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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 17:47:26 +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/t1563464912ysdruxvvynsz6v6.htm/, Retrieved Wed, 12 Aug 2020 17:37:24 +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 17:37:24 +0200
QR Codes:

Original text written by user:
IsPrivate?This computation is private
User-defined keywords
Estimated Impact0
Dataseries X:
16
35
30
33
35
26
19
21
40
27
16
26
40
12
15
28
42
28
18
14
16
34
30
33
66
16
3
12
23
33
27
4
17
18
42
23
19
26
47
14
21
17
21
42
29
22
17
40
20
18
21
24
Dataseries Y:
1
12
6
25
13
15
9
10
8
7
6
12
10
14
7
4
10
27
3
9
7
6
8
9
6
5
3
7
5
11
15
1
3
11
13
8
13
5
7
9
7
3
9
5
8
2
11
11
5
8
9
22




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])
-140.0619672890229602
-130.190088949592502
-120.135043946132381
-11-0.0910910720841036
-10-0.0682230125006119
-90.111093680864605
-80.0482548810693446
-7-0.1811669777741
-60.128453026203845
-50.11125649056932
-4-0.0165970128395138
-3-0.272886303777575
-2-0.048948258870603
-10.158680084440471
00.215088858603619
10.0394574107898424
2-0.168843240831291
3-0.159789105840826
40.15827784869941
50.232446288534569
6-0.13641154765199
70.228019779974601
80.0423286078177052
9-0.0924912355446558
10-0.114911089588107
110.114137264638637
12-0.0230232076550439
130.180826035098343
14-0.154715189278579

\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 & 0.0619672890229602 \tabularnewline
-13 & 0.190088949592502 \tabularnewline
-12 & 0.135043946132381 \tabularnewline
-11 & -0.0910910720841036 \tabularnewline
-10 & -0.0682230125006119 \tabularnewline
-9 & 0.111093680864605 \tabularnewline
-8 & 0.0482548810693446 \tabularnewline
-7 & -0.1811669777741 \tabularnewline
-6 & 0.128453026203845 \tabularnewline
-5 & 0.11125649056932 \tabularnewline
-4 & -0.0165970128395138 \tabularnewline
-3 & -0.272886303777575 \tabularnewline
-2 & -0.048948258870603 \tabularnewline
-1 & 0.158680084440471 \tabularnewline
0 & 0.215088858603619 \tabularnewline
1 & 0.0394574107898424 \tabularnewline
2 & -0.168843240831291 \tabularnewline
3 & -0.159789105840826 \tabularnewline
4 & 0.15827784869941 \tabularnewline
5 & 0.232446288534569 \tabularnewline
6 & -0.13641154765199 \tabularnewline
7 & 0.228019779974601 \tabularnewline
8 & 0.0423286078177052 \tabularnewline
9 & -0.0924912355446558 \tabularnewline
10 & -0.114911089588107 \tabularnewline
11 & 0.114137264638637 \tabularnewline
12 & -0.0230232076550439 \tabularnewline
13 & 0.180826035098343 \tabularnewline
14 & -0.154715189278579 \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]0.0619672890229602[/C][/ROW]
[ROW][C]-13[/C][C]0.190088949592502[/C][/ROW]
[ROW][C]-12[/C][C]0.135043946132381[/C][/ROW]
[ROW][C]-11[/C][C]-0.0910910720841036[/C][/ROW]
[ROW][C]-10[/C][C]-0.0682230125006119[/C][/ROW]
[ROW][C]-9[/C][C]0.111093680864605[/C][/ROW]
[ROW][C]-8[/C][C]0.0482548810693446[/C][/ROW]
[ROW][C]-7[/C][C]-0.1811669777741[/C][/ROW]
[ROW][C]-6[/C][C]0.128453026203845[/C][/ROW]
[ROW][C]-5[/C][C]0.11125649056932[/C][/ROW]
[ROW][C]-4[/C][C]-0.0165970128395138[/C][/ROW]
[ROW][C]-3[/C][C]-0.272886303777575[/C][/ROW]
[ROW][C]-2[/C][C]-0.048948258870603[/C][/ROW]
[ROW][C]-1[/C][C]0.158680084440471[/C][/ROW]
[ROW][C]0[/C][C]0.215088858603619[/C][/ROW]
[ROW][C]1[/C][C]0.0394574107898424[/C][/ROW]
[ROW][C]2[/C][C]-0.168843240831291[/C][/ROW]
[ROW][C]3[/C][C]-0.159789105840826[/C][/ROW]
[ROW][C]4[/C][C]0.15827784869941[/C][/ROW]
[ROW][C]5[/C][C]0.232446288534569[/C][/ROW]
[ROW][C]6[/C][C]-0.13641154765199[/C][/ROW]
[ROW][C]7[/C][C]0.228019779974601[/C][/ROW]
[ROW][C]8[/C][C]0.0423286078177052[/C][/ROW]
[ROW][C]9[/C][C]-0.0924912355446558[/C][/ROW]
[ROW][C]10[/C][C]-0.114911089588107[/C][/ROW]
[ROW][C]11[/C][C]0.114137264638637[/C][/ROW]
[ROW][C]12[/C][C]-0.0230232076550439[/C][/ROW]
[ROW][C]13[/C][C]0.180826035098343[/C][/ROW]
[ROW][C]14[/C][C]-0.154715189278579[/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])
-140.0619672890229602
-130.190088949592502
-120.135043946132381
-11-0.0910910720841036
-10-0.0682230125006119
-90.111093680864605
-80.0482548810693446
-7-0.1811669777741
-60.128453026203845
-50.11125649056932
-4-0.0165970128395138
-3-0.272886303777575
-2-0.048948258870603
-10.158680084440471
00.215088858603619
10.0394574107898424
2-0.168843240831291
3-0.159789105840826
40.15827784869941
50.232446288534569
6-0.13641154765199
70.228019779974601
80.0423286078177052
9-0.0924912355446558
10-0.114911089588107
110.114137264638637
12-0.0230232076550439
130.180826035098343
14-0.154715189278579



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):
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')