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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 computationSun, 30 Nov 2008 11:56:35 -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/Nov/30/t12280714259igoyrs7e7xu69j.htm/, Retrieved Sun, 19 May 2024 12:20:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26687, Retrieved Sun, 19 May 2024 12:20:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact187

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [] [2008-11-30 18:56:35] [6d40a467de0f28bd2350f82ac9522c51] [Current]
-    D    [Cross Correlation Function] [Q7] [2008-12-01 23:31:21] [73d6180dc45497329efd1b6934a84aba]
Feedback Forum
2008-12-05 16:56:46 [Kristof Van Esbroeck] [reply] 
De vraagstelling heeft betrekking op de cross correlatie functie. Deze tracht een voorspelling neer te zetten van een datareeks adhv een verschillende variabele. Dit in tegenstelling tot de autocorrelatie, welke tracht een voorspelling te maken adhv het verleden van de reeks.

We noteren voorts op de x as van de cross correlatie functie de verschillende lags.

We merken verschillende k waarden op in de bijhorende tabel, k = 0 verwijst naar de autocorrelatie. Een negatieve waarde heeft betrekking op het verleden, een positieve daarentegen verwijst naar de toekomst.
2008-12-08 08:42:47 [Dorien Peeters] [reply] 
De student heeft deze vraag volgens mij goed uitgevoerd. We gaan hier naar de cross-correlatie functie kijken. Hier gaan we aan de hand van een aantal variabelen, een voorspelling proberen te doen met de data.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
299,63
305,945
382,252
348,846
335,367
373,617
312,612
312,232
337,161
331,476
350,103
345,127
297,256
295,979
361,007
321,803
354,937
349,432
290,979
349,576
327,625
349,377
336,777
339,134
323,321
318,86
373,583
333,03
408,556
414,646
291,514
348,857
349,368
375,765
364,136
349,53
348,167
332,856
360,551
346,969
392,815
372,02
371,027
342,672
367,343
390,786
343,785
362,6
349,468
340,624
369,536
407,782
392,239
404,824
373,669
344,902
396,7
398,911
366,009
392,484
Dataseries Y:
Download CSV
Histogram 
154,783
187,646
237,863
215,54
231,745
199,548
164,147
159,388
203,514
224,901
211,539
211,16
181,712
203,908
240,774
232,819
255,221
246,7
206,263
211,679
236,601
237,43
233,767
219,52
222,625
216,238
248,587
221,376
242,453
246,539
189,351
185,956
213,175
228,732
212,93
218,254
227,103
219,026
264,529
262,057
258,779
231,928
211,167
205,439
224,883
228,624
209,435
215,607
287,356
306,015
338,546
344,16
328,412
342,006
277,668
290,477
314,967
324,627
290,646
315,033

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'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 & 0 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26687&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26687&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26687&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'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)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])
-140.061819893137789
-130.138134902709968
-120.326805943929688
-110.251920483269249
-100.196426106183129
-90.165181622412942
-80.171315859057678
-70.291962684089554
-60.394421770929548
-50.318230472388177
-40.194571863980822
-30.199473237299325
-20.166763713391238
-10.334817093914325
00.659021971841534
10.479044357273823
20.357672818769611
30.363200152443236
40.243210614016607
50.295693017759730
60.393186066459802
70.291502019850907
80.163520632340290
90.0670646218949132
10-0.0378310763228190
110.0786231290218252
120.252631834850761
130.12099628343496
14-0.00225722378305681

