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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 09:29:36 -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/02/t12282354698vocvyw3zswh9v6.htm/, Retrieved Fri, 17 May 2024 03:42:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28042, Retrieved Fri, 17 May 2024 03:42:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154

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] [Non Stationary Ti...] [2008-12-02 16:29:36] [b72e060d4eaf5aae1831b15bc791ef7e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97.3
101
113.2
101
105.7
113.9
86.4
96.5
103.3
114.9
105.8
94.2
98.4
99.4
108.8
112.6
104.4
112.2
81.1
97.1
112.6
113.8
107.8
103.2
103.3
101.2
107.7
110.4
101.9
115.9
89.9
88.6
117.2
123.9
100
103.6
94.1
98.7
119.5
112.7
104.4
124.7
89.1
97
121.6
118.8
114
111.5
97.2
102.5
113.4
109.8
104.9
126.1
80
96.8
117.2
112.3
117.3
111.1
102.2
104.3
122.9
107.6
121.3
131.5
89
104.4
128.9
135.9
133.3
121.3
120.5
120.4
137.9
126.1
133.2
151.1
105
119
140.4
156.6
137.1
122.7
Dataseries Y:
Download CSV
Histogram 
93.5
94.7
112.9
99.2
105.6
113
83.1
81.1
96.9
104.3
97.7
102.6
89.9
96
112.7
107.1
106.2
121
101.2
83.2
105.1
113.3
99.1
100.3
93.5
98.8
106.2
98.3
102.1
117.1
101.5
80.5
105.9
109.5
97.2
114.5
93.5
100.9
121.1
116.5
109.3
118.1
108.3
105.4
116.2
111.2
105.8
122.7
99.5
107.9
124.6
115
110.3
132.7
99.7
96.5
118.7
112.9
130.5
137.9
115
116.8
140.9
120.7
134.2
147.3
112.4
107.1
128.4
137.7
135
151
137.4
132.4
161.3
139.8
146
166.5
143.3
121
152.6
154.4
154.6
158

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'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28042&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28042&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28042&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'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series-1.1
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 series0
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-15-0.0288662027211724
-140.043893038882568
-130.0450729596085745
-12-0.0879295955436
-11-0.0198225795469259
-100.0303905804939563
-90.147550080542874
-8-0.156493428130063
-7-0.0871663445594668
-6-0.00297012703836807
-50.325408846178446
-4-0.369982590816568
-30.135185468826792
-20.0350636537585582
-1-0.229057035013158
00.472278421476994
1-0.34791672378219
20.0658840730628552
30.0825077962791559
4-0.0338287168123127
5-0.00650069948021166
60.127405748855040
7-0.235408418234558
80.130738298572978
90.138424116503879
10-0.165901326107215
11-0.0172437887908498
120.0772927287357272
13-0.071446791288912
140.0504256835004863
150.0138163365575111

