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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 22:55:40 +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/t15634834046q3jsvcz0vcrc65.htm/, Retrieved Wed, 12 Aug 2020 17:36:50 +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 17:36:50 +0200
QR Codes:

Original text written by user:
IsPrivate?This computation is private
User-defined keywords
Estimated Impact0
Dataseries X:
57
74
95
112
137
85
79
159
69
66
121
81
109
75
86
81
82
91
96
130
67
131
255
169
79
26
53
65
72
62
136
113
133
60
159
180
152
128
97
97
89
109
52
59
100
75
82
179
82
98
199
55
Dataseries Y:
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




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])
-140.00748446402514207
-13-0.00692080252465253
-120.100277707635693
-110.0818645151037225
-10-0.00913266729421667
-9-0.161325025166076
-8-0.104200071153081
-7-0.154987398388268
-60.0853523113657397
-50.10837987261277
-4-0.18133838589917
-3-0.0909596550011024
-2-0.0905536085673277
-10.0436796382742873
00.224999260475039
10.282693129824081
20.141976649904931
30.0796038647182705
4-0.161037565306306
5-0.13993440896907
6-0.00349004790582736
70.0225517138551388
8-0.0762196440715231
9-0.0764200403706318
10-0.133633559040162
11-0.189806818269375
120.0845972601413822
13-0.00326713516861647
140.12949353662112

\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 & 0.00748446402514207 \tabularnewline
-13 & -0.00692080252465253 \tabularnewline
-12 & 0.100277707635693 \tabularnewline
-11 & 0.0818645151037225 \tabularnewline
-10 & -0.00913266729421667 \tabularnewline
-9 & -0.161325025166076 \tabularnewline
-8 & -0.104200071153081 \tabularnewline
-7 & -0.154987398388268 \tabularnewline
-6 & 0.0853523113657397 \tabularnewline
-5 & 0.10837987261277 \tabularnewline
-4 & -0.18133838589917 \tabularnewline
-3 & -0.0909596550011024 \tabularnewline
-2 & -0.0905536085673277 \tabularnewline
-1 & 0.0436796382742873 \tabularnewline
0 & 0.224999260475039 \tabularnewline
1 & 0.282693129824081 \tabularnewline
2 & 0.141976649904931 \tabularnewline
3 & 0.0796038647182705 \tabularnewline
4 & -0.161037565306306 \tabularnewline
5 & -0.13993440896907 \tabularnewline
6 & -0.00349004790582736 \tabularnewline
7 & 0.0225517138551388 \tabularnewline
8 & -0.0762196440715231 \tabularnewline
9 & -0.0764200403706318 \tabularnewline
10 & -0.133633559040162 \tabularnewline
11 & -0.189806818269375 \tabularnewline
12 & 0.0845972601413822 \tabularnewline
13 & -0.00326713516861647 \tabularnewline
14 & 0.12949353662112 \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]0.00748446402514207[/C][/ROW]
[ROW][C]-13[/C][C]-0.00692080252465253[/C][/ROW]
[ROW][C]-12[/C][C]0.100277707635693[/C][/ROW]
[ROW][C]-11[/C][C]0.0818645151037225[/C][/ROW]
[ROW][C]-10[/C][C]-0.00913266729421667[/C][/ROW]
[ROW][C]-9[/C][C]-0.161325025166076[/C][/ROW]
[ROW][C]-8[/C][C]-0.104200071153081[/C][/ROW]
[ROW][C]-7[/C][C]-0.154987398388268[/C][/ROW]
[ROW][C]-6[/C][C]0.0853523113657397[/C][/ROW]
[ROW][C]-5[/C][C]0.10837987261277[/C][/ROW]
[ROW][C]-4[/C][C]-0.18133838589917[/C][/ROW]
[ROW][C]-3[/C][C]-0.0909596550011024[/C][/ROW]
[ROW][C]-2[/C][C]-0.0905536085673277[/C][/ROW]
[ROW][C]-1[/C][C]0.0436796382742873[/C][/ROW]
[ROW][C]0[/C][C]0.224999260475039[/C][/ROW]
[ROW][C]1[/C][C]0.282693129824081[/C][/ROW]
[ROW][C]2[/C][C]0.141976649904931[/C][/ROW]
[ROW][C]3[/C][C]0.0796038647182705[/C][/ROW]
[ROW][C]4[/C][C]-0.161037565306306[/C][/ROW]
[ROW][C]5[/C][C]-0.13993440896907[/C][/ROW]
[ROW][C]6[/C][C]-0.00349004790582736[/C][/ROW]
[ROW][C]7[/C][C]0.0225517138551388[/C][/ROW]
[ROW][C]8[/C][C]-0.0762196440715231[/C][/ROW]
[ROW][C]9[/C][C]-0.0764200403706318[/C][/ROW]
[ROW][C]10[/C][C]-0.133633559040162[/C][/ROW]
[ROW][C]11[/C][C]-0.189806818269375[/C][/ROW]
[ROW][C]12[/C][C]0.0845972601413822[/C][/ROW]
[ROW][C]13[/C][C]-0.00326713516861647[/C][/ROW]
[ROW][C]14[/C][C]0.12949353662112[/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])
-140.00748446402514207
-13-0.00692080252465253
-120.100277707635693
-110.0818645151037225
-10-0.00913266729421667
-9-0.161325025166076
-8-0.104200071153081
-7-0.154987398388268
-60.0853523113657397
-50.10837987261277
-4-0.18133838589917
-3-0.0909596550011024
-2-0.0905536085673277
-10.0436796382742873
00.224999260475039
10.282693129824081
20.141976649904931
30.0796038647182705
4-0.161037565306306
5-0.13993440896907
6-0.00349004790582736
70.0225517138551388
8-0.0762196440715231
9-0.0764200403706318
10-0.133633559040162
11-0.189806818269375
120.0845972601413822
13-0.00326713516861647
140.12949353662112



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')