\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
-14 & 0.061819893137789 \tabularnewline
-13 & 0.138134902709968 \tabularnewline
-12 & 0.326805943929688 \tabularnewline
-11 & 0.251920483269249 \tabularnewline
-10 & 0.196426106183129 \tabularnewline
-9 & 0.165181622412942 \tabularnewline
-8 & 0.171315859057678 \tabularnewline
-7 & 0.291962684089554 \tabularnewline
-6 & 0.394421770929548 \tabularnewline
-5 & 0.318230472388177 \tabularnewline
-4 & 0.194571863980822 \tabularnewline
-3 & 0.199473237299325 \tabularnewline
-2 & 0.166763713391238 \tabularnewline
-1 & 0.334817093914325 \tabularnewline
0 & 0.659021971841534 \tabularnewline
1 & 0.479044357273823 \tabularnewline
2 & 0.357672818769611 \tabularnewline
3 & 0.363200152443236 \tabularnewline
4 & 0.243210614016607 \tabularnewline
5 & 0.295693017759730 \tabularnewline
6 & 0.393186066459802 \tabularnewline
7 & 0.291502019850907 \tabularnewline
8 & 0.163520632340290 \tabularnewline
9 & 0.0670646218949132 \tabularnewline
10 & -0.0378310763228190 \tabularnewline
11 & 0.0786231290218252 \tabularnewline
12 & 0.252631834850761 \tabularnewline
13 & 0.12099628343496 \tabularnewline
14 & -0.00225722378305681 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26687&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]-14[/C][C]0.061819893137789[/C][/ROW]
[ROW][C]-13[/C][C]0.138134902709968[/C][/ROW]
[ROW][C]-12[/C][C]0.326805943929688[/C][/ROW]
[ROW][C]-11[/C][C]0.251920483269249[/C][/ROW]
[ROW][C]-10[/C][C]0.196426106183129[/C][/ROW]
[ROW][C]-9[/C][C]0.165181622412942[/C][/ROW]
[ROW][C]-8[/C][C]0.171315859057678[/C][/ROW]
[ROW][C]-7[/C][C]0.291962684089554[/C][/ROW]
[ROW][C]-6[/C][C]0.394421770929548[/C][/ROW]
[ROW][C]-5[/C][C]0.318230472388177[/C][/ROW]
[ROW][C]-4[/C][C]0.194571863980822[/C][/ROW]
[ROW][C]-3[/C][C]0.199473237299325[/C][/ROW]
[ROW][C]-2[/C][C]0.166763713391238[/C][/ROW]
[ROW][C]-1[/C][C]0.334817093914325[/C][/ROW]
[ROW][C]0[/C][C]0.659021971841534[/C][/ROW]
[ROW][C]1[/C][C]0.479044357273823[/C][/ROW]
[ROW][C]2[/C][C]0.357672818769611[/C][/ROW]
[ROW][C]3[/C][C]0.363200152443236[/C][/ROW]
[ROW][C]4[/C][C]0.243210614016607[/C][/ROW]
[ROW][C]5[/C][C]0.295693017759730[/C][/ROW]
[ROW][C]6[/C][C]0.393186066459802[/C][/ROW]
[ROW][C]7[/C][C]0.291502019850907[/C][/ROW]
[ROW][C]8[/C][C]0.163520632340290[/C][/ROW]
[ROW][C]9[/C][C]0.0670646218949132[/C][/ROW]
[ROW][C]10[/C][C]-0.0378310763228190[/C][/ROW]
[ROW][C]11[/C][C]0.0786231290218252[/C][/ROW]
[ROW][C]12[/C][C]0.252631834850761[/C][/ROW]
[ROW][C]13[/C][C]0.12099628343496[/C][/ROW]
[ROW][C]14[/C][C]-0.00225722378305681[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26687&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26687&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])
-140.061819893137789
-130.138134902709968
-120.326805943929688
-110.251920483269249
-100.196426106183129
-90.165181622412942
-80.171315859057678
-70.291962684089554
-60.394421770929548
-50.318230472388177
-40.194571863980822
-30.199473237299325
-20.166763713391238
-10.334817093914325
00.659021971841534
10.479044357273823
20.357672818769611
30.363200152443236
40.243210614016607
50.295693017759730
60.393186066459802
70.291502019850907
80.163520632340290
90.0670646218949132
10-0.0378310763228190
110.0786231290218252
120.252631834850761
130.12099628343496
14-0.00225722378305681


Figures (Output of Computation)

Input Parameters & R Code

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