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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 06:18:05 -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/t122822410723q0bd7ukwt143p.htm/, Retrieved Sat, 25 May 2024 01:36:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27739, Retrieved Sat, 25 May 2024 01:36:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact258

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  [Spectral Analysis] [airline data] [2008-12-02 12:35:12] [0e5eff269cdcaf8789c45b6ee36b0c3d]
F RMPD      [Cross Correlation Function] [airline data] [2008-12-02 13:18:05] [35c75b0726318bf2908e4a56ed2df1a9] [Current]
F   P         [Cross Correlation Function] [airline data] [2008-12-02 13:46:07] [0e5eff269cdcaf8789c45b6ee36b0c3d]
- RMPD        [Bivariate Kernel Density Estimation] [paper] [2008-12-02 14:12:36] [0e5eff269cdcaf8789c45b6ee36b0c3d]
- RMPD        [Trivariate Scatterplots] [trivariate scatte...] [2008-12-02 14:28:20] [0e5eff269cdcaf8789c45b6ee36b0c3d]
- RMPD        [Univariate Data Series] [paper] [2008-12-02 14:39:06] [0e5eff269cdcaf8789c45b6ee36b0c3d]
- RMPD        [Univariate Data Series] [paper] [2008-12-02 14:41:39] [0e5eff269cdcaf8789c45b6ee36b0c3d]
- RMPD        [(Partial) Autocorrelation Function] [paper] [2008-12-02 14:51:55] [0e5eff269cdcaf8789c45b6ee36b0c3d]
-   P           [(Partial) Autocorrelation Function] [acf] [2008-12-09 12:52:58] [a4602103a5e123497aa555277d0e627b]
-   P           [(Partial) Autocorrelation Function] [acf] [2008-12-09 12:56:04] [a4602103a5e123497aa555277d0e627b]
-   P           [(Partial) Autocorrelation Function] [acf] [2008-12-09 12:58:07] [a4602103a5e123497aa555277d0e627b]
- RMPD            [Variance Reduction Matrix] [vrm] [2008-12-09 14:08:41] [a4602103a5e123497aa555277d0e627b]
- RMPD            [Spectral Analysis] [SA] [2008-12-09 14:10:16] [a4602103a5e123497aa555277d0e627b]
- RMPD            [Spectral Analysis] [SA] [2008-12-09 14:12:05] [a4602103a5e123497aa555277d0e627b]
-   P               [Spectral Analysis] [fdqsdf] [2008-12-15 14:34:13] [5387335d8669ad018e3e2def51162329]
- RMPD            [Spectral Analysis] [SA] [2008-12-09 14:15:20] [a4602103a5e123497aa555277d0e627b]
- RMPD            [Standard Deviation-Mean Plot] [SDMP] [2008-12-09 14:18:25] [a4602103a5e123497aa555277d0e627b]
- RMPD            [ARIMA Backward Selection] [arma] [2008-12-09 14:24:45] [a4602103a5e123497aa555277d0e627b]
- RMP           [Variance Reduction Matrix] [VRM] [2008-12-09 13:01:09] [a4602103a5e123497aa555277d0e627b]
- RMP           [Spectral Analysis] [SA] [2008-12-09 13:03:25] [a4602103a5e123497aa555277d0e627b]
- RMP           [Spectral Analysis] [SA] [2008-12-09 13:06:47] [a4602103a5e123497aa555277d0e627b]
- RMP           [Spectral Analysis] [SA] [2008-12-09 13:08:47] [a4602103a5e123497aa555277d0e627b]
- RMP           [Standard Deviation-Mean Plot] [SDMP] [2008-12-09 13:12:48] [a4602103a5e123497aa555277d0e627b]
- RMPD        [(Partial) Autocorrelation Function] [paper] [2008-12-02 14:55:43] [0e5eff269cdcaf8789c45b6ee36b0c3d]
-   P           [(Partial) Autocorrelation Function] [acf] [2008-12-09 13:54:35] [a4602103a5e123497aa555277d0e627b]
-   P           [(Partial) Autocorrelation Function] [acf] [2008-12-09 13:57:45] [a4602103a5e123497aa555277d0e627b]
-   P             [(Partial) Autocorrelation Function] [qdsf] [2008-12-15 14:17:02] [5387335d8669ad018e3e2def51162329]
-   P           [(Partial) Autocorrelation Function] [cf] [2008-12-09 14:01:52] [a4602103a5e123497aa555277d0e627b]
Feedback Forum
2008-12-04 08:41:31 [Julie Govaerts] [reply] 
= de cross correlation tussen verschillende reeksen van gegevens
bv hoe Yt voorspellen = dmv Xt en het verleden van Xt?
Eerst berekenen we de cross correlation met de ruwe gegevens = niet getransformeerd
Correlatie tussen Yt en Xt +k --> X wordt verschoven in de tijd om na te gaan in welke mate Yt verklaard kan worden dmv het verleden van Xt (een leading indicator voor Yt)
Eerst zijn de waarden van k negatief = het verleden ; er na worden deze positief = in de toekomst = toekomstige waarden
De Rho waarde is de correlatiecoefficient.
Natuurlijk zoeken we hier de hoogste coëfficiënt 0 0.580471399357484
Deze valt dan ook duidelijk op nul. Dwz dat er geen vertraging of versnelling is op de gegevens, want de hoogste correlatie vind je op het huidige moment.

