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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 computationSun, 30 Nov 2008 05:47:38 -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/Nov/30/t1228049398cixe15ofcp0ficu.htm/, Retrieved Sun, 19 May 2024 08:49:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26481, Retrieved Sun, 19 May 2024 08:49:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact219

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]
-    D  [Univariate Data Series] [Non Stationary Ti...] [2008-11-30 09:45:17] [b82ef11dce0545f3fd4676ec3ebed828]
- RMP     [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-11-30 11:06:50] [b82ef11dce0545f3fd4676ec3ebed828]
-   P       [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-11-30 11:40:46] [b82ef11dce0545f3fd4676ec3ebed828]
-   P         [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-11-30 11:42:33] [b82ef11dce0545f3fd4676ec3ebed828]
- RMP           [Variance Reduction Matrix] [Non Stationary Ti...] [2008-11-30 12:00:29] [b82ef11dce0545f3fd4676ec3ebed828]
- RMP             [Spectral Analysis] [Non Stationary Ti...] [2008-11-30 12:06:20] [b82ef11dce0545f3fd4676ec3ebed828]
- RMPD                [Cross Correlation Function] [Non Stationary Ti...] [2008-11-30 12:47:38] [4b953869c7238aca4b6e0cfb0c5cddd6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97,4
97,0
105,4
102,7
98,1
104,5
87,4
89,9
109,8
111,7
98,6
96,9
95,1
97,0
112,7
102,9
97,4
111,4
87,4
96,8
114,1
110,3
103,9
101,6
94,6
95,9
104,7
102,8
98,1
113,9
80,9
95,7
113,2
105,9
108,8
102,3
99,0
100,7
115,5
100,7
109,9
114,6
85,4
100,5
114,8
116,5
112,9
102,0
106,0
105,3
118,8
106,1
109,3
117,2
92,5
104,2
112,5
122,4
113,3
100,0
110,7
112,8
109,8
117,3
109,1
115,9
95,7
Dataseries Y:
Download CSV
Histogram 
104,2
103,2
112,7
106,4
102,6
110,6
95,2
89,0
112,5
116,8
107,2
113,6
101,8
102,6
122,7
110,3
110,5
121,6
100,3
100,7
123,4
127,1
124,1
131,2
111,6
114,2
130,1
125,9
119,0
133,8
107,5
113,5
134,4
126,8
135,6
139,9
129,8
131,0
153,1
134,1
144,1
155,9
123,3
128,1
144,3
153,0
149,9
150,9
141,0
138,9
157,4
142,9
151,7
161,0
138,5
135,9
151,5
164,0
159,1
157,0
142,1
144,8
152,1
154,6
148,7
157,7
146,7

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=26481&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=26481&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26481&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 series0
Degree of seasonal differencing (D) of X series0
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 series0
krho(Y[t],X[t+k])
-150.0912155565387236
-140.0695305733715643
-130.222483163528892
-120.409972504111823
-110.0522899259797385
-10-0.0131272729913660
-90.255587186404979
-80.185865318179429
-70.316199954446794
-60.463746381446092
-50.243805345702995
-40.179083193854642
-30.326210492912755
-20.286075915629299
-10.465342732073016
00.7005352976756
10.234000141891714
20.149624668313012
30.360576372498428
40.271164477339670
50.394776742501801
60.501355194938643
70.230277063401053
80.151307545092506
90.228554642546875
100.168916007027035
110.334192575332466
120.481422605131478
130.112774079688864
140.045254466814791
150.198352859482036

