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 08:51:56 -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/t1228233261kmrcournagp41er.htm/, Retrieved Sun, 19 May 2024 09:24:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27977, Retrieved Sun, 19 May 2024 09:24:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175

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    [Cross Correlation Function] [Workshop 7, Q7] [2008-12-02 15:51:56] [d300b7a0882cee7d84584ad37a3d4ede] [Current]
Feedback Forum
2008-12-06 09:56:50 [Loïque Verhasselt] [reply] 
Q7: De student voert de cross correlatie functie correct op haar tijdreeks uit. Er is geen seizoenaliteit zichtbaar in de output. De student vergeet wel te vermelden dat we een duidelijk dalende trend kunnen zien in de autocorrelaties in de toekomst van Xt en een stijgende trend in het verleden van Xt. Dit betekend dat we Yt kunnen voorspellen door het verleden van Xt te gaan bekijken(verleden heeft een stijgend verloop) en ook deels voorspellen met de toekomst van Xt.
2008-12-06 10:59:58 [Britt Severijns] [reply] 
De output is goed maar geen interpretatie. Met de crosscorrelation gaan we proberen een voorspelling te maken van y(t) aan de hand van het verleden van
x(t). Er zijn vele verticale staafjes die significant zijn. We kunnen dus een goede voorspelling maken van y(t) aan de hand van het verleden van x(t).
2008-12-06 13:41:34 [Nicolaj Wuyts] [reply] 
Uit de grafiek kunnen we afleiden dat er heel wat significante waarden zijn. We kunnen dus kijken naar het verleden om voosprellingen te doen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101,02
100,67
100,47
100,38
100,33
100,34
100,37
100,39
100,21
100,21
100,22
100,28
100,25
100,25
100,21
100,16
100,18
100,1
99,96
99,88
99,88
99,86
99,84
99,8
99,82
99,81
99,92
100,03
99,99
100,02
100,01
100,13
100,33
100,13
99,96
100,05
99,83
99,8
100,01
100,1
100,13
100,16
100,41
101,34
101,65
101,85
102,07
102,12
102,14
102,21
102,28
102,19
102,33
102,54
102,44
102,78
102,9
103,08
102,77
102,65
102,71
103,29
102,86
103,45
103,72
103,65
103,83
104,45
105,14
105,07
105,31
105,19
105,3
105,02
105,17
105,28
105,45
105,38
105,8
105,96
105,08
105,11
105,61
105,5
Dataseries Y:
Download CSV
Histogram 
103,68
103,64
103,37
104,3
104,15
104,09
104,21
104,27
104
103,36
104,2
104,12
103,79
104,65
103,84
103,98
103,83
104,34
103,76
103,57
103,06
103,06
102,6
103,41
103,15
103,33
103,96
104,91
104,23
103,68
104,16
104,49
104,23
104,21
103,74
103,96
104,02
104,15
103,74
103,23
103,69
103,46
102,14
102,39
102,19
102,02
102,64
103,52
103,32
103,65
104,25
101,74
102,08
101,35
102,79
102,21
101,78
101,25
101,8
103
104,17
104,08
105,24
104,72
104,77
104,39
104,14
105,15
105,07
104,54
106,03
107,24
108,2
109,15
110,1
109,48
109,96
110,13
110,53
110,82
110,06
110,05
109,49
109,95

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27977&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27977&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27977&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'George Udny Yule' @ 72.249.76.132






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])
-160.477371768678956
-150.501130888436213
-140.517836417842789
-130.537625633150106
-120.55930423868451
-110.585066306703481
-100.602087376035606
-90.620474251526346
-80.63621577221826
-70.658189325821267
-60.673814874642382
-50.688949118994303
-40.703548528297092
-30.698522351794462
-20.690072549046823
-10.687276751343563
00.68201209714473
10.62686995373318
20.573772000532124
30.517996442050407
40.45920759200244
50.396443931215088
60.331618344781629
70.26959171349168
80.20616805923057
90.142406246612811
100.070458361394234
110.0136730506921101
12-0.0363654306752818
13-0.0768598658758853
14-0.101322786302566
15-0.116433221319894
16-0.134229446531611

