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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 computationSun, 30 Nov 2008 17:17:04 -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/01/t1228090680xymwndz75ceeonb.htm/, Retrieved Sun, 05 May 2024 18:36:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26804, Retrieved Sun, 05 May 2024 18:36:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact240

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [Q7 - non stationa...] [2008-12-01 00:17:04] [0831954c833179c36e9320daee0825b5] [Current]
-   P     [Cross Correlation Function] [] [2008-12-05 18:45:45] [57850c80fd59ccfb28f882be994e814e]
-   P       [Cross Correlation Function] [Verbetering Q7 - ...] [2008-12-05 18:48:38] [57850c80fd59ccfb28f882be994e814e]
F   P     [Cross Correlation Function] [q9 - variance red...] [2008-12-05 19:07:41] [57850c80fd59ccfb28f882be994e814e]
Feedback Forum
2008-12-05 18:50:37 [Bob Leysen] [reply] 
Ik maak een nieuwe link met d=1 en D=1

http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/05/t12285028302pjp55qgtfssgbs.htm

Op deze link zien we duidelijk autocorrelatie.

Op de link hieronder, zet ik d=2 en D=2

http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/05/t1228502956oxqitc1923v98ks.htm

Ik merk dan op dat de seizonaliteit al beter is weggewerkt.
  2008-12-05 18:52:36 [Bob Leysen] [reply] 
Bij de eerste link zien we dus duidelijk seasonaliteit en geen autocorrelatie

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15107
15024
12083
15761
16943
15070
13660
14769
14725
15998
15371
14957
15470
15102
11704
16284
16727
14969
14861
14583
15306
17904
16379
15420
17871
15913
13867
17823
17872
17422
16705
15991
16584
19124
17839
17209
18587
16258
15142
19202
17747
19090
18040
17516
17752
21073
17170
19440
19795
17575
16165
19465
19932
19961
17343
18924
18574
21351
18595
19823
20844
19640
17735
19814
22239
20682
17819
21872
22117
21866
Dataseries Y:
Download CSV
Histogram 
12055
12113
9617
12646
13581
12162
10970
11880
11888
12927
12300
12093
12381
12197
9455
13168
13428
11981
11885
11692
12234
14341
13131
12421
14286
12865
11160
14316
14389
14014
13419
12770
13316
15333
14243
13824
14963
13203
12199
15509
14200
15170
14058
13786
14148
16542
13588
15582
15803
14131
12923
15612
16034
16037
14038
15331
15038
17402
14993
16044
16930
15921
14417
15961
17852
16484
14216
17430
17840
17629

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=26804&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=26804&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26804&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)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])
-150.218837754940751
-140.20589305499289
-130.27490347608305
-120.575699088027702
-110.370695472525139
-100.285127238937388
-90.4100541659774
-80.496615494984702
-70.42783657516926
-60.55454728531017
-50.529024788171398
-40.606114776883331
-30.542908982431682
-20.474998632035226
-10.615857367911664
00.998183884114919
10.616892901822097
20.472413288654192
30.540907309618925
40.604370628502162
50.527375019196856
60.560792519615313
70.435499239294766
80.503058283877075
90.410762111620601
100.280085113512224
110.364589269244756
120.570102478284111
130.269107090322292
140.196211477064554
150.199448007885065

