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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 10:47:04 -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/t1228240080qk6011sgxplmbub.htm/, Retrieved Sun, 19 May 2024 10:44:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28141, Retrieved Sun, 19 May 2024 10:44:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSeverijns Britt
Estimated Impact158

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] [non stationary ti...] [2008-12-02 17:47:04] [78308c9f3efc33d1da821bcd963df161] [Current]
Feedback Forum
2008-12-08 17:55:55 [Jessica Alves Pires] [reply] 
Goede berekening. Je had misschien je eigen tijdreeksen erbij kunnen voegen en meer uitleg kunnen geven erover. Je had ook nog iets kunnen zeggen over de waarden in de tabel , bijvoorbeeld: De correlatie tussen Yt en Xt met 16 perioden vertraagd ten opzichte van Yt is -0.0367316101886205.
2008-12-08 19:57:15 [Vincent Dolhain] [reply] 
goede berekening
2008-12-08 19:57:15 [Vincent Dolhain] [reply] 
goede berekening

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98,1
101,1
111,1
93,3
100
108
70,4
75,4
105,5
112,3
102,5
93,5
86,7
95,2
103,8
97
95,5
101
67,5
64
106,7
100,6
101,2
93,1
84,2
85,8
91,8
92,4
80,3
79,7
62,5
57,1
100,8
100,7
86,2
83,2
71,7
77,5
89,8
80,3
78,7
93,8
57,6
60,6
91
85,3
77,4
77,3
68,3
69,9
81,7
75,1
69,9
84
54,3
60
89,9
77
85,3
77,6
69,2
75,5
85,7
72,2
79,9
85,3
52,2
61,2
82,4
85,4
78,2
70,2
70,2
69,3
77,5
66,1
69
79,2
56,2
63,3
77,8
92
78,1
65,1
71,1
70,9
72
81,9
70,6
72,5
65,1
54,9
Dataseries Y:
Download CSV
Histogram 
467037
460070
447988
442867
436087
431328
484015
509673
512927
502831
470984
471067
476049
474605
470439
461251
454724
455626
516847
525192
522975
518585
509239
512238
519164
517009
509933
509127
500857
506971
569323
579714
577992
565464
547344
554788
562325
560854
555332
543599
536662
542722
593530
610763
612613
611324
594167
595454
590865
589379
584428
573100
567456
569028
620735
628884
628232
612117
595404
597141
593408
590072
579799
574205
572775
572942
619567
625809
619916
587625
565742
557274
560576
548854
531673
525919
511038
498662
555362
564591
541657
527070
509846
514258
516922
507561
492622
490243
469357
477580
528379
533590

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'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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28141&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28141&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28141&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'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 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])
-16-0.0367316101886205
-15-0.126181763180703
-14-0.204542964280387
-13-0.247040972822727
-12-0.267937353739319
-11-0.116975966837270
-10-0.0210093767679633
-9-0.134446542858562
-8-0.256395517816844
-7-0.31324660007399
-6-0.316013515349562
-5-0.288738032055173
-4-0.278044401586320
-3-0.374425631351850
-2-0.477335109498284
-1-0.516895798296587
0-0.504786648375146
1-0.309918523300158
2-0.174015538376377
3-0.269842291673533
4-0.401642750787541
5-0.446085149639695
6-0.424096887351309
7-0.402539801784781
8-0.371184258819317
9-0.416133497349581
10-0.474419158584071
11-0.466263818989752
12-0.440373662791963
13-0.27144153205152
14-0.151244114996984
15-0.230982667973388
16-0.323038618896181

