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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 computationWed, 28 Nov 2007 09:04:03 -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/2007/Nov/28/t1196265258v1hkd91oti2b7u1.htm/, Retrieved Thu, 02 May 2024 06:34:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=7116, Retrieved Thu, 02 May 2024 06:34:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKlaas Van Pelt
Estimated Impact174

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [Cross Correlation 2] [2007-11-28 16:04:03] [6abd901c2e17b7d5559c695bbff3d863] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
37702
30364
32609
30212
29965
28352
25814
22414
20506
28806
22228
13971
36845
35338
35022
34777
26887
23970
22780
17351
21382
24561
17409
11514
31514
27071
29462
26105
22397
23843
21705
18089
20764
25316
17704
15548
28029
29383
36438
32034
22679
24319
18004
17537
20366
22782
19169
13807
29743
25591
29096
26482
22405
27044
17970
18730
19684
19785
18479
10698
31956
29506
34506
27165
26736
23691
18157
17328
18205
20995
17382
9367
31124
26551
30651
25859
25100
25778
20418
18688
Dataseries Y:
Download CSV
Histogram 
99.9
99.9
99.9
99.9
99.9
99.7
99.6
99.5
100.6
100.2
100.1
100.2
99.1
99.5
99.5
99.6
99.5
99.6
99.8
99.9
100.5
100.5
100.5
100.5
99.5
99.9
100.4
99.6
99.5
99.6
98.4
99.9
100.3
100.3
101.3
101
99.7
99.4
99.9
100.7
99.8
98.8
99.6
99.1
100.3
100.5
100.8
100.6
99.1
98.8
99
99.9
99.5
99.2
99.6
100.1
99.8
101.6
101.7
101.9
100
102
102
102.9
102.7
102.7
102.7
102.7
102.6
102.6
102.5
102.5
102.1
103.5
103.2
102.1
103.8
104
103.9
104.1

Tables (Output of Computation)





Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 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=7116&T=0

[TABLE]
[ROW][C]Summary of compuational 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]3 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=7116&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=7116&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 compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-150.160936465123226
-14-0.12632975511917
-130.0796362854082165
-120.0218964468332547
-110.0189272083438251
-10-0.148908845339558
-90.123004049909402
-8-0.0426391177378368
-70.00602802869853858
-60.0055134667737513
-50.0955579751796061
-4-0.233413807692342
-30.184612190576351
-2-0.0421569913245095
-1-0.139952393471063
00.0862767261654055
1-0.0125624077277678
20.0487558966007323
30.0680893410015199
4-0.0933792079818476
5-0.0292564832205857
60.0888961132355565
7-0.120860823432563
80.148610856121884
9-0.0312000332366745
100.064505220950675
11-0.088777020842129
120.0261904342121807
13-0.0649604032230492
14-0.00258224949964986
150.119116595779275

