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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 computationTue, 02 Dec 2008 11:22:47 -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/t1228242194ns3msw6ip93bs9b.htm/, Retrieved Sat, 18 May 2024 07:29:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28214, Retrieved Sat, 18 May 2024 07:29:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165

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] [] [2008-12-02 16:29:33] [b53e8d20687f12ca59f39c9b7c3a629a]
F         [Cross Correlation Function] [Q9] [2008-12-02 18:22:47] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-12-03 19:36:44 [407693b66d7f2e0b350979005057872d] [reply] 
Dit antwoord is volledig juist. Grafiek: typisch fenomeen
onderzoeken of ere en verband is tussen Yt en Xt , maar de correlatie kan vertekend zijn door Zt , een variabele die invloed heeft op Yt en Xt.
2008-12-04 19:12:14 [c97d2ae59c98cf77a04815c1edffab5a] [reply] 
Q8 was niet opgelost, dus eigenlijk kan Q9 ook niet gemaakt worden.
bij Q8 moest d, D en lambda bepaald worden waarmee je x(t) EN y(t) gaat differentiëren/transformeren. d en D kon je nagaan door:
*Autocorrelatie
Lange termijn trend?
Seizoenaliteit?
Spreiding autocorrelatie?
*Variantie reductie matrix
Kleinste variantie
D?
d?
*Spectrum analysis
-Raw periodogram: LT trend (langzaam stijgende/dalende lijn),seizoenaliteit(terugkerende pieken in jaren 4,6,12,..)
-Cumulative periodogram: LT trend (steil stijgende/dalende grafiek bij lage freq),Seizoenaliteit (trappen)

het klop inderdaad dat de cross correlatie in Q7 een nonsenscorrelatie on zijn, doordat daar de invloed van een derde variabele z op x en y nog niet was weggewerkt.
het is dus ook logisch dat het aantal significante correlaties bij Q9 lager ligt dan bij Q7.
2008-12-04 19:12:51 [c97d2ae59c98cf77a04815c1edffab5a] [reply] 
ik was vergeten te zeggen dat je lambda kan nagaan dmv de standard deviation-mean plot.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7.5
7.2
6.9
6.7
6.4
6.3
6.8
7.3
7.1
7.1
6.8
6.5
6.3
6.1
6.1
6.3
6.3
6
6.2
6.4
6.8
7.5
7.5
7.6
7.6
7.4
7.3
7.1
6.9
6.8
7.5
7.6
7.8
8.0
8.1
8.2
8.3
8.2
8.0
7.9
7.6
7.6
8.2
8.3
8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.5
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.6
8.2
8.1
8.0
8.6
8.7
8.8
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8.1
8.2
8.1
8.1
7.9
7.9
7.9
8.0
8.0
7.9
8.0
7.7
7.2
7.5
7.3
7.0
7.0
7.0
7.2
7.3
7.1
6.8
6.6
6.2
6.2
6.8
6.9
6.8
Dataseries Y:
Download CSV
Histogram 
15.9
15.5
15.3
14.5
14.4
14.7
19.1
21.6
20.2
17.9
15.7
14.5
14.1
13.9
14.2
15.3
15.4
15.2
16.5
18.2
18.6
21.0
19.2
18.7
18.4
17.8
17.2
16.2
15.5
15.3
18.3
19.2
19.0
18.7
18.1
18.5
21.1
21.0
20.4
19.5
18.6
18.8
23.7
24.8
25.0
23.6
22.3
21.8
20.8
19.7
18.3
17.4
17.0
18.1
23.9
25.6
25.3
23.6
21.9
21.4
20.6
20.5
20.2
20.6
19.7
19.3
22.8
23.5
23.8
22.6
22.0
21.7
20.7
20.2
19.1
19.5
18.7
18.6
22.2
23.2
23.5
21.3
20.0
18.7
18.9
18.3
18.4
19.9
19.2
18.5
20.9
20.5
19.4
18.1
17.0
17.0
17.3
16.7
15.5
15.3
13.7
14.1
17.3
18.1
18.1

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=28214&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=28214&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28214&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 series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-17-0.120489580146780
-16-0.202474750912598
-15-0.197006936183794
-14-0.09638749276413
-130.0404911573110885
-120.439511016007128
-110.103899834303140
-10-0.0336780408811036
-90.0127200909114158
-80.00209968852846959
-70.140471167703204
-60.146851848473226
-5-0.115889173371662
-4-0.359759411662646
-3-0.389390877472803
-2-0.234315624727332
-10.170616925942105
00.746762108846864
10.285219301245167
2-0.0220575203084153
3-0.113634146223441
4-0.164998604276901
50.0492390810751717
60.146835098637607
7-0.0117115575398368
8-0.204637251324637
9-0.237993542358556
10-0.216571589731008
110.0554216225637522
120.442896576531556
130.106330573669246
14-0.0318775946082062
15-0.0700222448996172
16-0.037689868095947
170.166742882230836

