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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 computationSat, 23 Jan 2010 12:21:52 -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/2010/Jan/23/t12642745525wsi0zhnglh28xn.htm/, Retrieved Sun, 05 May 2024 00:27:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=72387, Retrieved Sun, 05 May 2024 00:27:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [VAC (Partiële) au...] [2008-12-14 13:47:12] [379d6c32f73e3218fd773d79e4063d07]
-    D  [(Partial) Autocorrelation Function] [VAC (Partiële) au...] [2008-12-14 14:21:15] [379d6c32f73e3218fd773d79e4063d07]
- RM D    [Cross Correlation Function] [VAC cross correla...] [2008-12-14 14:31:56] [379d6c32f73e3218fd773d79e4063d07]
-   PD      [Cross Correlation Function] [VAC cross correla...] [2008-12-23 15:12:44] [379d6c32f73e3218fd773d79e4063d07]
-  M            [Cross Correlation Function] [Cross Cerelation ...] [2010-01-23 19:21:52] [f32a893c5a60da9308cd5d37e6977c4f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
124.1
124.4
115.7
108.3
102.3
104.6
104
103.5
96
96.6
95.4
92.1
93
90.4
93.3
97.1
111
114.1
113.3
111
107.2
118.3
134.1
139
116.7
112.5
122.8
130
125.6
123.8
135.8
136.4
135.3
149.5
159.6
161.4
175.2
199.5
245
257.8
Dataseries Y:
Download CSV
Histogram 
188.5
188.6
191.9
193.5
194.9
194.9
196.2
196.2
198
198.6
201.3
203.5
204.1
204.8
206.5
207.8
208.6
209.7
210
211.7
212.4
213.7
214.8
216.4
217.5
218.6
220.4
221.8
222.5
223.4
225.5
226.5
227.8
228.5
229.1
229.9
230.8
231.9
236
237.5

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=72387&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=72387&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72387&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 series0.1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)4
Box-Cox transformation parameter (lambda) of Y series1.1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-120.119189962920873
-110.0175563352217132
-10-0.267246557807399
-9-0.0161144574441146
-80.133934351552059
-70.0163149822038114
-6-0.0357416312044549
-5-0.174543736871255
-4-0.0760955139218173
-3-0.0473854811330874
-20.126863353996811
-1-0.0320551176162835
00.0594593872363008
1-0.0832201237177125
20.163543008033248
30.068112799634177
40.0426672839146407
5-0.226587102250659
6-0.207475693033417
70.0131160889891469
80.221559294596080
90.244083428981055
10-0.0102576417301859
11-0.124911556495311
12-0.273886249227939

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0.1 \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) & 4 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1.1 \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
-12 & 0.119189962920873 \tabularnewline
-11 & 0.0175563352217132 \tabularnewline
-10 & -0.267246557807399 \tabularnewline
-9 & -0.0161144574441146 \tabularnewline
-8 & 0.133934351552059 \tabularnewline
-7 & 0.0163149822038114 \tabularnewline
-6 & -0.0357416312044549 \tabularnewline
-5 & -0.174543736871255 \tabularnewline
-4 & -0.0760955139218173 \tabularnewline
-3 & -0.0473854811330874 \tabularnewline
-2 & 0.126863353996811 \tabularnewline
-1 & -0.0320551176162835 \tabularnewline
0 & 0.0594593872363008 \tabularnewline
1 & -0.0832201237177125 \tabularnewline
2 & 0.163543008033248 \tabularnewline
3 & 0.068112799634177 \tabularnewline
4 & 0.0426672839146407 \tabularnewline
5 & -0.226587102250659 \tabularnewline
6 & -0.207475693033417 \tabularnewline
7 & 0.0131160889891469 \tabularnewline
8 & 0.221559294596080 \tabularnewline
9 & 0.244083428981055 \tabularnewline
10 & -0.0102576417301859 \tabularnewline
11 & -0.124911556495311 \tabularnewline
12 & -0.273886249227939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72387&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]0.1[/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]4[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]1.1[/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]-12[/C][C]0.119189962920873[/C][/ROW]
[ROW][C]-11[/C][C]0.0175563352217132[/C][/ROW]
[ROW][C]-10[/C][C]-0.267246557807399[/C][/ROW]
[ROW][C]-9[/C][C]-0.0161144574441146[/C][/ROW]
[ROW][C]-8[/C][C]0.133934351552059[/C][/ROW]
[ROW][C]-7[/C][C]0.0163149822038114[/C][/ROW]
[ROW][C]-6[/C][C]-0.0357416312044549[/C][/ROW]
[ROW][C]-5[/C][C]-0.174543736871255[/C][/ROW]
[ROW][C]-4[/C][C]-0.0760955139218173[/C][/ROW]
[ROW][C]-3[/C][C]-0.0473854811330874[/C][/ROW]
[ROW][C]-2[/C][C]0.126863353996811[/C][/ROW]
[ROW][C]-1[/C][C]-0.0320551176162835[/C][/ROW]
[ROW][C]0[/C][C]0.0594593872363008[/C][/ROW]
[ROW][C]1[/C][C]-0.0832201237177125[/C][/ROW]
[ROW][C]2[/C][C]0.163543008033248[/C][/ROW]
[ROW][C]3[/C][C]0.068112799634177[/C][/ROW]
[ROW][C]4[/C][C]0.0426672839146407[/C][/ROW]
[ROW][C]5[/C][C]-0.226587102250659[/C][/ROW]
[ROW][C]6[/C][C]-0.207475693033417[/C][/ROW]
[ROW][C]7[/C][C]0.0131160889891469[/C][/ROW]
[ROW][C]8[/C][C]0.221559294596080[/C][/ROW]
[ROW][C]9[/C][C]0.244083428981055[/C][/ROW]
[ROW][C]10[/C][C]-0.0102576417301859[/C][/ROW]
[ROW][C]11[/C][C]-0.124911556495311[/C][/ROW]
[ROW][C]12[/C][C]-0.273886249227939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72387&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72387&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 series0.1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)4
Box-Cox transformation parameter (lambda) of Y series1.1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-120.119189962920873
-110.0175563352217132
-10-0.267246557807399
-9-0.0161144574441146
-80.133934351552059
-70.0163149822038114
-6-0.0357416312044549
-5-0.174543736871255
-4-0.0760955139218173
-3-0.0473854811330874
-20.126863353996811
-1-0.0320551176162835
00.0594593872363008
1-0.0832201237177125
20.163543008033248
30.068112799634177
40.0426672839146407
5-0.226587102250659
6-0.207475693033417
70.0131160889891469
80.221559294596080
90.244083428981055
10-0.0102576417301859
11-0.124911556495311
12-0.273886249227939


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.1 ; par2 = 1 ; par3 = 1 ; par4 = 4 ; par5 = 1.1 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 0.1 ; par2 = 1 ; par3 = 1 ; par4 = 4 ; par5 = 1.1 ; 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')