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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 07:54:47 -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/02/t1228229829yurfgu6tdwqe7n4.htm/, Retrieved Fri, 17 May 2024 07:01:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27907, Retrieved Fri, 17 May 2024 07:01:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact206

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [Q7] [2008-12-02 14:54:47] [d41d8cd98f00b204e9800998ecf8427e] [Current]
F   P     [Cross Correlation Function] [Q9] [2008-12-02 15:34:12] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-04 13:33:15 [72e979bcc364082694890d2eccc1a66f] [reply] 
Er word geen uitleg gegeven welke gegevens er precies worden gebruikt.
Er word wel een goede interpretatie gegeven.
2008-12-07 20:33:06 [Stefan Temmerman] [reply] 
Het is vaag welke gegevens gebruikt werden, maar de CCF wordt juist geïnterpreteerd: De pieken op de CCF duiden op een voorspellingswaarde van X(t) tot over Y(t).

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97.4
97
105.4
102.7
98.1
104.5
87.4
89.9
109.8
111.7
98.6
96.9
95.1
97
112.7
102.9
97.4
111.4
87.4
96.8
114.1
110.3
103.9
101.6
94.6
95.9
104.7
102.8
98.1
113.9
80.9
95.7
113.2
105.9
108.8
102.3
99
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102
106
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100
110.7
112.8
109.8
117.3
109.1
115.9
96
97.6
Dataseries Y:
Download CSV
Histogram 
91.2
99.2
108.2
101.5
106.9
104.4
77.9
60
99.5
95
105.6
102.5
93.3
97.3
127
111.7
96.4
133
72.2
95.8
124.1
127.6
110.7
104.6
112.7
115.3
139.4
119
97.4
154
81.5
88.8
127.7
105.1
114.9
106.4
104.5
121.6
141.4
99
126.7
134.1
81.3
88.6
132.7
132.9
134.4
103.7
119.7
115
132.9
108.5
113.9
142
97.7
92.2
128.8
134.9
128.2
114.8
117.9
119.1
120.7
129.1
117.6
129.2
100
87.3

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27907&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27907&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27907&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'Gwilym Jenkins' @ 72.249.127.135






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])
-150.0216337706743795
-14-0.246840176320931
-130.0454072794191193
-120.568472617528632
-11-0.095589422652575
-10-0.258344701827382
-90.0524827586460621
-80.0216444571163734
-70.113709089373134
-60.312089401783199
-50.173622262632162
-4-0.0492412957976012
-30.145825698955719
-2-0.11037433875871
-10.188756961136676
00.835660246645692
1-0.0350963914367127
2-0.163378597109044
30.148115027189430
40.05567313215847
50.178590810698426
60.291097425040208
70.170414180730866
80.00508537136905992
90.0896862748142333
10-0.160492710432634
110.147123797952095
120.63129748245142
13-0.0564813836104345
14-0.150282933145620
150.102898655282781

