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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 computationSun, 14 Dec 2008 07:01:08 -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/14/t1229263696urr69jtuyzf4dnv.htm/, Retrieved Wed, 15 May 2024 01:46:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33382, Retrieved Wed, 15 May 2024 01:46:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180

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] [d300b7a0882cee7d84584ad37a3d4ede] [Current]
-   PD    [Cross Correlation Function] [Cross correlatie:...] [2008-12-17 10:35:40] [f77c9ab3b413812d7baee6b7ec69a15d]
-  M D      [Cross Correlation Function] [Crosscorrelatie n...] [2010-12-03 09:35:23] [ff7c1e95cf99a1dae07ec89975494dde]
-  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] [ff7c1e95cf99a1dae07ec89975494dde]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101,02
100,67
100,47
100,38
100,33
100,34
100,37
100,39
100,21
100,21
100,22
100,28
100,25
100,25
100,21
100,16
100,18
100,1
99,96
99,88
99,88
99,86
99,84
99,8
99,82
99,81
99,92
100,03
99,99
100,02
100,01
100,13
100,33
100,13
99,96
100,05
99,83
99,8
100,01
100,1
100,13
100,16
100,41
101,34
101,65
101,85
102,07
102,12
102,14
102,21
102,28
102,19
102,33
102,54
102,44
102,78
102,9
103,08
102,77
102,65
102,71
103,29
102,86
103,45
103,72
103,65
103,83
104,45
105,14
105,07
105,31
105,19
105,3
105,02
105,17
105,28
105,45
105,38
105,8
105,96
105,08
105,11
105,61
105,5
Dataseries Y:
Download CSV
Histogram 
101,73
101,63
101,43
101,34
101,01
100,89
100,93
100,77
100,3
99,86
99,71
99,93
99,88
99,92
99,87
99,63
100,05
99,88
100,11
100,05
100,07
100,2
100,21
99,76
99,41
99,24
99,65
99,7
99,79
99,84
101
101,62
101,98
101,46
102,28
102,14
102,02
102,21
101,61
102,38
102,19
102,04
101,76
101,9
102,01
102,37
103,04
103,42
103,76
104,41
104,75
104,28
103,89
104,09
103,8
105,03
105,86
106,04
106,03
106,13
107,21
107,66
108,08
108,76
108,26
108,71
108,65
108,61
108,86
109,54
108,22
108,77
109,9
110,13
109,6
110,42
110,6
109,73
110,72
111,08
111,14
111,01
110,56
111,57

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=33382&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=33382&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33382&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 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])
-160.392996401305583
-150.440231772437339
-140.483551501583507
-130.529628442173577
-120.5745079250533
-110.61652827118223
-100.653425026081096
-90.691652013474608
-80.73152228010324
-70.76834315715625
-60.803670855405347
-50.842257029045416
-40.877946764463152
-30.899518464319117
-20.921823118808869
-10.94700193241894
00.96784674223386
10.942618823736122
20.92037712725572
30.897467848365937
40.870472733253391
50.842114651989281
60.816192776090859
70.792174923457945
80.760011320815545
90.727729226173895
100.698554798604007
110.663202468669881
120.627298692220778
130.595802351501152
140.563113717836132
150.521251919249724
160.481840011863722

