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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 computationFri, 05 Dec 2008 10:18:17 -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/05/t1228497640kbrzes9t627um45.htm/, Retrieved Thu, 16 May 2024 15:23:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29355, Retrieved Thu, 16 May 2024 15:23:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact235

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
-    D  [Univariate Data Series] [Non Stationary Ti...] [2008-11-30 09:45:17] [b82ef11dce0545f3fd4676ec3ebed828]
- RMP     [Standard Deviation-Mean Plot] [Non Stationary Ti...] [2008-11-30 10:32:49] [b82ef11dce0545f3fd4676ec3ebed828]
-   PD      [Standard Deviation-Mean Plot] [Non Stationary Ti...] [2008-11-30 13:22:54] [b82ef11dce0545f3fd4676ec3ebed828]
F RM D        [Cross Correlation Function] [Non Stationary Ti...] [2008-11-30 13:40:30] [b82ef11dce0545f3fd4676ec3ebed828]
-   P             [Cross Correlation Function] [Non Stationary Ti...] [2008-12-05 17:18:17] [4b953869c7238aca4b6e0cfb0c5cddd6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97.4
97.0
105.4
102.7
98.1
104.5
87.4
89.9
109.8
111.7
98.6
96.9
95.1
97.0
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
99.0
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102.0
106.0
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100.0
110.7
112.8
109.8
117.3
109.1
115.9
95.7
Dataseries Y:
Download CSV
Histogram 
104.2
103.2
112.7
106.4
102.6
110.6
95.2
89.0
112.5
116.8
107.2
113.6
101.8
102.6
122.7
110.3
110.5
121.6
100.3
100.7
123.4
127.1
124.1
131.2
111.6
114.2
130.1
125.9
119.0
133.8
107.5
113.5
134.4
126.8
135.6
139.9
129.8
131.0
153.1
134.1
144.1
155.9
123.3
128.1
144.3
153.0
149.9
150.9
141.0
138.9
157.4
142.9
151.7
161.0
138.5
135.9
151.5
164.0
159.1
157.0
142.1
144.8
152.1
154.6
148.7
157.7
146.7

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=29355&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=29355&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29355&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 series-0.3
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series0.5
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-14-0.235778306444663
-13-0.230253380889309
-12-0.244467710911105
-11-0.329854940088454
-10-0.379847853414333
-9-0.000465319261871513
-8-0.128254095348509
-7-0.114691930875737
-6-0.00506215365865071
-50.0117994566927144
-4-0.0239560119145054
-30.289083082584787
-20.126069271337667
-1-0.094247396014724
00.583604805581002
1-0.133254149841139
20.0858743335167844
30.0984898776372128
4-0.014028103167248
5-0.0206423261921723
60.113228448579180
7-0.103854310850484
80.00373249282143615
90.202636676744424
10-0.0971517418490357
110.0411555400139146
120.108265336508705
130.177298227285291
140.150954708375570

