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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationThu, 22 Nov 2007 07:00:03 -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/2007/Nov/22/t1195739564naqvo5gjfkpvdew.htm/, Retrieved Fri, 03 May 2024 00:29:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=5999, Retrieved Fri, 03 May 2024 00:29:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS7, Q4, Cross correlation function, marleen
Estimated Impact218

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] [WS7 Q4 Cross corr...] [2007-11-22 14:00:03] [87b6915e48e03972eaa4a0940182012f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.5
126.6
93.9
89.8
93.4
101.5
110.4
105.9
108.4
113.9
86.1
69.4
101.2
100.5
98
106.6
90.1
96.9
125.9
112
100
123.9
79.8
83.4
113.6
112.9
104
109.9
99
106.3
128.9
111.1
102.9
130
87
87.5
117.6
103.4
110.8
112.6
102.5
112.4
135.6
105.1
127.7
137
91
90.5
122.4
123.3
124.3
120
118.1
119
142.7
123.6
129.6
146.9
108.7
99.4
Dataseries Y:
Download CSV
Histogram 
108.4
117
103.8
100.8
110.6
104
112.6
107.3
98.9
109.8
104.9
102.2
123.9
124.9
112.7
121.9
100.6
104.3
120.4
107.5
102.9
125.6
107.5
108.8
128.4
121.1
119.5
128.7
108.7
105.5
119.8
111.3
110.6
120.1
97.5
107.7
127.3
117.2
119.8
116.2
111
112.4
130.6
109.1
118.8
123.9
101.6
112.8
128
129.6
125.8
119.5
115.7
113.6
129.7
112
116.8
126.3
112.9
115.9

Tables (Output of Computation)





Summary of compuational 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 compuational 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=5999&T=0

[TABLE]
[ROW][C]Summary of compuational 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=5999&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5999&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 compuational 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 series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t-k])
-130.0725976639662733
-120.33010932651514
-11-0.232461063258519
-100.125460843558205
-9-0.0629900764061857
-8-0.273065990872567
-70.136822063147694
-6-0.0204398360065237
-5-0.232794651501339
-40.083608944176853
-3-0.112736952166615
-2-0.304928888663417
-10.0131650442719975
0-0.392872063319585
1-0.137386344453439
20.169049822978702
3-0.182724133796478
4-0.00544034562901674
50.156121825188664
6-0.152697465696146
70.0225435873801688
80.0902720642698751
9-0.157098654527660
100.0933765980471474
110.140322009687457
12-0.119227297504527
130.0841418745572608

\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 & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t-k]) \tabularnewline
-13 & 0.0725976639662733 \tabularnewline
-12 & 0.33010932651514 \tabularnewline
-11 & -0.232461063258519 \tabularnewline
-10 & 0.125460843558205 \tabularnewline
-9 & -0.0629900764061857 \tabularnewline
-8 & -0.273065990872567 \tabularnewline
-7 & 0.136822063147694 \tabularnewline
-6 & -0.0204398360065237 \tabularnewline
-5 & -0.232794651501339 \tabularnewline
-4 & 0.083608944176853 \tabularnewline
-3 & -0.112736952166615 \tabularnewline
-2 & -0.304928888663417 \tabularnewline
-1 & 0.0131650442719975 \tabularnewline
0 & -0.392872063319585 \tabularnewline
1 & -0.137386344453439 \tabularnewline
2 & 0.169049822978702 \tabularnewline
3 & -0.182724133796478 \tabularnewline
4 & -0.00544034562901674 \tabularnewline
5 & 0.156121825188664 \tabularnewline
6 & -0.152697465696146 \tabularnewline
7 & 0.0225435873801688 \tabularnewline
8 & 0.0902720642698751 \tabularnewline
9 & -0.157098654527660 \tabularnewline
10 & 0.0933765980471474 \tabularnewline
11 & 0.140322009687457 \tabularnewline
12 & -0.119227297504527 \tabularnewline
13 & 0.0841418745572608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5999&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]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]-13[/C][C]0.0725976639662733[/C][/ROW]
[ROW][C]-12[/C][C]0.33010932651514[/C][/ROW]
[ROW][C]-11[/C][C]-0.232461063258519[/C][/ROW]
[ROW][C]-10[/C][C]0.125460843558205[/C][/ROW]
[ROW][C]-9[/C][C]-0.0629900764061857[/C][/ROW]
[ROW][C]-8[/C][C]-0.273065990872567[/C][/ROW]
[ROW][C]-7[/C][C]0.136822063147694[/C][/ROW]
[ROW][C]-6[/C][C]-0.0204398360065237[/C][/ROW]
[ROW][C]-5[/C][C]-0.232794651501339[/C][/ROW]
[ROW][C]-4[/C][C]0.083608944176853[/C][/ROW]
[ROW][C]-3[/C][C]-0.112736952166615[/C][/ROW]
[ROW][C]-2[/C][C]-0.304928888663417[/C][/ROW]
[ROW][C]-1[/C][C]0.0131650442719975[/C][/ROW]
[ROW][C]0[/C][C]-0.392872063319585[/C][/ROW]
[ROW][C]1[/C][C]-0.137386344453439[/C][/ROW]
[ROW][C]2[/C][C]0.169049822978702[/C][/ROW]
[ROW][C]3[/C][C]-0.182724133796478[/C][/ROW]
[ROW][C]4[/C][C]-0.00544034562901674[/C][/ROW]
[ROW][C]5[/C][C]0.156121825188664[/C][/ROW]
[ROW][C]6[/C][C]-0.152697465696146[/C][/ROW]
[ROW][C]7[/C][C]0.0225435873801688[/C][/ROW]
[ROW][C]8[/C][C]0.0902720642698751[/C][/ROW]
[ROW][C]9[/C][C]-0.157098654527660[/C][/ROW]
[ROW][C]10[/C][C]0.0933765980471474[/C][/ROW]
[ROW][C]11[/C][C]0.140322009687457[/C][/ROW]
[ROW][C]12[/C][C]-0.119227297504527[/C][/ROW]
[ROW][C]13[/C][C]0.0841418745572608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5999&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5999&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 series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t-k])
-130.0725976639662733
-120.33010932651514
-11-0.232461063258519
-100.125460843558205
-9-0.0629900764061857
-8-0.273065990872567
-70.136822063147694
-6-0.0204398360065237
-5-0.232794651501339
-40.083608944176853
-3-0.112736952166615
-2-0.304928888663417
-10.0131650442719975
0-0.392872063319585
1-0.137386344453439
20.169049822978702
3-0.182724133796478
4-0.00544034562901674
50.156121825188664
6-0.152697465696146
70.0225435873801688
80.0902720642698751
9-0.157098654527660
100.0933765980471474
110.140322009687457
12-0.119227297504527
130.0841418745572608


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 1 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; 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) x <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',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')