\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
-16 & 0.477371768678956 \tabularnewline
-15 & 0.501130888436213 \tabularnewline
-14 & 0.517836417842789 \tabularnewline
-13 & 0.537625633150106 \tabularnewline
-12 & 0.55930423868451 \tabularnewline
-11 & 0.585066306703481 \tabularnewline
-10 & 0.602087376035606 \tabularnewline
-9 & 0.620474251526346 \tabularnewline
-8 & 0.63621577221826 \tabularnewline
-7 & 0.658189325821267 \tabularnewline
-6 & 0.673814874642382 \tabularnewline
-5 & 0.688949118994303 \tabularnewline
-4 & 0.703548528297092 \tabularnewline
-3 & 0.698522351794462 \tabularnewline
-2 & 0.690072549046823 \tabularnewline
-1 & 0.687276751343563 \tabularnewline
0 & 0.68201209714473 \tabularnewline
1 & 0.62686995373318 \tabularnewline
2 & 0.573772000532124 \tabularnewline
3 & 0.517996442050407 \tabularnewline
4 & 0.45920759200244 \tabularnewline
5 & 0.396443931215088 \tabularnewline
6 & 0.331618344781629 \tabularnewline
7 & 0.26959171349168 \tabularnewline
8 & 0.20616805923057 \tabularnewline
9 & 0.142406246612811 \tabularnewline
10 & 0.070458361394234 \tabularnewline
11 & 0.0136730506921101 \tabularnewline
12 & -0.0363654306752818 \tabularnewline
13 & -0.0768598658758853 \tabularnewline
14 & -0.101322786302566 \tabularnewline
15 & -0.116433221319894 \tabularnewline
16 & -0.134229446531611 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27977&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]-16[/C][C]0.477371768678956[/C][/ROW]
[ROW][C]-15[/C][C]0.501130888436213[/C][/ROW]
[ROW][C]-14[/C][C]0.517836417842789[/C][/ROW]
[ROW][C]-13[/C][C]0.537625633150106[/C][/ROW]
[ROW][C]-12[/C][C]0.55930423868451[/C][/ROW]
[ROW][C]-11[/C][C]0.585066306703481[/C][/ROW]
[ROW][C]-10[/C][C]0.602087376035606[/C][/ROW]
[ROW][C]-9[/C][C]0.620474251526346[/C][/ROW]
[ROW][C]-8[/C][C]0.63621577221826[/C][/ROW]
[ROW][C]-7[/C][C]0.658189325821267[/C][/ROW]
[ROW][C]-6[/C][C]0.673814874642382[/C][/ROW]
[ROW][C]-5[/C][C]0.688949118994303[/C][/ROW]
[ROW][C]-4[/C][C]0.703548528297092[/C][/ROW]
[ROW][C]-3[/C][C]0.698522351794462[/C][/ROW]
[ROW][C]-2[/C][C]0.690072549046823[/C][/ROW]
[ROW][C]-1[/C][C]0.687276751343563[/C][/ROW]
[ROW][C]0[/C][C]0.68201209714473[/C][/ROW]
[ROW][C]1[/C][C]0.62686995373318[/C][/ROW]
[ROW][C]2[/C][C]0.573772000532124[/C][/ROW]
[ROW][C]3[/C][C]0.517996442050407[/C][/ROW]
[ROW][C]4[/C][C]0.45920759200244[/C][/ROW]
[ROW][C]5[/C][C]0.396443931215088[/C][/ROW]
[ROW][C]6[/C][C]0.331618344781629[/C][/ROW]
[ROW][C]7[/C][C]0.26959171349168[/C][/ROW]
[ROW][C]8[/C][C]0.20616805923057[/C][/ROW]
[ROW][C]9[/C][C]0.142406246612811[/C][/ROW]
[ROW][C]10[/C][C]0.070458361394234[/C][/ROW]
[ROW][C]11[/C][C]0.0136730506921101[/C][/ROW]
[ROW][C]12[/C][C]-0.0363654306752818[/C][/ROW]
[ROW][C]13[/C][C]-0.0768598658758853[/C][/ROW]
[ROW][C]14[/C][C]-0.101322786302566[/C][/ROW]
[ROW][C]15[/C][C]-0.116433221319894[/C][/ROW]
[ROW][C]16[/C][C]-0.134229446531611[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27977&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27977&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])
-160.477371768678956
-150.501130888436213
-140.517836417842789
-130.537625633150106
-120.55930423868451
-110.585066306703481
-100.602087376035606
-90.620474251526346
-80.63621577221826
-70.658189325821267
-60.673814874642382
-50.688949118994303
-40.703548528297092
-30.698522351794462
-20.690072549046823
-10.687276751343563
00.68201209714473
10.62686995373318
20.573772000532124
30.517996442050407
40.45920759200244
50.396443931215088
60.331618344781629
70.26959171349168
80.20616805923057
90.142406246612811
100.070458361394234
110.0136730506921101
12-0.0363654306752818
13-0.0768598658758853
14-0.101322786302566
15-0.116433221319894
16-0.134229446531611


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')