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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 12:54:33 -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/t1228161472o9rgs5wqy61b0ob.htm/, Retrieved Sun, 05 May 2024 13:36:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27284, Retrieved Sun, 05 May 2024 13:36:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact197

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] [Buitenlandse handel] [2008-10-13 10:26:18] [1ce0d16c8f4225c977b42c8fa93bc163]
F   PD  [Univariate Data Series] [Aantal gebouwde w...] [2008-10-13 20:51:57] [05e9a6d53ace945e674f09e419f751d6]
-   PD    [Univariate Data Series] [Aantal begonnen w...] [2008-10-20 19:29:55] [a7f04e0e73ce3683561193958d653479]
-           [Univariate Data Series] [Aantal begonnen w...] [2008-10-20 19:33:36] [a7f04e0e73ce3683561193958d653479]
-             [Univariate Data Series] [Aantal begonnen w...] [2008-10-20 20:27:32] [a7f04e0e73ce3683561193958d653479]
F RMPD          [Cross Correlation Function] [Q7 Cross Correlation] [2008-12-01 19:21:29] [a7f04e0e73ce3683561193958d653479]
F   P               [Cross Correlation Function] [Q8 - Met transfor...] [2008-12-01 19:54:33] [f1a30f1149cef3ef3ef69d586c6c3c1c] [Current]
Feedback Forum
2008-12-04 16:26:56 [Stijn Van de Velde] [reply] 
Zie Q3: op deze manier kan je te weten komen hoeveel keer we d en D zouden moeten transformeren.

d=0 wil zeggen dat er geen lange termijn trend is
D=0 wil zeggen dat er geen seizoenaliteit is.

Als er wel 1 van de 2 vorige is, gaan we d en/of D op 1, 2 of 3 zetten (afhankelijk van welke orde de trend/seizoenaliteit is) om zo de trend of seizoenaliteit er uit te halen. Op die manier word de tijdreeks stationair gemaakt. Dit noemt men differentiëren.

Zie Q5: Zo kan je de lambda berekenen. Door deze in te vullen ga je de tijdreeks wederom transformeren en zo de reeks nog meer stationair maken.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10
12
12
13
17
12
15
12
14
19
16
17
16
19
17
17
20
18
16
19
18
23
20
20
15
17
16
15
10
13
10
19
21
17
16
17
14
18
17
14
15
16
11
15
13
17
16
9
17
15
12
12
12
12
4
7
4
3
3
0
5
Dataseries Y:
Download CSV
Histogram 
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
3132
4226
3723
3576
3397

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27284&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27284&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27284&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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 series1
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 series1
krho(Y[t],X[t+k])
-130.302935004218909
-120.301977720765034
-110.300003895052969
-100.307963885667919
-90.295483355468720
-80.193866354294087
-70.268781472642544
-60.171002747868262
-50.234138750883028
-40.164470043840897
-30.212641138590072
-20.381916680646288
-10.322662951616216
00.333978819072315
10.325753799600231
20.25925275947555
30.225360171771599
40.292538647370964
50.167066891616462
60.260472578354328
70.250243193046321
80.250569655829503
90.259318861955564
100.143629654306023
110.182704549118855
120.236910351121808
130.0802095061203473

\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 & 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 & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & 0.302935004218909 \tabularnewline
-12 & 0.301977720765034 \tabularnewline
-11 & 0.300003895052969 \tabularnewline
-10 & 0.307963885667919 \tabularnewline
-9 & 0.295483355468720 \tabularnewline
-8 & 0.193866354294087 \tabularnewline
-7 & 0.268781472642544 \tabularnewline
-6 & 0.171002747868262 \tabularnewline
-5 & 0.234138750883028 \tabularnewline
-4 & 0.164470043840897 \tabularnewline
-3 & 0.212641138590072 \tabularnewline
-2 & 0.381916680646288 \tabularnewline
-1 & 0.322662951616216 \tabularnewline
0 & 0.333978819072315 \tabularnewline
1 & 0.325753799600231 \tabularnewline
2 & 0.25925275947555 \tabularnewline
3 & 0.225360171771599 \tabularnewline
4 & 0.292538647370964 \tabularnewline
5 & 0.167066891616462 \tabularnewline
6 & 0.260472578354328 \tabularnewline
7 & 0.250243193046321 \tabularnewline
8 & 0.250569655829503 \tabularnewline
9 & 0.259318861955564 \tabularnewline
10 & 0.143629654306023 \tabularnewline
11 & 0.182704549118855 \tabularnewline
12 & 0.236910351121808 \tabularnewline
13 & 0.0802095061203473 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27284&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]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]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]0.302935004218909[/C][/ROW]
[ROW][C]-12[/C][C]0.301977720765034[/C][/ROW]
[ROW][C]-11[/C][C]0.300003895052969[/C][/ROW]
[ROW][C]-10[/C][C]0.307963885667919[/C][/ROW]
[ROW][C]-9[/C][C]0.295483355468720[/C][/ROW]
[ROW][C]-8[/C][C]0.193866354294087[/C][/ROW]
[ROW][C]-7[/C][C]0.268781472642544[/C][/ROW]
[ROW][C]-6[/C][C]0.171002747868262[/C][/ROW]
[ROW][C]-5[/C][C]0.234138750883028[/C][/ROW]
[ROW][C]-4[/C][C]0.164470043840897[/C][/ROW]
[ROW][C]-3[/C][C]0.212641138590072[/C][/ROW]
[ROW][C]-2[/C][C]0.381916680646288[/C][/ROW]
[ROW][C]-1[/C][C]0.322662951616216[/C][/ROW]
[ROW][C]0[/C][C]0.333978819072315[/C][/ROW]
[ROW][C]1[/C][C]0.325753799600231[/C][/ROW]
[ROW][C]2[/C][C]0.25925275947555[/C][/ROW]
[ROW][C]3[/C][C]0.225360171771599[/C][/ROW]
[ROW][C]4[/C][C]0.292538647370964[/C][/ROW]
[ROW][C]5[/C][C]0.167066891616462[/C][/ROW]
[ROW][C]6[/C][C]0.260472578354328[/C][/ROW]
[ROW][C]7[/C][C]0.250243193046321[/C][/ROW]
[ROW][C]8[/C][C]0.250569655829503[/C][/ROW]
[ROW][C]9[/C][C]0.259318861955564[/C][/ROW]
[ROW][C]10[/C][C]0.143629654306023[/C][/ROW]
[ROW][C]11[/C][C]0.182704549118855[/C][/ROW]
[ROW][C]12[/C][C]0.236910351121808[/C][/ROW]
[ROW][C]13[/C][C]0.0802095061203473[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27284&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27284&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 series1
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 series1
krho(Y[t],X[t+k])
-130.302935004218909
-120.301977720765034
-110.300003895052969
-100.307963885667919
-90.295483355468720
-80.193866354294087
-70.268781472642544
-60.171002747868262
-50.234138750883028
-40.164470043840897
-30.212641138590072
-20.381916680646288
-10.322662951616216
00.333978819072315
10.325753799600231
20.25925275947555
30.225360171771599
40.292538647370964
50.167066891616462
60.260472578354328
70.250243193046321
80.250569655829503
90.259318861955564
100.143629654306023
110.182704549118855
120.236910351121808
130.0802095061203473


Figures (Output of Computation)

Input Parameters & R Code

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