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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 13:01:33 -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/t1228248133k3y3tpwu669t8r8.htm/, Retrieved Sat, 25 May 2024 03:06:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28310, Retrieved Sat, 25 May 2024 03:06:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsgdm
Estimated Impact162

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:40:39] [b98453cac15ba1066b407e146608df68]
F       [Law of Averages] [WS7 Task 4] [2008-11-30 15:52:14] [11ac052cc87d77b9933b02bea117068e]
- RMPD    [Spectral Analysis] [WS7 Task 6d] [2008-12-02 18:21:54] [11ac052cc87d77b9933b02bea117068e]
- RMPD      [Cross Correlation Function] [WS7 Task 9a] [2008-12-02 19:58:23] [11ac052cc87d77b9933b02bea117068e]
-   P           [Cross Correlation Function] [WS7 Task 9b] [2008-12-02 20:01:33] [99f79d508deef838ee89a56fb32f134e] [Current]
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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 time2 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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28310&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]2 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=28310&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28310&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series-0.5
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series2
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-16-0.0123383700749481
-150.0235046581009547
-14-0.140853844138907
-130.0986121324567467
-120.0167932917660784
-110.0718127064292567
-10-0.0490027121119286
-9-0.0556158500602669
-80.0634365644386577
-70.0479050471722053
-60.0340910717614555
-50.0440118324384058
-4-0.0260743371309403
-3-0.0374185154561079
-20.00747687255180176
-1-0.0172607047355573
00.108493402573497
1-0.105624605722842
20.0257549375788142
30.0516608005297031
4-0.13798865421153
50.00175453238526143
6-0.0318795252033083
7-0.0115307093266151
80.00933977278353637
90.0370298829427971
100.0683991516027174
11-0.0944769243598815
12-0.0246193552987801
130.054917534170014
14-0.0302694239499528
150.0382996836317581
160.0740187854087489

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & -0.5 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 1 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 1 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 2 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-16 & -0.0123383700749481 \tabularnewline
-15 & 0.0235046581009547 \tabularnewline
-14 & -0.140853844138907 \tabularnewline
-13 & 0.0986121324567467 \tabularnewline
-12 & 0.0167932917660784 \tabularnewline
-11 & 0.0718127064292567 \tabularnewline
-10 & -0.0490027121119286 \tabularnewline
-9 & -0.0556158500602669 \tabularnewline
-8 & 0.0634365644386577 \tabularnewline
-7 & 0.0479050471722053 \tabularnewline
-6 & 0.0340910717614555 \tabularnewline
-5 & 0.0440118324384058 \tabularnewline
-4 & -0.0260743371309403 \tabularnewline
-3 & -0.0374185154561079 \tabularnewline
-2 & 0.00747687255180176 \tabularnewline
-1 & -0.0172607047355573 \tabularnewline
0 & 0.108493402573497 \tabularnewline
1 & -0.105624605722842 \tabularnewline
2 & 0.0257549375788142 \tabularnewline
3 & 0.0516608005297031 \tabularnewline
4 & -0.13798865421153 \tabularnewline
5 & 0.00175453238526143 \tabularnewline
6 & -0.0318795252033083 \tabularnewline
7 & -0.0115307093266151 \tabularnewline
8 & 0.00933977278353637 \tabularnewline
9 & 0.0370298829427971 \tabularnewline
10 & 0.0683991516027174 \tabularnewline
11 & -0.0944769243598815 \tabularnewline
12 & -0.0246193552987801 \tabularnewline
13 & 0.054917534170014 \tabularnewline
14 & -0.0302694239499528 \tabularnewline
15 & 0.0382996836317581 \tabularnewline
16 & 0.0740187854087489 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28310&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]-0.5[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of X series[/C][C]1[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]1[/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]2[/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]-0.0123383700749481[/C][/ROW]
[ROW][C]-15[/C][C]0.0235046581009547[/C][/ROW]
[ROW][C]-14[/C][C]-0.140853844138907[/C][/ROW]
[ROW][C]-13[/C][C]0.0986121324567467[/C][/ROW]
[ROW][C]-12[/C][C]0.0167932917660784[/C][/ROW]
[ROW][C]-11[/C][C]0.0718127064292567[/C][/ROW]
[ROW][C]-10[/C][C]-0.0490027121119286[/C][/ROW]
[ROW][C]-9[/C][C]-0.0556158500602669[/C][/ROW]
[ROW][C]-8[/C][C]0.0634365644386577[/C][/ROW]
[ROW][C]-7[/C][C]0.0479050471722053[/C][/ROW]
[ROW][C]-6[/C][C]0.0340910717614555[/C][/ROW]
[ROW][C]-5[/C][C]0.0440118324384058[/C][/ROW]
[ROW][C]-4[/C][C]-0.0260743371309403[/C][/ROW]
[ROW][C]-3[/C][C]-0.0374185154561079[/C][/ROW]
[ROW][C]-2[/C][C]0.00747687255180176[/C][/ROW]
[ROW][C]-1[/C][C]-0.0172607047355573[/C][/ROW]
[ROW][C]0[/C][C]0.108493402573497[/C][/ROW]
[ROW][C]1[/C][C]-0.105624605722842[/C][/ROW]
[ROW][C]2[/C][C]0.0257549375788142[/C][/ROW]
[ROW][C]3[/C][C]0.0516608005297031[/C][/ROW]
[ROW][C]4[/C][C]-0.13798865421153[/C][/ROW]
[ROW][C]5[/C][C]0.00175453238526143[/C][/ROW]
[ROW][C]6[/C][C]-0.0318795252033083[/C][/ROW]
[ROW][C]7[/C][C]-0.0115307093266151[/C][/ROW]
[ROW][C]8[/C][C]0.00933977278353637[/C][/ROW]
[ROW][C]9[/C][C]0.0370298829427971[/C][/ROW]
[ROW][C]10[/C][C]0.0683991516027174[/C][/ROW]
[ROW][C]11[/C][C]-0.0944769243598815[/C][/ROW]
[ROW][C]12[/C][C]-0.0246193552987801[/C][/ROW]
[ROW][C]13[/C][C]0.054917534170014[/C][/ROW]
[ROW][C]14[/C][C]-0.0302694239499528[/C][/ROW]
[ROW][C]15[/C][C]0.0382996836317581[/C][/ROW]
[ROW][C]16[/C][C]0.0740187854087489[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28310&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28310&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-0.5
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series2
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-16-0.0123383700749481
-150.0235046581009547
-14-0.140853844138907
-130.0986121324567467
-120.0167932917660784
-110.0718127064292567
-10-0.0490027121119286
-9-0.0556158500602669
-80.0634365644386577
-70.0479050471722053
-60.0340910717614555
-50.0440118324384058
-4-0.0260743371309403
-3-0.0374185154561079
-20.00747687255180176
-1-0.0172607047355573
00.108493402573497
1-0.105624605722842
20.0257549375788142
30.0516608005297031
4-0.13798865421153
50.00175453238526143
6-0.0318795252033083
7-0.0115307093266151
80.00933977278353637
90.0370298829427971
100.0683991516027174
11-0.0944769243598815
12-0.0246193552987801
130.054917534170014
14-0.0302694239499528
150.0382996836317581
160.0740187854087489


Figures (Output of Computation)

Input Parameters & R Code

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