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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 08:15:22 -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/17/t12295270655575x50zw8hlmo2.htm/, Retrieved Sat, 18 May 2024 10:08:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=34396, Retrieved Sat, 18 May 2024 10:08:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169

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...] [2008-12-17 14:49:46] [d811f621c525a990f9b60f1ae1e2e8fd]
-   PD    [Cross Correlation Function] [stationair] [2008-12-17 15:15:22] [f4914427e726625a358be9269a8b7d03] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
17409
11514
31514
27071
29462
26105
22397
23843
21705
18089
20764
25316
17704
15548
28029
29383
36438
32034
22679
24319
18004
17537
20366
22782
19169
13807
29743
25591
29096
26482
22405
27044
17970
18730
19684
19785
18479
10698
31956
29506
34506
27165
26736
23691
18157
17328
18205
20995
17382
9367
Dataseries Y:
Download CSV
Histogram 
117.09
116.77
119.39
122.49
124.08
118.29
112.94
113.79
114.43
118.70
120.36
118.27
118.34
117.82
117.65
118.18
121.02
124.78
131.16
130.14
131.75
134.73
135.35
140.32
136.35
131.60
128.90
133.89
138.25
146.23
144.76
149.30
156.80
159.08
165.12
163.14
153.43
151.01
154.72
154.58
155.63
161.67
163.51
162.91
164.80
164.98
154.54
148.60
149.19
150.61

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=34396&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=34396&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34396&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 series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-120.134506462828295
-11-0.061845060093562
-100.0183948425012255
-9-0.0825734480417203
-8-0.0556696506784992
-7-0.0306017893260736
-6-0.228915686807167
-5-0.127130995945525
-40.0203421040667182
-30.135379057909341
-20.115975574341801
-1-0.007195098188219
0-0.190904090446446
1-0.104648557422963
20.192686650215182
3-0.0749188905474538
40.0492579430101938
5-0.08305233361543
60.00428088712188356
7-0.0207474449770875
8-0.0236517172934824
9-0.0848986229540962
100.0935481079397172
110.267571816120977
120.232442332851115

\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 & 1 \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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-12 & 0.134506462828295 \tabularnewline
-11 & -0.061845060093562 \tabularnewline
-10 & 0.0183948425012255 \tabularnewline
-9 & -0.0825734480417203 \tabularnewline
-8 & -0.0556696506784992 \tabularnewline
-7 & -0.0306017893260736 \tabularnewline
-6 & -0.228915686807167 \tabularnewline
-5 & -0.127130995945525 \tabularnewline
-4 & 0.0203421040667182 \tabularnewline
-3 & 0.135379057909341 \tabularnewline
-2 & 0.115975574341801 \tabularnewline
-1 & -0.007195098188219 \tabularnewline
0 & -0.190904090446446 \tabularnewline
1 & -0.104648557422963 \tabularnewline
2 & 0.192686650215182 \tabularnewline
3 & -0.0749188905474538 \tabularnewline
4 & 0.0492579430101938 \tabularnewline
5 & -0.08305233361543 \tabularnewline
6 & 0.00428088712188356 \tabularnewline
7 & -0.0207474449770875 \tabularnewline
8 & -0.0236517172934824 \tabularnewline
9 & -0.0848986229540962 \tabularnewline
10 & 0.0935481079397172 \tabularnewline
11 & 0.267571816120977 \tabularnewline
12 & 0.232442332851115 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34396&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]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]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]1[/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]0.134506462828295[/C][/ROW]
[ROW][C]-11[/C][C]-0.061845060093562[/C][/ROW]
[ROW][C]-10[/C][C]0.0183948425012255[/C][/ROW]
[ROW][C]-9[/C][C]-0.0825734480417203[/C][/ROW]
[ROW][C]-8[/C][C]-0.0556696506784992[/C][/ROW]
[ROW][C]-7[/C][C]-0.0306017893260736[/C][/ROW]
[ROW][C]-6[/C][C]-0.228915686807167[/C][/ROW]
[ROW][C]-5[/C][C]-0.127130995945525[/C][/ROW]
[ROW][C]-4[/C][C]0.0203421040667182[/C][/ROW]
[ROW][C]-3[/C][C]0.135379057909341[/C][/ROW]
[ROW][C]-2[/C][C]0.115975574341801[/C][/ROW]
[ROW][C]-1[/C][C]-0.007195098188219[/C][/ROW]
[ROW][C]0[/C][C]-0.190904090446446[/C][/ROW]
[ROW][C]1[/C][C]-0.104648557422963[/C][/ROW]
[ROW][C]2[/C][C]0.192686650215182[/C][/ROW]
[ROW][C]3[/C][C]-0.0749188905474538[/C][/ROW]
[ROW][C]4[/C][C]0.0492579430101938[/C][/ROW]
[ROW][C]5[/C][C]-0.08305233361543[/C][/ROW]
[ROW][C]6[/C][C]0.00428088712188356[/C][/ROW]
[ROW][C]7[/C][C]-0.0207474449770875[/C][/ROW]
[ROW][C]8[/C][C]-0.0236517172934824[/C][/ROW]
[ROW][C]9[/C][C]-0.0848986229540962[/C][/ROW]
[ROW][C]10[/C][C]0.0935481079397172[/C][/ROW]
[ROW][C]11[/C][C]0.267571816120977[/C][/ROW]
[ROW][C]12[/C][C]0.232442332851115[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34396&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34396&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 series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-120.134506462828295
-11-0.061845060093562
-100.0183948425012255
-9-0.0825734480417203
-8-0.0556696506784992
-7-0.0306017893260736
-6-0.228915686807167
-5-0.127130995945525
-40.0203421040667182
-30.135379057909341
-20.115975574341801
-1-0.007195098188219
0-0.190904090446446
1-0.104648557422963
20.192686650215182
3-0.0749188905474538
40.0492579430101938
5-0.08305233361543
60.00428088712188356
7-0.0207474449770875
8-0.0236517172934824
9-0.0848986229540962
100.0935481079397172
110.267571816120977
120.232442332851115


Figures (Output of Computation)

Input Parameters & R Code

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