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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 11:32:30 -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/t1228156554639vkdjjr5kz2lo.htm/, Retrieved Sun, 05 May 2024 11:49:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27114, Retrieved Sun, 05 May 2024 11:49:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact244

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_Q6] [2008-12-01 18:32:30] [a9e6d7cd6e144e8b311d9f96a24c5a25] [Current]
Feedback Forum
2008-12-08 17:04:11 [Sandra Hofmans] [reply] 

Klopt. Bij de cross correlation functie onderzoeken we correlaties tussen verschillende reeksen. We willen onderzoeken op basis van wat we Yt kunnen voorspellen?
De cijfers in de tabel geven de correlatiecoefficiënten weer, die grafisch worden voorgesteld in de cross correlation function. De correlaties die buiten de stippellijnen zijn niet toe te schrijven aan het toeval.
2008-12-10 08:24:31 [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.
2008-12-10 10:16:51 [Peter Van Doninck] [reply] 
De cross correlatie functie toont aan dat er een zekere mate van voorspelbaarheid is. Dit is er omdat de waarde bij 0, 12 en -12 buiten het 95% betrouwbaarheidsinterval liggen.

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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 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=27114&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=27114&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27114&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 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])
-14-0.179695923041466
-13-0.0746256626514006
-120.465610485201663
-110.0478230924362985
-10-0.270455007530446
-9-0.256174389562238
-8-0.182775666231738
-70.0610210109515518
-60.168180207676889
-50.23998057641088
-4-0.207960257649896
-3-0.264937409579879
-2-0.317131747988862
-1-0.109689656376152
00.669402558855973
10.0482236481243995
2-0.261022999758397
3-0.288245409299749
4-0.182901117824859
50.163733446432813
60.297429761382786
70.189471913474940
8-0.193917653239945
9-0.177416550468062
10-0.22575046887886
110.00505196930813801
120.600798781084409
130.0435654691794933
14-0.0852157357492086

\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 & -0.179695923041466 \tabularnewline
-13 & -0.0746256626514006 \tabularnewline
-12 & 0.465610485201663 \tabularnewline
-11 & 0.0478230924362985 \tabularnewline
-10 & -0.270455007530446 \tabularnewline
-9 & -0.256174389562238 \tabularnewline
-8 & -0.182775666231738 \tabularnewline
-7 & 0.0610210109515518 \tabularnewline
-6 & 0.168180207676889 \tabularnewline
-5 & 0.23998057641088 \tabularnewline
-4 & -0.207960257649896 \tabularnewline
-3 & -0.264937409579879 \tabularnewline
-2 & -0.317131747988862 \tabularnewline
-1 & -0.109689656376152 \tabularnewline
0 & 0.669402558855973 \tabularnewline
1 & 0.0482236481243995 \tabularnewline
2 & -0.261022999758397 \tabularnewline
3 & -0.288245409299749 \tabularnewline
4 & -0.182901117824859 \tabularnewline
5 & 0.163733446432813 \tabularnewline
6 & 0.297429761382786 \tabularnewline
7 & 0.189471913474940 \tabularnewline
8 & -0.193917653239945 \tabularnewline
9 & -0.177416550468062 \tabularnewline
10 & -0.22575046887886 \tabularnewline
11 & 0.00505196930813801 \tabularnewline
12 & 0.600798781084409 \tabularnewline
13 & 0.0435654691794933 \tabularnewline
14 & -0.0852157357492086 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27114&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]-0.179695923041466[/C][/ROW]
[ROW][C]-13[/C][C]-0.0746256626514006[/C][/ROW]
[ROW][C]-12[/C][C]0.465610485201663[/C][/ROW]
[ROW][C]-11[/C][C]0.0478230924362985[/C][/ROW]
[ROW][C]-10[/C][C]-0.270455007530446[/C][/ROW]
[ROW][C]-9[/C][C]-0.256174389562238[/C][/ROW]
[ROW][C]-8[/C][C]-0.182775666231738[/C][/ROW]
[ROW][C]-7[/C][C]0.0610210109515518[/C][/ROW]
[ROW][C]-6[/C][C]0.168180207676889[/C][/ROW]
[ROW][C]-5[/C][C]0.23998057641088[/C][/ROW]
[ROW][C]-4[/C][C]-0.207960257649896[/C][/ROW]
[ROW][C]-3[/C][C]-0.264937409579879[/C][/ROW]
[ROW][C]-2[/C][C]-0.317131747988862[/C][/ROW]
[ROW][C]-1[/C][C]-0.109689656376152[/C][/ROW]
[ROW][C]0[/C][C]0.669402558855973[/C][/ROW]
[ROW][C]1[/C][C]0.0482236481243995[/C][/ROW]
[ROW][C]2[/C][C]-0.261022999758397[/C][/ROW]
[ROW][C]3[/C][C]-0.288245409299749[/C][/ROW]
[ROW][C]4[/C][C]-0.182901117824859[/C][/ROW]
[ROW][C]5[/C][C]0.163733446432813[/C][/ROW]
[ROW][C]6[/C][C]0.297429761382786[/C][/ROW]
[ROW][C]7[/C][C]0.189471913474940[/C][/ROW]
[ROW][C]8[/C][C]-0.193917653239945[/C][/ROW]
[ROW][C]9[/C][C]-0.177416550468062[/C][/ROW]
[ROW][C]10[/C][C]-0.22575046887886[/C][/ROW]
[ROW][C]11[/C][C]0.00505196930813801[/C][/ROW]
[ROW][C]12[/C][C]0.600798781084409[/C][/ROW]
[ROW][C]13[/C][C]0.0435654691794933[/C][/ROW]
[ROW][C]14[/C][C]-0.0852157357492086[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27114&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27114&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])
-14-0.179695923041466
-13-0.0746256626514006
-120.465610485201663
-110.0478230924362985
-10-0.270455007530446
-9-0.256174389562238
-8-0.182775666231738
-70.0610210109515518
-60.168180207676889
-50.23998057641088
-4-0.207960257649896
-3-0.264937409579879
-2-0.317131747988862
-1-0.109689656376152
00.669402558855973
10.0482236481243995
2-0.261022999758397
3-0.288245409299749
4-0.182901117824859
50.163733446432813
60.297429761382786
70.189471913474940
8-0.193917653239945
9-0.177416550468062
10-0.22575046887886
110.00505196930813801
120.600798781084409
130.0435654691794933
14-0.0852157357492086


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')