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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 computationSat, 29 Nov 2008 07:03:12 -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/29/t122796744783of4wvxwsvzlpj.htm/, Retrieved Mon, 20 May 2024 08:37:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26286, Retrieved Mon, 20 May 2024 08:37:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact182

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] [CCF xd = 1 xD=1 y...] [2008-11-29 14:03:12] [9b05d7ef5dbcfba4217d280d9092f628] [Current]
Feedback Forum
2008-12-08 20:17:51 [8e2cc0b2ef568da46d009b2f601285b2] [reply] 
Correcte CCF, beide reeksen zijn stationair, nu zien we dat er geen verband is tussen x en y, x kan dus niet gebruikt worden om het verleden van y te verklaren en x kan de toekomst van y ook niet voorspellen.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
150739
159129
157928
147768
137507
136919
136151
133001
125554
119647
114158
116193
152803
161761
160942
149470
139208
134588
130322
126611
122401
117352
112135
112879
148729
157230
157221
146681
136524
132111
125326
122716
116615
113719
110737
112093
143565
149946
149147
134339
122683
115614
116566
111272
104609
101802
94542
93051
124129
130374
123946
114971
105531
104919
104782
101281
94545
93248
84031
87486
115867
Dataseries Y:
Download CSV
Histogram 
374556
375021
375787
372720
364431
370490
376974
377632
378205
370861
369167
371551
382842
381903
384502
392058
384359
388884
386586
387495
385705
378670
377367
376911
389827
387820
387267
380575
372402
376740
377795
376126
370804
367980
367866
366121
379421
378519
372423
355072
344693
342892
344178
337606
327103
323953
316532
306307
327225
329573
313761
307836
300074
304198
306122
300414
292133
290616
280244
285179
305486

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=26286&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=26286&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26286&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 series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
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])
-13-0.0114835534276636
-120.0597265539982125
-110.0482405821758069
-10-0.104003102408306
-90.074131096882253
-80.0365086841271687
-70.0781941666911744
-6-0.0053721494774298
-50.0221363133388464
-40.0536874727289123
-30.0870590290643611
-2-0.137915355376149
-10.113563656902416
0-0.316101664522849
1-0.159379858097569
2-0.128321284762782
3-0.00306137319788006
40.00669991603544697
5-0.0071882653294758
60.0926658929709522
70.0273793989917275
8-0.0467211330746369
9-0.00780834640931086
10-0.0115691467841052
110.103006260178025
12-0.232753850394278
13-0.0531174525617953

\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 & 1 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & -0.0114835534276636 \tabularnewline
-12 & 0.0597265539982125 \tabularnewline
-11 & 0.0482405821758069 \tabularnewline
-10 & -0.104003102408306 \tabularnewline
-9 & 0.074131096882253 \tabularnewline
-8 & 0.0365086841271687 \tabularnewline
-7 & 0.0781941666911744 \tabularnewline
-6 & -0.0053721494774298 \tabularnewline
-5 & 0.0221363133388464 \tabularnewline
-4 & 0.0536874727289123 \tabularnewline
-3 & 0.0870590290643611 \tabularnewline
-2 & -0.137915355376149 \tabularnewline
-1 & 0.113563656902416 \tabularnewline
0 & -0.316101664522849 \tabularnewline
1 & -0.159379858097569 \tabularnewline
2 & -0.128321284762782 \tabularnewline
3 & -0.00306137319788006 \tabularnewline
4 & 0.00669991603544697 \tabularnewline
5 & -0.0071882653294758 \tabularnewline
6 & 0.0926658929709522 \tabularnewline
7 & 0.0273793989917275 \tabularnewline
8 & -0.0467211330746369 \tabularnewline
9 & -0.00780834640931086 \tabularnewline
10 & -0.0115691467841052 \tabularnewline
11 & 0.103006260178025 \tabularnewline
12 & -0.232753850394278 \tabularnewline
13 & -0.0531174525617953 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26286&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]1[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]1[/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]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]-13[/C][C]-0.0114835534276636[/C][/ROW]
[ROW][C]-12[/C][C]0.0597265539982125[/C][/ROW]
[ROW][C]-11[/C][C]0.0482405821758069[/C][/ROW]
[ROW][C]-10[/C][C]-0.104003102408306[/C][/ROW]
[ROW][C]-9[/C][C]0.074131096882253[/C][/ROW]
[ROW][C]-8[/C][C]0.0365086841271687[/C][/ROW]
[ROW][C]-7[/C][C]0.0781941666911744[/C][/ROW]
[ROW][C]-6[/C][C]-0.0053721494774298[/C][/ROW]
[ROW][C]-5[/C][C]0.0221363133388464[/C][/ROW]
[ROW][C]-4[/C][C]0.0536874727289123[/C][/ROW]
[ROW][C]-3[/C][C]0.0870590290643611[/C][/ROW]
[ROW][C]-2[/C][C]-0.137915355376149[/C][/ROW]
[ROW][C]-1[/C][C]0.113563656902416[/C][/ROW]
[ROW][C]0[/C][C]-0.316101664522849[/C][/ROW]
[ROW][C]1[/C][C]-0.159379858097569[/C][/ROW]
[ROW][C]2[/C][C]-0.128321284762782[/C][/ROW]
[ROW][C]3[/C][C]-0.00306137319788006[/C][/ROW]
[ROW][C]4[/C][C]0.00669991603544697[/C][/ROW]
[ROW][C]5[/C][C]-0.0071882653294758[/C][/ROW]
[ROW][C]6[/C][C]0.0926658929709522[/C][/ROW]
[ROW][C]7[/C][C]0.0273793989917275[/C][/ROW]
[ROW][C]8[/C][C]-0.0467211330746369[/C][/ROW]
[ROW][C]9[/C][C]-0.00780834640931086[/C][/ROW]
[ROW][C]10[/C][C]-0.0115691467841052[/C][/ROW]
[ROW][C]11[/C][C]0.103006260178025[/C][/ROW]
[ROW][C]12[/C][C]-0.232753850394278[/C][/ROW]
[ROW][C]13[/C][C]-0.0531174525617953[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26286&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26286&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 series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
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])
-13-0.0114835534276636
-120.0597265539982125
-110.0482405821758069
-10-0.104003102408306
-90.074131096882253
-80.0365086841271687
-70.0781941666911744
-6-0.0053721494774298
-50.0221363133388464
-40.0536874727289123
-30.0870590290643611
-2-0.137915355376149
-10.113563656902416
0-0.316101664522849
1-0.159379858097569
2-0.128321284762782
3-0.00306137319788006
40.00669991603544697
5-0.0071882653294758
60.0926658929709522
70.0273793989917275
8-0.0467211330746369
9-0.00780834640931086
10-0.0115691467841052
110.103006260178025
12-0.232753850394278
13-0.0531174525617953


Figures (Output of Computation)

Input Parameters & R Code

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