Grafisch
Alle negatieve lag-waarden stellen het verleden voor. Lag 0 stelt het heden voor en de positieve lag-waarden de toekomst. De hoogste correlatie (die het 95% betrouwbaarheidsinterval overschrijdt) ligt op lag 0

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
103.1
100.6
103.1
95.5
90.5
90.9
88.8
90.7
94.3
104.6
111.1
110.8
107.2
99.0
99.0
91.0
96.2
96.9
96.2
100.1
99.0
115.4
106.9
107.1
99.3
99.2
108.3
105.6
99.5
107.4
93.1
88.1
110.7
113.1
99.6
93.6
98.6
99.6
114.3
107.8
101.2
112.5
100.5
93.9
116.2
112.0
106.4
95.7
96.0
95.8
103.0
102.2
98.4
111.4
86.6
91.3
107.9
101.8
104.4
93.4
100.1
98.5
112.9
101.4
107.1
110.8
90.3
95.5
111.4
113.0
107.5
95.9
106.3
105.2
117.2
106.9
108.2
113.0
97.2
99.9
108.1
118.1
109.1
93.3
112.1
Dataseries Y:
Download CSV
Histogram 
119.5	
125.0	
145.0	
105.3	
116.9	
120.1	
88.9	
78.4	
114.6	
113.3	
117.0	
99.6	
99.4	
101.9	
115.2	
108.5	
113.8	
121.0	
92.2	
90.2	
101.5	
126.6	
93.9	
89.8	
93.4	
101.5	
110.4	
105.9	
108.4	
113.9	
86.1	
69.4	
101.2	
100.5	
98.0	
106.6	
90.1	
96.9	
125.9	
112.0	
100.0	
123.9	
79.8	
83.4	
113.6	
112.9	
104.0	
109.9	
99.0	
106.3	
128.9	
111.1	
102.9	
130.0	
87.0	
87.5	
117.6	
103.4	
110.8	
112.6	
102.5	
112.4	
135.6	
105.1	
127.7	
137.0	
91.0	
90.5	
122.4	
123.3	
124.3	
120.0	
118.1	
119.0	
142.7	
123.6	
129.6	
151.6	
110.4	
99.2	
130.5	
136.2	
129.7	
128.0	
121.6

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=27739&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=27739&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27739&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 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])
-16-0.0310946605113673
-150.0604131181495997
-14-0.0535231887826068
-130.00687404157874838
-120.381017816923367
-11-0.00583408742077162
-10-0.284078039319054
-90.0617655605992916
-80.101768840219023
-70.139523392197282
-60.281937485806357
-50.120182543053055
-4-0.0398352384780394
-30.129910527117841
-20.00448710883175743
-10.113869725255704
00.580471399357484
10.047595900957849
2-0.271959218171184
3-0.0261616297225557
40.0360728593729617
50.0402209236664738
60.143522733670509
70.0875819585907979
8-0.00434602815547668
90.0444974714238492
10-0.0819841021821792
110.0979330696712692
120.417860717736214
13-0.051159609837347
14-0.326773606494212
15-0.105350669758714
16-0.00460481347624146

