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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, 29 Dec 2010 21:35:43 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t1293658402lnlteu3qikeuv6o.htm/, Retrieved Fri, 03 May 2024 09:34:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117130, Retrieved Fri, 03 May 2024 09:34:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

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] [2010-12-26 18:13:52] [c4f608d390ad7371b1365a9b84541edb]
-       [Cross Correlation Function] [] [2010-12-29 20:21:54] [a2638725f7f7c6bd63902ba17eba666b]
-           [Cross Correlation Function] [cross correlation] [2010-12-29 21:35:43] [46438e6f3f2a44df4f74f36960f4a09a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
16198.90
16554.20
19554.20
15903.80
18003.80
18329.60
16260.70
14851.90
18174.10
18406.60
18466.50
16016.50
17428.50
17167.20
19630.00
17183.60
18344.70
19301.40
18147.50
16192.90
18374.40
20515.20
18957.20
16471.50
18746.80
19009.50
19211.20
20547.70
19325.80
20605.50
20056.90
16141.40
20359.80
19711.60
15638.60
14384.50
13721.40
14134.30
15021.70
14212.60
13635.00
15446.90
14762.10
12521.00
16236.80
16065.00
16032.10
15794.30
15160.00
15692.10
18908.90
17424.50
17014.20
19790.40
17681.20
16006.90
19601.70
Dataseries Y:
Download CSV
Histogram 
16896.20
16698.00
19691.60
15930.70
17444.60
17699.40
15189.80
15672.70
17180.80
17664.90
17862.90
16162.30
17463.60
16772.10
19106.90
16721.30
18161.30
18509.90
17802.70
16409.90
17967.70
20286.60
19537.30
18021.90
20194.30
19049.60
20244.70
21473.30
19673.60
21053.20
20159.50
18203.60
21289.50
20432.30
17180.40
15816.80
15076.60
14531.60
15761.30
14345.50
13916.80
15496.80
14285.60
13597.30
16263.10
16773.30
15986.90
16842.60
16014.60
15878.60
18664.90
17690.50
17107.60
19165.70
17203.60
16579.00
18885.10

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117130&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117130&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117130&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 time2 seconds
R Server'George Udny Yule' @ 72.249.76.132






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series1
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])
-13-0.0639956580936962
-120.259663333626318
-11-0.0203454414914486
-10-0.0502958476583632
-90.328514944454435
-8-0.289133384759825
-70.171033587190889
-60.100349570679663
-5-0.240545059929103
-40.0823603252388359
-3-0.00338875621070821
-2-0.243448740480947
-10.240414958732262
0-0.443911542979687
10.0388760140488733
20.0535722643929428
3-0.128267822235212
4-0.0485254069893682
50.0797676751214759
6-0.106541435976749
70.0538747180593616
80.046248557544372
9-0.0343532030005636
10-0.0297839996668698
110.181010872279776
12-0.244432150291668
130.198488924211172

\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 & 1 \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
-13 & -0.0639956580936962 \tabularnewline
-12 & 0.259663333626318 \tabularnewline
-11 & -0.0203454414914486 \tabularnewline
-10 & -0.0502958476583632 \tabularnewline
-9 & 0.328514944454435 \tabularnewline
-8 & -0.289133384759825 \tabularnewline
-7 & 0.171033587190889 \tabularnewline
-6 & 0.100349570679663 \tabularnewline
-5 & -0.240545059929103 \tabularnewline
-4 & 0.0823603252388359 \tabularnewline
-3 & -0.00338875621070821 \tabularnewline
-2 & -0.243448740480947 \tabularnewline
-1 & 0.240414958732262 \tabularnewline
0 & -0.443911542979687 \tabularnewline
1 & 0.0388760140488733 \tabularnewline
2 & 0.0535722643929428 \tabularnewline
3 & -0.128267822235212 \tabularnewline
4 & -0.0485254069893682 \tabularnewline
5 & 0.0797676751214759 \tabularnewline
6 & -0.106541435976749 \tabularnewline
7 & 0.0538747180593616 \tabularnewline
8 & 0.046248557544372 \tabularnewline
9 & -0.0343532030005636 \tabularnewline
10 & -0.0297839996668698 \tabularnewline
11 & 0.181010872279776 \tabularnewline
12 & -0.244432150291668 \tabularnewline
13 & 0.198488924211172 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117130&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]1[/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]-13[/C][C]-0.0639956580936962[/C][/ROW]
[ROW][C]-12[/C][C]0.259663333626318[/C][/ROW]
[ROW][C]-11[/C][C]-0.0203454414914486[/C][/ROW]
[ROW][C]-10[/C][C]-0.0502958476583632[/C][/ROW]
[ROW][C]-9[/C][C]0.328514944454435[/C][/ROW]
[ROW][C]-8[/C][C]-0.289133384759825[/C][/ROW]
[ROW][C]-7[/C][C]0.171033587190889[/C][/ROW]
[ROW][C]-6[/C][C]0.100349570679663[/C][/ROW]
[ROW][C]-5[/C][C]-0.240545059929103[/C][/ROW]
[ROW][C]-4[/C][C]0.0823603252388359[/C][/ROW]
[ROW][C]-3[/C][C]-0.00338875621070821[/C][/ROW]
[ROW][C]-2[/C][C]-0.243448740480947[/C][/ROW]
[ROW][C]-1[/C][C]0.240414958732262[/C][/ROW]
[ROW][C]0[/C][C]-0.443911542979687[/C][/ROW]
[ROW][C]1[/C][C]0.0388760140488733[/C][/ROW]
[ROW][C]2[/C][C]0.0535722643929428[/C][/ROW]
[ROW][C]3[/C][C]-0.128267822235212[/C][/ROW]
[ROW][C]4[/C][C]-0.0485254069893682[/C][/ROW]
[ROW][C]5[/C][C]0.0797676751214759[/C][/ROW]
[ROW][C]6[/C][C]-0.106541435976749[/C][/ROW]
[ROW][C]7[/C][C]0.0538747180593616[/C][/ROW]
[ROW][C]8[/C][C]0.046248557544372[/C][/ROW]
[ROW][C]9[/C][C]-0.0343532030005636[/C][/ROW]
[ROW][C]10[/C][C]-0.0297839996668698[/C][/ROW]
[ROW][C]11[/C][C]0.181010872279776[/C][/ROW]
[ROW][C]12[/C][C]-0.244432150291668[/C][/ROW]
[ROW][C]13[/C][C]0.198488924211172[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117130&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117130&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 series1
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])
-13-0.0639956580936962
-120.259663333626318
-11-0.0203454414914486
-10-0.0502958476583632
-90.328514944454435
-8-0.289133384759825
-70.171033587190889
-60.100349570679663
-5-0.240545059929103
-40.0823603252388359
-3-0.00338875621070821
-2-0.243448740480947
-10.240414958732262
0-0.443911542979687
10.0388760140488733
20.0535722643929428
3-0.128267822235212
4-0.0485254069893682
50.0797676751214759
6-0.106541435976749
70.0538747180593616
80.046248557544372
9-0.0343532030005636
10-0.0297839996668698
110.181010872279776
12-0.244432150291668
130.198488924211172


Figures (Output of Computation)

Input Parameters & R Code

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