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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 10:57:58 -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/t1228240704qhzz0531d0rotgq.htm/, Retrieved Fri, 17 May 2024 07:01:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28154, Retrieved Fri, 17 May 2024 07:01:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153

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] [] [2008-12-02 17:57:58] [e02910eed3830f1815f587e12f46cbdb] [Current]
Feedback Forum
2008-12-06 10:53:17 [Angelique Van de Vijver] [reply] 
goede berekening en goede vaststellingen van de student.
De kruiscorrelatiefunctie heeft niks te maken met een trend. Het geeft het verband weer tussen 2 verschillende tijdreeksen.
Als k negatief is kan je verleden van X gebruiken om de Y te voorspellen.
Als k positief is kan je de toekomst van X gebruiken om Y te voorspellen.
Een leading indicator is een voorspellende waarde, iets dat vooroploopt. Het is een indicator die je op voorhand zegt wat het verloop van een andere variabele is.
Deze kruiscorrelatie kan evenwel beïvloed zijn door een derde variabele. Om deze invloed te elimineren moeten we de partiële correlatie nemen. We moeten dus eerst de 3e variabele elimineren om dan het zuiver verband tussen X en Y te hebben.
2008-12-09 21:30:56 [Anouk Greeve] [reply] 
Het lijkt allicht een makkelijke oplossing, maar ik kan niet veel meer toevoegen aan wat angelique reeds verwoord heeft. De berekeningen zien er correct uit.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
118,4
121,4
128,8
131,7
141,7
142,9
139,4
134,7
125,0
113,6
111,5
108,5
112,3
116,6
115,5
120,1
132,9
128,1
129,3
132,5
131,0
124,9
120,8
122,0
122,1
127,4
135,2
137,3
135,0
136,0
138,4
134,7
138,4
133,9
133,6
141,2
151,8
155,4
156,6
161,6
160,7
156,0
159,5
168,7
169,9
169,9
185,9
190,8
195,8
211,9
227,1
251,3
256,7
251,9
251,2
270,3
267,2
243,0
229,9
187,2
Dataseries Y:
Download CSV
Histogram 
107.1
110.7
117.1
118.7
126.5
127.5
134.6
131.8
135.9
142.7
141.7
153.4
145.0
137.7
148.3
152.2
169.4
168.6
161.1
174.1
179.0
190.6
190.0
181.6
174.8
180.5
196.8
193.8
197.0
216.3
221.4
217.9
229.7
227.4
204.2
196.6
198.8
207.5
190.7
201.6
210.5
223.5
223.8
231.2
244.0
234.7
250.2
265.7
287.6
283.3
295.4
312.3
333.8
347.7
383.2
407.1
413.6
362.7
321.9
239.4

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=28154&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=28154&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28154&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])
-140.0696232589589059
-130.103344804627444
-120.140225797901003
-110.177797893887537
-100.224677414525524
-90.279833884611009
-80.347700992541387
-70.432496469791598
-60.523176027114058
-50.614627950167337
-40.698742787628194
-30.77783088544257
-20.845388844777876
-10.89965907799631
00.928381013034265
10.89676361871178
20.839148968315796
30.768631889698596
40.692491607688836
50.626793302502646
60.57327571678276
70.52790217414409
80.482444306291108
90.436799967936786
100.389153465133968
110.339823097869472
120.292411994506373
130.25536641194379
140.229215571311416

