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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationThu, 26 Oct 2023 19:57:37 +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/2023/Oct/26/t1698343593y2mb2prx0se02g6.htm/, Retrieved Thu, 13 Aug 2026 17:51:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319976, Retrieved Thu, 13 Aug 2026 17:51:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact418

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] [] [2023-10-26 17:57:37] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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Dataseries Y:
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Histogram 
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1819

Tables (Output of Computation)





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
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319976&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] [ROW]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319976&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319976&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






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])
-230.671787964159572
-220.679184707444795
-210.716089237750407
-200.750217664504006
-190.74734406403583
-180.726135797845854
-170.706019981885346
-160.690985495998584
-150.693537513953531
-140.729975913606778
-130.76361550876126
-120.760710779751736
-110.735219198837318
-100.705545148555029
-90.692670742882974
-80.692222283851699
-70.727096653359828
-60.759491453289002
-50.750978456840487
-40.722639267592746
-30.69040240341198
-20.66954733649681
-10.670430808936217
00.706161077983438
10.735923298906549
20.705840988168237
30.673158051398928
40.633646916115765
50.601944694315972
60.592352141296355
70.61395377222505
80.63364750538397
90.609277423385721
100.578239505968076
110.544350489982261
120.512787376529433
130.499954014299177
140.521152976468228
150.53608683918156
160.52350334858987
170.492231736409141
180.46061665405219
190.428150550689399
200.415436636641488
210.434989576298891
220.449745443257097
230.438879711290564