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & -0.3 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 0.5 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & -0.235778306444663 \tabularnewline
-13 & -0.230253380889309 \tabularnewline
-12 & -0.244467710911105 \tabularnewline
-11 & -0.329854940088454 \tabularnewline
-10 & -0.379847853414333 \tabularnewline
-9 & -0.000465319261871513 \tabularnewline
-8 & -0.128254095348509 \tabularnewline
-7 & -0.114691930875737 \tabularnewline
-6 & -0.00506215365865071 \tabularnewline
-5 & 0.0117994566927144 \tabularnewline
-4 & -0.0239560119145054 \tabularnewline
-3 & 0.289083082584787 \tabularnewline
-2 & 0.126069271337667 \tabularnewline
-1 & -0.094247396014724 \tabularnewline
0 & 0.583604805581002 \tabularnewline
1 & -0.133254149841139 \tabularnewline
2 & 0.0858743335167844 \tabularnewline
3 & 0.0984898776372128 \tabularnewline
4 & -0.014028103167248 \tabularnewline
5 & -0.0206423261921723 \tabularnewline
6 & 0.113228448579180 \tabularnewline
7 & -0.103854310850484 \tabularnewline
8 & 0.00373249282143615 \tabularnewline
9 & 0.202636676744424 \tabularnewline
10 & -0.0971517418490357 \tabularnewline
11 & 0.0411555400139146 \tabularnewline
12 & 0.108265336508705 \tabularnewline
13 & 0.177298227285291 \tabularnewline
14 & 0.150954708375570 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29355&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]-0.3[/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]1[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]0.5[/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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-14[/C][C]-0.235778306444663[/C][/ROW]
[ROW][C]-13[/C][C]-0.230253380889309[/C][/ROW]
[ROW][C]-12[/C][C]-0.244467710911105[/C][/ROW]
[ROW][C]-11[/C][C]-0.329854940088454[/C][/ROW]
[ROW][C]-10[/C][C]-0.379847853414333[/C][/ROW]
[ROW][C]-9[/C][C]-0.000465319261871513[/C][/ROW]
[ROW][C]-8[/C][C]-0.128254095348509[/C][/ROW]
[ROW][C]-7[/C][C]-0.114691930875737[/C][/ROW]
[ROW][C]-6[/C][C]-0.00506215365865071[/C][/ROW]
[ROW][C]-5[/C][C]0.0117994566927144[/C][/ROW]
[ROW][C]-4[/C][C]-0.0239560119145054[/C][/ROW]
[ROW][C]-3[/C][C]0.289083082584787[/C][/ROW]
[ROW][C]-2[/C][C]0.126069271337667[/C][/ROW]
[ROW][C]-1[/C][C]-0.094247396014724[/C][/ROW]
[ROW][C]0[/C][C]0.583604805581002[/C][/ROW]
[ROW][C]1[/C][C]-0.133254149841139[/C][/ROW]
[ROW][C]2[/C][C]0.0858743335167844[/C][/ROW]
[ROW][C]3[/C][C]0.0984898776372128[/C][/ROW]
[ROW][C]4[/C][C]-0.014028103167248[/C][/ROW]
[ROW][C]5[/C][C]-0.0206423261921723[/C][/ROW]
[ROW][C]6[/C][C]0.113228448579180[/C][/ROW]
[ROW][C]7[/C][C]-0.103854310850484[/C][/ROW]
[ROW][C]8[/C][C]0.00373249282143615[/C][/ROW]
[ROW][C]9[/C][C]0.202636676744424[/C][/ROW]
[ROW][C]10[/C][C]-0.0971517418490357[/C][/ROW]
[ROW][C]11[/C][C]0.0411555400139146[/C][/ROW]
[ROW][C]12[/C][C]0.108265336508705[/C][/ROW]
[ROW][C]13[/C][C]0.177298227285291[/C][/ROW]
[ROW][C]14[/C][C]0.150954708375570[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29355&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29355&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-0.3
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series0.5
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-14-0.235778306444663
-13-0.230253380889309
-12-0.244467710911105
-11-0.329854940088454
-10-0.379847853414333
-9-0.000465319261871513
-8-0.128254095348509
-7-0.114691930875737
-6-0.00506215365865071
-50.0117994566927144
-4-0.0239560119145054
-30.289083082584787
-20.126069271337667
-1-0.094247396014724
00.583604805581002
1-0.133254149841139
20.0858743335167844
30.0984898776372128
4-0.014028103167248
5-0.0206423261921723
60.113228448579180
7-0.103854310850484
80.00373249282143615
90.202636676744424
10-0.0971517418490357
110.0411555400139146
120.108265336508705
130.177298227285291
140.150954708375570


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = -0.3 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 0.5 ; par6 = 0 ; par7 = 1 ;
Parameters (R input):
par1 = -0.3 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 0.5 ; par6 = 0 ; par7 = 1 ;
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')