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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 21:41:04 +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/t15634789193grum20mxnl2h84.htm/, Retrieved Wed, 12 Aug 2020 17:07:13 +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 17:07:13 +0200
QR Codes:

Original text written by user:
IsPrivate?This computation is private
User-defined keywords
Estimated Impact0
Dataseries X:
9
18
18
17
12
31
11
12
18
14
21
17
13
22
16
14
22
14
17
13
12
14
28
21
17
2
4
25
19
22
9
6
24
20
18
27
15
15
13
14
24
6
23
21
25
28
18
19
12
16
13
13
Dataseries Y:
5
5
13
21
14
19
10
13
14
14
8
17
18
6
9
13
18
21
6
5
9
15
15
17
30
6
1
7
9
16
14
1
9
12
22
8
8
12
24
6
15
11
12
24
14
13
12
29
10
12
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])
-140.072377773720809
-13-0.130095009562664
-120.0270128271100998
-110.236093725157112
-100.0209285128831255
-9-0.141948476122943
-8-0.0965321044946184
-70.127132193904925
-60.100586599812014
-5-0.0127717704462629
-4-0.23482279257891
-3-0.0652421852051604
-20.183453607840085
-10.306648114681912
00.287809661443285
1-0.159906092274133
2-0.0233212454884449
3-0.186770521476258
4-0.152193074274978
50.15654656933765
60.18342642604506
7-0.052137621430617
8-0.0972579318861
9-0.268041884672043
100.0244812469572197
110.294761589181737
120.0903890188189412
13-0.0542746982340785
14-0.299131246392802

\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 & 0.072377773720809 \tabularnewline
-13 & -0.130095009562664 \tabularnewline
-12 & 0.0270128271100998 \tabularnewline
-11 & 0.236093725157112 \tabularnewline
-10 & 0.0209285128831255 \tabularnewline
-9 & -0.141948476122943 \tabularnewline
-8 & -0.0965321044946184 \tabularnewline
-7 & 0.127132193904925 \tabularnewline
-6 & 0.100586599812014 \tabularnewline
-5 & -0.0127717704462629 \tabularnewline
-4 & -0.23482279257891 \tabularnewline
-3 & -0.0652421852051604 \tabularnewline
-2 & 0.183453607840085 \tabularnewline
-1 & 0.306648114681912 \tabularnewline
0 & 0.287809661443285 \tabularnewline
1 & -0.159906092274133 \tabularnewline
2 & -0.0233212454884449 \tabularnewline
3 & -0.186770521476258 \tabularnewline
4 & -0.152193074274978 \tabularnewline
5 & 0.15654656933765 \tabularnewline
6 & 0.18342642604506 \tabularnewline
7 & -0.052137621430617 \tabularnewline
8 & -0.0972579318861 \tabularnewline
9 & -0.268041884672043 \tabularnewline
10 & 0.0244812469572197 \tabularnewline
11 & 0.294761589181737 \tabularnewline
12 & 0.0903890188189412 \tabularnewline
13 & -0.0542746982340785 \tabularnewline
14 & -0.299131246392802 \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]0.072377773720809[/C][/ROW]
[ROW][C]-13[/C][C]-0.130095009562664[/C][/ROW]
[ROW][C]-12[/C][C]0.0270128271100998[/C][/ROW]
[ROW][C]-11[/C][C]0.236093725157112[/C][/ROW]
[ROW][C]-10[/C][C]0.0209285128831255[/C][/ROW]
[ROW][C]-9[/C][C]-0.141948476122943[/C][/ROW]
[ROW][C]-8[/C][C]-0.0965321044946184[/C][/ROW]
[ROW][C]-7[/C][C]0.127132193904925[/C][/ROW]
[ROW][C]-6[/C][C]0.100586599812014[/C][/ROW]
[ROW][C]-5[/C][C]-0.0127717704462629[/C][/ROW]
[ROW][C]-4[/C][C]-0.23482279257891[/C][/ROW]
[ROW][C]-3[/C][C]-0.0652421852051604[/C][/ROW]
[ROW][C]-2[/C][C]0.183453607840085[/C][/ROW]
[ROW][C]-1[/C][C]0.306648114681912[/C][/ROW]
[ROW][C]0[/C][C]0.287809661443285[/C][/ROW]
[ROW][C]1[/C][C]-0.159906092274133[/C][/ROW]
[ROW][C]2[/C][C]-0.0233212454884449[/C][/ROW]
[ROW][C]3[/C][C]-0.186770521476258[/C][/ROW]
[ROW][C]4[/C][C]-0.152193074274978[/C][/ROW]
[ROW][C]5[/C][C]0.15654656933765[/C][/ROW]
[ROW][C]6[/C][C]0.18342642604506[/C][/ROW]
[ROW][C]7[/C][C]-0.052137621430617[/C][/ROW]
[ROW][C]8[/C][C]-0.0972579318861[/C][/ROW]
[ROW][C]9[/C][C]-0.268041884672043[/C][/ROW]
[ROW][C]10[/C][C]0.0244812469572197[/C][/ROW]
[ROW][C]11[/C][C]0.294761589181737[/C][/ROW]
[ROW][C]12[/C][C]0.0903890188189412[/C][/ROW]
[ROW][C]13[/C][C]-0.0542746982340785[/C][/ROW]
[ROW][C]14[/C][C]-0.299131246392802[/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])
-140.072377773720809
-13-0.130095009562664
-120.0270128271100998
-110.236093725157112
-100.0209285128831255
-9-0.141948476122943
-8-0.0965321044946184
-70.127132193904925
-60.100586599812014
-5-0.0127717704462629
-4-0.23482279257891
-3-0.0652421852051604
-20.183453607840085
-10.306648114681912
00.287809661443285
1-0.159906092274133
2-0.0233212454884449
3-0.186770521476258
4-0.152193074274978
50.15654656933765
60.18342642604506
7-0.052137621430617
8-0.0972579318861
9-0.268041884672043
100.0244812469572197
110.294761589181737
120.0903890188189412
13-0.0542746982340785
14-0.299131246392802



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')