\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 & 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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-17 & -0.120489580146780 \tabularnewline
-16 & -0.202474750912598 \tabularnewline
-15 & -0.197006936183794 \tabularnewline
-14 & -0.09638749276413 \tabularnewline
-13 & 0.0404911573110885 \tabularnewline
-12 & 0.439511016007128 \tabularnewline
-11 & 0.103899834303140 \tabularnewline
-10 & -0.0336780408811036 \tabularnewline
-9 & 0.0127200909114158 \tabularnewline
-8 & 0.00209968852846959 \tabularnewline
-7 & 0.140471167703204 \tabularnewline
-6 & 0.146851848473226 \tabularnewline
-5 & -0.115889173371662 \tabularnewline
-4 & -0.359759411662646 \tabularnewline
-3 & -0.389390877472803 \tabularnewline
-2 & -0.234315624727332 \tabularnewline
-1 & 0.170616925942105 \tabularnewline
0 & 0.746762108846864 \tabularnewline
1 & 0.285219301245167 \tabularnewline
2 & -0.0220575203084153 \tabularnewline
3 & -0.113634146223441 \tabularnewline
4 & -0.164998604276901 \tabularnewline
5 & 0.0492390810751717 \tabularnewline
6 & 0.146835098637607 \tabularnewline
7 & -0.0117115575398368 \tabularnewline
8 & -0.204637251324637 \tabularnewline
9 & -0.237993542358556 \tabularnewline
10 & -0.216571589731008 \tabularnewline
11 & 0.0554216225637522 \tabularnewline
12 & 0.442896576531556 \tabularnewline
13 & 0.106330573669246 \tabularnewline
14 & -0.0318775946082062 \tabularnewline
15 & -0.0700222448996172 \tabularnewline
16 & -0.037689868095947 \tabularnewline
17 & 0.166742882230836 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28214&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]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]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]-17[/C][C]-0.120489580146780[/C][/ROW]
[ROW][C]-16[/C][C]-0.202474750912598[/C][/ROW]
[ROW][C]-15[/C][C]-0.197006936183794[/C][/ROW]
[ROW][C]-14[/C][C]-0.09638749276413[/C][/ROW]
[ROW][C]-13[/C][C]0.0404911573110885[/C][/ROW]
[ROW][C]-12[/C][C]0.439511016007128[/C][/ROW]
[ROW][C]-11[/C][C]0.103899834303140[/C][/ROW]
[ROW][C]-10[/C][C]-0.0336780408811036[/C][/ROW]
[ROW][C]-9[/C][C]0.0127200909114158[/C][/ROW]
[ROW][C]-8[/C][C]0.00209968852846959[/C][/ROW]
[ROW][C]-7[/C][C]0.140471167703204[/C][/ROW]
[ROW][C]-6[/C][C]0.146851848473226[/C][/ROW]
[ROW][C]-5[/C][C]-0.115889173371662[/C][/ROW]
[ROW][C]-4[/C][C]-0.359759411662646[/C][/ROW]
[ROW][C]-3[/C][C]-0.389390877472803[/C][/ROW]
[ROW][C]-2[/C][C]-0.234315624727332[/C][/ROW]
[ROW][C]-1[/C][C]0.170616925942105[/C][/ROW]
[ROW][C]0[/C][C]0.746762108846864[/C][/ROW]
[ROW][C]1[/C][C]0.285219301245167[/C][/ROW]
[ROW][C]2[/C][C]-0.0220575203084153[/C][/ROW]
[ROW][C]3[/C][C]-0.113634146223441[/C][/ROW]
[ROW][C]4[/C][C]-0.164998604276901[/C][/ROW]
[ROW][C]5[/C][C]0.0492390810751717[/C][/ROW]
[ROW][C]6[/C][C]0.146835098637607[/C][/ROW]
[ROW][C]7[/C][C]-0.0117115575398368[/C][/ROW]
[ROW][C]8[/C][C]-0.204637251324637[/C][/ROW]
[ROW][C]9[/C][C]-0.237993542358556[/C][/ROW]
[ROW][C]10[/C][C]-0.216571589731008[/C][/ROW]
[ROW][C]11[/C][C]0.0554216225637522[/C][/ROW]
[ROW][C]12[/C][C]0.442896576531556[/C][/ROW]
[ROW][C]13[/C][C]0.106330573669246[/C][/ROW]
[ROW][C]14[/C][C]-0.0318775946082062[/C][/ROW]
[ROW][C]15[/C][C]-0.0700222448996172[/C][/ROW]
[ROW][C]16[/C][C]-0.037689868095947[/C][/ROW]
[ROW][C]17[/C][C]0.166742882230836[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28214&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28214&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 series0
Seasonal Period (s)1
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])
-17-0.120489580146780
-16-0.202474750912598
-15-0.197006936183794
-14-0.09638749276413
-130.0404911573110885
-120.439511016007128
-110.103899834303140
-10-0.0336780408811036
-90.0127200909114158
-80.00209968852846959
-70.140471167703204
-60.146851848473226
-5-0.115889173371662
-4-0.359759411662646
-3-0.389390877472803
-2-0.234315624727332
-10.170616925942105
00.746762108846864
10.285219301245167
2-0.0220575203084153
3-0.113634146223441
4-0.164998604276901
50.0492390810751717
60.146835098637607
7-0.0117115575398368
8-0.204637251324637
9-0.237993542358556
10-0.216571589731008
110.0554216225637522
120.442896576531556
130.106330573669246
14-0.0318775946082062
15-0.0700222448996172
16-0.037689868095947
170.166742882230836


Figures (Output of Computation)

Input Parameters & R Code

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