\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 & 1 \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 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-15 & 0.160936465123226 \tabularnewline
-14 & -0.12632975511917 \tabularnewline
-13 & 0.0796362854082165 \tabularnewline
-12 & 0.0218964468332547 \tabularnewline
-11 & 0.0189272083438251 \tabularnewline
-10 & -0.148908845339558 \tabularnewline
-9 & 0.123004049909402 \tabularnewline
-8 & -0.0426391177378368 \tabularnewline
-7 & 0.00602802869853858 \tabularnewline
-6 & 0.0055134667737513 \tabularnewline
-5 & 0.0955579751796061 \tabularnewline
-4 & -0.233413807692342 \tabularnewline
-3 & 0.184612190576351 \tabularnewline
-2 & -0.0421569913245095 \tabularnewline
-1 & -0.139952393471063 \tabularnewline
0 & 0.0862767261654055 \tabularnewline
1 & -0.0125624077277678 \tabularnewline
2 & 0.0487558966007323 \tabularnewline
3 & 0.0680893410015199 \tabularnewline
4 & -0.0933792079818476 \tabularnewline
5 & -0.0292564832205857 \tabularnewline
6 & 0.0888961132355565 \tabularnewline
7 & -0.120860823432563 \tabularnewline
8 & 0.148610856121884 \tabularnewline
9 & -0.0312000332366745 \tabularnewline
10 & 0.064505220950675 \tabularnewline
11 & -0.088777020842129 \tabularnewline
12 & 0.0261904342121807 \tabularnewline
13 & -0.0649604032230492 \tabularnewline
14 & -0.00258224949964986 \tabularnewline
15 & 0.119116595779275 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=7116&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]1[/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[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]1[/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]0.160936465123226[/C][/ROW]
[ROW][C]-14[/C][C]-0.12632975511917[/C][/ROW]
[ROW][C]-13[/C][C]0.0796362854082165[/C][/ROW]
[ROW][C]-12[/C][C]0.0218964468332547[/C][/ROW]
[ROW][C]-11[/C][C]0.0189272083438251[/C][/ROW]
[ROW][C]-10[/C][C]-0.148908845339558[/C][/ROW]
[ROW][C]-9[/C][C]0.123004049909402[/C][/ROW]
[ROW][C]-8[/C][C]-0.0426391177378368[/C][/ROW]
[ROW][C]-7[/C][C]0.00602802869853858[/C][/ROW]
[ROW][C]-6[/C][C]0.0055134667737513[/C][/ROW]
[ROW][C]-5[/C][C]0.0955579751796061[/C][/ROW]
[ROW][C]-4[/C][C]-0.233413807692342[/C][/ROW]
[ROW][C]-3[/C][C]0.184612190576351[/C][/ROW]
[ROW][C]-2[/C][C]-0.0421569913245095[/C][/ROW]
[ROW][C]-1[/C][C]-0.139952393471063[/C][/ROW]
[ROW][C]0[/C][C]0.0862767261654055[/C][/ROW]
[ROW][C]1[/C][C]-0.0125624077277678[/C][/ROW]
[ROW][C]2[/C][C]0.0487558966007323[/C][/ROW]
[ROW][C]3[/C][C]0.0680893410015199[/C][/ROW]
[ROW][C]4[/C][C]-0.0933792079818476[/C][/ROW]
[ROW][C]5[/C][C]-0.0292564832205857[/C][/ROW]
[ROW][C]6[/C][C]0.0888961132355565[/C][/ROW]
[ROW][C]7[/C][C]-0.120860823432563[/C][/ROW]
[ROW][C]8[/C][C]0.148610856121884[/C][/ROW]
[ROW][C]9[/C][C]-0.0312000332366745[/C][/ROW]
[ROW][C]10[/C][C]0.064505220950675[/C][/ROW]
[ROW][C]11[/C][C]-0.088777020842129[/C][/ROW]
[ROW][C]12[/C][C]0.0261904342121807[/C][/ROW]
[ROW][C]13[/C][C]-0.0649604032230492[/C][/ROW]
[ROW][C]14[/C][C]-0.00258224949964986[/C][/ROW]
[ROW][C]15[/C][C]0.119116595779275[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=7116&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=7116&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 series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-150.160936465123226
-14-0.12632975511917
-130.0796362854082165
-120.0218964468332547
-110.0189272083438251
-10-0.148908845339558
-90.123004049909402
-8-0.0426391177378368
-70.00602802869853858
-60.0055134667737513
-50.0955579751796061
-4-0.233413807692342
-30.184612190576351
-2-0.0421569913245095
-1-0.139952393471063
00.0862767261654055
1-0.0125624077277678
20.0487558966007323
30.0680893410015199
4-0.0933792079818476
5-0.0292564832205857
60.0888961132355565
7-0.120860823432563
80.148610856121884
9-0.0312000332366745
100.064505220950675
11-0.088777020842129
120.0261904342121807
13-0.0649604032230492
14-0.00258224949964986
150.119116595779275


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; 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) x <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',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')