\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
-23 & 0.671787964159572 \tabularnewline
-22 & 0.679184707444795 \tabularnewline
-21 & 0.716089237750407 \tabularnewline
-20 & 0.750217664504006 \tabularnewline
-19 & 0.74734406403583 \tabularnewline
-18 & 0.726135797845854 \tabularnewline
-17 & 0.706019981885346 \tabularnewline
-16 & 0.690985495998584 \tabularnewline
-15 & 0.693537513953531 \tabularnewline
-14 & 0.729975913606778 \tabularnewline
-13 & 0.76361550876126 \tabularnewline
-12 & 0.760710779751736 \tabularnewline
-11 & 0.735219198837318 \tabularnewline
-10 & 0.705545148555029 \tabularnewline
-9 & 0.692670742882974 \tabularnewline
-8 & 0.692222283851699 \tabularnewline
-7 & 0.727096653359828 \tabularnewline
-6 & 0.759491453289002 \tabularnewline
-5 & 0.750978456840487 \tabularnewline
-4 & 0.722639267592746 \tabularnewline
-3 & 0.69040240341198 \tabularnewline
-2 & 0.66954733649681 \tabularnewline
-1 & 0.670430808936217 \tabularnewline
0 & 0.706161077983438 \tabularnewline
1 & 0.735923298906549 \tabularnewline
2 & 0.705840988168237 \tabularnewline
3 & 0.673158051398928 \tabularnewline
4 & 0.633646916115765 \tabularnewline
5 & 0.601944694315972 \tabularnewline
6 & 0.592352141296355 \tabularnewline
7 & 0.61395377222505 \tabularnewline
8 & 0.63364750538397 \tabularnewline
9 & 0.609277423385721 \tabularnewline
10 & 0.578239505968076 \tabularnewline
11 & 0.544350489982261 \tabularnewline
12 & 0.512787376529433 \tabularnewline
13 & 0.499954014299177 \tabularnewline
14 & 0.521152976468228 \tabularnewline
15 & 0.53608683918156 \tabularnewline
16 & 0.52350334858987 \tabularnewline
17 & 0.492231736409141 \tabularnewline
18 & 0.46061665405219 \tabularnewline
19 & 0.428150550689399 \tabularnewline
20 & 0.415436636641488 \tabularnewline
21 & 0.434989576298891 \tabularnewline
22 & 0.449745443257097 \tabularnewline
23 & 0.438879711290564 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319976&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]-23[/C][C]0.671787964159572[/C][/ROW]
[ROW][C]-22[/C][C]0.679184707444795[/C][/ROW]
[ROW][C]-21[/C][C]0.716089237750407[/C][/ROW]
[ROW][C]-20[/C][C]0.750217664504006[/C][/ROW]
[ROW][C]-19[/C][C]0.74734406403583[/C][/ROW]
[ROW][C]-18[/C][C]0.726135797845854[/C][/ROW]
[ROW][C]-17[/C][C]0.706019981885346[/C][/ROW]
[ROW][C]-16[/C][C]0.690985495998584[/C][/ROW]
[ROW][C]-15[/C][C]0.693537513953531[/C][/ROW]
[ROW][C]-14[/C][C]0.729975913606778[/C][/ROW]
[ROW][C]-13[/C][C]0.76361550876126[/C][/ROW]
[ROW][C]-12[/C][C]0.760710779751736[/C][/ROW]
[ROW][C]-11[/C][C]0.735219198837318[/C][/ROW]
[ROW][C]-10[/C][C]0.705545148555029[/C][/ROW]
[ROW][C]-9[/C][C]0.692670742882974[/C][/ROW]
[ROW][C]-8[/C][C]0.692222283851699[/C][/ROW]
[ROW][C]-7[/C][C]0.727096653359828[/C][/ROW]
[ROW][C]-6[/C][C]0.759491453289002[/C][/ROW]
[ROW][C]-5[/C][C]0.750978456840487[/C][/ROW]
[ROW][C]-4[/C][C]0.722639267592746[/C][/ROW]
[ROW][C]-3[/C][C]0.69040240341198[/C][/ROW]
[ROW][C]-2[/C][C]0.66954733649681[/C][/ROW]
[ROW][C]-1[/C][C]0.670430808936217[/C][/ROW]
[ROW][C]0[/C][C]0.706161077983438[/C][/ROW]
[ROW][C]1[/C][C]0.735923298906549[/C][/ROW]
[ROW][C]2[/C][C]0.705840988168237[/C][/ROW]
[ROW][C]3[/C][C]0.673158051398928[/C][/ROW]
[ROW][C]4[/C][C]0.633646916115765[/C][/ROW]
[ROW][C]5[/C][C]0.601944694315972[/C][/ROW]
[ROW][C]6[/C][C]0.592352141296355[/C][/ROW]
[ROW][C]7[/C][C]0.61395377222505[/C][/ROW]
[ROW][C]8[/C][C]0.63364750538397[/C][/ROW]
[ROW][C]9[/C][C]0.609277423385721[/C][/ROW]
[ROW][C]10[/C][C]0.578239505968076[/C][/ROW]
[ROW][C]11[/C][C]0.544350489982261[/C][/ROW]
[ROW][C]12[/C][C]0.512787376529433[/C][/ROW]
[ROW][C]13[/C][C]0.499954014299177[/C][/ROW]
[ROW][C]14[/C][C]0.521152976468228[/C][/ROW]
[ROW][C]15[/C][C]0.53608683918156[/C][/ROW]
[ROW][C]16[/C][C]0.52350334858987[/C][/ROW]
[ROW][C]17[/C][C]0.492231736409141[/C][/ROW]
[ROW][C]18[/C][C]0.46061665405219[/C][/ROW]
[ROW][C]19[/C][C]0.428150550689399[/C][/ROW]
[ROW][C]20[/C][C]0.415436636641488[/C][/ROW]
[ROW][C]21[/C][C]0.434989576298891[/C][/ROW]
[ROW][C]22[/C][C]0.449745443257097[/C][/ROW]
[ROW][C]23[/C][C]0.438879711290564[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319976&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319976&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 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])
-230.671787964159572
-220.679184707444795
-210.716089237750407
-200.750217664504006
-190.74734406403583
-180.726135797845854
-170.706019981885346
-160.690985495998584
-150.693537513953531
-140.729975913606778
-130.76361550876126
-120.760710779751736
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50.601944694315972
60.592352141296355
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100.578239505968076
110.544350489982261
120.512787376529433
130.499954014299177
140.521152976468228
150.53608683918156
160.52350334858987
170.492231736409141
180.46061665405219
190.428150550689399
200.415436636641488
210.434989576298891
220.449745443257097
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Figures (Output of Computation)

Input Parameters & R Code

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')