\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 & 0.218837754940751 \tabularnewline
-14 & 0.20589305499289 \tabularnewline
-13 & 0.27490347608305 \tabularnewline
-12 & 0.575699088027702 \tabularnewline
-11 & 0.370695472525139 \tabularnewline
-10 & 0.285127238937388 \tabularnewline
-9 & 0.4100541659774 \tabularnewline
-8 & 0.496615494984702 \tabularnewline
-7 & 0.42783657516926 \tabularnewline
-6 & 0.55454728531017 \tabularnewline
-5 & 0.529024788171398 \tabularnewline
-4 & 0.606114776883331 \tabularnewline
-3 & 0.542908982431682 \tabularnewline
-2 & 0.474998632035226 \tabularnewline
-1 & 0.615857367911664 \tabularnewline
0 & 0.998183884114919 \tabularnewline
1 & 0.616892901822097 \tabularnewline
2 & 0.472413288654192 \tabularnewline
3 & 0.540907309618925 \tabularnewline
4 & 0.604370628502162 \tabularnewline
5 & 0.527375019196856 \tabularnewline
6 & 0.560792519615313 \tabularnewline
7 & 0.435499239294766 \tabularnewline
8 & 0.503058283877075 \tabularnewline
9 & 0.410762111620601 \tabularnewline
10 & 0.280085113512224 \tabularnewline
11 & 0.364589269244756 \tabularnewline
12 & 0.570102478284111 \tabularnewline
13 & 0.269107090322292 \tabularnewline
14 & 0.196211477064554 \tabularnewline
15 & 0.199448007885065 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26804&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]0.218837754940751[/C][/ROW]
[ROW][C]-14[/C][C]0.20589305499289[/C][/ROW]
[ROW][C]-13[/C][C]0.27490347608305[/C][/ROW]
[ROW][C]-12[/C][C]0.575699088027702[/C][/ROW]
[ROW][C]-11[/C][C]0.370695472525139[/C][/ROW]
[ROW][C]-10[/C][C]0.285127238937388[/C][/ROW]
[ROW][C]-9[/C][C]0.4100541659774[/C][/ROW]
[ROW][C]-8[/C][C]0.496615494984702[/C][/ROW]
[ROW][C]-7[/C][C]0.42783657516926[/C][/ROW]
[ROW][C]-6[/C][C]0.55454728531017[/C][/ROW]
[ROW][C]-5[/C][C]0.529024788171398[/C][/ROW]
[ROW][C]-4[/C][C]0.606114776883331[/C][/ROW]
[ROW][C]-3[/C][C]0.542908982431682[/C][/ROW]
[ROW][C]-2[/C][C]0.474998632035226[/C][/ROW]
[ROW][C]-1[/C][C]0.615857367911664[/C][/ROW]
[ROW][C]0[/C][C]0.998183884114919[/C][/ROW]
[ROW][C]1[/C][C]0.616892901822097[/C][/ROW]
[ROW][C]2[/C][C]0.472413288654192[/C][/ROW]
[ROW][C]3[/C][C]0.540907309618925[/C][/ROW]
[ROW][C]4[/C][C]0.604370628502162[/C][/ROW]
[ROW][C]5[/C][C]0.527375019196856[/C][/ROW]
[ROW][C]6[/C][C]0.560792519615313[/C][/ROW]
[ROW][C]7[/C][C]0.435499239294766[/C][/ROW]
[ROW][C]8[/C][C]0.503058283877075[/C][/ROW]
[ROW][C]9[/C][C]0.410762111620601[/C][/ROW]
[ROW][C]10[/C][C]0.280085113512224[/C][/ROW]
[ROW][C]11[/C][C]0.364589269244756[/C][/ROW]
[ROW][C]12[/C][C]0.570102478284111[/C][/ROW]
[ROW][C]13[/C][C]0.269107090322292[/C][/ROW]
[ROW][C]14[/C][C]0.196211477064554[/C][/ROW]
[ROW][C]15[/C][C]0.199448007885065[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26804&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26804&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])
-150.218837754940751
-140.20589305499289
-130.27490347608305
-120.575699088027702
-110.370695472525139
-100.285127238937388
-90.4100541659774
-80.496615494984702
-70.42783657516926
-60.55454728531017
-50.529024788171398
-40.606114776883331
-30.542908982431682
-20.474998632035226
-10.615857367911664
00.998183884114919
10.616892901822097
20.472413288654192
30.540907309618925
40.604370628502162
50.527375019196856
60.560792519615313
70.435499239294766
80.503058283877075
90.410762111620601
100.280085113512224
110.364589269244756
120.570102478284111
130.269107090322292
140.196211477064554
150.199448007885065


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')