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Description of Statistical Computation

Author's title

H2: cross correlation function - France & The Netherlands (d=0 D=1 // d=0 D...

Author*The author of this computation has been verified*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationSun, 07 Dec 2008 17:35:08 -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/08/t1228696555cnayl6ctq7acoi2.htm/, Retrieved Thu, 16 May 2024 07:18:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30349, Retrieved Thu, 16 May 2024 07:18:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact241

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] [H2: cross correla...] [2008-12-04 17:29:14] [1e1d8320a8a1170c475bf6e4ce119de6]
-   P     [Cross Correlation Function] [H2: cross correla...] [2008-12-08 00:35:08] [fdd69703d301fae09456f660b2f52997] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3258.1
3140.1
3627.4
3279.4
3204
3515.6
3146.6
2271.7
3627.9
3553.4
3018.3
3355.4
3242
3311.1
4125.2
3423
3120.3
3863
3240.8
2837.4
3945
3684.1
3659.6
3769.6
3592.7
3754
4507.8
3853.2
3817.2
3958.4
3428.9
3125.7
3977
3983.3
4299.6
4306.9
4259.5
3986
4755.6
3925.6
4206.5
4323.4
3816.1
3410.7
4227.4
4296.9
4351.7
3800
4277
4100.2
4672.5
4189.9
4231.9
4654.9
4298.5
3635.9
4505.1
4891.9
4894.2
4093.2
Dataseries Y:
Download CSV
Histogram 
2236
2084.9
2409.5
2199.3
2203.5
2254.1
1975.8
1742.2
2520.6
2438.1
2126.3
2267.5
2201.1
2128.5
2596
2458.2
2210.5
2621.2
2231.4
2103.6
2685.8
2539.3
2462.4
2693.3
2307.7
2385.9
2737.6
2653.9
2545.4
2848.8
2359.5
2488.3
2861.1
2717.9
2844
2749
2652.9
2660.2
3187.1
2774.1
3158.2
3244.6
2665.5
2820.8
2983.4
3077.4
3024.8
2731.8
3046.2
2834.8
3292.8
2946.1
3196.9
3284.2
3003
2979
3137.4
3630.2
3270.7
2942.3

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=30349&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=30349&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30349&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)1
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])
-14-0.0878101647592761
-13-0.33264466572739
-120.586652521155078
-11-0.111331402573383
-10-0.388798975386144
-90.300561064115529
-8-0.067049404635693
-7-0.232234949419981
-60.427621963174972
-5-0.181424753074085
-4-0.147870782597591
-30.340788297241022
-2-0.268639668021724
-1-0.398888838232237
00.838389156522094
1-0.183996006976661
2-0.391960783869345
30.291267731548352
4-0.115699854688877
5-0.16196976798909
60.437725426178507
7-0.265977290674976
8-0.0847725059429086
90.278507998376205
10-0.240628253904025
11-0.232174903777514
120.530676160564521
13-0.112258124362592
14-0.234072782375020

\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) & 1 \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
-14 & -0.0878101647592761 \tabularnewline
-13 & -0.33264466572739 \tabularnewline
-12 & 0.586652521155078 \tabularnewline
-11 & -0.111331402573383 \tabularnewline
-10 & -0.388798975386144 \tabularnewline
-9 & 0.300561064115529 \tabularnewline
-8 & -0.067049404635693 \tabularnewline
-7 & -0.232234949419981 \tabularnewline
-6 & 0.427621963174972 \tabularnewline
-5 & -0.181424753074085 \tabularnewline
-4 & -0.147870782597591 \tabularnewline
-3 & 0.340788297241022 \tabularnewline
-2 & -0.268639668021724 \tabularnewline
-1 & -0.398888838232237 \tabularnewline
0 & 0.838389156522094 \tabularnewline
1 & -0.183996006976661 \tabularnewline
2 & -0.391960783869345 \tabularnewline
3 & 0.291267731548352 \tabularnewline
4 & -0.115699854688877 \tabularnewline
5 & -0.16196976798909 \tabularnewline
6 & 0.437725426178507 \tabularnewline
7 & -0.265977290674976 \tabularnewline
8 & -0.0847725059429086 \tabularnewline
9 & 0.278507998376205 \tabularnewline
10 & -0.240628253904025 \tabularnewline
11 & -0.232174903777514 \tabularnewline
12 & 0.530676160564521 \tabularnewline
13 & -0.112258124362592 \tabularnewline
14 & -0.234072782375020 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30349&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]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]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]-14[/C][C]-0.0878101647592761[/C][/ROW]
[ROW][C]-13[/C][C]-0.33264466572739[/C][/ROW]
[ROW][C]-12[/C][C]0.586652521155078[/C][/ROW]
[ROW][C]-11[/C][C]-0.111331402573383[/C][/ROW]
[ROW][C]-10[/C][C]-0.388798975386144[/C][/ROW]
[ROW][C]-9[/C][C]0.300561064115529[/C][/ROW]
[ROW][C]-8[/C][C]-0.067049404635693[/C][/ROW]
[ROW][C]-7[/C][C]-0.232234949419981[/C][/ROW]
[ROW][C]-6[/C][C]0.427621963174972[/C][/ROW]
[ROW][C]-5[/C][C]-0.181424753074085[/C][/ROW]
[ROW][C]-4[/C][C]-0.147870782597591[/C][/ROW]
[ROW][C]-3[/C][C]0.340788297241022[/C][/ROW]
[ROW][C]-2[/C][C]-0.268639668021724[/C][/ROW]
[ROW][C]-1[/C][C]-0.398888838232237[/C][/ROW]
[ROW][C]0[/C][C]0.838389156522094[/C][/ROW]
[ROW][C]1[/C][C]-0.183996006976661[/C][/ROW]
[ROW][C]2[/C][C]-0.391960783869345[/C][/ROW]
[ROW][C]3[/C][C]0.291267731548352[/C][/ROW]
[ROW][C]4[/C][C]-0.115699854688877[/C][/ROW]
[ROW][C]5[/C][C]-0.16196976798909[/C][/ROW]
[ROW][C]6[/C][C]0.437725426178507[/C][/ROW]
[ROW][C]7[/C][C]-0.265977290674976[/C][/ROW]
[ROW][C]8[/C][C]-0.0847725059429086[/C][/ROW]
[ROW][C]9[/C][C]0.278507998376205[/C][/ROW]
[ROW][C]10[/C][C]-0.240628253904025[/C][/ROW]
[ROW][C]11[/C][C]-0.232174903777514[/C][/ROW]
[ROW][C]12[/C][C]0.530676160564521[/C][/ROW]
[ROW][C]13[/C][C]-0.112258124362592[/C][/ROW]
[ROW][C]14[/C][C]-0.234072782375020[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30349&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30349&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)1
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])
-14-0.0878101647592761
-13-0.33264466572739
-120.586652521155078
-11-0.111331402573383
-10-0.388798975386144
-90.300561064115529
-8-0.067049404635693
-7-0.232234949419981
-60.427621963174972
-5-0.181424753074085
-4-0.147870782597591
-30.340788297241022
-2-0.268639668021724
-1-0.398888838232237
00.838389156522094
1-0.183996006976661
2-0.391960783869345
30.291267731548352
4-0.115699854688877
5-0.16196976798909
60.437725426178507
7-0.265977290674976
8-0.0847725059429086
90.278507998376205
10-0.240628253904025
11-0.232174903777514
120.530676160564521
13-0.112258124362592
14-0.234072782375020


Figures (Output of Computation)

Input Parameters & R Code

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