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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationWed, 16 Feb 2011 12:48:09 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Feb/16/t12978603307di4gc58hl09ioa.htm/, Retrieved Mon, 20 May 2024 01:10:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=118341, Retrieved Mon, 20 May 2024 01:10:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact243

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 with NA] [2011-02-16 12:48:09] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
99.2
99
100
111.6
122.2
117.6
121.1
136
154.2
153.6
158.5
140.6
136.2
168
154.3
149
165.5
Dataseries Y:
Download CSV
Histogram 
96.7
98.1
NA
104.9
104.9
109.5
110.8
112.3
109.3
105.3
101.7
95.4
96.4
97.6
102.4
101.6
103.8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Herman Ole Andreas Wold' @ www.yougetit.org

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 0 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ www.yougetit.org \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=118341&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ www.yougetit.org[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=118341&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=118341&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Herman Ole Andreas Wold' @ www.yougetit.org






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])
-90.246882174426315
-80.0658612346011844
-7-0.154589360354105
-6-0.319755577889252
-5-0.497925891907501
-4-0.55326180685978
-3-0.571076703933587
-2-0.525383101391581
-1-0.266433399845124
00.00738791379425843
10.109275007756465
20.145778346399347
30.128533075155108
40.152082445406382
50.231216247329911
60.321865813598666
70.28371506192287
80.247353130397773
90.17438167560823

\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
-9 & 0.246882174426315 \tabularnewline
-8 & 0.0658612346011844 \tabularnewline
-7 & -0.154589360354105 \tabularnewline
-6 & -0.319755577889252 \tabularnewline
-5 & -0.497925891907501 \tabularnewline
-4 & -0.55326180685978 \tabularnewline
-3 & -0.571076703933587 \tabularnewline
-2 & -0.525383101391581 \tabularnewline
-1 & -0.266433399845124 \tabularnewline
0 & 0.00738791379425843 \tabularnewline
1 & 0.109275007756465 \tabularnewline
2 & 0.145778346399347 \tabularnewline
3 & 0.128533075155108 \tabularnewline
4 & 0.152082445406382 \tabularnewline
5 & 0.231216247329911 \tabularnewline
6 & 0.321865813598666 \tabularnewline
7 & 0.28371506192287 \tabularnewline
8 & 0.247353130397773 \tabularnewline
9 & 0.17438167560823 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=118341&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]-9[/C][C]0.246882174426315[/C][/ROW]
[ROW][C]-8[/C][C]0.0658612346011844[/C][/ROW]
[ROW][C]-7[/C][C]-0.154589360354105[/C][/ROW]
[ROW][C]-6[/C][C]-0.319755577889252[/C][/ROW]
[ROW][C]-5[/C][C]-0.497925891907501[/C][/ROW]
[ROW][C]-4[/C][C]-0.55326180685978[/C][/ROW]
[ROW][C]-3[/C][C]-0.571076703933587[/C][/ROW]
[ROW][C]-2[/C][C]-0.525383101391581[/C][/ROW]
[ROW][C]-1[/C][C]-0.266433399845124[/C][/ROW]
[ROW][C]0[/C][C]0.00738791379425843[/C][/ROW]
[ROW][C]1[/C][C]0.109275007756465[/C][/ROW]
[ROW][C]2[/C][C]0.145778346399347[/C][/ROW]
[ROW][C]3[/C][C]0.128533075155108[/C][/ROW]
[ROW][C]4[/C][C]0.152082445406382[/C][/ROW]
[ROW][C]5[/C][C]0.231216247329911[/C][/ROW]
[ROW][C]6[/C][C]0.321865813598666[/C][/ROW]
[ROW][C]7[/C][C]0.28371506192287[/C][/ROW]
[ROW][C]8[/C][C]0.247353130397773[/C][/ROW]
[ROW][C]9[/C][C]0.17438167560823[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=118341&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=118341&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])
-90.246882174426315
-80.0658612346011844
-7-0.154589360354105
-6-0.319755577889252
-5-0.497925891907501
-4-0.55326180685978
-3-0.571076703933587
-2-0.525383101391581
-1-0.266433399845124
00.00738791379425843
10.109275007756465
20.145778346399347
30.128533075155108
40.152082445406382
50.231216247329911
60.321865813598666
70.28371506192287
80.247353130397773
90.17438167560823


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.pass ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = na.pass ;
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)
x
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')