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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 computationThu, 04 Dec 2008 04:44:08 -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/04/t1228391128tyml8s6fb3l5o5o.htm/, Retrieved Sun, 19 May 2024 04:27:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28917, Retrieved Sun, 19 May 2024 04:27:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact238

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]
- RMPD  [Standard Deviation-Mean Plot] [blok 17 Q5 standa...] [2008-12-02 20:05:22] [6173c35e31b784a490c8cd5476f785d4]
-    D    [Standard Deviation-Mean Plot] [blok 17 Q8 standa...] [2008-12-04 11:29:25] [6173c35e31b784a490c8cd5476f785d4]
- RMPD        [Cross Correlation Function] [blok 17 Q9 cross ...] [2008-12-04 11:44:08] [1237f4df7e9be807e4c0a07b90c45721] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98.6
98
106.8
96.6
100.1
107.7
91.5
97.8
107.4
117.5
105.6
97.4
99.5
98
104.3
100.6
101.1
103.9
96.9
95.5
108.4
117
103.8
100.8
110.6
104
112.6
107.3
98.9
109.8
104.9
102.2
123.9
124.9
112.7
121.9
100.6
104.3
120.4
107.5
102.9
125.6
107.5
108.8
128.4
121.1
119.5
128.7
108.7
105.5
119.8
111.3
110.6
120.1
97.5
107.7
127.3
117.2
119.8
116.2
111
112.4
130.6
109.1
118.8
123.9
101.6
112.8
128
129.6
125.8
119.5
Dataseries Y:
Download CSV
Histogram 
98.1
101.1
111.1
93.3
100
108
70.4
75.4
105.5
112.3
102.5
93.5
86.7
95.2
103.8
97
95.5
101
67.5
64
106.7
100.6
101.2
93.1
84.2
85.8
91.8
92.4
80.3
79.7
62.5
57.1
100.8
100.7
86.2
83.2
71.7
77.5
89.8
80.3
78.7
93.8
57.6
60.6
91
85.3
77.4
77.3
68.3
69.9
81.7
75.1
69.9
84
54.3
60
89.9
77
85.3
77.6
69.2
75.5
85.7
72.2
79.9
85.3
52.2
61.2
82.4
85.4
78.2
70.2

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=28917&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=28917&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28917&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 series-1.9
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 series0
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-140.0432677065934309
-13-0.0768064461342353
-120.0180610910794010
-11-0.118438998531633
-10-0.251343775168955
-90.0640319317908126
-80.00445822710540056
-7-0.147801781758117
-6-0.0402128900393475
-5-0.42907844990777
-4-0.446703603810631
-3-0.076436533417495
-2-0.255186165483198
-1-0.292158832488169
00.217802852510561
1-0.132282394423244
20.0682209427551899
3-0.0169855126760604
4-0.158307109223681
50.0372689506226071
6-0.109999466852542
70.0405067596291359
80.200688287080771
90.108671546894821
100.0863147210691597
110.110892329421087
12-0.156261117651209
13-0.23577120151781
14-0.0496376258915339

