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Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationThu, 18 Jul 2019 23:00:19 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Jul/18/t15634836599t5ugnqb523piye.htm/, Retrieved Wed, 12 Aug 2020 16:19:37 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 12 Aug 2020 16:19:37 +0200
QR Codes:

Original text written by user:
IsPrivate?This computation is private
User-defined keywords
Estimated Impact0
Dataseries X:
7
31
16
13
11
23
8
13
15
12
14
13
15
16
10
16
20
10
14
15
11
17
29
13
12
3
2
23
13
22
9
7
18
24
17
24
13
16
18
9
17
9
14
17
11
11
12
9
9
17
12
11
Dataseries Y:
6
5
12
20
13
11
5
12
18
10
5
15
12
5
5
9
17
10
11
5
6
17
16
14
23
4
2
6
10
15
10
2
10
8
21
17
10
14
24
7
12
7
9
19
7
8
8
15
8
7
7
12




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center







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 series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.0354825086594146
-13-0.0378904480146166
-12-0.233216460972037
-110.0703530378403039
-10-0.0368602313045366
-9-0.20560357786813
-8-0.0054985894854423
-70.0936882151420579
-60.14410117791323
-50.100300053729139
-4-0.301155408909616
-30.0702546290799381
-20.320572687439304
-10.230396434932803
00.335659980512895
1-0.14294179970517
2-0.28017589130912
3-0.106425999061914
4-0.118890083617002
50.147646968560162
60.1351090774348
7-0.216975940237909
8-0.0971909519563301
9-0.164456414940155
10-0.121614776169631
110.291637436617981
12-0.00227570258346045
130.0276866896741817
140.0391236327929513