\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
-15 & 0.0216337706743795 \tabularnewline
-14 & -0.246840176320931 \tabularnewline
-13 & 0.0454072794191193 \tabularnewline
-12 & 0.568472617528632 \tabularnewline
-11 & -0.095589422652575 \tabularnewline
-10 & -0.258344701827382 \tabularnewline
-9 & 0.0524827586460621 \tabularnewline
-8 & 0.0216444571163734 \tabularnewline
-7 & 0.113709089373134 \tabularnewline
-6 & 0.312089401783199 \tabularnewline
-5 & 0.173622262632162 \tabularnewline
-4 & -0.0492412957976012 \tabularnewline
-3 & 0.145825698955719 \tabularnewline
-2 & -0.11037433875871 \tabularnewline
-1 & 0.188756961136676 \tabularnewline
0 & 0.835660246645692 \tabularnewline
1 & -0.0350963914367127 \tabularnewline
2 & -0.163378597109044 \tabularnewline
3 & 0.148115027189430 \tabularnewline
4 & 0.05567313215847 \tabularnewline
5 & 0.178590810698426 \tabularnewline
6 & 0.291097425040208 \tabularnewline
7 & 0.170414180730866 \tabularnewline
8 & 0.00508537136905992 \tabularnewline
9 & 0.0896862748142333 \tabularnewline
10 & -0.160492710432634 \tabularnewline
11 & 0.147123797952095 \tabularnewline
12 & 0.63129748245142 \tabularnewline
13 & -0.0564813836104345 \tabularnewline
14 & -0.150282933145620 \tabularnewline
15 & 0.102898655282781 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27907&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]-15[/C][C]0.0216337706743795[/C][/ROW]
[ROW][C]-14[/C][C]-0.246840176320931[/C][/ROW]
[ROW][C]-13[/C][C]0.0454072794191193[/C][/ROW]
[ROW][C]-12[/C][C]0.568472617528632[/C][/ROW]
[ROW][C]-11[/C][C]-0.095589422652575[/C][/ROW]
[ROW][C]-10[/C][C]-0.258344701827382[/C][/ROW]
[ROW][C]-9[/C][C]0.0524827586460621[/C][/ROW]
[ROW][C]-8[/C][C]0.0216444571163734[/C][/ROW]
[ROW][C]-7[/C][C]0.113709089373134[/C][/ROW]
[ROW][C]-6[/C][C]0.312089401783199[/C][/ROW]
[ROW][C]-5[/C][C]0.173622262632162[/C][/ROW]
[ROW][C]-4[/C][C]-0.0492412957976012[/C][/ROW]
[ROW][C]-3[/C][C]0.145825698955719[/C][/ROW]
[ROW][C]-2[/C][C]-0.11037433875871[/C][/ROW]
[ROW][C]-1[/C][C]0.188756961136676[/C][/ROW]
[ROW][C]0[/C][C]0.835660246645692[/C][/ROW]
[ROW][C]1[/C][C]-0.0350963914367127[/C][/ROW]
[ROW][C]2[/C][C]-0.163378597109044[/C][/ROW]
[ROW][C]3[/C][C]0.148115027189430[/C][/ROW]
[ROW][C]4[/C][C]0.05567313215847[/C][/ROW]
[ROW][C]5[/C][C]0.178590810698426[/C][/ROW]
[ROW][C]6[/C][C]0.291097425040208[/C][/ROW]
[ROW][C]7[/C][C]0.170414180730866[/C][/ROW]
[ROW][C]8[/C][C]0.00508537136905992[/C][/ROW]
[ROW][C]9[/C][C]0.0896862748142333[/C][/ROW]
[ROW][C]10[/C][C]-0.160492710432634[/C][/ROW]
[ROW][C]11[/C][C]0.147123797952095[/C][/ROW]
[ROW][C]12[/C][C]0.63129748245142[/C][/ROW]
[ROW][C]13[/C][C]-0.0564813836104345[/C][/ROW]
[ROW][C]14[/C][C]-0.150282933145620[/C][/ROW]
[ROW][C]15[/C][C]0.102898655282781[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27907&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27907&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])
-150.0216337706743795
-14-0.246840176320931
-130.0454072794191193
-120.568472617528632
-11-0.095589422652575
-10-0.258344701827382
-90.0524827586460621
-80.0216444571163734
-70.113709089373134
-60.312089401783199
-50.173622262632162
-4-0.0492412957976012
-30.145825698955719
-2-0.11037433875871
-10.188756961136676
00.835660246645692
1-0.0350963914367127
2-0.163378597109044
30.148115027189430
40.05567313215847
50.178590810698426
60.291097425040208
70.170414180730866
80.00508537136905992
90.0896862748142333
10-0.160492710432634
110.147123797952095
120.63129748245142
13-0.0564813836104345
14-0.150282933145620
150.102898655282781


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')