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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 computationMon, 01 Dec 2008 12:14:32 -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/01/t1228159115ldb4jbzv8ovxvsj.htm/, Retrieved Sun, 05 May 2024 13:55:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27209, Retrieved Sun, 05 May 2024 13:55:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact217

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  [(Partial) Autocorrelation Function] [NSTS_Q5] [2008-11-30 17:55:01] [9f5bfe3b95f9ec3d2ed4c0a560a9648a]
F RMPD      [Cross Correlation Function] [NSTS_Q7 (woningen)] [2008-12-01 19:14:32] [a9e6d7cd6e144e8b311d9f96a24c5a25] [Current]
Feedback Forum
2008-12-08 17:07:05 [Sandra Hofmans] [reply] 

Het klopt dat we het verband verdwenen is. Dit is te verklaren doordat de correlatie vertekend kan worden door een derde variabele. Deze kan een invloed uitoefenen op zowel x als y. We moeten deze variabele toch elimineren.
2008-12-10 08:27:35 [Lana Van Wesemael] [reply] 
De cross-correlation function toont het verband tussen 2 verschillende variabelen. De cross-correlation function kan ons vertellen in welke mate we Y(t) kunnen verklaren door naar het verleden van X(t) te kijken. De cross-correlation functie die ik hier gemaakt hebt zal niet helemaal kloppen. Het was immers niet nodig om de lamda waarde aan te passen (zie Q8), wat ik wel gedaan heb..
2008-12-10 10:33:13 [Peter Van Doninck] [reply] 
Dit klopt. Deze grafiek is veel betrouwbaarder dan de vorige! Toen was er sprake van een nonsenscorrelatie, die hier verdwenen is!

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
82.7
88.9
105.9
100.8
94
105
58.5
87.6
113.1
112.5
89.6
74.5
82.7
90.1
109.4
96
89.2
109.1
49.1
92.9
107.7
103.5
91.1
79.8
71.9
82.9
90.1
100.7
90.7
108.8
44.1
93.6
107.4
96.5
93.6
76.5
76.7
84
103.3
88.5
99
105.9
44.7
94
107.1
104.8
102.5
77.7
85.2
91.3
106.5
92.4
97.5
107
51.1
98.6
102.2
114.3
99.4
72.5
Dataseries Y:
Download CSV
Histogram 
2916
3180
4151
4023
3431
3874
2617
3580
5267
3832
3441
3228
3397
3971
4625
4486
4131
4686
3174
4282
4209
4159
3936
3153
3620
4227
4441
4808
4850
5040
3546
4669
5410
5134
4864
3999
4459
4622
5360
4658
5173
4845
3325
4720
4895
5071
4895
3805
4187
4435
4475
4774
5161
4529
3284
4303
4610
4691
4200
3471

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=27209&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=27209&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27209&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 series2
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1.5
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.15093760013791
-12-0.429678308993222
-11-0.100645753070148
-10-0.332712231645253
-9-0.217262068763443
-8-0.309650419479773
-7-0.355085367243801
-6-0.272758467332468
-5-0.063295242510736
-4-0.257106745784193
-3-0.248671910082628
-2-0.142656743845357
-1-0.336100025124361
00.0883534882811493
1-0.162414376791839
2-0.0896577394667244
3-0.127188434046481
4-0.0753416737453197
5-0.100611443218147
60.171992068992666
7-0.298138467975319
8-0.0452120691218067
9-0.0659498735329168
100.0744694902453523
110.00734431753510476
120.0661778230387482
130.143061963478648

