FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationTue, 02 Dec 2008 08:56:53 -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/t1228233474tclgy4turrz2jpi.htm/, Retrieved Thu, 23 May 2024 00:34:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27985, Retrieved Thu, 23 May 2024 00:34:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact163

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [Spectral Analysis] [question 6] [2008-12-01 14:22:49] [379d6c32f73e3218fd773d79e4063d07]
F RM D      [Cross Correlation Function] [cross correlation...] [2008-12-02 15:56:53] [52d1f7c78552cd0e785e1b7a3cade101] [Current]
Feedback Forum
2008-12-08 19:31:49 [94a54c888ac7f7d6874c3108eb0e1808] [reply] 
de student heeft geeft een goede interpretatie aan zijn gevonden gegevens.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
168.8
169.8
171.2
171.3
171.5
172.4
172.8
172.8
173.7
174
174.1
174
175.1
175.8
176.2
176.9
177.7
178
177.5
177.5
178.3
177.7
177.4
176.7
177.1
177.8
178.8
179.8
179.8
179.9
180.1
180.7
181
181.3
181.3
180.9
Dataseries Y:
Download CSV
Histogram 
179.3
180.5
181.5
181.4
181.4
182
182.8
183.1
184.4
184.6
184.6
184.2
184.9
185.3
186.4
186.6
187.3
188.3
187.8
188.1
188
187.8
187.8
187.3
188.5
189.9
191.1
191.8
191.4
191.5
192
193.1
193.3
193.7
193.4
193.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27985&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27985&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27985&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-12-0.0511231527090112
-11-0.0473272053247805
-100.0724850126164274
-90.100377555423185
-80.092477395839684
-7-0.062469780410285
-6-0.066375632998312
-5-0.117449738099928
-4-0.134549035547340
-3-0.0873682499842006
-2-0.0873261919621346
-1-0.186412120220516
0-0.267492033100152
1-0.108993424442382
2-0.00903861842152274
3-0.00344562454712587
40.0233926959758364
5-0.041072852066126
6-0.0678350102110188
7-0.0473993908698159
80.0322970661550017
90.0185680744472876
10-0.0715715461199525
11-0.141490778370094
12-0.184095193193965

\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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-12 & -0.0511231527090112 \tabularnewline
-11 & -0.0473272053247805 \tabularnewline
-10 & 0.0724850126164274 \tabularnewline
-9 & 0.100377555423185 \tabularnewline
-8 & 0.092477395839684 \tabularnewline
-7 & -0.062469780410285 \tabularnewline
-6 & -0.066375632998312 \tabularnewline
-5 & -0.117449738099928 \tabularnewline
-4 & -0.134549035547340 \tabularnewline
-3 & -0.0873682499842006 \tabularnewline
-2 & -0.0873261919621346 \tabularnewline
-1 & -0.186412120220516 \tabularnewline
0 & -0.267492033100152 \tabularnewline
1 & -0.108993424442382 \tabularnewline
2 & -0.00903861842152274 \tabularnewline
3 & -0.00344562454712587 \tabularnewline
4 & 0.0233926959758364 \tabularnewline
5 & -0.041072852066126 \tabularnewline
6 & -0.0678350102110188 \tabularnewline
7 & -0.0473993908698159 \tabularnewline
8 & 0.0322970661550017 \tabularnewline
9 & 0.0185680744472876 \tabularnewline
10 & -0.0715715461199525 \tabularnewline
11 & -0.141490778370094 \tabularnewline
12 & -0.184095193193965 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27985&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]1[/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]-12[/C][C]-0.0511231527090112[/C][/ROW]
[ROW][C]-11[/C][C]-0.0473272053247805[/C][/ROW]
[ROW][C]-10[/C][C]0.0724850126164274[/C][/ROW]
[ROW][C]-9[/C][C]0.100377555423185[/C][/ROW]
[ROW][C]-8[/C][C]0.092477395839684[/C][/ROW]
[ROW][C]-7[/C][C]-0.062469780410285[/C][/ROW]
[ROW][C]-6[/C][C]-0.066375632998312[/C][/ROW]
[ROW][C]-5[/C][C]-0.117449738099928[/C][/ROW]
[ROW][C]-4[/C][C]-0.134549035547340[/C][/ROW]
[ROW][C]-3[/C][C]-0.0873682499842006[/C][/ROW]
[ROW][C]-2[/C][C]-0.0873261919621346[/C][/ROW]
[ROW][C]-1[/C][C]-0.186412120220516[/C][/ROW]
[ROW][C]0[/C][C]-0.267492033100152[/C][/ROW]
[ROW][C]1[/C][C]-0.108993424442382[/C][/ROW]
[ROW][C]2[/C][C]-0.00903861842152274[/C][/ROW]
[ROW][C]3[/C][C]-0.00344562454712587[/C][/ROW]
[ROW][C]4[/C][C]0.0233926959758364[/C][/ROW]
[ROW][C]5[/C][C]-0.041072852066126[/C][/ROW]
[ROW][C]6[/C][C]-0.0678350102110188[/C][/ROW]
[ROW][C]7[/C][C]-0.0473993908698159[/C][/ROW]
[ROW][C]8[/C][C]0.0322970661550017[/C][/ROW]
[ROW][C]9[/C][C]0.0185680744472876[/C][/ROW]
[ROW][C]10[/C][C]-0.0715715461199525[/C][/ROW]
[ROW][C]11[/C][C]-0.141490778370094[/C][/ROW]
[ROW][C]12[/C][C]-0.184095193193965[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27985&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27985&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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-12-0.0511231527090112
-11-0.0473272053247805
-100.0724850126164274
-90.100377555423185
-80.092477395839684
-7-0.062469780410285
-6-0.066375632998312
-5-0.117449738099928
-4-0.134549035547340
-3-0.0873682499842006
-2-0.0873261919621346
-1-0.186412120220516
0-0.267492033100152
1-0.108993424442382
2-0.00903861842152274
3-0.00344562454712587
40.0233926959758364
5-0.041072852066126
6-0.0678350102110188
7-0.0473993908698159
80.0322970661550017
90.0185680744472876
10-0.0715715461199525
11-0.141490778370094
12-0.184095193193965


Figures (Output of Computation)

Input Parameters & R Code

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