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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 computationTue, 02 Dec 2008 10:29:01 -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/t1228239118xa98s90yjm56lm6.htm/, Retrieved Sun, 19 May 2024 09:25:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28120, Retrieved Sun, 19 May 2024 09:25:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176

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] [Q7 Workshop 4] [2008-12-02 17:29:01] [56fd94b954e08a6655cb7790b21ee404] [Current]
Feedback Forum
2008-12-06 16:18:35 [Ken Wright] [reply] 
goed, Met de cross correlatiefunctie kan men nagaan in hoeverre Y te verklaren valt door het verleden van X. Wanneer k groter is dan 0 geeft het de correlatie weer tussen de toekomstige x en het heden van Y . En als k negatief is bijvoorbeeld -3; als x vandaag verandert, dan gaat het invloed hebben op y met 3maanden vertraging
2008-12-08 17:25:03 [Birgit Van Dyck] [reply] 
goed geantwoord, de cross correlatie functie geeft weer in welke mate je Yt kan verklaren adhv Xt+k.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,9059
0,8883
0,8924
0,8833
0,87
0,8758
0,8858
0,917
0,9554
0,9922
0,9778
0,9808
0,9811
1,0014
1,0183
1,0622
1,0773
1,0807
1,0848
1,1582
1,1663
1,1372
1,1139
1,1222
1,1692
1,1702
1,2286
1,2613
1,2646
1,2262
1,1985
1,2007
1,2138
1,2266
1,2176
1,2218
1,249
1,2991
1,3408
1,3119
1,3014
1,3201
1,2938
1,2694
1,2165
1,2037
1,2292
1,2256
1,2015
1,1786
1,1856
1,2103
1,1938
1,202
1,2271
1,277
1,265
1,2684
1,2811
1,2727
1,2611
1,2881
1,3213
1,2999
1,3074
1,3242
1,3516
1,3511
1,3419
1,3716
1,3622
1,3896
1,4227
1,4684
Dataseries Y:
Download CSV
Histogram 
109,86
108,68
113,38
117,12
116,23
114,75
115,81
115,86
117,80
117,11
116,31
118,38
121,57
121,65
124,20
126,12
128,60
128,16
130,12
135,83
138,05
134,99
132,38
128,94
128,12
127,84
132,43
134,13
134,78
133,13
129,08
134,48
132,86
134,08
134,54
134,51
135,97
136,09
139,14
135,63
136,55
138,83
138,84
135,37
132,22
134,75
135,98
136,06
138,05
139,59
140,58
139,81
140,77
140,96
143,59
142,70
145,11
146,70
148,53
148,99
149,65
151,11
154,82
156,56
157,60
155,24
160,68
163,22
164,55
166,76
159,05
159,82
164,95
162,89

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=28120&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=28120&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28120&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])
-150.262618523180247
-140.290075329918661
-130.319826746535023
-120.354663756589351
-110.396295855537777
-100.433441011826995
-90.469030219846059
-80.50703101472472
-70.552643918695092
-60.592780233851096
-50.636473834080514
-40.680204514804654
-30.720856786893121
-20.770231975279068
-10.833421267858984
00.897737265927056
10.852163989566381
20.79441606379361
30.75073094324953
40.710408877186031
50.65117909604179
60.586769343932279
70.520378515187498
80.457740482735743
90.409244582899609
100.359310288334588
110.305199370208806
120.254436919180201
130.205408148145640
140.158677250835179
150.116829034493985