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 2 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1.5 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & -0.15093760013791 \tabularnewline
-12 & -0.429678308993222 \tabularnewline
-11 & -0.100645753070148 \tabularnewline
-10 & -0.332712231645253 \tabularnewline
-9 & -0.217262068763443 \tabularnewline
-8 & -0.309650419479773 \tabularnewline
-7 & -0.355085367243801 \tabularnewline
-6 & -0.272758467332468 \tabularnewline
-5 & -0.063295242510736 \tabularnewline
-4 & -0.257106745784193 \tabularnewline
-3 & -0.248671910082628 \tabularnewline
-2 & -0.142656743845357 \tabularnewline
-1 & -0.336100025124361 \tabularnewline
0 & 0.0883534882811493 \tabularnewline
1 & -0.162414376791839 \tabularnewline
2 & -0.0896577394667244 \tabularnewline
3 & -0.127188434046481 \tabularnewline
4 & -0.0753416737453197 \tabularnewline
5 & -0.100611443218147 \tabularnewline
6 & 0.171992068992666 \tabularnewline
7 & -0.298138467975319 \tabularnewline
8 & -0.0452120691218067 \tabularnewline
9 & -0.0659498735329168 \tabularnewline
10 & 0.0744694902453523 \tabularnewline
11 & 0.00734431753510476 \tabularnewline
12 & 0.0661778230387482 \tabularnewline
13 & 0.143061963478648 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27209&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]2[/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]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]1.5[/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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]-0.15093760013791[/C][/ROW]
[ROW][C]-12[/C][C]-0.429678308993222[/C][/ROW]
[ROW][C]-11[/C][C]-0.100645753070148[/C][/ROW]
[ROW][C]-10[/C][C]-0.332712231645253[/C][/ROW]
[ROW][C]-9[/C][C]-0.217262068763443[/C][/ROW]
[ROW][C]-8[/C][C]-0.309650419479773[/C][/ROW]
[ROW][C]-7[/C][C]-0.355085367243801[/C][/ROW]
[ROW][C]-6[/C][C]-0.272758467332468[/C][/ROW]
[ROW][C]-5[/C][C]-0.063295242510736[/C][/ROW]
[ROW][C]-4[/C][C]-0.257106745784193[/C][/ROW]
[ROW][C]-3[/C][C]-0.248671910082628[/C][/ROW]
[ROW][C]-2[/C][C]-0.142656743845357[/C][/ROW]
[ROW][C]-1[/C][C]-0.336100025124361[/C][/ROW]
[ROW][C]0[/C][C]0.0883534882811493[/C][/ROW]
[ROW][C]1[/C][C]-0.162414376791839[/C][/ROW]
[ROW][C]2[/C][C]-0.0896577394667244[/C][/ROW]
[ROW][C]3[/C][C]-0.127188434046481[/C][/ROW]
[ROW][C]4[/C][C]-0.0753416737453197[/C][/ROW]
[ROW][C]5[/C][C]-0.100611443218147[/C][/ROW]
[ROW][C]6[/C][C]0.171992068992666[/C][/ROW]
[ROW][C]7[/C][C]-0.298138467975319[/C][/ROW]
[ROW][C]8[/C][C]-0.0452120691218067[/C][/ROW]
[ROW][C]9[/C][C]-0.0659498735329168[/C][/ROW]
[ROW][C]10[/C][C]0.0744694902453523[/C][/ROW]
[ROW][C]11[/C][C]0.00734431753510476[/C][/ROW]
[ROW][C]12[/C][C]0.0661778230387482[/C][/ROW]
[ROW][C]13[/C][C]0.143061963478648[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27209&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27209&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 series2
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1.5
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.15093760013791
-12-0.429678308993222
-11-0.100645753070148
-10-0.332712231645253
-9-0.217262068763443
-8-0.309650419479773
-7-0.355085367243801
-6-0.272758467332468
-5-0.063295242510736
-4-0.257106745784193
-3-0.248671910082628
-2-0.142656743845357
-1-0.336100025124361
00.0883534882811493
1-0.162414376791839
2-0.0896577394667244
3-0.127188434046481
4-0.0753416737453197
5-0.100611443218147
60.171992068992666
7-0.298138467975319
8-0.0452120691218067
9-0.0659498735329168
100.0744694902453523
110.00734431753510476
120.0661778230387482
130.143061963478648


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 2 ; par5 = 12 ;
Parameters (R input):
par1 = 2.0 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 1.5 ; par6 = 0 ; par7 = 1 ;
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')