\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
-14 & 0.0696232589589059 \tabularnewline
-13 & 0.103344804627444 \tabularnewline
-12 & 0.140225797901003 \tabularnewline
-11 & 0.177797893887537 \tabularnewline
-10 & 0.224677414525524 \tabularnewline
-9 & 0.279833884611009 \tabularnewline
-8 & 0.347700992541387 \tabularnewline
-7 & 0.432496469791598 \tabularnewline
-6 & 0.523176027114058 \tabularnewline
-5 & 0.614627950167337 \tabularnewline
-4 & 0.698742787628194 \tabularnewline
-3 & 0.77783088544257 \tabularnewline
-2 & 0.845388844777876 \tabularnewline
-1 & 0.89965907799631 \tabularnewline
0 & 0.928381013034265 \tabularnewline
1 & 0.89676361871178 \tabularnewline
2 & 0.839148968315796 \tabularnewline
3 & 0.768631889698596 \tabularnewline
4 & 0.692491607688836 \tabularnewline
5 & 0.626793302502646 \tabularnewline
6 & 0.57327571678276 \tabularnewline
7 & 0.52790217414409 \tabularnewline
8 & 0.482444306291108 \tabularnewline
9 & 0.436799967936786 \tabularnewline
10 & 0.389153465133968 \tabularnewline
11 & 0.339823097869472 \tabularnewline
12 & 0.292411994506373 \tabularnewline
13 & 0.25536641194379 \tabularnewline
14 & 0.229215571311416 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28154&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]-14[/C][C]0.0696232589589059[/C][/ROW]
[ROW][C]-13[/C][C]0.103344804627444[/C][/ROW]
[ROW][C]-12[/C][C]0.140225797901003[/C][/ROW]
[ROW][C]-11[/C][C]0.177797893887537[/C][/ROW]
[ROW][C]-10[/C][C]0.224677414525524[/C][/ROW]
[ROW][C]-9[/C][C]0.279833884611009[/C][/ROW]
[ROW][C]-8[/C][C]0.347700992541387[/C][/ROW]
[ROW][C]-7[/C][C]0.432496469791598[/C][/ROW]
[ROW][C]-6[/C][C]0.523176027114058[/C][/ROW]
[ROW][C]-5[/C][C]0.614627950167337[/C][/ROW]
[ROW][C]-4[/C][C]0.698742787628194[/C][/ROW]
[ROW][C]-3[/C][C]0.77783088544257[/C][/ROW]
[ROW][C]-2[/C][C]0.845388844777876[/C][/ROW]
[ROW][C]-1[/C][C]0.89965907799631[/C][/ROW]
[ROW][C]0[/C][C]0.928381013034265[/C][/ROW]
[ROW][C]1[/C][C]0.89676361871178[/C][/ROW]
[ROW][C]2[/C][C]0.839148968315796[/C][/ROW]
[ROW][C]3[/C][C]0.768631889698596[/C][/ROW]
[ROW][C]4[/C][C]0.692491607688836[/C][/ROW]
[ROW][C]5[/C][C]0.626793302502646[/C][/ROW]
[ROW][C]6[/C][C]0.57327571678276[/C][/ROW]
[ROW][C]7[/C][C]0.52790217414409[/C][/ROW]
[ROW][C]8[/C][C]0.482444306291108[/C][/ROW]
[ROW][C]9[/C][C]0.436799967936786[/C][/ROW]
[ROW][C]10[/C][C]0.389153465133968[/C][/ROW]
[ROW][C]11[/C][C]0.339823097869472[/C][/ROW]
[ROW][C]12[/C][C]0.292411994506373[/C][/ROW]
[ROW][C]13[/C][C]0.25536641194379[/C][/ROW]
[ROW][C]14[/C][C]0.229215571311416[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28154&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28154&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])
-140.0696232589589059
-130.103344804627444
-120.140225797901003
-110.177797893887537
-100.224677414525524
-90.279833884611009
-80.347700992541387
-70.432496469791598
-60.523176027114058
-50.614627950167337
-40.698742787628194
-30.77783088544257
-20.845388844777876
-10.89965907799631
00.928381013034265
10.89676361871178
20.839148968315796
30.768631889698596
40.692491607688836
50.626793302502646
60.57327571678276
70.52790217414409
80.482444306291108
90.436799967936786
100.389153465133968
110.339823097869472
120.292411994506373
130.25536641194379
140.229215571311416


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')