\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 & 0 \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 & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-15 & 0.0912155565387236 \tabularnewline
-14 & 0.0695305733715643 \tabularnewline
-13 & 0.222483163528892 \tabularnewline
-12 & 0.409972504111823 \tabularnewline
-11 & 0.0522899259797385 \tabularnewline
-10 & -0.0131272729913660 \tabularnewline
-9 & 0.255587186404979 \tabularnewline
-8 & 0.185865318179429 \tabularnewline
-7 & 0.316199954446794 \tabularnewline
-6 & 0.463746381446092 \tabularnewline
-5 & 0.243805345702995 \tabularnewline
-4 & 0.179083193854642 \tabularnewline
-3 & 0.326210492912755 \tabularnewline
-2 & 0.286075915629299 \tabularnewline
-1 & 0.465342732073016 \tabularnewline
0 & 0.7005352976756 \tabularnewline
1 & 0.234000141891714 \tabularnewline
2 & 0.149624668313012 \tabularnewline
3 & 0.360576372498428 \tabularnewline
4 & 0.271164477339670 \tabularnewline
5 & 0.394776742501801 \tabularnewline
6 & 0.501355194938643 \tabularnewline
7 & 0.230277063401053 \tabularnewline
8 & 0.151307545092506 \tabularnewline
9 & 0.228554642546875 \tabularnewline
10 & 0.168916007027035 \tabularnewline
11 & 0.334192575332466 \tabularnewline
12 & 0.481422605131478 \tabularnewline
13 & 0.112774079688864 \tabularnewline
14 & 0.045254466814791 \tabularnewline
15 & 0.198352859482036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26481&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]0[/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]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-15[/C][C]0.0912155565387236[/C][/ROW]
[ROW][C]-14[/C][C]0.0695305733715643[/C][/ROW]
[ROW][C]-13[/C][C]0.222483163528892[/C][/ROW]
[ROW][C]-12[/C][C]0.409972504111823[/C][/ROW]
[ROW][C]-11[/C][C]0.0522899259797385[/C][/ROW]
[ROW][C]-10[/C][C]-0.0131272729913660[/C][/ROW]
[ROW][C]-9[/C][C]0.255587186404979[/C][/ROW]
[ROW][C]-8[/C][C]0.185865318179429[/C][/ROW]
[ROW][C]-7[/C][C]0.316199954446794[/C][/ROW]
[ROW][C]-6[/C][C]0.463746381446092[/C][/ROW]
[ROW][C]-5[/C][C]0.243805345702995[/C][/ROW]
[ROW][C]-4[/C][C]0.179083193854642[/C][/ROW]
[ROW][C]-3[/C][C]0.326210492912755[/C][/ROW]
[ROW][C]-2[/C][C]0.286075915629299[/C][/ROW]
[ROW][C]-1[/C][C]0.465342732073016[/C][/ROW]
[ROW][C]0[/C][C]0.7005352976756[/C][/ROW]
[ROW][C]1[/C][C]0.234000141891714[/C][/ROW]
[ROW][C]2[/C][C]0.149624668313012[/C][/ROW]
[ROW][C]3[/C][C]0.360576372498428[/C][/ROW]
[ROW][C]4[/C][C]0.271164477339670[/C][/ROW]
[ROW][C]5[/C][C]0.394776742501801[/C][/ROW]
[ROW][C]6[/C][C]0.501355194938643[/C][/ROW]
[ROW][C]7[/C][C]0.230277063401053[/C][/ROW]
[ROW][C]8[/C][C]0.151307545092506[/C][/ROW]
[ROW][C]9[/C][C]0.228554642546875[/C][/ROW]
[ROW][C]10[/C][C]0.168916007027035[/C][/ROW]
[ROW][C]11[/C][C]0.334192575332466[/C][/ROW]
[ROW][C]12[/C][C]0.481422605131478[/C][/ROW]
[ROW][C]13[/C][C]0.112774079688864[/C][/ROW]
[ROW][C]14[/C][C]0.045254466814791[/C][/ROW]
[ROW][C]15[/C][C]0.198352859482036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26481&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26481&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 series0
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 series0
krho(Y[t],X[t+k])
-150.0912155565387236
-140.0695305733715643
-130.222483163528892
-120.409972504111823
-110.0522899259797385
-10-0.0131272729913660
-90.255587186404979
-80.185865318179429
-70.316199954446794
-60.463746381446092
-50.243805345702995
-40.179083193854642
-30.326210492912755
-20.286075915629299
-10.465342732073016
00.7005352976756
10.234000141891714
20.149624668313012
30.360576372498428
40.271164477339670
50.394776742501801
60.501355194938643
70.230277063401053
80.151307545092506
90.228554642546875
100.168916007027035
110.334192575332466
120.481422605131478
130.112774079688864
140.045254466814791
150.198352859482036


Figures (Output of Computation)

Input Parameters & R Code

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