FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 24 Dec 2008 04:47:14 -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/24/t123011929376m47f1wliytiei.htm/, Retrieved Sat, 18 May 2024 19:52:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=36469, Retrieved Sat, 18 May 2024 19:52:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact209

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 correlation...] [2007-12-24 07:47:07] [707a919fab5d6f3020ea3c395672cd86]
-   PD    [Cross Correlation Function] [] [2008-12-24 11:47:14] [e7fa5259715477c9f32960f5b339b707] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
467
460
448
443
436
431
484
510
513
503
471
471
476
475
470
461
455
456
517
525
523
519
509
512
519
517
510
509
501
507
569
580
578
565
547
555
Dataseries Y:
Download CSV
Histogram 
98,1
101,1
111,1
93,3
100
108
70,4
75,4
105,5
112,3
102,5
93,5
86,7
95,2
103,8
97
95,5
101
67,5
64
106,7
100,6
101,2
93,1
84,2
85,8
91,8
92,4
80,3
79,7
62,5
57,1
100,8
100,7
86,2
83,2

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'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 & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36469&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36469&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36469&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'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 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])
-12-0.0876755578918423
-11-0.182684628216183
-10-0.222902987712360
-9-0.151071472819103
-8-0.092796597055852
-7-0.117887736260041
-6-0.157828460762054
-5-0.215053074190718
-4-0.183993368730336
-3-0.0212219562288619
-20.121607767546836
-1-0.120975407575130
0-0.47186677027019
1-0.547936383349351
2-0.518187361848083
3-0.398738915892519
4-0.277242356585011
5-0.201045555830190
6-0.174462961437356
7-0.138128437874531
8-0.0607211485451575
90.0558273998963696
100.15525697459778
110.0114111067434436
12-0.16635953631771

\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 & -0.0876755578918423 \tabularnewline
-11 & -0.182684628216183 \tabularnewline
-10 & -0.222902987712360 \tabularnewline
-9 & -0.151071472819103 \tabularnewline
-8 & -0.092796597055852 \tabularnewline
-7 & -0.117887736260041 \tabularnewline
-6 & -0.157828460762054 \tabularnewline
-5 & -0.215053074190718 \tabularnewline
-4 & -0.183993368730336 \tabularnewline
-3 & -0.0212219562288619 \tabularnewline
-2 & 0.121607767546836 \tabularnewline
-1 & -0.120975407575130 \tabularnewline
0 & -0.47186677027019 \tabularnewline
1 & -0.547936383349351 \tabularnewline
2 & -0.518187361848083 \tabularnewline
3 & -0.398738915892519 \tabularnewline
4 & -0.277242356585011 \tabularnewline
5 & -0.201045555830190 \tabularnewline
6 & -0.174462961437356 \tabularnewline
7 & -0.138128437874531 \tabularnewline
8 & -0.0607211485451575 \tabularnewline
9 & 0.0558273998963696 \tabularnewline
10 & 0.15525697459778 \tabularnewline
11 & 0.0114111067434436 \tabularnewline
12 & -0.16635953631771 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36469&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]-0.0876755578918423[/C][/ROW]
[ROW][C]-11[/C][C]-0.182684628216183[/C][/ROW]
[ROW][C]-10[/C][C]-0.222902987712360[/C][/ROW]
[ROW][C]-9[/C][C]-0.151071472819103[/C][/ROW]
[ROW][C]-8[/C][C]-0.092796597055852[/C][/ROW]
[ROW][C]-7[/C][C]-0.117887736260041[/C][/ROW]
[ROW][C]-6[/C][C]-0.157828460762054[/C][/ROW]
[ROW][C]-5[/C][C]-0.215053074190718[/C][/ROW]
[ROW][C]-4[/C][C]-0.183993368730336[/C][/ROW]
[ROW][C]-3[/C][C]-0.0212219562288619[/C][/ROW]
[ROW][C]-2[/C][C]0.121607767546836[/C][/ROW]
[ROW][C]-1[/C][C]-0.120975407575130[/C][/ROW]
[ROW][C]0[/C][C]-0.47186677027019[/C][/ROW]
[ROW][C]1[/C][C]-0.547936383349351[/C][/ROW]
[ROW][C]2[/C][C]-0.518187361848083[/C][/ROW]
[ROW][C]3[/C][C]-0.398738915892519[/C][/ROW]
[ROW][C]4[/C][C]-0.277242356585011[/C][/ROW]
[ROW][C]5[/C][C]-0.201045555830190[/C][/ROW]
[ROW][C]6[/C][C]-0.174462961437356[/C][/ROW]
[ROW][C]7[/C][C]-0.138128437874531[/C][/ROW]
[ROW][C]8[/C][C]-0.0607211485451575[/C][/ROW]
[ROW][C]9[/C][C]0.0558273998963696[/C][/ROW]
[ROW][C]10[/C][C]0.15525697459778[/C][/ROW]
[ROW][C]11[/C][C]0.0114111067434436[/C][/ROW]
[ROW][C]12[/C][C]-0.16635953631771[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36469&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36469&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])
-12-0.0876755578918423
-11-0.182684628216183
-10-0.222902987712360
-9-0.151071472819103
-8-0.092796597055852
-7-0.117887736260041
-6-0.157828460762054
-5-0.215053074190718
-4-0.183993368730336
-3-0.0212219562288619
-20.121607767546836
-1-0.120975407575130
0-0.47186677027019
1-0.547936383349351
2-0.518187361848083
3-0.398738915892519
4-0.277242356585011
5-0.201045555830190
6-0.174462961437356
7-0.138128437874531
8-0.0607211485451575
90.0558273998963696
100.15525697459778
110.0114111067434436
12-0.16635953631771


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) x <- 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')