\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
-16 & 0.392996401305583 \tabularnewline
-15 & 0.440231772437339 \tabularnewline
-14 & 0.483551501583507 \tabularnewline
-13 & 0.529628442173577 \tabularnewline
-12 & 0.5745079250533 \tabularnewline
-11 & 0.61652827118223 \tabularnewline
-10 & 0.653425026081096 \tabularnewline
-9 & 0.691652013474608 \tabularnewline
-8 & 0.73152228010324 \tabularnewline
-7 & 0.76834315715625 \tabularnewline
-6 & 0.803670855405347 \tabularnewline
-5 & 0.842257029045416 \tabularnewline
-4 & 0.877946764463152 \tabularnewline
-3 & 0.899518464319117 \tabularnewline
-2 & 0.921823118808869 \tabularnewline
-1 & 0.94700193241894 \tabularnewline
0 & 0.96784674223386 \tabularnewline
1 & 0.942618823736122 \tabularnewline
2 & 0.92037712725572 \tabularnewline
3 & 0.897467848365937 \tabularnewline
4 & 0.870472733253391 \tabularnewline
5 & 0.842114651989281 \tabularnewline
6 & 0.816192776090859 \tabularnewline
7 & 0.792174923457945 \tabularnewline
8 & 0.760011320815545 \tabularnewline
9 & 0.727729226173895 \tabularnewline
10 & 0.698554798604007 \tabularnewline
11 & 0.663202468669881 \tabularnewline
12 & 0.627298692220778 \tabularnewline
13 & 0.595802351501152 \tabularnewline
14 & 0.563113717836132 \tabularnewline
15 & 0.521251919249724 \tabularnewline
16 & 0.481840011863722 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33382&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]-16[/C][C]0.392996401305583[/C][/ROW]
[ROW][C]-15[/C][C]0.440231772437339[/C][/ROW]
[ROW][C]-14[/C][C]0.483551501583507[/C][/ROW]
[ROW][C]-13[/C][C]0.529628442173577[/C][/ROW]
[ROW][C]-12[/C][C]0.5745079250533[/C][/ROW]
[ROW][C]-11[/C][C]0.61652827118223[/C][/ROW]
[ROW][C]-10[/C][C]0.653425026081096[/C][/ROW]
[ROW][C]-9[/C][C]0.691652013474608[/C][/ROW]
[ROW][C]-8[/C][C]0.73152228010324[/C][/ROW]
[ROW][C]-7[/C][C]0.76834315715625[/C][/ROW]
[ROW][C]-6[/C][C]0.803670855405347[/C][/ROW]
[ROW][C]-5[/C][C]0.842257029045416[/C][/ROW]
[ROW][C]-4[/C][C]0.877946764463152[/C][/ROW]
[ROW][C]-3[/C][C]0.899518464319117[/C][/ROW]
[ROW][C]-2[/C][C]0.921823118808869[/C][/ROW]
[ROW][C]-1[/C][C]0.94700193241894[/C][/ROW]
[ROW][C]0[/C][C]0.96784674223386[/C][/ROW]
[ROW][C]1[/C][C]0.942618823736122[/C][/ROW]
[ROW][C]2[/C][C]0.92037712725572[/C][/ROW]
[ROW][C]3[/C][C]0.897467848365937[/C][/ROW]
[ROW][C]4[/C][C]0.870472733253391[/C][/ROW]
[ROW][C]5[/C][C]0.842114651989281[/C][/ROW]
[ROW][C]6[/C][C]0.816192776090859[/C][/ROW]
[ROW][C]7[/C][C]0.792174923457945[/C][/ROW]
[ROW][C]8[/C][C]0.760011320815545[/C][/ROW]
[ROW][C]9[/C][C]0.727729226173895[/C][/ROW]
[ROW][C]10[/C][C]0.698554798604007[/C][/ROW]
[ROW][C]11[/C][C]0.663202468669881[/C][/ROW]
[ROW][C]12[/C][C]0.627298692220778[/C][/ROW]
[ROW][C]13[/C][C]0.595802351501152[/C][/ROW]
[ROW][C]14[/C][C]0.563113717836132[/C][/ROW]
[ROW][C]15[/C][C]0.521251919249724[/C][/ROW]
[ROW][C]16[/C][C]0.481840011863722[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33382&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33382&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])
-160.392996401305583
-150.440231772437339
-140.483551501583507
-130.529628442173577
-120.5745079250533
-110.61652827118223
-100.653425026081096
-90.691652013474608
-80.73152228010324
-70.76834315715625
-60.803670855405347
-50.842257029045416
-40.877946764463152
-30.899518464319117
-20.921823118808869
-10.94700193241894
00.96784674223386
10.942618823736122
20.92037712725572
30.897467848365937
40.870472733253391
50.842114651989281
60.816192776090859
70.792174923457945
80.760011320815545
90.727729226173895
100.698554798604007
110.663202468669881
120.627298692220778
130.595802351501152
140.563113717836132
150.521251919249724
160.481840011863722


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')