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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 computationWed, 17 Dec 2008 23:46:47 -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/18/t122958286200oxynrwi4g2agr.htm/, Retrieved Sat, 11 May 2024 14:44:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=34612, Retrieved Sat, 11 May 2024 14:44:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact213

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMPD  [Standard Deviation-Mean Plot] [Identification an...] [2008-12-07 14:45:52] [b943bd7078334192ff8343563ee31113]
- RM      [Variance Reduction Matrix] [Identification an...] [2008-12-07 14:47:22] [b943bd7078334192ff8343563ee31113]
- RMP       [(Partial) Autocorrelation Function] [Identification an...] [2008-12-07 14:51:36] [b943bd7078334192ff8343563ee31113]
- RMPD        [Cross Correlation Function] [Kruiselingse corr...] [2008-12-12 19:54:41] [d32f94eec6fe2d8c421bd223368a5ced]
-    D            [Cross Correlation Function] [Test cross correl...] [2008-12-18 06:46:47] [382e90e66f02be5ed86892bdc1574692] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
120
145
159
170
180
200
211
217
230
245
246
280
298
306
306
320
333
343
350
365
380
400
401
420
430
452
450
440
460
462
471
484
498
503
506
Dataseries Y:
Download CSV
Histogram 
0
0
0
0
0
120
145
159
170
180
200
211
217
230
245
246
280
298
306
306
320
333
343
350
365
380
400
401
420
430
452
450
440
460
462

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'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34612&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34612&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34612&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'Herman Ole Andreas Wold' @ 193.190.124.10:1001






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])
-120.0204235406647403
-110.0819779096102516
-100.145891458438157
-90.218726746586896
-80.287695090773168
-70.351227689345685
-60.420909434443949
-50.49459079274785
-40.604667212196705
-30.708061961469023
-20.807242591294183
-10.899347042182347
00.983282518063488
10.90263816275202
20.826015094815586
30.754053479800076
40.675168960130703
50.591426316321686
60.516514584428561
70.437869112380857
80.358536724673002
90.27918859160442
100.207752745285561
110.134723604532058
120.0751599545891919

\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 & 0.0204235406647403 \tabularnewline
-11 & 0.0819779096102516 \tabularnewline
-10 & 0.145891458438157 \tabularnewline
-9 & 0.218726746586896 \tabularnewline
-8 & 0.287695090773168 \tabularnewline
-7 & 0.351227689345685 \tabularnewline
-6 & 0.420909434443949 \tabularnewline
-5 & 0.49459079274785 \tabularnewline
-4 & 0.604667212196705 \tabularnewline
-3 & 0.708061961469023 \tabularnewline
-2 & 0.807242591294183 \tabularnewline
-1 & 0.899347042182347 \tabularnewline
0 & 0.983282518063488 \tabularnewline
1 & 0.90263816275202 \tabularnewline
2 & 0.826015094815586 \tabularnewline
3 & 0.754053479800076 \tabularnewline
4 & 0.675168960130703 \tabularnewline
5 & 0.591426316321686 \tabularnewline
6 & 0.516514584428561 \tabularnewline
7 & 0.437869112380857 \tabularnewline
8 & 0.358536724673002 \tabularnewline
9 & 0.27918859160442 \tabularnewline
10 & 0.207752745285561 \tabularnewline
11 & 0.134723604532058 \tabularnewline
12 & 0.0751599545891919 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34612&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]0.0204235406647403[/C][/ROW]
[ROW][C]-11[/C][C]0.0819779096102516[/C][/ROW]
[ROW][C]-10[/C][C]0.145891458438157[/C][/ROW]
[ROW][C]-9[/C][C]0.218726746586896[/C][/ROW]
[ROW][C]-8[/C][C]0.287695090773168[/C][/ROW]
[ROW][C]-7[/C][C]0.351227689345685[/C][/ROW]
[ROW][C]-6[/C][C]0.420909434443949[/C][/ROW]
[ROW][C]-5[/C][C]0.49459079274785[/C][/ROW]
[ROW][C]-4[/C][C]0.604667212196705[/C][/ROW]
[ROW][C]-3[/C][C]0.708061961469023[/C][/ROW]
[ROW][C]-2[/C][C]0.807242591294183[/C][/ROW]
[ROW][C]-1[/C][C]0.899347042182347[/C][/ROW]
[ROW][C]0[/C][C]0.983282518063488[/C][/ROW]
[ROW][C]1[/C][C]0.90263816275202[/C][/ROW]
[ROW][C]2[/C][C]0.826015094815586[/C][/ROW]
[ROW][C]3[/C][C]0.754053479800076[/C][/ROW]
[ROW][C]4[/C][C]0.675168960130703[/C][/ROW]
[ROW][C]5[/C][C]0.591426316321686[/C][/ROW]
[ROW][C]6[/C][C]0.516514584428561[/C][/ROW]
[ROW][C]7[/C][C]0.437869112380857[/C][/ROW]
[ROW][C]8[/C][C]0.358536724673002[/C][/ROW]
[ROW][C]9[/C][C]0.27918859160442[/C][/ROW]
[ROW][C]10[/C][C]0.207752745285561[/C][/ROW]
[ROW][C]11[/C][C]0.134723604532058[/C][/ROW]
[ROW][C]12[/C][C]0.0751599545891919[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34612&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34612&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])
-120.0204235406647403
-110.0819779096102516
-100.145891458438157
-90.218726746586896
-80.287695090773168
-70.351227689345685
-60.420909434443949
-50.49459079274785
-40.604667212196705
-30.708061961469023
-20.807242591294183
-10.899347042182347
00.983282518063488
10.90263816275202
20.826015094815586
30.754053479800076
40.675168960130703
50.591426316321686
60.516514584428561
70.437869112380857
80.358536724673002
90.27918859160442
100.207752745285561
110.134723604532058
120.0751599545891919


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')