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Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationMon, 01 Dec 2008 13:48:24 -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/01/t1228164537350r4de3q14qopp.htm/, Retrieved Sun, 05 May 2024 11:33:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27385, Retrieved Sun, 05 May 2024 11:33:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97
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]
- RMPD  [Variance Reduction Matrix] [Q6 reproduce vari...] [2008-12-01 20:00:25] [4242609301e759e844b9196c1994e4ef]
- RM D    [Cross Correlation Function] [Q7 cross correlation] [2008-12-01 20:19:39] [4242609301e759e844b9196c1994e4ef]
-    D        [Cross Correlation Function] [Q9] [2008-12-01 20:48:24] [c040f376c7eef5bfe1cb52dcc7980437] [Current]
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Dataseries X:
113.5
121.2
130.4
115.2
117.9
110.7
107.6
124.3
115.1
112.5
127.9
117.4
119.3
130.4
126
125.4
130.5
115.9
108.7
124
119.4
118.6
131.3
111.1
124.8
132.3
126.7
131.7
130.9
122.1
113.2
133.6
119.2
129.4
131.4
117.1
130.5
132.3
140.8
137.5
128.6
126.7
120.8
139.3
128.6
131.3
136.3
128.8
133.2
136.3
151.1
145
134.4
135.7
128.7
129.2
138.6
132.7
132.5
135.2
Dataseries Y:
41.1
58
63
53.8
54.7
55.5
56.1
69.6
69.4
57.2
68
53.3
47.9
60.8
61.7
57.8
51.4
50.5
48.1
58.7
54
56.1
60.4
51.2
50.7
56.4
53.3
52.6
47.7
49.5
48.5
55.3
49.8
57.4
64.6
53
41.5
55.9
58.4
53.5
50.6
58.5
49.1
61.1
52.3
58.4
65.5
61.7
45.1
52.1
59.3
57.9
45
64.9
63.8
69.4
71.1
62.9
73.5
62.6




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=27385&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=27385&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27385&T=0

As an alternative you can also use a 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 series2
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 series-2
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-130.0942535617143938
-120.115658260348118
-11-0.0416714066227259
-100.053099320155962
-90.139332099683928
-80.129964964400441
-70.0262692905130147
-60.115184187579430
-50.0294011215577501
-40.209131984997979
-30.106714595470937
-20.103438914152498
-1-0.119057874310774
00.193409300689555
1-0.0220489574866620
2-0.209221800751730
30.331105652072423
40.0641514581438475
50.131477335469175
60.201477983114514
7-0.124176469142343
8-0.0227821164778549
90.226161538195657
10-0.0646056855308783
11-0.262472732206518
120.0590132664297176
13-0.0652895992797194

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 2 \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 & -2 \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 & 0.0942535617143938 \tabularnewline
-12 & 0.115658260348118 \tabularnewline
-11 & -0.0416714066227259 \tabularnewline
-10 & 0.053099320155962 \tabularnewline
-9 & 0.139332099683928 \tabularnewline
-8 & 0.129964964400441 \tabularnewline
-7 & 0.0262692905130147 \tabularnewline
-6 & 0.115184187579430 \tabularnewline
-5 & 0.0294011215577501 \tabularnewline
-4 & 0.209131984997979 \tabularnewline
-3 & 0.106714595470937 \tabularnewline
-2 & 0.103438914152498 \tabularnewline
-1 & -0.119057874310774 \tabularnewline
0 & 0.193409300689555 \tabularnewline
1 & -0.0220489574866620 \tabularnewline
2 & -0.209221800751730 \tabularnewline
3 & 0.331105652072423 \tabularnewline
4 & 0.0641514581438475 \tabularnewline
5 & 0.131477335469175 \tabularnewline
6 & 0.201477983114514 \tabularnewline
7 & -0.124176469142343 \tabularnewline
8 & -0.0227821164778549 \tabularnewline
9 & 0.226161538195657 \tabularnewline
10 & -0.0646056855308783 \tabularnewline
11 & -0.262472732206518 \tabularnewline
12 & 0.0590132664297176 \tabularnewline
13 & -0.0652895992797194 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27385&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]2[/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]-2[/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]0.0942535617143938[/C][/ROW]
[ROW][C]-12[/C][C]0.115658260348118[/C][/ROW]
[ROW][C]-11[/C][C]-0.0416714066227259[/C][/ROW]
[ROW][C]-10[/C][C]0.053099320155962[/C][/ROW]
[ROW][C]-9[/C][C]0.139332099683928[/C][/ROW]
[ROW][C]-8[/C][C]0.129964964400441[/C][/ROW]
[ROW][C]-7[/C][C]0.0262692905130147[/C][/ROW]
[ROW][C]-6[/C][C]0.115184187579430[/C][/ROW]
[ROW][C]-5[/C][C]0.0294011215577501[/C][/ROW]
[ROW][C]-4[/C][C]0.209131984997979[/C][/ROW]
[ROW][C]-3[/C][C]0.106714595470937[/C][/ROW]
[ROW][C]-2[/C][C]0.103438914152498[/C][/ROW]
[ROW][C]-1[/C][C]-0.119057874310774[/C][/ROW]
[ROW][C]0[/C][C]0.193409300689555[/C][/ROW]
[ROW][C]1[/C][C]-0.0220489574866620[/C][/ROW]
[ROW][C]2[/C][C]-0.209221800751730[/C][/ROW]
[ROW][C]3[/C][C]0.331105652072423[/C][/ROW]
[ROW][C]4[/C][C]0.0641514581438475[/C][/ROW]
[ROW][C]5[/C][C]0.131477335469175[/C][/ROW]
[ROW][C]6[/C][C]0.201477983114514[/C][/ROW]
[ROW][C]7[/C][C]-0.124176469142343[/C][/ROW]
[ROW][C]8[/C][C]-0.0227821164778549[/C][/ROW]
[ROW][C]9[/C][C]0.226161538195657[/C][/ROW]
[ROW][C]10[/C][C]-0.0646056855308783[/C][/ROW]
[ROW][C]11[/C][C]-0.262472732206518[/C][/ROW]
[ROW][C]12[/C][C]0.0590132664297176[/C][/ROW]
[ROW][C]13[/C][C]-0.0652895992797194[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27385&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27385&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series2
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 series-2
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-130.0942535617143938
-120.115658260348118
-11-0.0416714066227259
-100.053099320155962
-90.139332099683928
-80.129964964400441
-70.0262692905130147
-60.115184187579430
-50.0294011215577501
-40.209131984997979
-30.106714595470937
-20.103438914152498
-1-0.119057874310774
00.193409300689555
1-0.0220489574866620
2-0.209221800751730
30.331105652072423
40.0641514581438475
50.131477335469175
60.201477983114514
7-0.124176469142343
8-0.0227821164778549
90.226161538195657
10-0.0646056855308783
11-0.262472732206518
120.0590132664297176
13-0.0652895992797194



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 2.0 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = -2.0 ; 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')