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & -1.1 \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 \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
-15 & -0.0288662027211724 \tabularnewline
-14 & 0.043893038882568 \tabularnewline
-13 & 0.0450729596085745 \tabularnewline
-12 & -0.0879295955436 \tabularnewline
-11 & -0.0198225795469259 \tabularnewline
-10 & 0.0303905804939563 \tabularnewline
-9 & 0.147550080542874 \tabularnewline
-8 & -0.156493428130063 \tabularnewline
-7 & -0.0871663445594668 \tabularnewline
-6 & -0.00297012703836807 \tabularnewline
-5 & 0.325408846178446 \tabularnewline
-4 & -0.369982590816568 \tabularnewline
-3 & 0.135185468826792 \tabularnewline
-2 & 0.0350636537585582 \tabularnewline
-1 & -0.229057035013158 \tabularnewline
0 & 0.472278421476994 \tabularnewline
1 & -0.34791672378219 \tabularnewline
2 & 0.0658840730628552 \tabularnewline
3 & 0.0825077962791559 \tabularnewline
4 & -0.0338287168123127 \tabularnewline
5 & -0.00650069948021166 \tabularnewline
6 & 0.127405748855040 \tabularnewline
7 & -0.235408418234558 \tabularnewline
8 & 0.130738298572978 \tabularnewline
9 & 0.138424116503879 \tabularnewline
10 & -0.165901326107215 \tabularnewline
11 & -0.0172437887908498 \tabularnewline
12 & 0.0772927287357272 \tabularnewline
13 & -0.071446791288912 \tabularnewline
14 & 0.0504256835004863 \tabularnewline
15 & 0.0138163365575111 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28042&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]-1.1[/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[/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]-15[/C][C]-0.0288662027211724[/C][/ROW]
[ROW][C]-14[/C][C]0.043893038882568[/C][/ROW]
[ROW][C]-13[/C][C]0.0450729596085745[/C][/ROW]
[ROW][C]-12[/C][C]-0.0879295955436[/C][/ROW]
[ROW][C]-11[/C][C]-0.0198225795469259[/C][/ROW]
[ROW][C]-10[/C][C]0.0303905804939563[/C][/ROW]
[ROW][C]-9[/C][C]0.147550080542874[/C][/ROW]
[ROW][C]-8[/C][C]-0.156493428130063[/C][/ROW]
[ROW][C]-7[/C][C]-0.0871663445594668[/C][/ROW]
[ROW][C]-6[/C][C]-0.00297012703836807[/C][/ROW]
[ROW][C]-5[/C][C]0.325408846178446[/C][/ROW]
[ROW][C]-4[/C][C]-0.369982590816568[/C][/ROW]
[ROW][C]-3[/C][C]0.135185468826792[/C][/ROW]
[ROW][C]-2[/C][C]0.0350636537585582[/C][/ROW]
[ROW][C]-1[/C][C]-0.229057035013158[/C][/ROW]
[ROW][C]0[/C][C]0.472278421476994[/C][/ROW]
[ROW][C]1[/C][C]-0.34791672378219[/C][/ROW]
[ROW][C]2[/C][C]0.0658840730628552[/C][/ROW]
[ROW][C]3[/C][C]0.0825077962791559[/C][/ROW]
[ROW][C]4[/C][C]-0.0338287168123127[/C][/ROW]
[ROW][C]5[/C][C]-0.00650069948021166[/C][/ROW]
[ROW][C]6[/C][C]0.127405748855040[/C][/ROW]
[ROW][C]7[/C][C]-0.235408418234558[/C][/ROW]
[ROW][C]8[/C][C]0.130738298572978[/C][/ROW]
[ROW][C]9[/C][C]0.138424116503879[/C][/ROW]
[ROW][C]10[/C][C]-0.165901326107215[/C][/ROW]
[ROW][C]11[/C][C]-0.0172437887908498[/C][/ROW]
[ROW][C]12[/C][C]0.0772927287357272[/C][/ROW]
[ROW][C]13[/C][C]-0.071446791288912[/C][/ROW]
[ROW][C]14[/C][C]0.0504256835004863[/C][/ROW]
[ROW][C]15[/C][C]0.0138163365575111[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28042&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28042&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-1.1
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 series0
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-15-0.0288662027211724
-140.043893038882568
-130.0450729596085745
-12-0.0879295955436
-11-0.0198225795469259
-100.0303905804939563
-90.147550080542874
-8-0.156493428130063
-7-0.0871663445594668
-6-0.00297012703836807
-50.325408846178446
-4-0.369982590816568
-30.135185468826792
-20.0350636537585582
-1-0.229057035013158
00.472278421476994
1-0.34791672378219
20.0658840730628552
30.0825077962791559
4-0.0338287168123127
5-0.00650069948021166
60.127405748855040
7-0.235408418234558
80.130738298572978
90.138424116503879
10-0.165901326107215
11-0.0172437887908498
120.0772927287357272
13-0.071446791288912
140.0504256835004863
150.0138163365575111


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = -1.1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 0.0 ; par6 = 1 ; par7 = 1 ;
Parameters (R input):
par1 = -1.1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 0.0 ; 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')