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 computationTue, 02 Dec 2008 08:47:26 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/02/t1228232915z2za6bttmam2bli.htm/, Retrieved Tue, 28 May 2024 01:48:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27972, Retrieved Tue, 28 May 2024 01:48:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact192

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Spectral Analysis] [] [2008-12-02 15:26:02] [74be16979710d4c4e7c6647856088456]
- RMPD    [Cross Correlation Function] [] [2008-12-02 15:47:26] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
132.7
128.6
127.8
128.9
124.6
129.2
130.5
124.3
125.8
123.5
120.7
123.1
122.0
121.0
121.2
117.4
113.0
113.1
116.1
121.3
108.6
114.3
113.5
111.2
109.3
108.2
102.7
110.4
108.1
112.8
108.1
102.6
109.2
108.2
107.1
108.4
103.6
104.0
111.5
105.4
Dataseries Y:
Download CSV
Histogram 
116.5
118.2
118.8
116.4
118.6
131.4
119.4
119.3
119.1
119.4
120.4
120.3
118.3
118.9
119.9
119.2
118.6
117.5
118.2
120.4
118.0
119.6
120.1
118.5
112.0
112.9
111.1
114.8
113.8
110.8
113.3
115.3
115.1
115.4
113.2
119.4
119.3
115.8
112.8
115.2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27972&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27972&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27972&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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)12
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])
-130.148359894770882
-120.157802594224671
-110.188222429806816
-100.236784653391863
-90.31064639828117
-80.383498617484774
-70.364170642516583
-60.391127401186493
-50.547390670645604
-40.58001207984313
-30.571205785381607
-20.583557534051318
-10.538640909622481
00.563597999814151
10.525940268875791
20.447951582983331
30.381306735315976
40.346238235856307
50.330614008723822
60.311402331114624
70.297556484430073
80.239149366358053
90.173392901294116
100.119561761250249
110.0125617300123364
12-0.0467386163863938
13-0.0296041388280976

\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) & 12 \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
-13 & 0.148359894770882 \tabularnewline
-12 & 0.157802594224671 \tabularnewline
-11 & 0.188222429806816 \tabularnewline
-10 & 0.236784653391863 \tabularnewline
-9 & 0.31064639828117 \tabularnewline
-8 & 0.383498617484774 \tabularnewline
-7 & 0.364170642516583 \tabularnewline
-6 & 0.391127401186493 \tabularnewline
-5 & 0.547390670645604 \tabularnewline
-4 & 0.58001207984313 \tabularnewline
-3 & 0.571205785381607 \tabularnewline
-2 & 0.583557534051318 \tabularnewline
-1 & 0.538640909622481 \tabularnewline
0 & 0.563597999814151 \tabularnewline
1 & 0.525940268875791 \tabularnewline
2 & 0.447951582983331 \tabularnewline
3 & 0.381306735315976 \tabularnewline
4 & 0.346238235856307 \tabularnewline
5 & 0.330614008723822 \tabularnewline
6 & 0.311402331114624 \tabularnewline
7 & 0.297556484430073 \tabularnewline
8 & 0.239149366358053 \tabularnewline
9 & 0.173392901294116 \tabularnewline
10 & 0.119561761250249 \tabularnewline
11 & 0.0125617300123364 \tabularnewline
12 & -0.0467386163863938 \tabularnewline
13 & -0.0296041388280976 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27972&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]12[/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]-13[/C][C]0.148359894770882[/C][/ROW]
[ROW][C]-12[/C][C]0.157802594224671[/C][/ROW]
[ROW][C]-11[/C][C]0.188222429806816[/C][/ROW]
[ROW][C]-10[/C][C]0.236784653391863[/C][/ROW]
[ROW][C]-9[/C][C]0.31064639828117[/C][/ROW]
[ROW][C]-8[/C][C]0.383498617484774[/C][/ROW]
[ROW][C]-7[/C][C]0.364170642516583[/C][/ROW]
[ROW][C]-6[/C][C]0.391127401186493[/C][/ROW]
[ROW][C]-5[/C][C]0.547390670645604[/C][/ROW]
[ROW][C]-4[/C][C]0.58001207984313[/C][/ROW]
[ROW][C]-3[/C][C]0.571205785381607[/C][/ROW]
[ROW][C]-2[/C][C]0.583557534051318[/C][/ROW]
[ROW][C]-1[/C][C]0.538640909622481[/C][/ROW]
[ROW][C]0[/C][C]0.563597999814151[/C][/ROW]
[ROW][C]1[/C][C]0.525940268875791[/C][/ROW]
[ROW][C]2[/C][C]0.447951582983331[/C][/ROW]
[ROW][C]3[/C][C]0.381306735315976[/C][/ROW]
[ROW][C]4[/C][C]0.346238235856307[/C][/ROW]
[ROW][C]5[/C][C]0.330614008723822[/C][/ROW]
[ROW][C]6[/C][C]0.311402331114624[/C][/ROW]
[ROW][C]7[/C][C]0.297556484430073[/C][/ROW]
[ROW][C]8[/C][C]0.239149366358053[/C][/ROW]
[ROW][C]9[/C][C]0.173392901294116[/C][/ROW]
[ROW][C]10[/C][C]0.119561761250249[/C][/ROW]
[ROW][C]11[/C][C]0.0125617300123364[/C][/ROW]
[ROW][C]12[/C][C]-0.0467386163863938[/C][/ROW]
[ROW][C]13[/C][C]-0.0296041388280976[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27972&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27972&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)12
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])
-130.148359894770882
-120.157802594224671
-110.188222429806816
-100.236784653391863
-90.31064639828117
-80.383498617484774
-70.364170642516583
-60.391127401186493
-50.547390670645604
-40.58001207984313
-30.571205785381607
-20.583557534051318
-10.538640909622481
00.563597999814151
10.525940268875791
20.447951582983331
30.381306735315976
40.346238235856307
50.330614008723822
60.311402331114624
70.297556484430073
80.239149366358053
90.173392901294116
100.119561761250249
110.0125617300123364
12-0.0467386163863938
13-0.0296041388280976


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
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 (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,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')