
0.79
0.79
0.8
0.8
0.8
0.8
0.81
0.8
0.82
0.85
0.85
0.86
0.85
0.83
0.81
0.82
0.82
0.78
0.78
0.73
0.68
0.65
0.62
0.6
0.6
0.59
0.6
0.6
0.6
0.59
0.58
0.56
0.55
0.54
0.55
0.55
0.54
0.54
0.54
0.53
0.53
0.53
0.53
Dataseries Y:
Download CSV
Histogram 
1.88
1.87
1.88
1.87
1.88
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.88
1.88
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.86
1.86
1.85
1.84
1.83
1.82
1.78
1.75
1.74
1.74
1.74
1.73
1.73
1.73
1.71
1.7
1.7
1.69
1.68
1.68
1.68
1.68
1.67
1.66
1.65
1.65
1.65

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113412&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113412&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113412&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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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 series2
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])
-11-0.0719792572269621
-10-0.00797044585988534
-90.165172162665612
-80.0759031690349025
-7-0.066093389515043
-60.0248923155316433
-50.13059268985811
-40.065235033807058
-3-0.146656203821880
-20.293189785399759
-1-0.0143468025477918
0-0.154504027437767
1-0.0670743674670299
2-0.224276084272757
3-0.0994466398825614
4-0.191658567369229
50.071734012738963
6-0.0106681352278465
70.0736959686429354
80.00846093483587723
90.304348409603599
10-0.160022028417685
110.186753677609299

\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 & 2 \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
-11 & -0.0719792572269621 \tabularnewline
-10 & -0.00797044585988534 \tabularnewline
-9 & 0.165172162665612 \tabularnewline
-8 & 0.0759031690349025 \tabularnewline
-7 & -0.066093389515043 \tabularnewline
-6 & 0.0248923155316433 \tabularnewline
-5 & 0.13059268985811 \tabularnewline
-4 & 0.065235033807058 \tabularnewline
-3 & -0.146656203821880 \tabularnewline
-2 & 0.293189785399759 \tabularnewline
-1 & -0.0143468025477918 \tabularnewline
0 & -0.154504027437767 \tabularnewline
1 & -0.0670743674670299 \tabularnewline
2 & -0.224276084272757 \tabularnewline
3 & -0.0994466398825614 \tabularnewline
4 & -0.191658567369229 \tabularnewline
5 & 0.071734012738963 \tabularnewline
6 & -0.0106681352278465 \tabularnewline
7 & 0.0736959686429354 \tabularnewline
8 & 0.00846093483587723 \tabularnewline
9 & 0.304348409603599 \tabularnewline
10 & -0.160022028417685 \tabularnewline
11 & 0.186753677609299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113412&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]2[/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]-11[/C][C]-0.0719792572269621[/C][/ROW]
[ROW][C]-10[/C][C]-0.00797044585988534[/C][/ROW]
[ROW][C]-9[/C][C]0.165172162665612[/C][/ROW]
[ROW][C]-8[/C][C]0.0759031690349025[/C][/ROW]
[ROW][C]-7[/C][C]-0.066093389515043[/C][/ROW]
[ROW][C]-6[/C][C]0.0248923155316433[/C][/ROW]
[ROW][C]-5[/C][C]0.13059268985811[/C][/ROW]
[ROW][C]-4[/C][C]0.065235033807058[/C][/ROW]
[ROW][C]-3[/C][C]-0.146656203821880[/C][/ROW]
[ROW][C]-2[/C][C]0.293189785399759[/C][/ROW]
[ROW][C]-1[/C][C]-0.0143468025477918[/C][/ROW]
[ROW][C]0[/C][C]-0.154504027437767[/C][/ROW]
[ROW][C]1[/C][C]-0.0670743674670299[/C][/ROW]
[ROW][C]2[/C][C]-0.224276084272757[/C][/ROW]
[ROW][C]3[/C][C]-0.0994466398825614[/C][/ROW]
[ROW][C]4[/C][C]-0.191658567369229[/C][/ROW]
[ROW][C]5[/C][C]0.071734012738963[/C][/ROW]
[ROW][C]6[/C][C]-0.0106681352278465[/C][/ROW]
[ROW][C]7[/C][C]0.0736959686429354[/C][/ROW]
[ROW][C]8[/C][C]0.00846093483587723[/C][/ROW]
[ROW][C]9[/C][C]0.304348409603599[/C][/ROW]
[ROW][C]10[/C][C]-0.160022028417685[/C][/ROW]
[ROW][C]11[/C][C]0.186753677609299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113412&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113412&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 series2
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])
-11-0.0719792572269621
-10-0.00797044585988534
-90.165172162665612
-80.0759031690349025
-7-0.066093389515043
-60.0248923155316433
-50.13059268985811
-40.065235033807058
-3-0.146656203821880
-20.293189785399759
-1-0.0143468025477918
0-0.154504027437767
1-0.0670743674670299
2-0.224276084272757
3-0.0994466398825614
4-0.191658567369229
50.071734012738963
6-0.0106681352278465
70.0736959686429354
80.00846093483587723
90.304348409603599
10-0.160022028417685
110.186753677609299


Figures (Output of Computation)

Input Parameters & R Code

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