\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 & 0 \tabularnewline
Seasonal Period (s) & 1 \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 & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & 0.0354825086594146 \tabularnewline
-13 & -0.0378904480146166 \tabularnewline
-12 & -0.233216460972037 \tabularnewline
-11 & 0.0703530378403039 \tabularnewline
-10 & -0.0368602313045366 \tabularnewline
-9 & -0.20560357786813 \tabularnewline
-8 & -0.0054985894854423 \tabularnewline
-7 & 0.0936882151420579 \tabularnewline
-6 & 0.14410117791323 \tabularnewline
-5 & 0.100300053729139 \tabularnewline
-4 & -0.301155408909616 \tabularnewline
-3 & 0.0702546290799381 \tabularnewline
-2 & 0.320572687439304 \tabularnewline
-1 & 0.230396434932803 \tabularnewline
0 & 0.335659980512895 \tabularnewline
1 & -0.14294179970517 \tabularnewline
2 & -0.28017589130912 \tabularnewline
3 & -0.106425999061914 \tabularnewline
4 & -0.118890083617002 \tabularnewline
5 & 0.147646968560162 \tabularnewline
6 & 0.1351090774348 \tabularnewline
7 & -0.216975940237909 \tabularnewline
8 & -0.0971909519563301 \tabularnewline
9 & -0.164456414940155 \tabularnewline
10 & -0.121614776169631 \tabularnewline
11 & 0.291637436617981 \tabularnewline
12 & -0.00227570258346045 \tabularnewline
13 & 0.0276866896741817 \tabularnewline
14 & 0.0391236327929513 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]1[/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]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-14[/C][C]0.0354825086594146[/C][/ROW]
[ROW][C]-13[/C][C]-0.0378904480146166[/C][/ROW]
[ROW][C]-12[/C][C]-0.233216460972037[/C][/ROW]
[ROW][C]-11[/C][C]0.0703530378403039[/C][/ROW]
[ROW][C]-10[/C][C]-0.0368602313045366[/C][/ROW]
[ROW][C]-9[/C][C]-0.20560357786813[/C][/ROW]
[ROW][C]-8[/C][C]-0.0054985894854423[/C][/ROW]
[ROW][C]-7[/C][C]0.0936882151420579[/C][/ROW]
[ROW][C]-6[/C][C]0.14410117791323[/C][/ROW]
[ROW][C]-5[/C][C]0.100300053729139[/C][/ROW]
[ROW][C]-4[/C][C]-0.301155408909616[/C][/ROW]
[ROW][C]-3[/C][C]0.0702546290799381[/C][/ROW]
[ROW][C]-2[/C][C]0.320572687439304[/C][/ROW]
[ROW][C]-1[/C][C]0.230396434932803[/C][/ROW]
[ROW][C]0[/C][C]0.335659980512895[/C][/ROW]
[ROW][C]1[/C][C]-0.14294179970517[/C][/ROW]
[ROW][C]2[/C][C]-0.28017589130912[/C][/ROW]
[ROW][C]3[/C][C]-0.106425999061914[/C][/ROW]
[ROW][C]4[/C][C]-0.118890083617002[/C][/ROW]
[ROW][C]5[/C][C]0.147646968560162[/C][/ROW]
[ROW][C]6[/C][C]0.1351090774348[/C][/ROW]
[ROW][C]7[/C][C]-0.216975940237909[/C][/ROW]
[ROW][C]8[/C][C]-0.0971909519563301[/C][/ROW]
[ROW][C]9[/C][C]-0.164456414940155[/C][/ROW]
[ROW][C]10[/C][C]-0.121614776169631[/C][/ROW]
[ROW][C]11[/C][C]0.291637436617981[/C][/ROW]
[ROW][C]12[/C][C]-0.00227570258346045[/C][/ROW]
[ROW][C]13[/C][C]0.0276866896741817[/C][/ROW]
[ROW][C]14[/C][C]0.0391236327929513[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.0354825086594146
-13-0.0378904480146166
-12-0.233216460972037
-110.0703530378403039
-10-0.0368602313045366
-9-0.20560357786813
-8-0.0054985894854423
-70.0936882151420579
-60.14410117791323
-50.100300053729139
-4-0.301155408909616
-30.0702546290799381
-20.320572687439304
-10.230396434932803
00.335659980512895
1-0.14294179970517
2-0.28017589130912
3-0.106425999061914
4-0.118890083617002
50.147646968560162
60.1351090774348
7-0.216975940237909
8-0.0971909519563301
9-0.164456414940155
10-0.121614776169631
110.291637436617981
12-0.00227570258346045
130.0276866896741817
140.0391236327929513



Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = na.fail ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = na.fail ;
R code (references can be found in the software module):
par8 <- 'na.fail'
par7 <- '0'
par6 <- '0'
par5 <- '1'
par4 <- '1'
par3 <- '0'
par2 <- '0'
par1 <- '1'
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 (par8=='na.fail') par8 <- na.fail else par8 <- na.pass
ccf <- function (x, y, lag.max = NULL, type = c('correlation', 'covariance'), plot = TRUE, na.action = na.fail, ...) {
type <- match.arg(type)
if (is.matrix(x) || is.matrix(y))
stop('univariate time series only')
X <- na.action(ts.intersect(as.ts(x), as.ts(y)))
colnames(X) <- c(deparse(substitute(x))[1L], deparse(substitute(y))[1L])
acf.out <- acf(X, lag.max = lag.max, plot = FALSE, type = type, na.action=na.action)
lag <- c(rev(acf.out$lag[-1, 2, 1]), acf.out$lag[, 1, 2])
y <- c(rev(acf.out$acf[-1, 2, 1]), acf.out$acf[, 1, 2])
acf.out$acf <- array(y, dim = c(length(y), 1L, 1L))
acf.out$lag <- array(lag, dim = c(length(y), 1L, 1L))
acf.out$snames <- paste(acf.out$snames, collapse = ' & ')
if (plot) {
plot(acf.out, ...)
return(invisible(acf.out))
}
else return(acf.out)
}
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)
print(x)
print(y)
bitmap(file='test1.png')
(r <- ccf(x,y,na.action=par8,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')