\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
-15 & 0.262618523180247 \tabularnewline
-14 & 0.290075329918661 \tabularnewline
-13 & 0.319826746535023 \tabularnewline
-12 & 0.354663756589351 \tabularnewline
-11 & 0.396295855537777 \tabularnewline
-10 & 0.433441011826995 \tabularnewline
-9 & 0.469030219846059 \tabularnewline
-8 & 0.50703101472472 \tabularnewline
-7 & 0.552643918695092 \tabularnewline
-6 & 0.592780233851096 \tabularnewline
-5 & 0.636473834080514 \tabularnewline
-4 & 0.680204514804654 \tabularnewline
-3 & 0.720856786893121 \tabularnewline
-2 & 0.770231975279068 \tabularnewline
-1 & 0.833421267858984 \tabularnewline
0 & 0.897737265927056 \tabularnewline
1 & 0.852163989566381 \tabularnewline
2 & 0.79441606379361 \tabularnewline
3 & 0.75073094324953 \tabularnewline
4 & 0.710408877186031 \tabularnewline
5 & 0.65117909604179 \tabularnewline
6 & 0.586769343932279 \tabularnewline
7 & 0.520378515187498 \tabularnewline
8 & 0.457740482735743 \tabularnewline
9 & 0.409244582899609 \tabularnewline
10 & 0.359310288334588 \tabularnewline
11 & 0.305199370208806 \tabularnewline
12 & 0.254436919180201 \tabularnewline
13 & 0.205408148145640 \tabularnewline
14 & 0.158677250835179 \tabularnewline
15 & 0.116829034493985 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28120&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]-15[/C][C]0.262618523180247[/C][/ROW]
[ROW][C]-14[/C][C]0.290075329918661[/C][/ROW]
[ROW][C]-13[/C][C]0.319826746535023[/C][/ROW]
[ROW][C]-12[/C][C]0.354663756589351[/C][/ROW]
[ROW][C]-11[/C][C]0.396295855537777[/C][/ROW]
[ROW][C]-10[/C][C]0.433441011826995[/C][/ROW]
[ROW][C]-9[/C][C]0.469030219846059[/C][/ROW]
[ROW][C]-8[/C][C]0.50703101472472[/C][/ROW]
[ROW][C]-7[/C][C]0.552643918695092[/C][/ROW]
[ROW][C]-6[/C][C]0.592780233851096[/C][/ROW]
[ROW][C]-5[/C][C]0.636473834080514[/C][/ROW]
[ROW][C]-4[/C][C]0.680204514804654[/C][/ROW]
[ROW][C]-3[/C][C]0.720856786893121[/C][/ROW]
[ROW][C]-2[/C][C]0.770231975279068[/C][/ROW]
[ROW][C]-1[/C][C]0.833421267858984[/C][/ROW]
[ROW][C]0[/C][C]0.897737265927056[/C][/ROW]
[ROW][C]1[/C][C]0.852163989566381[/C][/ROW]
[ROW][C]2[/C][C]0.79441606379361[/C][/ROW]
[ROW][C]3[/C][C]0.75073094324953[/C][/ROW]
[ROW][C]4[/C][C]0.710408877186031[/C][/ROW]
[ROW][C]5[/C][C]0.65117909604179[/C][/ROW]
[ROW][C]6[/C][C]0.586769343932279[/C][/ROW]
[ROW][C]7[/C][C]0.520378515187498[/C][/ROW]
[ROW][C]8[/C][C]0.457740482735743[/C][/ROW]
[ROW][C]9[/C][C]0.409244582899609[/C][/ROW]
[ROW][C]10[/C][C]0.359310288334588[/C][/ROW]
[ROW][C]11[/C][C]0.305199370208806[/C][/ROW]
[ROW][C]12[/C][C]0.254436919180201[/C][/ROW]
[ROW][C]13[/C][C]0.205408148145640[/C][/ROW]
[ROW][C]14[/C][C]0.158677250835179[/C][/ROW]
[ROW][C]15[/C][C]0.116829034493985[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28120&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28120&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])
-150.262618523180247
-140.290075329918661
-130.319826746535023
-120.354663756589351
-110.396295855537777
-100.433441011826995
-90.469030219846059
-80.50703101472472
-70.552643918695092
-60.592780233851096
-50.636473834080514
-40.680204514804654
-30.720856786893121
-20.770231975279068
-10.833421267858984
00.897737265927056
10.852163989566381
20.79441606379361
30.75073094324953
40.710408877186031
50.65117909604179
60.586769343932279
70.520378515187498
80.457740482735743
90.409244582899609
100.359310288334588
110.305199370208806
120.254436919180201
130.205408148145640
140.158677250835179
150.116829034493985


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')