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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 13:51:18 -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/t1228251132kr7e0r6vr773grr.htm/, Retrieved Sun, 19 May 2024 11:37:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28436, Retrieved Sun, 19 May 2024 11:37:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109

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] [Gilliam Schoorel] [2008-12-02 20:51:18] [4a7b7ae341cb1fe8993cedd56bcfa583] [Current]
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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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28436&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28436&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28436&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
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 series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.253938744900300
-12-0.0103656011005439
-110.210281183048926
-10-0.353834703455968
-90.247156365357525
-80.0957018278830976
-7-0.329569015247490
-60.155440860798282
-50.159787890223056
-4-0.337871873518989
-30.394295202079732
-2-0.0329913908710726
-1-0.545164264130922
00.800476131772886
1-0.322650865765199
2-0.0912586064572096
30.292732465455816
4-0.272348270013033
50.104821975217055
60.186959878210026
7-0.256624186806126
80.0335199000157541
90.142681953029057
10-0.17763316555165
110.0256165657388603
120.12220095384185
13-0.235918677824066

\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 & 1 \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 & -0.253938744900300 \tabularnewline
-12 & -0.0103656011005439 \tabularnewline
-11 & 0.210281183048926 \tabularnewline
-10 & -0.353834703455968 \tabularnewline
-9 & 0.247156365357525 \tabularnewline
-8 & 0.0957018278830976 \tabularnewline
-7 & -0.329569015247490 \tabularnewline
-6 & 0.155440860798282 \tabularnewline
-5 & 0.159787890223056 \tabularnewline
-4 & -0.337871873518989 \tabularnewline
-3 & 0.394295202079732 \tabularnewline
-2 & -0.0329913908710726 \tabularnewline
-1 & -0.545164264130922 \tabularnewline
0 & 0.800476131772886 \tabularnewline
1 & -0.322650865765199 \tabularnewline
2 & -0.0912586064572096 \tabularnewline
3 & 0.292732465455816 \tabularnewline
4 & -0.272348270013033 \tabularnewline
5 & 0.104821975217055 \tabularnewline
6 & 0.186959878210026 \tabularnewline
7 & -0.256624186806126 \tabularnewline
8 & 0.0335199000157541 \tabularnewline
9 & 0.142681953029057 \tabularnewline
10 & -0.17763316555165 \tabularnewline
11 & 0.0256165657388603 \tabularnewline
12 & 0.12220095384185 \tabularnewline
13 & -0.235918677824066 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28436&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]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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]-0.253938744900300[/C][/ROW]
[ROW][C]-12[/C][C]-0.0103656011005439[/C][/ROW]
[ROW][C]-11[/C][C]0.210281183048926[/C][/ROW]
[ROW][C]-10[/C][C]-0.353834703455968[/C][/ROW]
[ROW][C]-9[/C][C]0.247156365357525[/C][/ROW]
[ROW][C]-8[/C][C]0.0957018278830976[/C][/ROW]
[ROW][C]-7[/C][C]-0.329569015247490[/C][/ROW]
[ROW][C]-6[/C][C]0.155440860798282[/C][/ROW]
[ROW][C]-5[/C][C]0.159787890223056[/C][/ROW]
[ROW][C]-4[/C][C]-0.337871873518989[/C][/ROW]
[ROW][C]-3[/C][C]0.394295202079732[/C][/ROW]
[ROW][C]-2[/C][C]-0.0329913908710726[/C][/ROW]
[ROW][C]-1[/C][C]-0.545164264130922[/C][/ROW]
[ROW][C]0[/C][C]0.800476131772886[/C][/ROW]
[ROW][C]1[/C][C]-0.322650865765199[/C][/ROW]
[ROW][C]2[/C][C]-0.0912586064572096[/C][/ROW]
[ROW][C]3[/C][C]0.292732465455816[/C][/ROW]
[ROW][C]4[/C][C]-0.272348270013033[/C][/ROW]
[ROW][C]5[/C][C]0.104821975217055[/C][/ROW]
[ROW][C]6[/C][C]0.186959878210026[/C][/ROW]
[ROW][C]7[/C][C]-0.256624186806126[/C][/ROW]
[ROW][C]8[/C][C]0.0335199000157541[/C][/ROW]
[ROW][C]9[/C][C]0.142681953029057[/C][/ROW]
[ROW][C]10[/C][C]-0.17763316555165[/C][/ROW]
[ROW][C]11[/C][C]0.0256165657388603[/C][/ROW]
[ROW][C]12[/C][C]0.12220095384185[/C][/ROW]
[ROW][C]13[/C][C]-0.235918677824066[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28436&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28436&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 series1
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 series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.253938744900300
-12-0.0103656011005439
-110.210281183048926
-10-0.353834703455968
-90.247156365357525
-80.0957018278830976
-7-0.329569015247490
-60.155440860798282
-50.159787890223056
-4-0.337871873518989
-30.394295202079732
-2-0.0329913908710726
-1-0.545164264130922
00.800476131772886
1-0.322650865765199
2-0.0912586064572096
30.292732465455816
4-0.272348270013033
50.104821975217055
60.186959878210026
7-0.256624186806126
80.0335199000157541
90.142681953029057
10-0.17763316555165
110.0256165657388603
120.12220095384185
13-0.235918677824066


Figures (Output of Computation)

Input Parameters & R Code

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