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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 13:08:32 -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/t1228248581hi51sf2cfjghjye.htm/, Retrieved Sun, 19 May 2024 12:19:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28329, Retrieved Sun, 19 May 2024 12:19:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsVan Dooren Leen
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] [NST Q7] [2008-12-02 20:08:32] [006ad2c49b6a7c2ad6ab685cfc1dae56] [Current]
Feedback Forum
2008-12-06 19:37:58 [Stefan Temmerman] [reply] 
De student voert de berekening juist uit, en merkt op dat er enkele correlaties buiten het betrouwbaarheidsinterval liggen. Dit wil zeggen dat we Y(t) nog kunnen voorspellen aan de hand van de tijdreeks X(t).
2008-12-07 11:23:04 [006ad2c49b6a7c2ad6ab685cfc1dae56] [reply] 
Goede berekening en goede uitleg.
2008-12-07 11:57:20 [Lana Van Wesemael] [reply] 
mooi uitgewerkt. Let er wel op dat je de volgende keer even kort verteld welke tijdreeks je gebruikt hebt. Dat maakt het iets duidelijker voor de lezer.
2008-12-08 19:40:37 [Birgit Van Dyck] [reply] 
De student geeft een goede oplossing. de Crosscorrelatiefunctie geeft aan in hoeverre Y te verklaren is door het verleden van X.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
127
123
118
114
108
111
151
159
158
148
138
137
136
133
126
120
114
116
153
162
161
149
139
135
130
127
122
117
112
113
149
157
157
147
137
132
125
123
117
114
111
112
144
150
149
134
123
116
117
111
105
102
95
93
124
130
124
115
106
105
105
101
95
93
84
87
116
120
117
109
Dataseries Y:
Download CSV
Histogram 
392
394
392
396
392
396
419
421
420
418
410
418
426
428
430
424
423
427
441
449
452
462
455
461
461
463
462
456
455
456
472
472
471
465
459
465
468
467
463
460
462
461
476
476
471
453
443
442
444
438
427
424
416
406
431
434
418
412
404
409
412
406
398
397
385
390
413
413
401
397

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28329&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28329&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28329&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 time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






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])
-150.290667768948981
-140.328690411343593
-130.415139858411423
-120.490279710165437
-110.456278045974434
-100.379311485052272
-90.316118913295665
-80.305404226301571
-70.34291672094931
-60.386558910108383
-50.400714717929207
-40.379275838082382
-30.365773558042877
-20.388426019175741
-10.467323061556177
00.528557426433485
10.430464094689757
20.285626495251627
30.161557612626386
40.0855764923829647
50.0375410122236516
6-0.00596334198517975
7-0.0297406630567994
8-0.0741860972707307
9-0.081422432158432
10-0.0583833994409818
11-0.0071650409479211
120.0134404919583613
13-0.0703940518293046
14-0.182453866375995
15-0.279614039663487

\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
-15 & 0.290667768948981 \tabularnewline
-14 & 0.328690411343593 \tabularnewline
-13 & 0.415139858411423 \tabularnewline
-12 & 0.490279710165437 \tabularnewline
-11 & 0.456278045974434 \tabularnewline
-10 & 0.379311485052272 \tabularnewline
-9 & 0.316118913295665 \tabularnewline
-8 & 0.305404226301571 \tabularnewline
-7 & 0.34291672094931 \tabularnewline
-6 & 0.386558910108383 \tabularnewline
-5 & 0.400714717929207 \tabularnewline
-4 & 0.379275838082382 \tabularnewline
-3 & 0.365773558042877 \tabularnewline
-2 & 0.388426019175741 \tabularnewline
-1 & 0.467323061556177 \tabularnewline
0 & 0.528557426433485 \tabularnewline
1 & 0.430464094689757 \tabularnewline
2 & 0.285626495251627 \tabularnewline
3 & 0.161557612626386 \tabularnewline
4 & 0.0855764923829647 \tabularnewline
5 & 0.0375410122236516 \tabularnewline
6 & -0.00596334198517975 \tabularnewline
7 & -0.0297406630567994 \tabularnewline
8 & -0.0741860972707307 \tabularnewline
9 & -0.081422432158432 \tabularnewline
10 & -0.0583833994409818 \tabularnewline
11 & -0.0071650409479211 \tabularnewline
12 & 0.0134404919583613 \tabularnewline
13 & -0.0703940518293046 \tabularnewline
14 & -0.182453866375995 \tabularnewline
15 & -0.279614039663487 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28329&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]-15[/C][C]0.290667768948981[/C][/ROW]
[ROW][C]-14[/C][C]0.328690411343593[/C][/ROW]
[ROW][C]-13[/C][C]0.415139858411423[/C][/ROW]
[ROW][C]-12[/C][C]0.490279710165437[/C][/ROW]
[ROW][C]-11[/C][C]0.456278045974434[/C][/ROW]
[ROW][C]-10[/C][C]0.379311485052272[/C][/ROW]
[ROW][C]-9[/C][C]0.316118913295665[/C][/ROW]
[ROW][C]-8[/C][C]0.305404226301571[/C][/ROW]
[ROW][C]-7[/C][C]0.34291672094931[/C][/ROW]
[ROW][C]-6[/C][C]0.386558910108383[/C][/ROW]
[ROW][C]-5[/C][C]0.400714717929207[/C][/ROW]
[ROW][C]-4[/C][C]0.379275838082382[/C][/ROW]
[ROW][C]-3[/C][C]0.365773558042877[/C][/ROW]
[ROW][C]-2[/C][C]0.388426019175741[/C][/ROW]
[ROW][C]-1[/C][C]0.467323061556177[/C][/ROW]
[ROW][C]0[/C][C]0.528557426433485[/C][/ROW]
[ROW][C]1[/C][C]0.430464094689757[/C][/ROW]
[ROW][C]2[/C][C]0.285626495251627[/C][/ROW]
[ROW][C]3[/C][C]0.161557612626386[/C][/ROW]
[ROW][C]4[/C][C]0.0855764923829647[/C][/ROW]
[ROW][C]5[/C][C]0.0375410122236516[/C][/ROW]
[ROW][C]6[/C][C]-0.00596334198517975[/C][/ROW]
[ROW][C]7[/C][C]-0.0297406630567994[/C][/ROW]
[ROW][C]8[/C][C]-0.0741860972707307[/C][/ROW]
[ROW][C]9[/C][C]-0.081422432158432[/C][/ROW]
[ROW][C]10[/C][C]-0.0583833994409818[/C][/ROW]
[ROW][C]11[/C][C]-0.0071650409479211[/C][/ROW]
[ROW][C]12[/C][C]0.0134404919583613[/C][/ROW]
[ROW][C]13[/C][C]-0.0703940518293046[/C][/ROW]
[ROW][C]14[/C][C]-0.182453866375995[/C][/ROW]
[ROW][C]15[/C][C]-0.279614039663487[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28329&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28329&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])
-150.290667768948981
-140.328690411343593
-130.415139858411423
-120.490279710165437
-110.456278045974434
-100.379311485052272
-90.316118913295665
-80.305404226301571
-70.34291672094931
-60.386558910108383
-50.400714717929207
-40.379275838082382
-30.365773558042877
-20.388426019175741
-10.467323061556177
00.528557426433485
10.430464094689757
20.285626495251627
30.161557612626386
40.0855764923829647
50.0375410122236516
6-0.00596334198517975
7-0.0297406630567994
8-0.0741860972707307
9-0.081422432158432
10-0.0583833994409818
11-0.0071650409479211
120.0134404919583613
13-0.0703940518293046
14-0.182453866375995
15-0.279614039663487


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')