\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
-16 & -0.0310946605113673 \tabularnewline
-15 & 0.0604131181495997 \tabularnewline
-14 & -0.0535231887826068 \tabularnewline
-13 & 0.00687404157874838 \tabularnewline
-12 & 0.381017816923367 \tabularnewline
-11 & -0.00583408742077162 \tabularnewline
-10 & -0.284078039319054 \tabularnewline
-9 & 0.0617655605992916 \tabularnewline
-8 & 0.101768840219023 \tabularnewline
-7 & 0.139523392197282 \tabularnewline
-6 & 0.281937485806357 \tabularnewline
-5 & 0.120182543053055 \tabularnewline
-4 & -0.0398352384780394 \tabularnewline
-3 & 0.129910527117841 \tabularnewline
-2 & 0.00448710883175743 \tabularnewline
-1 & 0.113869725255704 \tabularnewline
0 & 0.580471399357484 \tabularnewline
1 & 0.047595900957849 \tabularnewline
2 & -0.271959218171184 \tabularnewline
3 & -0.0261616297225557 \tabularnewline
4 & 0.0360728593729617 \tabularnewline
5 & 0.0402209236664738 \tabularnewline
6 & 0.143522733670509 \tabularnewline
7 & 0.0875819585907979 \tabularnewline
8 & -0.00434602815547668 \tabularnewline
9 & 0.0444974714238492 \tabularnewline
10 & -0.0819841021821792 \tabularnewline
11 & 0.0979330696712692 \tabularnewline
12 & 0.417860717736214 \tabularnewline
13 & -0.051159609837347 \tabularnewline
14 & -0.326773606494212 \tabularnewline
15 & -0.105350669758714 \tabularnewline
16 & -0.00460481347624146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27739&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]-16[/C][C]-0.0310946605113673[/C][/ROW]
[ROW][C]-15[/C][C]0.0604131181495997[/C][/ROW]
[ROW][C]-14[/C][C]-0.0535231887826068[/C][/ROW]
[ROW][C]-13[/C][C]0.00687404157874838[/C][/ROW]
[ROW][C]-12[/C][C]0.381017816923367[/C][/ROW]
[ROW][C]-11[/C][C]-0.00583408742077162[/C][/ROW]
[ROW][C]-10[/C][C]-0.284078039319054[/C][/ROW]
[ROW][C]-9[/C][C]0.0617655605992916[/C][/ROW]
[ROW][C]-8[/C][C]0.101768840219023[/C][/ROW]
[ROW][C]-7[/C][C]0.139523392197282[/C][/ROW]
[ROW][C]-6[/C][C]0.281937485806357[/C][/ROW]
[ROW][C]-5[/C][C]0.120182543053055[/C][/ROW]
[ROW][C]-4[/C][C]-0.0398352384780394[/C][/ROW]
[ROW][C]-3[/C][C]0.129910527117841[/C][/ROW]
[ROW][C]-2[/C][C]0.00448710883175743[/C][/ROW]
[ROW][C]-1[/C][C]0.113869725255704[/C][/ROW]
[ROW][C]0[/C][C]0.580471399357484[/C][/ROW]
[ROW][C]1[/C][C]0.047595900957849[/C][/ROW]
[ROW][C]2[/C][C]-0.271959218171184[/C][/ROW]
[ROW][C]3[/C][C]-0.0261616297225557[/C][/ROW]
[ROW][C]4[/C][C]0.0360728593729617[/C][/ROW]
[ROW][C]5[/C][C]0.0402209236664738[/C][/ROW]
[ROW][C]6[/C][C]0.143522733670509[/C][/ROW]
[ROW][C]7[/C][C]0.0875819585907979[/C][/ROW]
[ROW][C]8[/C][C]-0.00434602815547668[/C][/ROW]
[ROW][C]9[/C][C]0.0444974714238492[/C][/ROW]
[ROW][C]10[/C][C]-0.0819841021821792[/C][/ROW]
[ROW][C]11[/C][C]0.0979330696712692[/C][/ROW]
[ROW][C]12[/C][C]0.417860717736214[/C][/ROW]
[ROW][C]13[/C][C]-0.051159609837347[/C][/ROW]
[ROW][C]14[/C][C]-0.326773606494212[/C][/ROW]
[ROW][C]15[/C][C]-0.105350669758714[/C][/ROW]
[ROW][C]16[/C][C]-0.00460481347624146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27739&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27739&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])
-16-0.0310946605113673
-150.0604131181495997
-14-0.0535231887826068
-130.00687404157874838
-120.381017816923367
-11-0.00583408742077162
-10-0.284078039319054
-90.0617655605992916
-80.101768840219023
-70.139523392197282
-60.281937485806357
-50.120182543053055
-4-0.0398352384780394
-30.129910527117841
-20.00448710883175743
-10.113869725255704
00.580471399357484
10.047595900957849
2-0.271959218171184
3-0.0261616297225557
40.0360728593729617
50.0402209236664738
60.143522733670509
70.0875819585907979
8-0.00434602815547668
90.0444974714238492
10-0.0819841021821792
110.0979330696712692
120.417860717736214
13-0.051159609837347
14-0.326773606494212
15-0.105350669758714
16-0.00460481347624146


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')