\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 & -0.0367316101886205 \tabularnewline
-15 & -0.126181763180703 \tabularnewline
-14 & -0.204542964280387 \tabularnewline
-13 & -0.247040972822727 \tabularnewline
-12 & -0.267937353739319 \tabularnewline
-11 & -0.116975966837270 \tabularnewline
-10 & -0.0210093767679633 \tabularnewline
-9 & -0.134446542858562 \tabularnewline
-8 & -0.256395517816844 \tabularnewline
-7 & -0.31324660007399 \tabularnewline
-6 & -0.316013515349562 \tabularnewline
-5 & -0.288738032055173 \tabularnewline
-4 & -0.278044401586320 \tabularnewline
-3 & -0.374425631351850 \tabularnewline
-2 & -0.477335109498284 \tabularnewline
-1 & -0.516895798296587 \tabularnewline
0 & -0.504786648375146 \tabularnewline
1 & -0.309918523300158 \tabularnewline
2 & -0.174015538376377 \tabularnewline
3 & -0.269842291673533 \tabularnewline
4 & -0.401642750787541 \tabularnewline
5 & -0.446085149639695 \tabularnewline
6 & -0.424096887351309 \tabularnewline
7 & -0.402539801784781 \tabularnewline
8 & -0.371184258819317 \tabularnewline
9 & -0.416133497349581 \tabularnewline
10 & -0.474419158584071 \tabularnewline
11 & -0.466263818989752 \tabularnewline
12 & -0.440373662791963 \tabularnewline
13 & -0.27144153205152 \tabularnewline
14 & -0.151244114996984 \tabularnewline
15 & -0.230982667973388 \tabularnewline
16 & -0.323038618896181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28141&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]-0.0367316101886205[/C][/ROW]
[ROW][C]-15[/C][C]-0.126181763180703[/C][/ROW]
[ROW][C]-14[/C][C]-0.204542964280387[/C][/ROW]
[ROW][C]-13[/C][C]-0.247040972822727[/C][/ROW]
[ROW][C]-12[/C][C]-0.267937353739319[/C][/ROW]
[ROW][C]-11[/C][C]-0.116975966837270[/C][/ROW]
[ROW][C]-10[/C][C]-0.0210093767679633[/C][/ROW]
[ROW][C]-9[/C][C]-0.134446542858562[/C][/ROW]
[ROW][C]-8[/C][C]-0.256395517816844[/C][/ROW]
[ROW][C]-7[/C][C]-0.31324660007399[/C][/ROW]
[ROW][C]-6[/C][C]-0.316013515349562[/C][/ROW]
[ROW][C]-5[/C][C]-0.288738032055173[/C][/ROW]
[ROW][C]-4[/C][C]-0.278044401586320[/C][/ROW]
[ROW][C]-3[/C][C]-0.374425631351850[/C][/ROW]
[ROW][C]-2[/C][C]-0.477335109498284[/C][/ROW]
[ROW][C]-1[/C][C]-0.516895798296587[/C][/ROW]
[ROW][C]0[/C][C]-0.504786648375146[/C][/ROW]
[ROW][C]1[/C][C]-0.309918523300158[/C][/ROW]
[ROW][C]2[/C][C]-0.174015538376377[/C][/ROW]
[ROW][C]3[/C][C]-0.269842291673533[/C][/ROW]
[ROW][C]4[/C][C]-0.401642750787541[/C][/ROW]
[ROW][C]5[/C][C]-0.446085149639695[/C][/ROW]
[ROW][C]6[/C][C]-0.424096887351309[/C][/ROW]
[ROW][C]7[/C][C]-0.402539801784781[/C][/ROW]
[ROW][C]8[/C][C]-0.371184258819317[/C][/ROW]
[ROW][C]9[/C][C]-0.416133497349581[/C][/ROW]
[ROW][C]10[/C][C]-0.474419158584071[/C][/ROW]
[ROW][C]11[/C][C]-0.466263818989752[/C][/ROW]
[ROW][C]12[/C][C]-0.440373662791963[/C][/ROW]
[ROW][C]13[/C][C]-0.27144153205152[/C][/ROW]
[ROW][C]14[/C][C]-0.151244114996984[/C][/ROW]
[ROW][C]15[/C][C]-0.230982667973388[/C][/ROW]
[ROW][C]16[/C][C]-0.323038618896181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28141&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28141&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])
-16-0.0367316101886205
-15-0.126181763180703
-14-0.204542964280387
-13-0.247040972822727
-12-0.267937353739319
-11-0.116975966837270
-10-0.0210093767679633
-9-0.134446542858562
-8-0.256395517816844
-7-0.31324660007399
-6-0.316013515349562
-5-0.288738032055173
-4-0.278044401586320
-3-0.374425631351850
-2-0.477335109498284
-1-0.516895798296587
0-0.504786648375146
1-0.309918523300158
2-0.174015538376377
3-0.269842291673533
4-0.401642750787541
5-0.446085149639695
6-0.424096887351309
7-0.402539801784781
8-0.371184258819317
9-0.416133497349581
10-0.474419158584071
11-0.466263818989752
12-0.440373662791963
13-0.27144153205152
14-0.151244114996984
15-0.230982667973388
16-0.323038618896181


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')