FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationWed, 03 May 2023 08:19:27 +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/May/03/t1683095362b7b25tzplwe0bo8.htm/, Retrieved Tue, 25 Aug 2026 18:06:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319896, Retrieved Tue, 25 Aug 2026 18:06:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact408

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] [CCF between sand ...] [2023-05-03 06:19:27] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
64.83
24.25
19.62
17.53
15.19
79.54
56.53
62.17
60.62
62.82
47.83
36.81
39.61
65.36
72.79
59.13
20.19
19.78
76.47
75.13
63.69
54.12
80.24
53.12
69.33
63.62
69.71
Dataseries Y:
Download CSV
Histogram 
20.67
30.21
29.54
36.3
31.32
11.43
24.67
29.94
21.28
23.72
28.09
33.47
31.91
21.44
9.69
18.69
38.61
36.95
12.94
13.66
28.18
24.1
14.67
22.34
23.1
26.84
22.23

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=319896&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=319896&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319896&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])
-110.283480388795812
-100.0441048226100796
-9-0.235155061168581
-8-0.18234517883394
-70.0560867799262779
-60.0298547801376603
-5-0.134536876157509
-4-0.0308208112512687
-30.229711900528975
-20.307209246219447
-1-0.182643854483813
0-0.850628259659183
1-0.270977744131269
20.294166347182819
30.185721892335551
4-0.110222062285025
5-0.170066054114309
60.00453066125860654
70.0257348110548293
8-0.0380590658752183
9-0.0510013625627221
10-0.0108193488268395
110.168924024184993

\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
-11 & 0.283480388795812 \tabularnewline
-10 & 0.0441048226100796 \tabularnewline
-9 & -0.235155061168581 \tabularnewline
-8 & -0.18234517883394 \tabularnewline
-7 & 0.0560867799262779 \tabularnewline
-6 & 0.0298547801376603 \tabularnewline
-5 & -0.134536876157509 \tabularnewline
-4 & -0.0308208112512687 \tabularnewline
-3 & 0.229711900528975 \tabularnewline
-2 & 0.307209246219447 \tabularnewline
-1 & -0.182643854483813 \tabularnewline
0 & -0.850628259659183 \tabularnewline
1 & -0.270977744131269 \tabularnewline
2 & 0.294166347182819 \tabularnewline
3 & 0.185721892335551 \tabularnewline
4 & -0.110222062285025 \tabularnewline
5 & -0.170066054114309 \tabularnewline
6 & 0.00453066125860654 \tabularnewline
7 & 0.0257348110548293 \tabularnewline
8 & -0.0380590658752183 \tabularnewline
9 & -0.0510013625627221 \tabularnewline
10 & -0.0108193488268395 \tabularnewline
11 & 0.168924024184993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319896&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]-11[/C][C]0.283480388795812[/C][/ROW]
[ROW][C]-10[/C][C]0.0441048226100796[/C][/ROW]
[ROW][C]-9[/C][C]-0.235155061168581[/C][/ROW]
[ROW][C]-8[/C][C]-0.18234517883394[/C][/ROW]
[ROW][C]-7[/C][C]0.0560867799262779[/C][/ROW]
[ROW][C]-6[/C][C]0.0298547801376603[/C][/ROW]
[ROW][C]-5[/C][C]-0.134536876157509[/C][/ROW]
[ROW][C]-4[/C][C]-0.0308208112512687[/C][/ROW]
[ROW][C]-3[/C][C]0.229711900528975[/C][/ROW]
[ROW][C]-2[/C][C]0.307209246219447[/C][/ROW]
[ROW][C]-1[/C][C]-0.182643854483813[/C][/ROW]
[ROW][C]0[/C][C]-0.850628259659183[/C][/ROW]
[ROW][C]1[/C][C]-0.270977744131269[/C][/ROW]
[ROW][C]2[/C][C]0.294166347182819[/C][/ROW]
[ROW][C]3[/C][C]0.185721892335551[/C][/ROW]
[ROW][C]4[/C][C]-0.110222062285025[/C][/ROW]
[ROW][C]5[/C][C]-0.170066054114309[/C][/ROW]
[ROW][C]6[/C][C]0.00453066125860654[/C][/ROW]
[ROW][C]7[/C][C]0.0257348110548293[/C][/ROW]
[ROW][C]8[/C][C]-0.0380590658752183[/C][/ROW]
[ROW][C]9[/C][C]-0.0510013625627221[/C][/ROW]
[ROW][C]10[/C][C]-0.0108193488268395[/C][/ROW]
[ROW][C]11[/C][C]0.168924024184993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319896&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319896&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])
-110.283480388795812
-100.0441048226100796
-9-0.235155061168581
-8-0.18234517883394
-70.0560867799262779
-60.0298547801376603
-5-0.134536876157509
-4-0.0308208112512687
-30.229711900528975
-20.307209246219447
-1-0.182643854483813
0-0.850628259659183
1-0.270977744131269
20.294166347182819
30.185721892335551
4-0.110222062285025
5-0.170066054114309
60.00453066125860654
70.0257348110548293
8-0.0380590658752183
9-0.0510013625627221
10-0.0108193488268395
110.168924024184993


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')