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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 computationSat, 06 Dec 2008 05:18:36 -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/06/t12285659362zevs4j6xy5e8px.htm/, Retrieved Fri, 17 May 2024 04:42:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29540, Retrieved Fri, 17 May 2024 04:42:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact219

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] [] [2008-12-03 08:17:18] [996314793dac993597edc1ca2281ff39]
-   PD    [Cross Correlation Function] [] [2008-12-06 12:18:36] [e02910eed3830f1815f587e12f46cbdb] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
118.4
121.4
128.8
131.7
141.7
142.9
139.4
134.7
125.0
113.6
111.5
108.5
112.3
116.6
115.5
120.1
132.9
128.1
129.3
132.5
131.0
124.9
120.8
122.0
122.1
127.4
135.2
137.3
135.0
136.0
138.4
134.7
138.4
133.9
133.6
141.2
151.8
155.4
156.6
161.6
160.7
156.0
159.5
168.7
169.9
169.9
185.9
190.8
195.8
211.9
227.1
251.3
256.7
251.9
251.2
270.3
267.2
243.0
229.9
187.2
Dataseries Y:
Download CSV
Histogram 
104.0
107.9
113.8
113.8
123.1
125.1
137.6
134.0
140.3
152.1
150.6
167.3
153.2
142.0
154.4
158.5
180.9
181.3
172.4
192.0
199.3
215.4
214.3
201.5
190.5
196.0
215.7
209.4
214.1
237.8
239.0
237.8
251.5
248.8
215.4
201.2
203.1
214.2
188.9
203.0
213.3
228.5
228.2
240.9
258.8
248.5
269.2
289.6
323.4
317.2
322.8
340.9
368.2
388.5
441.2
474.3
483.9
417.9
365.9
263.0

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=29540&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=29540&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29540&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 series-1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series-0.2
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.123200310008927
-12-0.114127296386663
-110.148892827649746
-100.167350766599013
-9-0.0528244067400646
-8-0.215265946654823
-70.0290283078521761
-60.0235341037629556
-5-0.0580848790424832
-40.191827069137861
-30.171674048716685
-2-0.143519414060112
-10.272966570773409
00.4745669411744
10.11946816751842
2-0.081573323569201
3-0.00766770317019954
40.126179392868570
5-0.0796509342595687
60.0385407994868843
70.0614624387068022
8-0.236436344091201
9-0.189467857150694
100.17852020668568
11-0.174076972814681
12-0.38762382680743
13-0.158541839726712

\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 & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & -0.2 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & -0.123200310008927 \tabularnewline
-12 & -0.114127296386663 \tabularnewline
-11 & 0.148892827649746 \tabularnewline
-10 & 0.167350766599013 \tabularnewline
-9 & -0.0528244067400646 \tabularnewline
-8 & -0.215265946654823 \tabularnewline
-7 & 0.0290283078521761 \tabularnewline
-6 & 0.0235341037629556 \tabularnewline
-5 & -0.0580848790424832 \tabularnewline
-4 & 0.191827069137861 \tabularnewline
-3 & 0.171674048716685 \tabularnewline
-2 & -0.143519414060112 \tabularnewline
-1 & 0.272966570773409 \tabularnewline
0 & 0.4745669411744 \tabularnewline
1 & 0.11946816751842 \tabularnewline
2 & -0.081573323569201 \tabularnewline
3 & -0.00766770317019954 \tabularnewline
4 & 0.126179392868570 \tabularnewline
5 & -0.0796509342595687 \tabularnewline
6 & 0.0385407994868843 \tabularnewline
7 & 0.0614624387068022 \tabularnewline
8 & -0.236436344091201 \tabularnewline
9 & -0.189467857150694 \tabularnewline
10 & 0.17852020668568 \tabularnewline
11 & -0.174076972814681 \tabularnewline
12 & -0.38762382680743 \tabularnewline
13 & -0.158541839726712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29540&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]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]-0.2[/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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]-0.123200310008927[/C][/ROW]
[ROW][C]-12[/C][C]-0.114127296386663[/C][/ROW]
[ROW][C]-11[/C][C]0.148892827649746[/C][/ROW]
[ROW][C]-10[/C][C]0.167350766599013[/C][/ROW]
[ROW][C]-9[/C][C]-0.0528244067400646[/C][/ROW]
[ROW][C]-8[/C][C]-0.215265946654823[/C][/ROW]
[ROW][C]-7[/C][C]0.0290283078521761[/C][/ROW]
[ROW][C]-6[/C][C]0.0235341037629556[/C][/ROW]
[ROW][C]-5[/C][C]-0.0580848790424832[/C][/ROW]
[ROW][C]-4[/C][C]0.191827069137861[/C][/ROW]
[ROW][C]-3[/C][C]0.171674048716685[/C][/ROW]
[ROW][C]-2[/C][C]-0.143519414060112[/C][/ROW]
[ROW][C]-1[/C][C]0.272966570773409[/C][/ROW]
[ROW][C]0[/C][C]0.4745669411744[/C][/ROW]
[ROW][C]1[/C][C]0.11946816751842[/C][/ROW]
[ROW][C]2[/C][C]-0.081573323569201[/C][/ROW]
[ROW][C]3[/C][C]-0.00766770317019954[/C][/ROW]
[ROW][C]4[/C][C]0.126179392868570[/C][/ROW]
[ROW][C]5[/C][C]-0.0796509342595687[/C][/ROW]
[ROW][C]6[/C][C]0.0385407994868843[/C][/ROW]
[ROW][C]7[/C][C]0.0614624387068022[/C][/ROW]
[ROW][C]8[/C][C]-0.236436344091201[/C][/ROW]
[ROW][C]9[/C][C]-0.189467857150694[/C][/ROW]
[ROW][C]10[/C][C]0.17852020668568[/C][/ROW]
[ROW][C]11[/C][C]-0.174076972814681[/C][/ROW]
[ROW][C]12[/C][C]-0.38762382680743[/C][/ROW]
[ROW][C]13[/C][C]-0.158541839726712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29540&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29540&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 series-1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series-0.2
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.123200310008927
-12-0.114127296386663
-110.148892827649746
-100.167350766599013
-9-0.0528244067400646
-8-0.215265946654823
-70.0290283078521761
-60.0235341037629556
-5-0.0580848790424832
-40.191827069137861
-30.171674048716685
-2-0.143519414060112
-10.272966570773409
00.4745669411744
10.11946816751842
2-0.081573323569201
3-0.00766770317019954
40.126179392868570
5-0.0796509342595687
60.0385407994868843
70.0614624387068022
8-0.236436344091201
9-0.189467857150694
100.17852020668568
11-0.174076972814681
12-0.38762382680743
13-0.158541839726712


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
Parameters (R input):
par1 = -1.0 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = -0.2 ; par6 = 1 ; 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')