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & -1.9 \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 & 0 \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 & 0.0432677065934309 \tabularnewline
-13 & -0.0768064461342353 \tabularnewline
-12 & 0.0180610910794010 \tabularnewline
-11 & -0.118438998531633 \tabularnewline
-10 & -0.251343775168955 \tabularnewline
-9 & 0.0640319317908126 \tabularnewline
-8 & 0.00445822710540056 \tabularnewline
-7 & -0.147801781758117 \tabularnewline
-6 & -0.0402128900393475 \tabularnewline
-5 & -0.42907844990777 \tabularnewline
-4 & -0.446703603810631 \tabularnewline
-3 & -0.076436533417495 \tabularnewline
-2 & -0.255186165483198 \tabularnewline
-1 & -0.292158832488169 \tabularnewline
0 & 0.217802852510561 \tabularnewline
1 & -0.132282394423244 \tabularnewline
2 & 0.0682209427551899 \tabularnewline
3 & -0.0169855126760604 \tabularnewline
4 & -0.158307109223681 \tabularnewline
5 & 0.0372689506226071 \tabularnewline
6 & -0.109999466852542 \tabularnewline
7 & 0.0405067596291359 \tabularnewline
8 & 0.200688287080771 \tabularnewline
9 & 0.108671546894821 \tabularnewline
10 & 0.0863147210691597 \tabularnewline
11 & 0.110892329421087 \tabularnewline
12 & -0.156261117651209 \tabularnewline
13 & -0.23577120151781 \tabularnewline
14 & -0.0496376258915339 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28917&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]-1.9[/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]0[/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]0.0432677065934309[/C][/ROW]
[ROW][C]-13[/C][C]-0.0768064461342353[/C][/ROW]
[ROW][C]-12[/C][C]0.0180610910794010[/C][/ROW]
[ROW][C]-11[/C][C]-0.118438998531633[/C][/ROW]
[ROW][C]-10[/C][C]-0.251343775168955[/C][/ROW]
[ROW][C]-9[/C][C]0.0640319317908126[/C][/ROW]
[ROW][C]-8[/C][C]0.00445822710540056[/C][/ROW]
[ROW][C]-7[/C][C]-0.147801781758117[/C][/ROW]
[ROW][C]-6[/C][C]-0.0402128900393475[/C][/ROW]
[ROW][C]-5[/C][C]-0.42907844990777[/C][/ROW]
[ROW][C]-4[/C][C]-0.446703603810631[/C][/ROW]
[ROW][C]-3[/C][C]-0.076436533417495[/C][/ROW]
[ROW][C]-2[/C][C]-0.255186165483198[/C][/ROW]
[ROW][C]-1[/C][C]-0.292158832488169[/C][/ROW]
[ROW][C]0[/C][C]0.217802852510561[/C][/ROW]
[ROW][C]1[/C][C]-0.132282394423244[/C][/ROW]
[ROW][C]2[/C][C]0.0682209427551899[/C][/ROW]
[ROW][C]3[/C][C]-0.0169855126760604[/C][/ROW]
[ROW][C]4[/C][C]-0.158307109223681[/C][/ROW]
[ROW][C]5[/C][C]0.0372689506226071[/C][/ROW]
[ROW][C]6[/C][C]-0.109999466852542[/C][/ROW]
[ROW][C]7[/C][C]0.0405067596291359[/C][/ROW]
[ROW][C]8[/C][C]0.200688287080771[/C][/ROW]
[ROW][C]9[/C][C]0.108671546894821[/C][/ROW]
[ROW][C]10[/C][C]0.0863147210691597[/C][/ROW]
[ROW][C]11[/C][C]0.110892329421087[/C][/ROW]
[ROW][C]12[/C][C]-0.156261117651209[/C][/ROW]
[ROW][C]13[/C][C]-0.23577120151781[/C][/ROW]
[ROW][C]14[/C][C]-0.0496376258915339[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28917&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28917&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-1.9
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 series0
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-140.0432677065934309
-13-0.0768064461342353
-120.0180610910794010
-11-0.118438998531633
-10-0.251343775168955
-90.0640319317908126
-80.00445822710540056
-7-0.147801781758117
-6-0.0402128900393475
-5-0.42907844990777
-4-0.446703603810631
-3-0.076436533417495
-2-0.255186165483198
-1-0.292158832488169
00.217802852510561
1-0.132282394423244
20.0682209427551899
3-0.0169855126760604
4-0.158307109223681
50.0372689506226071
6-0.109999466852542
70.0405067596291359
80.200688287080771
90.108671546894821
100.0863147210691597
110.110892329421087
12-0.156261117651209
13-0.23577120151781
14-0.0496376258915339


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = -1.9 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 0.0 ; par6 = 0 ; par7 = 1 ;
Parameters (R input):
par1 = -1.9 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 0.0 ; 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')