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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 05:33:18 -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/t12282212354rqwf8gg0wpnxg8.htm/, Retrieved Sun, 19 May 2024 11:31:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27685, Retrieved Sun, 19 May 2024 11:31:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199

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] [Non Stationary Ti...] [2008-12-02 11:59:49] [74be16979710d4c4e7c6647856088456]
F RM D    [Cross Correlation Function] [Non Stationary Ti...] [2008-12-02 12:07:59] [8ac58ef7b35dc5a117bc162cf16850e9]
F    D        [Cross Correlation Function] [Non Stationary Ti...] [2008-12-02 12:33:18] [acca1d0ee7cc95ffc080d0867a313954] [Current]
Feedback Forum
2008-12-04 13:47:23 [Glenn Maras] [reply] 
Goede bespreking en vergelijking met Q7, de student zegt duidelijk dat in Q7 er nonsenscorrelatie te zien was en deze er nu allemaal uitgewerkt is.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110.40
96.40
101.90
106.20
81.00
94.70
101.00
109.40
102.30
90.70
96.20
96.10
106.00
103.10
102.00
104.70
86.00
92.10
106.90
112.60
101.70
92.00
97.40
97.00
105.40
102.70
98.10
104.50
87.40
89.90
109.80
111.70
98.60
96.90
95.10
97.00
112.70
102.90
97.40
111.40
87.40
96.80
114.10
110.30
103.90
101.60
94.60
95.90
104.70
102.80
98.10
113.90
80.90
95.70
113.20
105.90
108.80
102.30
99.00
100.70
115.50
Dataseries Y:
Download CSV
Histogram 
109.20
88.60
94.30
98.30
86.40
80.60
104.10
108.20
93.40
71.90
94.10
94.90
96.40
91.10
84.40
86.40
88.00
75.10
109.70
103.00
82.10
68.00
96.40
94.30
90.00
88.00
76.10
82.50
81.40
66.50
97.20
94.10
80.70
70.50
87.80
89.50
99.60
84.20
75.10
92.00
80.80
73.10
99.80
90.00
83.10
72.40
78.80
87.30
91.00
80.10
73.60
86.40
74.50
71.20
92.40
81.50
85.30
69.90
84.20
90.70
100.30

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27685&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27685&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27685&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'George Udny Yule' @ 72.249.76.132






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)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 series1
krho(Y[t],X[t+k])
-13-0.242913085874240
-12-0.213715176882144
-11-0.106280206522092
-10-0.367143352300753
-90.0345392462755281
-8-0.054627753751998
-7-0.279361771081202
-60.0127453909866063
-50.0564001789340234
-4-0.106675337090398
-30.334071353928195
-20.123032644297837
-1-0.0918359225623047
00.777675949978544
10.100547319148974
20.136188834764014
30.246433682621054
4-0.0944420581616984
50.05177640717465
60.0417432037988897
7-0.220022019199529
8-0.114342461951056
9-0.0635148393973354
10-0.248503525459659
11-0.167022377769392
12-0.110898168158601
13-0.240307383923383

\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) & 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 & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & -0.242913085874240 \tabularnewline
-12 & -0.213715176882144 \tabularnewline
-11 & -0.106280206522092 \tabularnewline
-10 & -0.367143352300753 \tabularnewline
-9 & 0.0345392462755281 \tabularnewline
-8 & -0.054627753751998 \tabularnewline
-7 & -0.279361771081202 \tabularnewline
-6 & 0.0127453909866063 \tabularnewline
-5 & 0.0564001789340234 \tabularnewline
-4 & -0.106675337090398 \tabularnewline
-3 & 0.334071353928195 \tabularnewline
-2 & 0.123032644297837 \tabularnewline
-1 & -0.0918359225623047 \tabularnewline
0 & 0.777675949978544 \tabularnewline
1 & 0.100547319148974 \tabularnewline
2 & 0.136188834764014 \tabularnewline
3 & 0.246433682621054 \tabularnewline
4 & -0.0944420581616984 \tabularnewline
5 & 0.05177640717465 \tabularnewline
6 & 0.0417432037988897 \tabularnewline
7 & -0.220022019199529 \tabularnewline
8 & -0.114342461951056 \tabularnewline
9 & -0.0635148393973354 \tabularnewline
10 & -0.248503525459659 \tabularnewline
11 & -0.167022377769392 \tabularnewline
12 & -0.110898168158601 \tabularnewline
13 & -0.240307383923383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27685&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]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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]-0.242913085874240[/C][/ROW]
[ROW][C]-12[/C][C]-0.213715176882144[/C][/ROW]
[ROW][C]-11[/C][C]-0.106280206522092[/C][/ROW]
[ROW][C]-10[/C][C]-0.367143352300753[/C][/ROW]
[ROW][C]-9[/C][C]0.0345392462755281[/C][/ROW]
[ROW][C]-8[/C][C]-0.054627753751998[/C][/ROW]
[ROW][C]-7[/C][C]-0.279361771081202[/C][/ROW]
[ROW][C]-6[/C][C]0.0127453909866063[/C][/ROW]
[ROW][C]-5[/C][C]0.0564001789340234[/C][/ROW]
[ROW][C]-4[/C][C]-0.106675337090398[/C][/ROW]
[ROW][C]-3[/C][C]0.334071353928195[/C][/ROW]
[ROW][C]-2[/C][C]0.123032644297837[/C][/ROW]
[ROW][C]-1[/C][C]-0.0918359225623047[/C][/ROW]
[ROW][C]0[/C][C]0.777675949978544[/C][/ROW]
[ROW][C]1[/C][C]0.100547319148974[/C][/ROW]
[ROW][C]2[/C][C]0.136188834764014[/C][/ROW]
[ROW][C]3[/C][C]0.246433682621054[/C][/ROW]
[ROW][C]4[/C][C]-0.0944420581616984[/C][/ROW]
[ROW][C]5[/C][C]0.05177640717465[/C][/ROW]
[ROW][C]6[/C][C]0.0417432037988897[/C][/ROW]
[ROW][C]7[/C][C]-0.220022019199529[/C][/ROW]
[ROW][C]8[/C][C]-0.114342461951056[/C][/ROW]
[ROW][C]9[/C][C]-0.0635148393973354[/C][/ROW]
[ROW][C]10[/C][C]-0.248503525459659[/C][/ROW]
[ROW][C]11[/C][C]-0.167022377769392[/C][/ROW]
[ROW][C]12[/C][C]-0.110898168158601[/C][/ROW]
[ROW][C]13[/C][C]-0.240307383923383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27685&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27685&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)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 series1
krho(Y[t],X[t+k])
-13-0.242913085874240
-12-0.213715176882144
-11-0.106280206522092
-10-0.367143352300753
-90.0345392462755281
-8-0.054627753751998
-7-0.279361771081202
-60.0127453909866063
-50.0564001789340234
-4-0.106675337090398
-30.334071353928195
-20.123032644297837
-1-0.0918359225623047
00.777675949978544
10.100547319148974
20.136188834764014
30.246433682621054
4-0.0944420581616984
50.05177640717465
60.0417432037988897
7-0.220022019199529
8-0.114342461951056
9-0.0635148393973354
10-0.248503525459659
11-0.167022377769392
12-0.110898168158601
13-0.240307383923383


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = -0.3 ; par3 = 1 ; par4 = 0 ; par5 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; 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')